Intrinsicis in the Industrial Robotics Industry

Intrinsic

intrinsic

This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors: (1) H. Wen, Department of Economics, University of Bath; (2) T. Huang, Faculty of Business and Law, University of Roehampton; (3) D. Xiao, School of Mathematical Sciences. Table of Links Abstract and Introduction Relevant Blockchain Technologies Basic Model Advanced Models for Real-World Scenarios Monte Carlo Simulations Conclusions Future Investigations, and References 6. Conclusions Building on the insights drawn from our Monte Carlo simulation results, this culminating section distills our findings and teases out the intricate nuances that emerged throughout our study. We commence by probing the bedrock of our model—Intrinsic Incentive Mechanisms for Integrity-driven Ratings—which underscores the spontaneous motivations driving genuine ratings. Progressing further, we then delineate the Incentive Mechanisms for Actions, shedding light on the various levers and pulleys that can be manipulated to promote desired agent behaviors. As a summary to the contribution incentive mechanisms, a crucial balancing act then comes into focus as we navigate the dynamics of Balancing Stakeholders’ Shares and Network Effects, emphasizing the delicate equilibrium necessary for a thriving ecosystem. Rounding off our discourse, we elevate our gaze to a holistic vista, capturing the grand vision of the The Everlasting Reputation System as a Universal Sustainable Oracle, which places our system in the broader context of the blockchain oracle landscape. 6.1. Intrinsic Incentive Mechanisms for Integrity-driven Ratings In the realm of online ratings, ensuring integrity-driven feedback is pivotal. Our system’s design incorporates a mechanism that penalizes agents whose ratings diverge from the consensus, especially when they have staked a significant amount. This is instrumental in thwarting malicious actors. Vendors offering subpar products or services, contemplating artificially boosting their ratings or undermining their rivals, confront imminent hefty losses. Even if spurious reviews momentarily influence the consensus, integrity-driven reviews, over time, can redress the balance. This may relegate their staked ratings to the minority, inflicting substantial losses. Concurrently, vendors deliberating on posting negative reviews against competitors with top-notch products or services encounter an analogous predicament. Considering that the majority of consumers are predisposed to furnish positive feedback for high-caliber products or services, a malevolent negative review can swiftly be overshadowed, culminating in substantial losses for the deceitful vendor. This architecture ensures that, viewed through the lens of economic incentives, agents are propelled to rate with an integrity-driven mindset grounded in their firsthand experiences. Venturing into game theory, while agents may kick off with endeavors to ascertain the predominant sentiment, the game is poised to settle into a state where the lion’s share of agents unambiguously rate driven by their integrity-driven experience. This resonates with specific game theoretic models where cyclical guessing games gravitate towards the integrity-driven modus operandi espoused by autonomous participants [88]. Such findings not only bolster our theoretical postulations but also accentuate the tangible applicability of our blockchain-anchored reputation system. By synergizing individual motivations with the overarching system mechanics, we unfurl a resilient platform wherein integrity-driven ratings gain momentum, while duplicitous manipulations face retribution. 6.2. Incentive Mechanisms for Actions From our Monte Carlo Simulations (MCS) results and interpretations, we’ve distilled core insights on action-related staking behaviors within a blockchain-based reputation system: A clear distinction arises when comparing the adaptive model, which includes learning and adjustment mechanisms, to its non-learning counterparts in the context of staking on actions. Agents receiving positive feedback often increase their stakes, aiming to maximize their rewards. Conversely, those encountering negative outcomes tend to reduce their stakes to minimize losses. The trends observed in high-staking scenarios further reinforce these findings. Agents reaping positive benefits amplify their staking commitments, whereas those on the receiving end of negative feedback tend to retract. This pattern is akin to free-market behaviors where tasks naturally shift towards more proficient agents, sidelining the less adept ones. The introduction of features such as the option for agents to skip rounds and the utilization of non-random consumer selection provides deeper insights into staking behaviors. Notably, tasks are predominantly assigned to agents who commit significant credit points, an indicator of their dedication or skill set in specific roles. Agents with higher stakes simplify the selection criteria for consumers, giving them a competitive advantage and attracting newcomers. As a result, our blockchain-based reputation system effectively manages an onchain marketplace, steering clear of the fake rating problems common in traditional platforms. In summary, these findings highlight the platform’s intrinsic mechanisms for encouraging integrity and cultivating a beneficial relationship between consumers and providers. Importantly, the ability of our platform to counter fake reviews gives it a competitive edge, drawing in users and third-party vendors from competing systems. This positive feedback not only enhances the platform’s reputation but also leverages the network effect [89, 90], strengthening its market presence. 6.3. Balancing Stakeholders’ Shares and Network Effects Through our simulations, which incorporate contribution incentive mechanisms, we elucidate the potential equilibriums in credit point distributions. While external incentives may reduce the shares of existing stakeholders, they can simultaneously enhance the intrinsic value of each credit point via the network effect. There is a pivotal balance, achievable through judiciously crafted mechanisms and DAO governance, where an optimal outcome that serves all stakeholders’ interests can be realized. The aim is to crystallize this equilibrium and possibly enshrine it within a constitutional framework, thus establishing a robust foundation for the reputation system’s functionality. 6.4. The Everlasting Reputation System as a Universal Sustainable Oracle The rapid progression of blockchain technology has opened new horizons for decentralized applications. Within this paradigm, the oracle problem—the challenge of securely and reliably transmitting external data to the blockchain—emerges as a significant hurdle. Although Chainlink’s decentralized oracle aimed to address this, it faces challenges concerning long-term sustainability and potential dilution of asset value. Drawing from the inherent features of blockchain such as transparency and immutability, the everlasting reputation system presents a straightforward and sustainable solution to the universal oracle problem. It is anchored in a decentralized, collective rating mechanism, making it organically adaptive to supply real-world data to smart contracts, thereby functioning as a dynamic oracle network. The reputation system’s intrinsic advantage stems from its foundational principles. It endows participants with a robust and enduring incentive to rate transparently and honestly. This distinct methodology yields unparalleled advantages. For consumers, it acts as a guiding beacon, steering them to vendors who meet their anticipated standards. For vendors, it stands as a defensive shield against unfounded negative feedback from malevolent competitors, enabling them to foster and uphold their credibility. As the system magnetically draws an escalating number of consumers and vendors, its network effect burgeons. This amplifying network effect continually reinforces the oracle. Moreover, its adaptability and universality negate the need for complex, case-specific designs. As it intuitively attracts diverse users and data providers, the reputation system is poised to rival other oracles like Chainlink. Furthermore, with the potential integration of cryptographic tools such as Ring Signatures and ZeroKnowledge Proofs (ZKPs) to the reputation system, the oracle offers enhanced privacy adaptability: : Ring Signatures allow for digital signing by a group member, disclosing that a group member signed it but concealing the signer’s identity. Within oracles, this protects data providers’ identities. Specifically, as oracle providers feed data to smart contracts, using Ring Signatures ensures that the data’s specific provider remains undisclosed, offering oracle participants a layer of privacy. 1. Ring Signature Oracles : ZKPs let a prover authenticate a statement’s veracity to a verifier without revealing further relevant information about the statement. In oracles, ZKPs protect the data contents submitted to smart contracts, permitting data verification without unveiling the data’s detailed specifics. By integrating ZKPs, the system can effortlessly merge with existing blockchain frameworks, ensuring data privacy without compromising its accuracy or truth. 2. ZKP Oracles The evolving nature of the reputation system, driven by agent participation and feedback, epitomizes a self-tuning mechanism. Incorporating a Decentralized Autonomous Organization (DAO) in future phases will endow the oracle with perpetual evolution, distinguishing it from static oracle systems reliant on manual interventions or enhancements. 6.4.1. Advantageous Scenarios for the Universal Sustainable Oracle : Integration of Ring Signatures and ZKPs makes this system ideal for applications demanding high data confidentiality, such as banking transactions, confidential business dealings, legal documents, medical records, and private voting mechanisms. 1. Privacy-Sensitive Applications : Precise data is the lifeblood of DeFi. The reputation-based oracle can enhance financial protocols with reliable market data, optimizing the efficacy of platforms related to lending, borrowing, trading, or even risk management and insurance. 2. Decentralized Finance (DeFi) : The reputation system, with its emphasis on transparency and accuracy, can ensure product authenticity throughout supply chains. Industries prioritizing authenticity like agriculture, pharmaceuticals, electronics, luxury goods, and even art and antiques can greatly benefit. 3. Supply Chain Management In summary, rooted in blockchain principles and supplemented with ZKPs, the everlasting reputation system offers a comprehensive solution to the pertinent oracle dilemma. As blockchain garners wider acceptance, the need for a trustworthy oracle will surge, positioning this model as a leading contender that harmoniously blends data privacy, reliability, and durability.

Key Takeaways from an Intrinsic Integrity-Driven Rating Model for Sustainable Reputation
System

This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors: (1) H. Wen, Department of Economics, University of Bath; (2) T. Huang, Faculty of Business and Law, University of Roehampton; (3) D. Xiao, School of Mathematical Sciences. Table of Links Abstract and Introduction Relevant Blockchain Technologies Basic Model Advanced Models for Real-World Scenarios Monte Carlo Simulations Conclusions, Future Investigations, and References 3. Basic Model In this section, we embark on a deep exploration of the foundational mechanisms underpinning our reputation-based blockchain. We initiate our discourse with the On-chain reputation system, elucidating how reputation metrics are embedded and maintained directly within the blockchain environment. Progressing further, we tackle the essential task of Connecting the real world to our digital framework, ensuring seamless integration and applicability. Recognizing the importance of clear foundational premises, we detail the Assumptions of the Gains or Losses for Any Agent, shedding light on the anticipated dynamics each participant might experience. This naturally leads us to an examination of the Distribution of Agent Actions within the system, offering insights into the varied interactions and behaviors exhibited by agents. Finally, we delve deep into the Action Analysis in Reputation Systems, providing a granular breakdown of the ripple effects and broader implications of agent activities within our reputation ecosystem. 3.1. On-chain reputation system The idea is to introduce a cross-rating system in the blockchain-based free market. All agent actions will be rated either positively or negatively by others using staked credit points. The incentive for rating lies in potential future profits, as the staked credit points for each comment will earn a profit if it aligns with the general sentiment (positive or negative) of future comments. Conversely, they will incur a loss if future comments contradict the initial sentiment. The more credit points one stakes, the higher the potential profit or loss one stands to gain based on future ratings. Simultaneously, this also amplifies the impact on all previous ratings in the same direction, reinforcing them if they align and diminishing them if they oppose. Here, an agent is defined as a blockchain wallet holder. An agent can choose to stake any amount of its credit points between 0 and the balance in the wallet minus the transaction fee (negligible at Layer 2 of the Ethereum blockchain [53, 54]). An agent can also distribute the amount across multiple wallets, but this won’t provide any advantage over operating as a single entity since weight is determined by credit point amounts, not the number of agents. The core proposition is to integrate a cross-rating system into the blockchain-based free market. Every agent’s action gets rated—either positively or negatively—by other agents leveraging staked credit points. The incentive for this rating mechanism emerges from potential future gains; the staked points for a comment will appreciate if subsequent comments resonate with its sentiment. In contrast, if future comments diverge, the staked points depreciate. The magnitude of potential gains or losses directly correlates with the number of staked credit points. Moreover, these stakes influence all preceding ratings in the same direction, either amplifying or attenuating them. In this context, an agent is delineated as a blockchain wallet holder. This agent can stake any quantity of its credit points, bounded between zero and the wallet balance after accounting for transaction fees. While agents can distribute their credit points across multiple wallets, this doesn’t confer any strategic advantage since the weight stems from the quantity of staked points, not the agent count. Figure 1 showcases an archetypal interaction round among agents within the reputation system. In any given round or block, agents, be it Alice, Bob, or any other participant, can partake in a mix of actions, ratings, or trades. For instance, if Alice rates Bob’s action positively in a round, Bob garners positive returns. On the other hand, a negative rating imposes a loss on Bob. Other agents can similarly rate Bob’s action within the round, influencing his credit point balance based on the rating’s nature. When delving deeper into ratings, if an agent’s rating of Bob’s action aligns with Alice’s sentiment, both Alice and that agent benefit. However, conflicting ratings lead to losses for both parties. The dynamic nature of potential gains and losses, contingent upon future ratings, fosters authentic feedback from agents, mitigating false ratings. Given that a significant portion of these ratings are genuine, the ecosystem offers a tangible advantage over traditional online platforms, which often grapple with counterfeit, low-cost ratings. Over time, this advantage is projected to burgeon, drawing an expanding user base. As the positive reinforcement loop of ratings solidifies, the continuously growing influence of the reputation system will inspire participants to proactively contribute, transforming these ratings into the system’s cornerstone. Such inherent positive externalities vouch for the sustainability of this reputation system, setting it apart from numerous other blockchain paradigms that lean heavily on credit point vesting, such as the renowned Chainlink 2.0 [18]. 3.2. Connecting the real world Following the introduction of ”soulbound credit points” by Ethereum’s co-founder, Vitalik Buterin [55, 56], and Elon Musk’s vision of integrating blockchain with Twitter accounts [57], a unique opportunity arises to associate each wallet address from the on-chain reputation system with a specific individual or entity in the real world. This association implies that actions undertaken offline could be evaluated, and these evaluations could subsequently result in tangible gains or losses based on evolving collective ratings. As blockchain continues to evolve and integrate with real world including Real World Assets (RWAs) [58, 59] via oracle or similar mechanisms, an increasing number of high-value economic activities are likely to find connections to the blockchain. Given the promising potentials discussed in Subsection 3.1, individuals with a higher risk tolerance might be incentivized to link their real-world actions to their blockchain wallet addresses. On the other hand, participants of the reputation system, essentially the wallet holders, are the ones recognizing the value of this association and are willing to submit their ratings in anticipation of future benefits. This sentiment is particularly pronounced during the initial phases of the project. Although speculators may invest in credit points expecting them to appreciate in value, they don’t possess a strong economic motive to submit misleading ratings. As a result, their activities should not compromise the system’s functionality. We’ll now transition from these theoretical underpinnings to a practical application, beginning with a rudimentary model. Let’s consider the following categories of agents participating in the reputation system: C1. Agents with Actions C2. Rating Participants C3. Investors Any given agent can belong to one, two, or all three of these categories simultaneously. C1 agents, due to potential gains or losses stemming from others’ ratings, are more likely to be responsive to rating if they have staked a higher number of credit points. It is logical to assume that agents staking more credit points will prioritize meeting consumer expectations. Since this staking information is transparent, consumers will naturally gravitate towards producers who have staked more credit points, creating an initial incentive for credit points purchase when someone decides to join the reputation system. While producers can also rate consumers, this mechanism naturally promotes responsible action across the board. To preserve individual privacy and provide flexibility in engagement, the model permits C1 agents to choose the amount they would like to stake – anywhere from zero to their full wallet balance (minus transaction fees) – for any given action they want to be rated on. The staking information is transparent on the chain, and therefore consumers may also use this information when choosing providers. They are more likely to prefer those who have staked more credit points due to the reasoning above. C2 agents similarly determine their staked amounts based on the confidence level of their beliefs when providing ratings. Since these agents are subject to potential gains or losses depending on the conformity of others’ ratings to the actions they have rated, they are incentivized to rate in a responsible and genuine manner. C3 agents, or traders between the credit points and other assets at anywhere external to the reputation system, are individuals who primarily buy or sell credit points without engaging in other actions within the system. Their actions are largely inconsequential to the reputation system’s functionity. However, traders could indirectly benefit the system in its initial phase by driving up credit point prices with their purchases. 3.3. Assumptions of the Gains or Losses for Any Agent Consider an agent i as a general example of any agent. We categorise its involvement in the reputation system into three parts: functioning as a C1, C2, and C3. Considering trader agents categorised as C3 are not directly affecting the credit point-number-based gains or losses of any other specific agents, we now focus on C1 and C2 categories and denote them as ”A” for action agents and ”R” for agents providing ratings. a category cannot exceed the credit points allocated for that specific category. This stipulation implies that every agent must earmark a specific portion of their credit for each staking category from the outset, and these cannot be liquidated until they are unstaked. In more advanced models, this foundational assumption can be replaced with intricate mechanisms that more closely mirror real-world scenarios, ensuring that total losses never surpass assets, even though total gains might exceed them. 3.4. Distribution of Agent Actions where x ∈ (−∞, ∞). where, once again, xi ∈ (−∞, ∞). Although this is an infinite interval, its cumulative distribution function (CDF) is: where the error function is defined as: 3.5. Action Analysis in Reputation Systems Considering a reputation system where agents rate each other based on action, we posit that an agent’s rating can either be positive or negative. Each agent’s action and rating can influence the gains or losses for others. Thus, the total gains agent i accrues from its participation in the reputation system are given by formula (6) Let us denote the probability of agent i providing a positive rating as P+ and that of negative rating as P−. The joint probabilities of rating interactions between i and j under different scenarios are: The chances that rating from two agents align in sentiment (either both positive or both negative) is: To ascertain whether the majority of agents will benefit from such a system without specially designed favourable mechanism, we examine the relative magnitude of probabilities for congruent rating (both positive or both negative) against incongruent rating. Specifically, we need to verify: Breaking it down and reordering: By the fundamental properties of real numbers, the square of any real number is always non-negative. This implies that the combined probability of agents giving congruent rating (either both positive or both negative) will always be at least as high as that of giving incongruent rating. Thus, when agents’ rating aligns, the majority in the system stands to benefit. This dynamic promotes a system where congruent ratings are more prevalent than incongruent ones, underscoring the collaborative nature of such reputation systems. In theory, this mechanism should encourage agents to participate actively in ratings. We can use Monte Carlo simulations to determine whether this is noticeable in practice.

Basic Intrinsic Integrity-Driven Rating Model for a Sustainable Reputation
System

This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors: (1) H. Wen, Department of Economics, University of Bath; (2) T. Huang, Faculty of Business and Law, University of Roehampton; (3) D. Xiao, School of Mathematical Sciences. Table of Links Abstract and Introduction Relevant Blockchain Technologies Basic Model Advanced Models for Real-World Scenarios Monte Carlo Simulations Conclusions Future Investigations, and References Abstract In the era of digital markets, the challenge for consumers is discerning quality amidst information asymmetry. While traditional markets use brand mechanisms to address this issue, transferring such systems to internet-based P2P markets—where misleading practices like fake ratings are rampant—remains challenging. Current internet platforms strive to counter this through verification algorithms, but these efforts find themselves in a continuous tug-of-war with counterfeit actions. Exploiting the transparency, immutability, and traceability of blockchain technology, this paper introduces a robust reputation voting system grounded in it. Unlike existing blockchain-based reputation systems, our model harnesses an intrinsically economically incentivized approach to bolster agent integrity. We optimize this model to mirror real-world user behavior, preserving the reputation system’s foundational sustainability. Through Monte-Carlo simulations, using both uniform and power-law distributions enabled by an innovative inverse transform method, we traverse a broad parameter landscape, replicating real-world complexity. The findings underscore the promise of a sustainable, transparent, and formidable reputation mechanism. Given its structure, our framework can potentially function as a universal, sustainable oracle for offchain-onchain bridging, aiding entities in perpetually cultivating their reputation. Future integration with technologies like Ring Signature and Zero Knowledge Proof could amplify the system’s privacy facets, rendering it particularly influential in the ever-evolving digital domain. Keywords: blockchain, reputation system, sustainable oracle, agent, credit point, integrity-driven rating, incentive mechanisms, Monte-Carlo simulation 1. Introduction Information asymmetry [1] introduces challenges for consumers trying to select products or services that best fit their needs. In traditional retail markets, consumers gravitate towards brands they trust based on personal experiences, the experiences of others, or advertisements [2]. Each brand embodies a certain value that can be monetized in open markets. Offering products or services that fall short of their targeted consumers’ expectations can diminish this brand value. These brand mechanisms [3] serve to benefit both consumers and producers in retail and even broader traditional market sectors [4]. With the increased convenience and diversity they provide, online products and services are becoming the preferred choice for many consumers. A notable trend in this domain is the rise of third-party P2P offerings on internet platforms [5]. This rapidly growing sector includes a wide array of services. Modern internet platforms such as Amazon, eBay, Taobao, Anyvan, ClickMechanic, and Mybuilder have transformed the way third-party service providers engage with consumers. Essentially, these platforms enable vendors to showcase their products or services, allowing customers to easily browse, compare, and purchase. Central to these operations is the role of customer reviews. A high number of positive reviews can significantly boost the attractiveness of a vendor, influencing other potential customers’ purchasing decisions. Conversely, negative reviews can deter future sales. This significant influence has, however, paved the way for manipulative tactics. The prevalence of deceptive practices, such as fake ratings and click farming, is notably higher online compared to traditional markets [6, 7, 8]. For instance, some vendors might engage in ’review farming,’ where they either purchase their products to leave positive reviews or hire individuals to write misleadingly favorable reviews for them. On the darker side, there might be instances of vendors leaving malicious negative reviews for competitors to tarnish their reputation. Hence, the establishment of reliable brand mechanisms presents substantial challenges. Given this scenario, verifying authentic consumer actions and crafting efficient reputation mechanisms become critical. While online platforms have the capability to employ algorithms to filter genuine from deceptive actions, thus allowing third-party producers to construct a legitimate reputation, the efficacy of such methods remains questionable. A persistent tension exists between the deceptive maneuvers of some third-party producers and the countermeasures introduced by platform verification algorithms. This dynamic essentially forms a cyclical battle. Present solutions might demand trade-offs, potentially compromising user privacy or other desirable attributes [9]. While machine learning offers a promising avenue [10], its implementation requires either an initial training phase with assured genuine user data or a substantial development period before it becomes truly effective. The advent of blockchain technology, pioneered by Satoshi Nakamoto [11], and augmented by Nick Szabo’s concept of the smart contract [12]—brought to fruition by Vitalik Buterin through Ethereum in 2014 [13]—has paved the way for innovative on-chain reputation systems.[1] Oracles play a pivotal role in bridging the gap between the real-world and blockchain technology, as they provide the crucial link by which off-chain data can be accessed and utilized by on-chain smart contracts. This function is imperative given that blockchains natively cannot access information outside their network. In 2017, Chainlink introduced its oracle mechanism [14], significantly amplifying the capabilities of decentralized systems. Its renowned use-case for price feeds laid the foundation for the blossoming of Decentralized Finance [15, 16]. Essentially, oracles act as third-party intermediaries, relaying external data to smart contracts. Chainlink’s decentralized oracle network assures the authenticity and integrity of data relayed into the blockchain. This is achieved by sourcing data from a plethora of independent oracles and consolidating their responses, mitigating risks associated with singular data points and enhancing the reliability and security of smart contracts dependent on external data. Yet, the functioning of these oracles primarily relies on incentive mechanisms, most notably vesting incentives—taking, for instance, the native asset ”Link” transitioning from non-circulation to circulation. Over a period, these incentives might have the potential to dilute the value for all participants. As the pool of these rewards nears depletion, apprehensions about the system’s enduring viability come to the fore. Successive enhancements, like Chainlink 2.0 [17], along with other contemporary solutions, have endeavored to surmount this challenge. Nonetheless, their effectiveness is a subject of continued discussion. While these refined models and strategies demonstrate potential, their ability to maintain prolonged and unwavering effectiveness remains under examination [18]. Hence, forging a truly sustainable oracle infrastructure becomes essential to fortifying the link between the real-world and blockchain technology. Recent studies have illuminated various dimensions of blockchain technology, notably the blockchainbased reputation systems [19, 20]. While these endeavors are commendable, some of them appear more theoretical and might encounter challenges when catering to all dynamic agent[2] interactions. Others have opted for centralized selection of trusted nodes, potentially introducing vulnerabilities associated with single points of failure [21, 22, 23, 24]. Mechanisms aiming to emulate real-world settings sometimes lean on intricate processes for identifying agent types and monitoring their on-chain activities. These processes occasionally overshadow more direct incentive mechanisms that have demonstrated spontaneous efficacy [25]. Some proposed economic incentive structures seem to lean on increasing blockchain asset ”numbers”, which might be susceptible to dilution over time [26], instead of furnishing concrete advantages. As mechanism designs grow in complexity, they might inadvertently elevate intervention costs or introduce unpredictabilities. Relying heavily on uncertainties akin to those proposed by chaos theory [27]—inspired by the renowned ”Butterfly Effect” [28]—might tread on delicate ground. While there appear to be promising solutions, such as ”Truth” [29]—a blockchain-aided reputation system aimed at obviating the need to trust e-commerce platforms and ensuring feedback authenticity and anonymity—the system is not without its potential shortcomings. ”Truth” introduces a hybrid model wherein the centralized e-commerce platform remains in charge of traditional trading services, but the blockchain oversees the authentication of feedback. Although ”Truth” presents an innovative approach to the age-old problem of authenticating e-commerce feedback, certain aspects warrant deeper scrutiny. For example, the paper does not adequately address the motivations that would drive consumers to engage in the feedback process. Understanding these incentives is vital for ensuring widespread adoption and sincere feedback. Furthermore, the system seems to overlook potential malicious behaviors by sellers, such as posting negative feedback against competitors or hiring fictitious consumers to generate positive reviews. While transactions may take place through the prescribed channels, there is a potential for compensating these ”hired” consumers via alternative methods, thereby circumventing the system’s safeguards. For ”Truth” to fully realize its potential and to demonstrate its effectiveness in real-world settings, these concerns need to be addressed in future versions or research Given these challenges, there’s merit in exploring a more streamlined, basic model that inherently incentivizes agent behavior integrity and fosters sustainability [30, 31, 32, 33, 34]. Initial evaluations of this model could employ Monte-Carlo simulations across vast parameter domains, capturing real-world intricacies. It’s essential to note that this rudimentary model need not capture every intricate detail of real-world scenarios. A basic reputation system, even if it offers only a singular credibility rating, can be invaluable in myriad applications. This value is realized when the reputation information it delivers is accurate and trustworthy, allowing users to rely on it rather than turning to other platforms that struggle to filter out low-cost fake ratings, as previously discussed. Building upon a well-defined basic model foundation allows for iteration and enhancement. By integrating corrective components to the foundational equations, we can preserve core incentives while refining the model towards an everlasting reputation system to mirror real-world scenarios more accurately. Ensuring this model’s competitive advantage over other platforms or blockchain solutions amplifies its resilience and sustainability. This strategy not only highlights the model’s practical utility but, as adoption grows, leverages the network effect. Widespread integration of our system fortifies commercial barriers against replication, making it increasingly arduous for potential competitors to emulate our unique model and its benefits. While the applications of the everlasting reputation system continue to expand across various domains and demographics, this system can function as a sustainable oracle network with demonstrated reliability. The integration of Ring Signatures [35, 36, 37, 38, 39] and Zero Knowledge Proofs (ZKPs) [40, 41, 42] can further enhance the privacy dimensions of such oracle mechanisms, especially in situations where privacy is paramount. Ring Signatures allow for digital signature from a group member without disclosing the identity of the specific signer. ZKPs represent an advanced cryptographic technique enabling agents to substantiate certain claims (like their assets or states) without disclosing the specifics. The structure of this paper is organized as follows: Section 2 introduces the relevant blockchain technologies. Following this, the basic model is presented in Section 3, which lays the foundation for understanding the incentive mechanisms that promote self-motivated integrity among agents. We then refine this basic model in Section 4 to mirror real-world scenarios more closely, ensuring that the incentives for integrity-driven ratings are preserved. After building a robust theoretical base, Section 5 uses Monte Carlo simulations to validate our propositions, followed by a detailed analysis and discussion of the results. Our findings from the simulations are summarized in Section 6, where we underscore that our reputation system functions effectively as a universal sustainable oracle. Finally, potential directions for future research are proposed in Section 7. [1] In our model, the term ”reputation system” is used generically to denote the alternative solution we propose. However, in practical application scenarios, it might also be referred to as a ”platform.” The two terms can be used interchangeably. [2] In our model, we employ the term ”agent” to represent entities involved in the system. Conversely, in real-world application scenarios, we refer to them as ”users”. Throughout this paper, the term ”agent” is used in the context of the model while ”user” is employed in the context of real-world applications. Both terms can be considered interchangeable in many scenarios.

Introducing an Intrinsic Integrity-Driven Rating Model for a Sustainable Reputation
System

একটি টেকসই খ্যাতির জন্য একটি অন্তর্নিহিত সততা-চালিত রেটিং মডেল প্রবর্তন করা হচ্ছে
পদ্ধতি

Einführung eines intrinsischen, integritätsorientierten Bewertungsmodells für eine nachhaltige Reputation
System

Presentación de un modelo de calificación basado en la integridad intrínseca para una reputación sostenible
Sistema

Présentation d’un modèle de notation intrinsèque axé sur l’intégrité pour une réputation durable
Système

स्थायी प्रतिष्ठा के लिए एक आंतरिक अखंडता-संचालित रेटिंग मॉडल का परिचय
प्रणाली

持続可能な評判を実現するための本質的な誠実性を重視した評価モデルの導入
システム

지속 가능한 평판을 위한 본질적인 청렴도 중심 평가 모델 도입
체계

Apresentando um modelo de classificação baseado na integridade intrínseca para uma reputação sustentável
Sistema

Представляем внутреннюю рейтинговую модель, основанную на честности, для устойчивой репутации
Система

Sürdürülebilir Bir İtibar için İçsel Dürüstlük Odaklı Derecelendirme Modelinin Tanıtılması
Sistem

Giới thiệu Mô hình xếp hạng dựa trên tính chính trực nội tại để có được danh tiếng bền vững
Hệ thống

引入内在诚信驱动的评级模型以实现可持续的声誉
系统

This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv Authors: (1) H. Wen, Department of Economics, University of Bath; (2) T. Huang, Faculty of Business and Law, University of Roehampton; (3) D. Xiao, School of Mathematical Sciences. Table of Links Abstract and Introduction Relevant Blockchain Technologies Basic Model Advanced Models for Real-World Scenarios Monte Carlo Simulations Conclusions Future Investigations, and References 7. Future Investigations As our research journey navigates the intricate tapestry of the reputation system and its myriad facets, there remain certain areas poised for deeper exploration in subsequent phases of our investigation. Our anticipation is to cast our intellectual net even wider, to capture the more nuanced dynamics and emergent properties that our initial inquiry might have only touched upon. Firstly, we delve into the realm of Additional and Multiple Ratings, seeking to comprehend how multiple layers of feedback can enrich the analytical depth of our system. Proceeding from there, the lens of our inquiry shifts to an anticipatory stance, focusing on the Early Detection of Potential Problems — a proactive measure to ensure the seamless functioning of the reputation mechanism. As we transition into the practicalities of system deployment, the Adaptive Incentive Mechanisms for Post-Launch Stage promises a set of guiding principles and methodologies tailored for the evolving needs of a live system. Lastly, but by no means least, our exploration culminates in the conceptualization of a Perpetually Self-Adaptively-Evolving Universal Oracle, envisaging a system that not only evolves but also continually refines itself, embodying the principles of perpetual learning and adaptation. 7.1. Additional and Multiple Ratings In our subsequent studies, we aim to delve deeper into the intricacies of a singular agent assigning multiple ratings to a counterpart. We are conceptualizing an innovative weighting formula that, rather than merely focusing on staking quantity, places added emphasis on recent ratings. Such a revision is aimed at capturing the fluidity of actions and potential shifts in an agent’s viewpoint over time. To operationalize this, we propose the incorporation of indices representing an agent’s historical actions, coupled with a decay function to account for intervals between successive ratings. Our objective with this methodology is to temper the impact of aged ratings, acknowledging that the context of the scrutinized action may transform. To ensure its feasibility, we plan to employ MCSs within an expansive parameter spectrum. This will entail examining an array of decay functions related to diverse time spans. 7.2. Early Detection of Potential Problems 7.2.1. Manual Intervention During Testnets Despite the inherent design of the reputation system, which instinctively promotes agent integrity, and its satisfactory performance in simulations, it might be prudent to retain the option for manual interventions, particularly during the testnet phase. Potential indicators, like abrupt transaction surges or atypical staking by an agent, could be warning signs of looming issues or exploitation. Given the feedback dynamics of reputation systems, user reviews and ratings might act as preliminary alert signals. For example, a sharp decline in an agent’s reputation could necessitate in-depth scrutiny. This can be optimally executed if the manual intervention guidelines are unambiguous, thereby ensuring seamless coexistence with the autonomous functioning of the reputation system. 7.2.2. Machine Learning Integration Employing advanced analytics and machine learning algorithms can facilitate real-time transaction pattern monitoring. By amalgamating continuous oversight with adaptive algorithms proficient in learning from past data, the system can proactively pinpoint and address concerns, fortifying the trustworthiness of the blockchain and all its stakeholders. This anticipatory stance not only reduces potential threats but also nurtures user trust, bolstering durability and adaptability in a swiftly changing digital landscape. 7.3. Adaptive Incentive Mechanisms for Post-Launch Stage 7.3.1. Incentive Mechanisms for Early Problem Finders To counteract the ”Keynesian beauty contest” effect, as delineated by Keynes in 1936 [87], we contemplate the application of skewed functions to endorse rating diversity. If the collective outcomes of this ”contest” exceed a predetermined threshold, the system might autonomously incentivize divergent actions, aiming for recalibration. Designing such mechanisms necessitates meticulous scheming, exhaustive simulation-based validation, and thorough field testing to ensure interventions remain well-measured and intentional. 7.3.2. Strengthening Community Participation Besides prioritizing early problem identification, it’s essential to reward the astute minority who identify these issues promptly. Echoing sentiments from earlier chapters, a renewed focus will be directed towards championing community engagement. Establishing an organic incentive framework can ensure sustained competitiveness of the reputation system. This strategy not only lauds individuals for their proactive contributions but also consolidates the efficacy and integrity of the overarching system. 7.3.3. Exploring Decentralized Autonomy through DAO The exploration of Decentralized Autonomous Organizations (DAOs) presents an innovative blueprint for a self-regulating, independent system. By their design, DAOs, anchored in predefined rules, circumvent centralized control, potentially ensuring unbiased decision-making mechanisms. These organizations can be tailored to autonomously reward contributors, manage resources, and make judgements rooted in the community’s collective wisdom. Incorporating DAOs into our system’s architecture might further bolster incentivized participation, aligning individual rewards with collective benefits. 7.4. Perpetually Self-Adaptively-Evolving Universal Oracle An astutely crafted reputation system with intrinsic incentive mechanisms for integrity ratings, coupled with the principles of DAO, paves the way for a perpetually self-operating, self-correcting, and adaptively evolving Universal Oracle. This union merges the immutable trust mechanism of a DAO with the dynamic adaptability of our model, crafting a system poised for long-term resilience and relevance. Users, being both beneficiaries and decision-makers regarding the oracle’s direction, ensure each update carries the legacy of existing participants while extending to new ones. This reinforces the network effect and establishes barriers against imitators. Consequently, such a system, by continuously recalibrating its parameters and practices in response to evolving circumstances, can potentially maintain its integrity and relevance indefinitely, surpassing any other competitors. References [1] Carlo Morelli, Information Costs and Information Asymmetry in British Food Retailing, The Service Industries Journal, Volume 19, 1999 - Issue 3, Pages 175-186. [2] T¨ulin Erdem, Joffre Swait, Brand Credibility, Brand Consideration, and Choice, Journal of Consumer Research, Volume 31, Issue 1, June 2004, Pages 191–198. [3] Tae Hyun Baek, Jooyoung Kim, Jay Hyunjae Yu, The Differential Roles of Brand Credibility and Brand Prestige in Consumer Brand Choice, Psychology & Marketing, Vol. 27(7): 662–678, July 2010. [4] Gerald Bloom, Hilary Standing, Robert Lloyd, Markets, information asymmetry and health care: Towards new social contracts, Social Science & Medicine, Volume 66, Issue 10, May 2008, Pages 2076-2087. [5] Xueping Zhen, Shuangshuang Xu, Yongjian Li, Dan Shi, When and how should a retailer use third-party platform channels? The Impact of spillover effects, European Journal of Operational Research, Volume 301, Issue 2, 1 September 2022, Pages 624-637. [6] Wu, Y., Ngai, E.W.T., Wu, P., & Wu, C. (2020). Fake online reviews: Literature review, synthesis, and directions for future research. Decision Support Systems, 132, [online] Available at: https://doi.org/10. 1016/j.dss.2020.113280. [7] Sekar, S. (2023). Technical Perspective: A Rare Glimpse of Tracking Fake Reviews. Communications of the ACM, 66(10), 97. Available at: https://doi.org/10.1145/3615429. [8] Harrison-Walker, L.J., & Jiang, Y. (2023). Suspicion of online product reviews as fake: Cues and consequences. Journal of Business Research, 160, [online] Available at: https://doi.org/10.1016/j.jbusres. 2023.113780. [9] Shukla, A.D., & Goh, J.M. (2023). Fighting fake reviews: Authenticated anonymous reviews using identity verification. Business Horizons, [In Press, Journal Pre-proof], [online] Available at: https://doi.org/10. 1016/j.bushor.2023.08.002. [10] JP Vergne, Decentralized vs. Distributed Organization: Blockchain, Machine Learning and the Future of the Digital Platform, Organization Theory, Volume 1, Issue 4, October-December 2020, https://doi. org/10.1177/2631787720977052. [11] Satoshi Nakamoto, 2008, Bitcoin: A Peer-to-Peer Electronic Cash System, https://www.bitcoin.org. [12] Jiin-Chiou Cheng, Narn-Yih Lee, Chien Chi, Yi-Hua Chen, Blockchain and smart contract for digital certificate, 2018 IEEE International Conference. [13] Ethereum whitepaper, https://ethereum.org/en/whitepaper/. [14] Steve Ellis, Ari Juels, and Sergey Nazarov, ChainLink: A Decentralized Oracle Network, 4 September 2017. [15] Giulio Caldarelli and Joshua Ellul, The Blockchain Oracle Problem in Decentralized Finance—A Multivocal Approach, Applied Sciences, Volume 11, Issue 16, 2021, https://doi.org/10.3390/app11167572. [16] Kenneth Hsien Yung Chung, Dan Li, Peter Adriaens, Technology-enabled financing of sustainable infrastructure: A case for blockchains and decentralized oracle networks, Technological Forecasting and Social Change, Volume 187, February 2023, https://doi.org/10.1016/j.techfore.2022.122258. [17] Lorenz Breidenbach et al., Chainlink 2.0: Next Steps in the Evolution of Decentralized Oracle Networks, 15 April 2021. [18] Eric Wall, What’s Wrong With the Chainlink 2.0 Whitepaper? (For Simpletons), May 1, 2021. [19] Ahmed S. Almasoud, Farookh Khadeer Hussain, Omar K. Hussain, Smart contracts for blockchainbased reputation systems: A systematic literature review, Journal of Network and Computer Applications, Volume 170, 15 November 2020.

Future Investigations on an Intrinsic Integrity-Driven Model for Sustainable Reputation Systems

We’re all guilty of it; it’s part of what makes us human. But we should always strive to be better and fairer, and to eliminate these biases when they arise. This is especially true in today's workplace, where the widespread adoption of artificial intelligence (AI) technology risks hardcoding these biases into the future of work. Intrinsic bias. As I’ve , AI is not inherently good or bad. It is a tool, and like all tools, it can be used in a multitude of ways. But we're quickly approaching a tipping point where crucial business decisions are based almost entirely on AI recommendations. If you're not concerned about AI integrity, you should be. discussed before If we are not judicious in our approach, Fortunately, there are clear opportunities to address and overcome this bias in our organizations. Here are a few steps you can take to ensure a better future at work and beyond. we risk further entrenching the same intrinsic biases we have been working so hard to overcome into the makeup of our technology. Take a Long Hard Look in The Mirror It's cliché but true: True change comes from within. In order to build a fair and ethical workplace, you must first admit that bias exists and commit to rectifying it. By facing a potentially difficult reality, you're better prepared to face future challenges. Intrinsically biased AI can have devastating consequences. Consider Amazon's , which was trained on resumes spanning a 10-year history. Based on the data it was fed, the algorithm learned a distinct male preference. Unfortunately, the team assumed that their historical data was free of bias, which resulted in a perpetuation of inequality and a significant PR disaster. now-retired recruiting engine All companies have biases. Successful companies face them. Ask yourself honestly: Where is our bias? Why is it occurring? How can we ensure it won’t happen again? Avoid 'Black Box' AI Imagine you tasked your team with developing new compensation guidelines. After their pitch, you'd have some questions. What data did you use? What assumptions did you make? Which best practices did you follow? You would be justifiably suspicious if they couldn't explain their conclusions. The same explainability should be expected of AI. Widespread misunderstandings about how reached its cancer treatment decisions contributed to its unfortunate and high-profile failures. To make matters worse, the lack of visibility into how and why Watson made its recommendations made doctors less likely to trust it -- even in cases where it could have been helpful. IBM’s Watson In order to effectively and ethically engage with technology, executives must understand how AI works in general, how to design ethical AI and how any active algorithms are augmenting companywide decision making. This ability to fully understand AI decision making is -- or should be -- a core competency of today's C-suite. Review, Revise, Repeat Finally, don't be complacent with your AI. Institute systems to continually check for bias. Falling into the trap of thinking your AI (or any AI) has the only real answer is tempting, but dangerous. AI that can fully understand context is still a long way off. Until then, there is always the risk of receiving answers that, while not technically wrong, may be inappropriate or fail to account for outside variables that only the human mind is capable of recognizing. Placing complete trust in AI as an indisputable source of truth could hold us back from eliminating biases and creating a fairer workplace and society. A primary aim of AI is to save time, so checking every answer is clearly counterproductive. But iteration is necessary for improvement. Ethical reviews for bias should be performed with at least as much regularity as checks for performance, efficiency and accuracy. You won’t always be able to predict where and when bias might occur, and how we define fairness is in a constant state of flux. If we commit to introspection now, we can ensure these technologies become forces of good for business and for humanity. AI could be humanity’s greatest tool in eliminating systemic bias, but we’ll need to be honest with ourselves and each other to confront our pre-existing biases. Photo by ali syaaban on Unsplash . Originally published on Forbes .

Looking Within to Battle Intrinsic Bias in AI 

Why I Quit a $500K job at Amazon to work for myself Last week I left my cushy job at Amazon after 8 years. Despite getting rewarded repeatedly with promotions, compensation, recognition, and praise, I wasn’t motivated enough to do another year. I spent my entire time in AWS building tools for developers. I liked that field so much that I would have been satisfied working in it for the rest of my life. I joined Amazon as an entry level developer. Within 3.5 years I had been promoted twice to a senior engineer, and I was practically guaranteed another promotion to principal engineer this year if I had stayed. My potential at the company was high, I was told. My esteem within the company grew along the years and I was regarded an expert and a leader in my field. People looked up to me and respected me. I made $75K in my first year and that gradually grew to $511K by my last year. I could have made another $1M if I stayed another couple of years. My work–life balance was good too, despite Amazon’s reputation. I didn’t need to prove myself anymore, and I could get everything done in 40 hours a week. My team worked from home one day a week, and I rarely opened my laptop at night or weekends. Also, the people I worked with were exceptional. I had three managers in total, and all were generous people with lots of empathy. I’m very grateful to everyone I worked with. Everything was going well and getting better. But despite all this, my motivation to go to work each morning was decreasing — almost in an inverse trend to my career and income growth. It would have been foolish of me to expect my motivation to start increasing if I got yet another promotion, or another compensation bump, or another big project. But there was something else that was trending down with my motivation. It was my freedom. The Motivation Decline For the first couple of years my motivation was off the charts. I was mostly working with another person on an internal tool, and there was very little scrutiny around it. It was a time where I had a lot of independence in choosing how to work and what to work on — at least relative to more recent years. It was just me and the other person improving this thing, talking to users, releasing updates, testing it, and everything else. Whatever we felt was important, we generally got to do. We did the best work we could for its own sake and we were mostly self-directed. The last couple of years, however, were quite different. I was leading the most important project in the history of my department, with many stakeholders and complex goals. What I could do was always bounded by my ability to convince all the people involved that it was the best way to navigate our goals. I was always going to be working on somebody else’s terms at Amazon. The terms were simple in the beginning (keep fixing the thing), but kept getting more complicated as the years passed by (maximize all goals; satisfy all stakeholders). Then there were other restrictions inherent to working in a large organization about how to do the work, what work to do, what goals to set, and what business was worth pursuing. This situation was squeezing me into doing things that I’d rather not do, and vice versa. Finding New Motivation What kind of work would I do if I had to do it forever? Not something that I did until I reached some milestone (an exit), but something that I would consider satisfactory if I continued to do it until I’m 80. What is out there that I could do that would make me excited waking up every day for the next 45 years that could also earn me enough money to cover my expenses? Is that too unambitious? I don’t think so. Because there are two types of drivers that get me out of bed in the morning. One comes from the outside in the form of a carrot or a stick. For instance, I’m not automatically driven to do my tax returns every April, but I make sure I do because I don’t want to go to prison. Or I might not want to work on something I dislike, but I do so anyway because I may need to pay the bills, or want to buy a fancy car. These are the extrinsic motivators. The other comes from within. This is what drives me to do things when there isn’t a carrot or a stick. Hobbies are one activity driven by this. But what I was looking for was something that I could do for a living that was also driven by this type of motivation: the intrinsic kind. Back to the question of whether this is too unambitious. See, I realized that extrinsic motivation doesn’t last. Whenever I got promoted, it felt good for a week, and then it was as over. When I first hit $100K income, I would take a peek at my W2 for a few days admiring the six digits, but then it wore off. When I hit $200K, $300K, $400K, and $500K, it was the same thing. I would be delusional to think that earning $1M, or $10M would suddenly make it different. And I feel the same with every other extrinsic reward or material possession. Getting them feels good for a while, but this wears off quickly. The things that don’t wear off are those that I’ve been doing since I was a kid, when nothing was forcing me to do them. Things such as writing code, selling my creations, charting my own path, calling it like I saw it. I know my strengths, and I know what motivates me, so why not do this all the time? I’m lucky to live in a time where I can do something independently in my area of expertise without requiring large amounts of capital or outside investors. So that’s what I’m doing. What’s Next? I’m going all in on independence, and I’m going to try to make a living with my own bare hands starting from nothing. I don’t expect to only do things that I like, but it will be on my terms. My target is to cover my family’s expenses before I run out of savings while doing something that intrinsically motivates me. What more would I ever want to be satisfied with my work? If you liked this article, I’m going to be writing more about: . How I take risks without consequential harm from uncertainty . How I set myself up financially before I took the plunge How I’m setting up my new independent business. And obviously about the lifestyle business that I’m starting. Now that I can use Twitter without being subject to Amazon’s social media policy, you can as I continue to document my journey. follow me there Originally published at danielvassallo.com on February 9, 2019.

Only Intrinsic Motivation Lasts

I can remember five years ago when I first heard of bitcoin. I read through the written by Satoshi Nakamoto. That’s not his real name. It’s a pseudonym. I think he was smart not to publicize his own name. It’s allowed us to focus on the technology, not a story which could be spun by the media. In this white paper, Nakamoto lays out a framework for how to manage a de-centralized ledger he calls the “Blockchain.” Blockchain technology could have many applications, but it’s first is implemented in the bitcoin platform. bitcoin white paper Every cryptocurrency has its own intrinsic value. Intrinsic value and money have been an argument going on for millennia. Does the U.S. Dollar have intrinsic value? Does gold have intrinsic value or is it a “barbarous relic?” Let’s start with bitcoin. It’s the first and largest market capitalized cryptocurrency, which makes it the most liquid. The intrinsic value of cryptocurrency is that it’s a de-centralized currency — no third party or “trusted” surrogate has the potential to dilute your purchasing power. No third party is charging you tons of fees at every stage of the transaction. There’s much less friction in each transaction. Bitcoin has intrinsic value because the money itself is a that allows for a store of value, a medium of exchange and ultimately a unit of account. The bitcoin platform itself provides the mechanisms for storage and transportation, and trading of value without the need of any other third party. As we’ll find, that’s a lot of intrinsic value, because no other money scheme provides that — not the U.S. Dollar, not any fiat currency, and not gold. technology Finally, if you want to see where and how blockchain technology is going to transform and disrupt most of the mainstream industries, . read this Store of Value — Cryptocurrency vs. Fiat Money If you think about what lies ahead for all developed countries, it all revolves around too much public debt. Whether its Europe with its or the United States with its or even Japan with its all developed countries have amassed huge debt. It’s going to take drastic fiscal policy, which is always politically difficult to push through, to resolve the debt issue. Added to the problem is the fact that most developed countries are fighting against aging demographics. 89% Debt to GDP 104% Debt to GDP 250% Debt to GDP All of these factors point to the fact that world reserve currencies, like the Yen or the U.S. Dollar, are going to get diluted through quantitative easing, money printing and other monetary policies. Or worse, massive deflation will happen if debt cannot be serviced, and massive amounts of money will get written off and go to money heaven. This bolsters the argument for storing purchasing power in a decentralized currency like bitcoin. Medium of Exchange — Cryptocurrency vs. National Fiat Money As for medium of exchange, there are many cryptocurrencies vying to provide this service. Bitcoin is primarily looking to be a store of value, not a medium of exchange. That may change with the coming , which will provide off-chain scalability and speed for transactions with low fees. Lightning Network Many other cryptocurrencies are looking to become the medium of exchange. Dash, Zcash, Monero, Bcash and others are all competing to become the dominant player for digital cash and transactions. They all have the potential to beat out fiat paper money in many ways. ACH payments can take 3–7 business days to settle. Fees and remittance services are high. Paper money only settles during business days/hours. It’s because the entire money system is based on paper. Cryptocurrencies as digital money solves all that. Cryptocurrencies can be transmitted 24/7 and can be settled in minutes. Fees are 1/10th that of their paper ancestors. They solve so many problems that paper money just can’t address. While we’re in the early stages, cryptocurrency networks are being built out to scale, and in the coming 1–2 years, will rival the scale for ACH and Visa, in terms of processing transactions per second. Unit of Account — Early Stages If you want a window into what can happen for developed countries without a large debt load, look no further than what is happening today in Venezuela, Argentina and Zimbabwe. As each of their respective national currencies experience hyperinflation, vendors and consumers have moved to seeing the value of bitcoin. You can see the . They do their own internal calculus to see what’s the safest way to maintain their wealth and purchasing power. They will take the volatility of bitcoin over the baked-in-inflation of their national currency. It’s so bad in Zimbabwe, there’s an for bitcoin. People are losing purchasing power by the day because of 1,000% to 10,000% inflation. People will look for alternatives and bitcoin seems to be solving the problem at the economy level. People are able to transact with a currency that makes more sense than the national currency which is experiencing systemic erosion of purchasing power. A couple years ago, . people are turning to bitcoin in Venezuela 85% premium on local exchanges Argentina was also experiencing inflation and people turned to bitcoin I’m not saying bitcoin should be the main global currency. I am saying that alternative currencies help keep national currencies honest. And when turmoil happens in a country’s monetary system like Venezuela, there is now an alternative that never existed before. Bitcoin is a self-contained system for storing and transferring value globally — and that is its intrinsic value. ________________________________________________________________ Disclaimer: The above references an opinion and is for information purposes only. It is not intended to be investment advice. Please do your own homework. Jake Ryan is the founder of Tradecraft Capital, a startup advisor, an angel investor & writer on investing. If you enjoyed this article “clap” to help others find it! For more, join us on Facebook , Twitter .

Money & the Debate of Intrinsic Value

This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license.
Authors:
(1) Brisha Jain, Independent researcher India and brishajain02@gmail.com;
(2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro
Related Work
Data Collection
Analysis Methodology
Results
Limitations
Concluding Discussion
References 5. RESULTS 5.1. Gender bias in interaction frequency and popularity of journalist-politician interactions (RQ1) In order to explore the answer to the first research question, we started with checking if there is a gender bias in the frequency of interaction (i.e., frequency of mentions) between politicians and journalists on Twitter. Male Politicians are more frequently mentioned by journalists: Figure 1a compares the CDF of the number tweets posted by journalists mentioning male and female politicians. We make an interesting observation from this figure. When the receiving politician is male (i.e., in MJ-MP and FJ-MP categories), the number of mentioned tweets (and hence the frequency of journalist-politician interaction) is higher when female politicians are at the receiving end. To that end, a Kruskal-Wallis test among the number of tweets per journalist within the four categories revealed quite statistically significant differences across categories (p << 0.05). Then we performed pairwise Mann-Whitney tests for following up among the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). There is no statistically significant difference when either a Male or Female journalist mentions Male politician accounts. Similarly, there is no statistically significant difference when either a Male or Female journalist mentions a Female politician account. However, there are statistically significant differences in how frequently Male/Female journalists mention Male politicians vs. how frequently they mention Female politicians (all p << 0.05). Next we compare the popularity per tweet directed toward Male vs. Female politicians. vely). Our observation implies that Twitter users in India seem to ascribe greater credibility to the views on male journalists on female politicians compared to the views of female journalists on female politicians. These observations hold for “retweets” as well. Overall, our popularity analysis of these four categories of tweets reveals that while journalists do not suffer from explicit bias in their interactions with politicians, there is evidence to support the existence of gender bias in the amount of interest these interactions generate from active Twitter users. 5.2. Gender bias within the content of journalist-politician tweets (RQ2) In the last section, our analysis showed a significant bias towards the Male politicians from both male and female journalists—the tweets mentioning male politicians are more frequent as well as more popular. However, to that end, we checked if the content of these tweets might be responsible for this bias. Specifically, we checked the emotion and the topic of tweets written by male / female journalists and directed towards male / female politicians. 5.2.1. Emotion analysis. : We used the TweetNLP tool for detecting the emotions of the tweets for each category [6]. TweetNLP provides a diachronic large-language model (TimeLMs) based approach for detecting emotion, specifically from multi-lingual tweets. The goal of this analysis is to determine if there are significant differences in the emotional scores of tweets—if there is that might indicate a gender bias inherently in the tweets based on the gender of sender and receiver. We considered four main emotions: anger, joy, optimism, and sadness and each tweet in each of the four categories were assigned for emotion score along these dimensions. Then we performed a Kruskal-Wallis test to identify if any of the emotions were different across the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). We found that the p-value for each of the four tests (one for each emotion) ranged from 0.16 to 0.99, hinting at no statistically significant difference within the emotions of the tweets. 5.2.2. Topic analysis. : To dig further, we performed a topic analysis of the tweets (using Latent Dirichlet Allocation or LDA) collected across four categories. The goal was to check if the topics of the tweets changed based on the gender of the sender or receiver. As described in Section 4 we identified the optimal number of topics (which are essentially clusters of words) for each category and identified the most significant five words for each topic using the LDA algorithm. For each of the four categories, the optimal number of topics were thirteen. Next, we identified the thirteen topics using the LDA algorithm for each category of tweets and performed a significant word analysis for the detected topics. Specifically, for each category of tweets we selected the topics (e.g., topics from MJ-MP) and picked the significant words representing each topic. Then for each topic we checked if those words also occured in the topics detected from other categories of tweets (if found it will signify that words representing topics are also present in topics detected from other categories of tweets). For each of the four categories of tweets on average 81.5% to 93.8% significant words (representing the topics) occur in topics detected from tweets of other categories. This analysis supports our observation from emotion analysis— the content of tweets across those four categories are the same. However, still the tweets directed towards male politicians attract more interaction compared to the tweets directed towards female politicians. Next we explore a potential reason for this gender bias. 5.3. Potential reason for the gender bias 5.3.1. Inherent Gender Bias in Indian Twitter: We checked a simple statistic regarding top politicians—how many of the most popular politicians (based on the number of Twitter followers) are male vs. they are female. To that end, we leveraged our dataset of top politicians and checked the gender of top 85 politicians (whose Twitter accounts are also part of this study). This analysis uncovered an unsettling gender imbalance among the top politicians—out of 85 top politicians 58 are male and 26 are female. Thus, popular male politicians are almost twice in number compared to popular female politicians. We postulate that this inequality is one of the key reasons behind our observed phenomenon of male politicians attracting significantly more interaction from the general public as well as journalists. In fact, this inequality reflects a systemic bias deeply ingrained in society. This gender disparity extends its influence even to the realm of Twitter, where male politicians tend to garner a larger number of followers than their female counterparts. This phenomenon isn’t isolated; it permeates various sectors, as illustrated by the dominance of men in top positions across industries. In corporate boardrooms, technology firms, and the entertainment sector, leadership roles are predominantly occupied by men. This systematic bias, rooted in societal norms, is further reinforced by the strong correlation between social capital and the attainment of positions of power. Consequently, popularity on Twitter serve as a stark reflection of this intrinsic bias. Addressing these disparities is paramount for fostering gender equality and dismantling deeply entrenched biases in society. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. This paper is available on arxiv under CC BY-NC-ND 4.0 DEED license. Authors: Authors: (1) Brisha Jain, Independent researcher India and brishajain02@gmail.com; (2) Mainack Mondal, IIT Kharagpur India and mainack@cse.iitkgp.ac.in. Table of Links Abstract and Intro Related Work Data Collection Analysis Methodology Results Limitations Concluding Discussion References Abstract and Intro Abstract and Intro Related Work Related Work Data Collection Data Collection Analysis Methodology Analysis Methodology Results Results Limitations Limitations Concluding Discussion Concluding Discussion References References 5. RESULTS 5.1. Gender bias in interaction frequency and popularity of journalist-politician interactions (RQ1) In order to explore the answer to the first research question, we started with checking if there is a gender bias in the frequency of interaction (i.e., frequency of mentions) between politicians and journalists on Twitter. Male Politicians are more frequently mentioned by journalists: Figure 1a compares the CDF of the number tweets posted by journalists mentioning male and female politicians. We make an interesting observation from this figure. When the receiving politician is male (i.e., in MJ-MP and FJ-MP categories), the number of mentioned tweets (and hence the frequency of journalist-politician interaction) is higher when female politicians are at the receiving end. To that end, a Kruskal-Wallis test among the number of tweets per journalist within the four categories revealed quite statistically significant differences across categories ( p << 0.05). Then we performed pairwise Mann-Whitney tests for following up among the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). There is no statistically significant difference when either a Male or Female journalist mentions Male politician accounts. Similarly, there is no statistically significant difference when either a Male or Female journalist mentions a Female politician account. However, there are statistically significant differences in how frequently Male/Female journalists mention Male politicians vs. how frequently they mention Female politicians (all p << 0.05). Next we compare the popularity per tweet directed toward Male vs. Female politicians. Male Politicians are more frequently mentioned by journalists: p p vely). Our observation implies that Twitter users in India seem to ascribe greater credibility to the views on male journalists on female politicians compared to the views of female journalists on female politicians. These observations hold for “retweets” as well. Overall, our popularity analysis of these four categories of tweets reveals that while journalists do not suffer from explicit bias in their interactions with politicians, there is evidence to support the existence of gender bias in the amount of interest these interactions generate from active Twitter users. 5.2. Gender bias within the content of journalist-politician tweets (RQ2) In the last section, our analysis showed a significant bias towards the Male politicians from both male and female journalists—the tweets mentioning male politicians are more frequent as well as more popular. However, to that end, we checked if the content of these tweets might be responsible for this bias. Specifically, we checked the emotion and the topic of tweets written by male / female journalists and directed towards male / female politicians. 5.2.1. Emotion analysis. : We used the TweetNLP tool for detecting the emotions of the tweets for each category [6]. TweetNLP provides a diachronic large-language model (TimeLMs) based approach for detecting emotion, specifically from multi-lingual tweets. The goal of this analysis is to determine if there are significant differences in the emotional scores of tweets—if there is that might indicate a gender bias inherently in the tweets based on the gender of sender and receiver. We considered four main emotions: anger, joy, optimism, and sadness and each tweet in each of the four categories were assigned for emotion score along these dimensions. Then we performed a Kruskal-Wallis test to identify if any of the emotions were different across the four categories (MJ-MP, MJ-FP, FJ-MP, FJ-FP). We found that the p -value for each of the four tests (one for each emotion) ranged from 0.16 to 0.99, hinting at no statistically significant difference within the emotions of the tweets. 5.2.1. Emotion analysis. p 5.2.2. Topic analysis. : To dig further, we performed a topic analysis of the tweets (using Latent Dirichlet Allocation or LDA) collected across four categories. The goal was to check if the topics of the tweets changed based on the gender of the sender or receiver. As described in Section 4 we identified the optimal number of topics (which are essentially clusters of words) for each category and identified the most significant five words for each topic using the LDA algorithm. For each of the four categories, the optimal number of topics were thirteen. Next, we identified the thirteen topics using the LDA algorithm for each category of tweets and performed a significant word analysis for the detected topics. Specifically, for each category of tweets we selected the topics (e.g., topics from MJ-MP) and picked the significant words representing each topic. Then for each topic we checked if those words also occured in the topics detected from other categories of tweets (if found it will signify that words representing topics are also present in topics detected from other categories of tweets). For each of the four categories of tweets on average 81.5% to 93.8% significant words (representing the topics) occur in topics detected from tweets of other categories. 5.2.2. Topic analysis. This analysis supports our observation from emotion analysis— the content of tweets across those four categories are the same. However, still the tweets directed towards male politicians attract more interaction compared to the tweets directed towards female politicians. Next we explore a potential reason for this gender bias. 5.3. Potential reason for the gender bias 5.3.1. Inherent Gender Bias in Indian Twitter : We checked a simple statistic regarding top politicians—how many of the most popular politicians (based on the number of Twitter followers) are male vs. they are female. To that end, we leveraged our dataset of top politicians and checked the gender of top 85 politicians (whose Twitter accounts are also part of this study). This analysis uncovered an unsettling gender imbalance among the top politicians—out of 85 top politicians 58 are male and 26 are female. Thus, popular male politicians are almost twice in number compared to popular female politicians. We postulate that this inequality is one of the key reasons behind our observed phenomenon of male politicians attracting significantly more interaction from the general public as well as journalists. 5.3.1. Inherent Gender Bias in Indian Twitter In fact, this inequality reflects a systemic bias deeply ingrained in society. This gender disparity extends its influence even to the realm of Twitter, where male politicians tend to garner a larger number of followers than their female counterparts. This phenomenon isn’t isolated; it permeates various sectors, as illustrated by the dominance of men in top positions across industries. In corporate boardrooms, technology firms, and the entertainment sector, leadership roles are predominantly occupied by men. This systematic bias, rooted in societal norms, is further reinforced by the strong correlation between social capital and the attainment of positions of power. Consequently, popularity on Twitter serve as a stark reflection of this intrinsic bias. Addressing these disparities is paramount for fostering gender equality and dismantling deeply entrenched biases in society.

Uncovering Gender Bias within Journalist-Politician Interaction in Indian Twitter: Results

ভারতীয় টুইটারে সাংবাদিক-রাজনীতিবিদ ইন্টারঅ্যাকশনের মধ্যে লিঙ্গ পক্ষপাত উন্মোচন: ফলাফল

Aufdeckung geschlechtsspezifischer Voreingenommenheit in der Interaktion zwischen Journalisten und Politikern auf Twitter in Indien: Ergebnisse

Descubriendo el sesgo de género en la interacción entre periodistas y políticos en Twitter indio: resultados

Découvrir les préjugés sexistes dans les interactions journalistes-hommes politiques sur Twitter indien : résultats

भारतीय ट्विटर पर पत्रकार-राजनेता बातचीत में लैंगिक पूर्वाग्रह का खुलासा: परिणाम

インドのツイッターにおけるジャーナリストと政治家のやり取りにおけるジェンダーバイアスの解明：結果

인도 트위터의 언론인-정치인 상호 작용에서 성 편견 발견: 결과

Descobrindo o preconceito de gênero na interação jornalista-político no Twitter indiano: resultados

Выявление гендерных предубеждений во взаимодействии журналистов и политиков в индийском Твиттере: результаты

Hint Twitter'ında Gazeteci-Politikacı Etkileşimindeki Cinsiyet Önyargısını Ortaya Çıkarmak: Sonuçlar

Khám phá thành kiến giới tính trong tương tác giữa nhà báo và chính trị gia trên Twitter của Ấn Độ: Kết quả

揭示印度推特上记者与政客互动中的性别偏见：结果

Authors:
(1) Omid Davoodi, Carleton University, School of Computer Science;
(2) Shayan Mohammadizadehsamakosh, Sharif University of Technology, Department of Computer Engineering;
(3) Majid Komeili, Carleton University, School of Computer Science. Table of Links Abstract and Intro Background Information Methodology Prototype Interpretability Prototype-query Similarity Interpretability of the Decision-Making Process The Effects of Low Prototype Counts Discussions Prototype Interpretability Our first set of experiments had the goal of finding out the interpretability of the prototypes created by each method. Many part prototype methods claim that their prototypes are intrinsically interpretable. In that case, humans should be able to understand the concept that is encoded within those prototypes. If a prototype represents a large area of an image without any coherent concept understandable to humans, it is not an interpretable prototype. In the first experiment, each person was shown a pair of prototypes (concept images in the case of ACE) on the same dataset coming from different methods. The annotators had to choose the prototype they found more interpretable out of the two. Every person had to complete 10 such pairs each time. Out of those 10, 2 were validation pairs handpicked by us to ensure answer quality. An example of such a pair and the instructions given to each person are show in Figure 4. Only people who had finished a qualification task consisting of 7 such pairs with perfect scores were allowed into the main study. Moreover, each 10-pair set of query samples was given to three different people to gauge overall agreement and find individuals who might have chosen randomly. Additionally, this experiment was repeated 3 times and the results were aggregated. This experiment was a purely comparative study between different methods. This ensured that people had a frame of reference when deciding which method worked better, and this is a common practice in many studies of subjective nature. But they are not without problems. One of the more important problems with this experiment was that it could not answer whether the prototypes from these methods were interpretable or not. It could only answer whether the prototypes of a particular method were more interpretable on average than the ones from another. To remedy this, we conducted another set of experiments. In the new absolute experiments the annotators were given the option to also choose "Both" and "None" if they thought that both or none of the images in the pair were interpretable. The reason we did not simply show them a single image asking for interpretability was to keep the layout of the experiments very similar. This not only simplified the logistics of creating query samples for the new experiment, but also gave us the opportunity to see whether the rankings of the methods seen in the comparative experiments remained the same when the new options were added. The assumption was that if a very similar experiment with those options added resulted in a drastic change in the rankings, it would reveal problems in our methodology that were hidden beforehand. Instructions given to each person were as follows: • For each pair of images, pick the one with the best highlighted patch. A good patch contains relevant and identifying regions/parts of the subject, and only such regions/parts. • If both images have good or bad patches, select "Both" or "None" respectively. • Imagine that someone points to the marked region and says: "This is an image of a bird/car/lion/etc. because of this part“. How much would you agree with that statement? It is important to note that new image pairs were created for this experiment. This was again to see if a new set of data changes the rankings considerably, revealing any potential hidden problems with the statistical significance of the results. A new qualification task and a validation set were also created. The rest of the setup was almost identical to the comparative experiment. Each time, the annotator was given 10 prototype image pairs and told to pick the best option for each pair, with two out of those 10 pairs coming from the validation set. The experiment was repeated 4 times and the results were aggregated. In total, 1104 pairs were compared and annotated by humans for the comparative study. Table 1 shows the percentage of times each method was chosen against others. As can be seen, prototypes selected by TesNet were chosen 73.26% of the time when compared to prototypes selected by other methods. In the second place was ProtoTree with 63.64% preference. It is also interesting to note the relative weakness of SPARROW and Deformable ProtoPNet. Table 2 shows the results of the same experiments but on a method-by-method basis. Each element of the table shows the results of the pairwise comparison between the two methods, with the method shown in the first column being the one selected out of the two. For example, the intersection between the row "ACE" and the column "ProtoPool" shows the percentage of times (42.86%) ACE was chosen against ProtoPool in the experiments. While there is more variation when looked at with this much detail, the general trends hold true. For the absolute experiments, 1304 pairs were compared and annotated by humans in total. Table 3 shows the percentage of times each method’s prototypes were chosen in general. As the options for "Both" and "None" also existed in this experiment, the results here show the percentage of prototypes from each method that the annotators found acceptable. While the overall rankings seem to have mostly remained the same, the higher percentages overall show that while some methods might lose in the comparative study, their prototypes are still interpretable, even if a bit less so than the more successful methods. These results also show that 95.18% of the prototypes created by TesNet and 89.56% of the ones by ProtoTree are seen as interpretable. These results match our own observations, where it seems that TesNet and ProtoTree offer consistently good prototypes in all of the datasets. Like before, Table 4 shows the results of the same experiments but on a method-by-method basis. Each row shows the percentage of times a method (shown on the first column) was selected either solely or as part of the "Both" option when paired with another method shown on the top row. Note that this time, the sum of mirrored cells does not necessarily add up to 100% due to the existence of "Both" and "None" options. The results show a fine-grained consistency in prototype interpretability rates across all methods. This paper is available on arxiv under CC 4.0 license. Authors: (1) Omid Davoodi, Carleton University, School of Computer Science; (2) Shayan Mohammadizadehsamakosh, Sharif University of Technology, Department of Computer Engineering; (3) Majid Komeili, Carleton University, School of Computer Science. Authors: Authors: (1) Omid Davoodi, Carleton University, School of Computer Science; (2) Shayan Mohammadizadehsamakosh, Sharif University of Technology, Department of Computer Engineering; (3) Majid Komeili, Carleton University, School of Computer Science. Table of Links Abstract and Intro Abstract and Intro Background Information Background Information Methodology Methodology Prototype Interpretability Prototype Interpretability Prototype-query Similarity Prototype-query Similarity Interpretability of the Decision-Making Process Interpretability of the Decision-Making Process The Effects of Low Prototype Counts The Effects of Low Prototype Counts Discussions Discussions Prototype Interpretability Our first set of experiments had the goal of finding out the interpretability of the prototypes created by each method. Many part prototype methods claim that their prototypes are intrinsically interpretable. In that case, humans should be able to understand the concept that is encoded within those prototypes. If a prototype represents a large area of an image without any coherent concept understandable to humans, it is not an interpretable prototype. In the first experiment, each person was shown a pair of prototypes (concept images in the case of ACE) on the same dataset coming from different methods. The annotators had to choose the prototype they found more interpretable out of the two. Every person had to complete 10 such pairs each time. Out of those 10, 2 were validation pairs handpicked by us to ensure answer quality. An example of such a pair and the instructions given to each person are show in Figure 4. Only people who had finished a qualification task consisting of 7 such pairs with perfect scores were allowed into the main study. Moreover, each 10-pair set of query samples was given to three different people to gauge overall agreement and find individuals who might have chosen randomly. Additionally, this experiment was repeated 3 times and the results were aggregated. This experiment was a purely comparative study between different methods. This ensured that people had a frame of reference when deciding which method worked better, and this is a common practice in many studies of subjective nature. But they are not without problems. One of the more important problems with this experiment was that it could not answer whether the prototypes from these methods were interpretable or not. It could only answer whether the prototypes of a particular method were more interpretable on average than the ones from another. To remedy this, we conducted another set of experiments. In the new absolute experiments the annotators were given the option to also choose "Both" and "None" if they thought that both or none of the images in the pair were interpretable. The reason we did not simply show them a single image asking for interpretability was to keep the layout of the experiments very similar. This not only simplified the logistics of creating query samples for the new experiment, but also gave us the opportunity to see whether the rankings of the methods seen in the comparative experiments remained the same when the new options were added. The assumption was that if a very similar experiment with those options added resulted in a drastic change in the rankings, it would reveal problems in our methodology that were hidden beforehand. Instructions given to each person were as follows: • For each pair of images, pick the one with the best highlighted patch. A good patch contains relevant and identifying regions/parts of the subject, and only such regions/parts. • If both images have good or bad patches, select "Both" or "None" respectively. • Imagine that someone points to the marked region and says: "This is an image of a bird/car/lion/etc. because of this part“. How much would you agree with that statement? It is important to note that new image pairs were created for this experiment. This was again to see if a new set of data changes the rankings considerably, revealing any potential hidden problems with the statistical significance of the results. A new qualification task and a validation set were also created. The rest of the setup was almost identical to the comparative experiment. Each time, the annotator was given 10 prototype image pairs and told to pick the best option for each pair, with two out of those 10 pairs coming from the validation set. The experiment was repeated 4 times and the results were aggregated. In total, 1104 pairs were compared and annotated by humans for the comparative study. Table 1 shows the percentage of times each method was chosen against others. As can be seen, prototypes selected by TesNet were chosen 73.26% of the time when compared to prototypes selected by other methods. In the second place was ProtoTree with 63.64% preference. It is also interesting to note the relative weakness of SPARROW and Deformable ProtoPNet. Table 2 shows the results of the same experiments but on a method-by-method basis. Each element of the table shows the results of the pairwise comparison between the two methods, with the method shown in the first column being the one selected out of the two. For example, the intersection between the row "ACE" and the column "ProtoPool" shows the percentage of times (42.86%) ACE was chosen against ProtoPool in the experiments. While there is more variation when looked at with this much detail, the general trends hold true. For the absolute experiments, 1304 pairs were compared and annotated by humans in total. Table 3 shows the percentage of times each method’s prototypes were chosen in general. As the options for "Both" and "None" also existed in this experiment, the results here show the percentage of prototypes from each method that the annotators found acceptable. While the overall rankings seem to have mostly remained the same, the higher percentages overall show that while some methods might lose in the comparative study, their prototypes are still interpretable, even if a bit less so than the more successful methods. These results also show that 95.18% of the prototypes created by TesNet and 89.56% of the ones by ProtoTree are seen as interpretable. These results match our own observations, where it seems that TesNet and ProtoTree offer consistently good prototypes in all of the datasets. Like before, Table 4 shows the results of the same experiments but on a method-by-method basis. Each row shows the percentage of times a method (shown on the first column) was selected either solely or as part of the "Both" option when paired with another method shown on the top row. Note that this time, the sum of mirrored cells does not necessarily add up to 100% due to the existence of "Both" and "None" options. The results show a fine-grained consistency in prototype interpretability rates across all methods. This paper is available on arxiv under CC 4.0 license. This paper is available on arxiv under CC 4.0 license. available on arxiv

Evaluating Prototype Interpretability

The Challenges of Building a Web3 Community from Scratch With Strategies for Success

Authors:
(1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk);
(2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk);
(3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction
Course information
Motivation and intervention
Findings
Discussion
Conclusion and References 6 Conclusion The results we present here are in line with previous research (for example [Lo and Hew, 2017], [Buckley and Doyle, 2016], [Dahlstrøm, 2012]) and show a stark increase in student engagement with pre-lecture activities on a course taught in flipped mode. As the pre-lecture activities of interest were quiz-based - with points awarded for attempting the quizzes on time rather than quiz score - we were able to investigate whether the increased engagement was meaningful or the result of students ‘gaming’ the leaderboard. In our study, there was no evidence of the latter. It is thought that the two statistics courses considered here are the only time the students came across the Level Up! plugin or other form of gamification. It is therefore not known whether the increased engagement is due to the novelty of a leaderboard and whether gamification would be as successful if implemented widely across multiple courses. The significant increase in student engagement after the adoption of a gamification strategy is certainly worthy of further research. Though the results from the gamification intervention were very positive, student engagement is still far from perfect. Diversity in teaching and learning approaches is surely the key to engaging students across their courses, with gamification a valuable strategy in a teacher’s toolbox. 7 References [Alomari et al., 2019] Alomari, I., Al-Samarraie, H., and Yousef, R. (2019). The role of gamification techniques in promoting student learning: A review and synthesis: A review and synthesis. Journal of Information Technology Education:Research, 18:395–417. [Blair, 2017] Blair, A. (2017). Understanding first-year students’ transition to university: A pilot study with implications for student engagement, assessment, and feedback. Politics, 37.2:215–228. [Bredow et al., 2021] Bredow, C. A., Roehling, P. V., Knorp, A. J., and Sweet, A. M. (2021). To flip or not to flip? a meta-analysis of the efficacy of flipped learning in higher education. Review of educational research, 91(6):878–918. [Buckley and Doyle, 2016] Buckley, P. and Doyle, E. (2016). Gamification and student motivation. Interactive Learning Environments, 24(6):1162–1175. [Cronhjort et al., 2017] Cronhjort, M., Filipsson, L., and Weurlander, M. (2017). Improved engagement and learning in flipped-classroom calculus. Teaching Mathematics and its Applications: An International Journal of the IMA, 37(3):113–121. [Dahlstrøm, 2012] Dahlstrøm, C. (2012). Impacts of gamification on intrinsic motivation. Education and Humanities Research, pages 1–11. [Deterding et al., 2011] Deterding, S., Khaled, R., Nacke, L. E., Dixon, D., et al. (2011). Gamification: Toward a definition. In CHI 2011 gamification workshop proceedings, volume 12, pages 1–79. Vancouver, Canadá. [Ding et al., 2018] Ding, L., Er, E., and Orey, M. (2018). An exploratory study of student engagement in gamified online discussions. Computers & Education, 120:213–226. [Domínguez et al., 2013] Domínguez, A., de Navarrete, J. S., de Marcos, L., Fernández-Sanz, L., Pagés, C., and Martínez-Herráiz, J.-J. (2013). Gamifying learning experiences: Practical implications and outcomes. Computers & Education, 63:380–392. [Dong et al., 2012] Dong, T., Dontcheva, M., Joseph, D., Karahalios, K., Newman, M., and Ackerman, M. (2012). Discovery-based games for learning software. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’12, page 2083–2086, New York, NY, USA. Association for Computing Machinery. [Ekici, 2021] Ekici, M. (2021). A systematic review of the use of gamification in flipped learning. Education and Information Technologies, 26(3):3327–3346. [Hakulinen et al., 2013] Hakulinen, L., Auvinen, T., and Korhonen, A. (2013). Empirical study on the effect of achievement badges in trakla2 online learning environment. In 2013 Learning and Teaching in Computing and Engineering, pages 47–54. [Hartikainen et al., 2019] Hartikainen, S., Rintala, H., Pylväs, L., and Nokelainen, P. (2019). The concept of active learning and the measurement of learning outcomes: A review of research in engineering higher education. Education Sciences, 9(4):276. [Heilbrunn et al., 2017] Heilbrunn, B., Herzig, P., and Schill, A. (2017). Gamification Analytics—Methods and Tools for Monitoring and Adapting Gamification Designs, pages 31–47. Springer International Publishing, Cham. [Huang and Hew, 2018] Huang, B. and Hew, K. F. (2018). Implementing a theory-driven gamification model in higher education flipped courses: Effects on out-of-class activity completion and quality of artifacts. Computers & Education, 125:254–272. [Ireland and Mouthaan, 2020] Ireland, J. and Mouthaan, M. (2020). Perspectives on curriculum design: comparing the spiral and the network models. Research Matters. [Kim and Castelli, 2021] Kim, J. and Castelli, D. M. (2021). Effects of gamification on behavioral change in education: A meta-analysis. International Journal of Environmental Research and Public Health, 18(7). [Leitner et al., 2017] Leitner, P., Khalil, M., and Ebner, M. (2017). Learning Analytics in Higher Education—A Literature Review, volume 94, pages 1–23. Springer International Publishing. [Lo and Hew, 2017] Lo, C. K. and Hew, K. F. (2017). A critical review of flipped classroom challenges in k-12 education: possible solutions and recommendations for future research. Research and Practice in Technology Enhanced Learning, 12(1):4. [Lo et al., 2017] Lo, C. K., Hew, K. F., and Chen, G. (2017). Toward a set of design principles for mathematics flipped classrooms: A synthesis of research in mathematics education. Educational Research Review, 22:50–73. [Marcey, 2014] Marcey, D. J. (2014). The lecture hall as an arena of inquiry: Using cinematic lectures and inverted classes (clic) to flip an introductory biology lecture course. Archive of the Academic Commons. [Owens et al., 2020] Owens, D. C., Sadler, T. D., Barlow, A. T., and Smith-Walters, C. (2020). Student motivation from and resistance to active learning rooted in essential science practices. Research in Science Education, 50(1):253–277. [Stone, 2012] Stone, B. B. (2012). Flip your classroom to increase active learning and student engagement. In Proceedings from 28th Annual Conference on Distance Teaching and Learning, Madison, Wisconsin, USA. [Touchton, 2015] Touchton, M. (2015). Flipping the classroom and student performance in advanced statistics: Evidence from a quasi-experiment. Journal of Political Science Education, 11(1):28–44. [Wester et al., 2021] Wester, E. R., Walsh, L. L., Arango-Caro, S., and Callis-Duehl, K. L. (2021). Student engagement declines in stem undergraduates during covid-19–driven remote learning. Journal of Microbiology & Biology Education, 22(1):10.1128/jmbe.v22i1.2385. [Wilson, 2013] Wilson, S. G. (2013). The flipped class: A method to address the challenges of an undergraduate statistics course. Teaching of Psychology, 40(3):193–199. [Wood, 2023] Wood, A. (2023). Effective Teaching in Large STEM Classes. IOP Series in Physics Education. IOP Publishing. Publisher Copyright: © IOP Publishing Ltd 2023. All rights reserved. [Zaric et al., 2017] Zaric, N., Scepanovic, S., Markovic, T., Ljucovic, J., and Davcev, D. (2017). The model for gamification of e-learning in higher education based on learning styles. In International Conference on ICT Innovations, pages 265–273. This paper is available on arxiv under CC BY 4.0 DEED license. Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Authors: Authors: (1) Eilidh Jack, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (eilidh.jack@glasgow.ac.uk); (2) Craig Alexander, School of Mathematics and Statistics, University of Glasgow, Glasgow, G12 8QQ (craig.alexander.2@glasgow.ac.uk); (3) Elinor Jones, Department of Statistical Science, University College London, London, WC1E 7HB (elinor.jones@ucl.ac.uk). Table of Links Abstract and Introduction Course information Motivation and intervention Findings Discussion Conclusion and References Abstract and Introduction Abstract and Introduction Course information Course information Motivation and intervention Motivation and intervention Findings Findings Discussion Discussion Conclusion and References Conclusion and References 6 Conclusion The results we present here are in line with previous research (for example [Lo and Hew, 2017], [Buckley and Doyle, 2016], [Dahlstrøm, 2012]) and show a stark increase in student engagement with pre-lecture activities on a course taught in flipped mode. As the pre-lecture activities of interest were quiz-based - with points awarded for attempting the quizzes on time rather than quiz score - we were able to investigate whether the increased engagement was meaningful or the result of students ‘gaming’ the leaderboard. In our study, there was no evidence of the latter. It is thought that the two statistics courses considered here are the only time the students came across the Level Up! plugin or other form of gamification. It is therefore not known whether the increased engagement is due to the novelty of a leaderboard and whether gamification would be as successful if implemented widely across multiple courses. The significant increase in student engagement after the adoption of a gamification strategy is certainly worthy of further research. Though the results from the gamification intervention were very positive, student engagement is still far from perfect. Diversity in teaching and learning approaches is surely the key to engaging students across their courses, with gamification a valuable strategy in a teacher’s toolbox. 7 References [Alomari et al., 2019] Alomari, I., Al-Samarraie, H., and Yousef, R. (2019). The role of gamification techniques in promoting student learning: A review and synthesis: A review and synthesis. Journal of Information Technology Education:Research, 18:395–417. [Blair, 2017] Blair, A. (2017). Understanding first-year students’ transition to university: A pilot study with implications for student engagement, assessment, and feedback. Politics, 37.2:215–228. [Bredow et al., 2021] Bredow, C. A., Roehling, P. V., Knorp, A. J., and Sweet, A. M. (2021). To flip or not to flip? a meta-analysis of the efficacy of flipped learning in higher education. Review of educational research, 91(6):878–918. [Buckley and Doyle, 2016] Buckley, P. and Doyle, E. (2016). Gamification and student motivation. Interactive Learning Environments, 24(6):1162–1175. [Cronhjort et al., 2017] Cronhjort, M., Filipsson, L., and Weurlander, M. (2017). Improved engagement and learning in flipped-classroom calculus. Teaching Mathematics and its Applications: An International Journal of the IMA, 37(3):113–121. [Dahlstrøm, 2012] Dahlstrøm, C. (2012). Impacts of gamification on intrinsic motivation. Education and Humanities Research, pages 1–11. [Deterding et al., 2011] Deterding, S., Khaled, R., Nacke, L. E., Dixon, D., et al. (2011). Gamification: Toward a definition. In CHI 2011 gamification workshop proceedings, volume 12, pages 1–79. Vancouver, Canadá. [Ding et al., 2018] Ding, L., Er, E., and Orey, M. (2018). An exploratory study of student engagement in gamified online discussions. Computers & Education, 120:213–226. [Domínguez et al., 2013] Domínguez, A., de Navarrete, J. S., de Marcos, L., Fernández-Sanz, L., Pagés, C., and Martínez-Herráiz, J.-J. (2013). Gamifying learning experiences: Practical implications and outcomes. Computers & Education, 63:380–392. [Dong et al., 2012] Dong, T., Dontcheva, M., Joseph, D., Karahalios, K., Newman, M., and Ackerman, M. (2012). Discovery-based games for learning software. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’12, page 2083–2086, New York, NY, USA. Association for Computing Machinery. [Ekici, 2021] Ekici, M. (2021). A systematic review of the use of gamification in flipped learning. Education and Information Technologies, 26(3):3327–3346. [Hakulinen et al., 2013] Hakulinen, L., Auvinen, T., and Korhonen, A. (2013). Empirical study on the effect of achievement badges in trakla2 online learning environment. In 2013 Learning and Teaching in Computing and Engineering, pages 47–54. [Hartikainen et al., 2019] Hartikainen, S., Rintala, H., Pylväs, L., and Nokelainen, P. (2019). The concept of active learning and the measurement of learning outcomes: A review of research in engineering higher education. Education Sciences, 9(4):276. [Heilbrunn et al., 2017] Heilbrunn, B., Herzig, P., and Schill, A. (2017). Gamification Analytics—Methods and Tools for Monitoring and Adapting Gamification Designs, pages 31–47. Springer International Publishing, Cham. [Huang and Hew, 2018] Huang, B. and Hew, K. F. (2018). Implementing a theory-driven gamification model in higher education flipped courses: Effects on out-of-class activity completion and quality of artifacts. Computers & Education, 125:254–272. [Ireland and Mouthaan, 2020] Ireland, J. and Mouthaan, M. (2020). Perspectives on curriculum design: comparing the spiral and the network models. Research Matters. [Kim and Castelli, 2021] Kim, J. and Castelli, D. M. (2021). Effects of gamification on behavioral change in education: A meta-analysis. International Journal of Environmental Research and Public Health, 18(7). [Leitner et al., 2017] Leitner, P., Khalil, M., and Ebner, M. (2017). Learning Analytics in Higher Education—A Literature Review, volume 94, pages 1–23. Springer International Publishing. [Lo and Hew, 2017] Lo, C. K. and Hew, K. F. (2017). A critical review of flipped classroom challenges in k-12 education: possible solutions and recommendations for future research. Research and Practice in Technology Enhanced Learning, 12(1):4. [Lo et al., 2017] Lo, C. K., Hew, K. F., and Chen, G. (2017). Toward a set of design principles for mathematics flipped classrooms: A synthesis of research in mathematics education. Educational Research Review, 22:50–73. [Marcey, 2014] Marcey, D. J. (2014). The lecture hall as an arena of inquiry: Using cinematic lectures and inverted classes (clic) to flip an introductory biology lecture course. Archive of the Academic Commons. [Owens et al., 2020] Owens, D. C., Sadler, T. D., Barlow, A. T., and Smith-Walters, C. (2020). Student motivation from and resistance to active learning rooted in essential science practices. Research in Science Education, 50(1):253–277. [Stone, 2012] Stone, B. B. (2012). Flip your classroom to increase active learning and student engagement. In Proceedings from 28th Annual Conference on Distance Teaching and Learning, Madison, Wisconsin, USA. [Touchton, 2015] Touchton, M. (2015). Flipping the classroom and student performance in advanced statistics: Evidence from a quasi-experiment. Journal of Political Science Education, 11(1):28–44. [Wester et al., 2021] Wester, E. R., Walsh, L. L., Arango-Caro, S., and Callis-Duehl, K. L. (2021). Student engagement declines in stem undergraduates during covid-19–driven remote learning. Journal of Microbiology & Biology Education, 22(1):10.1128/jmbe.v22i1.2385. [Wilson, 2013] Wilson, S. G. (2013). The flipped class: A method to address the challenges of an undergraduate statistics course. Teaching of Psychology, 40(3):193–199. [Wood, 2023] Wood, A. (2023). Effective Teaching in Large STEM Classes. IOP Series in Physics Education. IOP Publishing. Publisher Copyright: © IOP Publishing Ltd 2023. All rights reserved. [Zaric et al., 2017] Zaric, N., Scepanovic, S., Markovic, T., Ljucovic, J., and Davcev, D. (2017). The model for gamification of e-learning in higher education based on learning styles. In International Conference on ICT Innovations, pages 265–273. This paper is available on arxiv under CC BY 4.0 DEED license. This paper is available on arxiv under CC BY 4.0 DEED license. available on arxiv

Exploring the Impact of Gamification in Flipped Classrooms: Conclusion and References

Fifteen years ago, crypto wasn’t on the mainstream media’s radar; but today, it frequently captures the spotlight on drive-time, lunch-time, and evening news channels, drawing both seasoned investors and curious newcomers into its orbit. While it’s exciting to witness this once-niche market gaining traction as the flood of crypto headlines inundate our newsfeeds, we have to ask: can we have too much of a good thing? Taking note of every headline as the ticker goes up; and down (sometimes multiple times a day) would be akin to suffering from investor whiplash… Sensationalism vs. substance Crypto headlines often oscillate between extremes. A prime example of this is a headline that read “Ethereum price lags”; while another read “Ethereum trader strikes gold as prices soar…” From exuberant predictions around astronomical gains to dire warnings of impending collapse - and everything in between - it can be hard to make head or tail of what’s actually going on. A broader view of crypto investment takes into account the long-term fundamentals that drive crypto adoption and value, including technological innovation, adoption by institutions and mainstream users, regulatory developments, macroeconomic trends, and societal acceptance. When investors read too much into market sentiment fueled only by headlines, they risk making short-sighted investment decisions fuelled by market manipulation and short-term noise. Understanding the broader context: the crypto market cycle “Do your own research" (DYOR) is a cardinal rule in investment circles, underlining the importance of doing your own independent analysis and due diligence before making financial decisions. In the dynamic world of crypto, this mantra holds particular significance where relying solely on hearsay (including mainstream media narratives) or social media hype can lead investors astray, exposing them to unnecessary risks and potential losses. To navigate the crypto landscape effectively, astute investors are those who have taken the time to understand the nuances of Bitcoin’s market cycle (and its effect on altcoins such as Ethereum). This four-year cycle is often referred to as the ‘halving cycle’ and has been historically closely linked to price, with the cycle progressing through ‘Bull’ (rising prices); ‘Bear’ (falling prices); ‘Accumulation’ (a leveling out of prices) and ‘Expansion’ (steady growth). Importantly, within each of these phases, there might be rallies, reactions, and a lot of volatility on (hopefully) the way up. Disclaimer: Crypto is volatile, carries risk and the value can go up and down. Past performance is not an indicator of future returns. Please DO YOUR OWN RESEARCH! Back to basics: time-tested investment strategies Amidst a frenzy of post-halving headlines, investor disappointment around the absence of immediate spikes in price is real, but now is not the time to abandon proven investment principles. Instead, investors should anchor their strategies in time-tested approaches designed to weather market volatility: Dollar-cost averaging (DCA): Embrace the discipline of regularly investing fixed amounts, irrespective of market conditions. DCA mitigates the impact of short-term price fluctuations by averaging out costs over time and promoting a steady accumulation of assets.


The Hold-On-for-Dear-Life (HODL) strategy: In a market rife with volatility, the HODL mantra focuses on the importance of long-term conviction over short-term speculation. By focusing on the intrinsic value and fundamentals of assets, rather than succumbing to market hysteria, investors can cultivate their resilience.


Diversification: Diversification remains a cornerstone of sound investment practice, and sees investors spread investments across different assets (such as types of coins) to minimize risk from volatile market swings. A diversified portfolio fosters resilience and safeguards against unforeseen market turbulence. Don’t let hype eclipse substance! While an influx of crypto headlines signifies exciting maturation of the digital asset ecosystem, excessive fixation on sensational news can prove highly detrimental to investors' long-term success. By exercising discernment amidst the noise, understanding the broader market context, and adhering to time-tested investment strategies, investors can navigate the crypto landscape with confidence. Fifteen years ago, crypto wasn’t on the mainstream media’s radar; but today, it frequently captures the spotlight on drive-time, lunch-time, and evening news channels, drawing both seasoned investors and curious newcomers into its orbit. crypto While it’s exciting to witness this once-niche market gaining traction as the flood of crypto headlines inundate our newsfeeds, we have to ask: can we have too much of a good thing? Taking note of every headline as the ticker goes up; and down (sometimes multiple times a day) would be akin to suffering from investor whiplash… Sensationalism vs. substance Sensationalism vs. substance Crypto headlines often oscillate between extremes. A prime example of this is a headline that read “Ethereum price lags”; while another read “Ethereum trader strikes gold as prices soar…” From exuberant predictions around astronomical gains to dire warnings of impending collapse - and everything in between - it can be hard to make head or tail of what’s actually going on. actually A broader view of crypto investment takes into account the long-term fundamentals that drive crypto adoption and value, including technological innovation, adoption by institutions and mainstream users, regulatory developments, macroeconomic trends, and societal acceptance. When investors read too much into market sentiment fueled only by headlines, they risk making short-sighted investment decisions fuelled by market manipulation and short-term noise. Understanding the broader context: the crypto market cycle Understanding the broader context: the crypto market cycle “Do your own research" (DYOR) is a cardinal rule in investment circles, underlining the importance of doing your own independent analysis and due diligence before making financial decisions. In the dynamic world of crypto, this mantra holds particular significance where relying solely on hearsay (including mainstream media narratives) or social media hype can lead investors astray, exposing them to unnecessary risks and potential losses. To navigate the crypto landscape effectively, astute investors are those who have taken the time to understand the nuances of Bitcoin’s market cycle (and its effect on altcoins such as Ethereum). This four-year cycle is often referred to as the ‘halving cycle’ and has been historically closely linked to price, with the cycle progressing through ‘Bull’ (rising prices); ‘Bear’ (falling prices); ‘Accumulation’ (a leveling out of prices) and ‘Expansion’ (steady growth). Importantly, within each of these phases, there might be rallies, reactions, and a lot of volatility on (hopefully) the way up. Disclaimer: Crypto is volatile, carries risk and the value can go up and down. Past performance is not an indicator of future returns. Please DO YOUR OWN RESEARCH! Disclaimer: Crypto is volatile, carries risk and the value can go up and down. Past performance is not an indicator of future returns. Please DO YOUR OWN RESEARCH! Back to basics: time-tested investment strategies Back to basics: time-tested investment strategies Amidst a frenzy of post-halving headlines, investor disappointment around the absence of immediate spikes in price is real, but now is not the time to abandon proven investment principles. Instead, investors should anchor their strategies in time-tested approaches designed to weather market volatility: absence of immediate spikes in price Dollar-cost averaging (DCA): Embrace the discipline of regularly investing fixed amounts, irrespective of market conditions. DCA mitigates the impact of short-term price fluctuations by averaging out costs over time and promoting a steady accumulation of assets. The Hold-On-for-Dear-Life (HODL) strategy: In a market rife with volatility, the HODL mantra focuses on the importance of long-term conviction over short-term speculation. By focusing on the intrinsic value and fundamentals of assets, rather than succumbing to market hysteria, investors can cultivate their resilience. Diversification: Diversification remains a cornerstone of sound investment practice, and sees investors spread investments across different assets (such as types of coins) to minimize risk from volatile market swings. A diversified portfolio fosters resilience and safeguards against unforeseen market turbulence. Dollar-cost averaging (DCA): Embrace the discipline of regularly investing fixed amounts, irrespective of market conditions. DCA mitigates the impact of short-term price fluctuations by averaging out costs over time and promoting a steady accumulation of assets. Dollar-cost averaging (DCA): Embrace the discipline of regularly investing fixed amounts, irrespective of market conditions. DCA mitigates the impact of short-term price fluctuations by averaging out costs over time and promoting a steady accumulation of assets. Dollar-cost averaging (DCA): The Hold-On-for-Dear-Life (HODL) strategy: In a market rife with volatility, the HODL mantra focuses on the importance of long-term conviction over short-term speculation. By focusing on the intrinsic value and fundamentals of assets, rather than succumbing to market hysteria, investors can cultivate their resilience. The Hold-On-for-Dear-Life (HODL) strategy: In a market rife with volatility, the HODL mantra focuses on the importance of long-term conviction over short-term speculation. By focusing on the intrinsic value and fundamentals of assets, rather than succumbing to market hysteria, investors can cultivate their resilience. The Hold-On-for-Dear-Life (HODL) strategy: Diversification: Diversification remains a cornerstone of sound investment practice, and sees investors spread investments across different assets (such as types of coins) to minimize risk from volatile market swings. A diversified portfolio fosters resilience and safeguards against unforeseen market turbulence. Diversification: Diversification remains a cornerstone of sound investment practice, and sees investors spread investments across different assets (such as types of coins) to minimize risk from volatile market swings. A diversified portfolio fosters resilience and safeguards against unforeseen market turbulence. Diversification: Don’t let hype eclipse substance! While an influx of crypto headlines signifies exciting maturation of the digital asset ecosystem, excessive fixation on sensational news can prove highly detrimental to investors' long-term success. By exercising discernment amidst the noise, understanding the broader market context, and adhering to time-tested investment strategies, investors can navigate the crypto landscape with confidence.

Why Obsessing Over Crypto Headlines Is a Dangerous Investment Habit

Adaptive Graph Neural Networks for Cosmological Data Generalization: Acknowledgements and Disclosure

Colliding Magnetospheres in The Young High-Eccentricity Binary DQ Tau: Flare Analyses

Really? So you're just going to open anyway? I really wanted you to skip this one. I was busy travelling, and I had nothing to write. Came back from an 18 hour flight and kept thinking of what to write, but I couldn’t. So I figured I would ask for a favor and have you pass this one. Yet here we are.. So thanks for that.. What I am supposed to do now? Let me think. Oh— You know what? I have a quick story to tell. I was visiting my brother on a small and (very) remote island of French Polynesia called Nuku Hiva. So small, in fact, that it doesn’t even appear on the map unless you zoom wayyy in. Don’t believe me? The point is, it’s a small airport, and you have to walk yourself to the terminal. They have cones that tell you where to walk, but most importantly, where they don’t want you to walk past. On the way in, I was tired from my long journey there. I just followed the line. On the way out, I was feeling a little more like myself. I saw these cones again. And they saw me. They were lined up like soldiers, proudly wearing bright orange. Probably to show off, and also form a contrast to the tarmac. The cones seemed to stare back at me with a challenge. I am pretty sure they were whispering “Go ahead, step over. We dare you.” Wait. What? I looked at them. My pulse quickened immediately. I was being challenged. “What do you mean you dare me to step over? Do you even know me? I can do whatever I want.” I glance around. Everyone else is obediently tracing the path to the plane. Not me. The impulse takes over. I step over the cone barrier, and my foot lands on the other side.. I suddenly feel like a moonwalker claiming uncharted territory. The hot Polynesian air crackled with tension. I could feel the cones' silent outrage at my audacity. The thrill of the forbidden surges through me. My heart thumps in exhilaration. I am euphoric. But then, as if on cue, “Excuse me, sir, please return to the path,” a voice cut through my daydream. A staff member waved her hands with a mix of fear and anger. And just like that—I was caught. My quest was over. I had to wave my white flag, and step back over. Although they reclaimed my newly acquired territory, they did not take away my victory. I stepped back into line, but I had a smirk on my lips. The thrill of defiance had sent a jolt of excitement through my veins. It was a potent mixture of satisfaction and adrenaline. A simple act of rebellion that felt disproportionately exhilarating. Anyway.. I make my way to my seat on the plane. And then I start thinking.. Why do these feelings emerge so strongly when we push against boundaries? You see, it wasn't about the cones or the airport—it was a snapshot of a deeper, almost instinctual drive within us all. This urge to defy the norm. The urge to resist restrictions. It taps into something fundamental about human nature. The intrinsic desire for autonomy and freedom. This instinct inspired today's letter. It's about the allure of the forbidden and the resistance to being told what not to do. As it turns out, it plays in various facets of our lives. The small acts of rebellion when we pick the road less traveled The significant decisions that define our individuality The way we make decisions in general How we are influenced by people, companies, and marketing 1. Why We Chase the Forbidden Reflecting on that moment of rebellion at the airport. It’s clear this isn't just personal. It's universal. This instinct to challenge the boundaries is deeply embedded in us all. Just look at how Apple used this trait with their "Think Different" campaign. Apple didn’t sell products—they sold an idea. You know how? They aligned their brand with Einstein, Dylan, and MLK. Apple celebrated those who defy norms. They joined those who tap our desire to be part of something different. The Genius of "Think Different" Apple's strategy was a cultural statement more than it was pure marketing. These icons weren’t just talented; they were revolutionaries. Their simple message? It's cool to be different; and it's powerful to think outside the box. This strategy wasn't about selling; it was about igniting a cultural shift. Think about it—how often do we gravitate towards things just because they offer us a taste of defiance? That’s what Apple mastered. It’s almost like they marketed a rebellion. 2. The Neuroscience Behind the Temptation When we're told not to do something, our brain's instinct isn't just to listen—it's often to rebel. This rebellion is powered by our brain's reward system. Neurotransmitters, like dopamine, play a crucial role in it. We often pair dopamine with pleasure. But it's also about motivation, learning, and seeking rewards. When faced with a prohibition, our brain perceives it as a challenge. Engaging in the forbidden act and succeeding triggers a release of dopamine, which feels rewarding. Then there’s the prefrontal cortex—our brain’s rational part that tries to keep us in check. Dopamine signals pleasure and potential reward. The prefrontal cortex calculates the consequences. It tries to apply the brakes, weighing long-term outcomes against immediate gratification. Fun fact. This part of the brain is still developing in young adults. This explains why teenagers often take more risks. And boy was mine still developing during my teenage years. When you and I were barely walking on two feet and had hair to make Chewbacca jealous, our survival relied on curiosity and exploration. Survival depended on exploring the unknown and overcoming obstacles. This required questioning and testing the environment all the time. It was not about liking to try new things; it's about the neurological reward systems that encouraged those traits. It was necessary for survival, and it was neural, too. I was necessary for things like discovering what to eat. Imagine our ancestors venturing into new territories in search of something to eat. They come across a mushroom, bright red with white spots—pretty much screaming, "Don't touch me bro". But, curiosity and defiance? It's built-in. So, someone had to take the first bite, right? And you can bet they regretted it right after. That’s the kind of trial and error that taught everyone else to steer clear of the fancy fungi. But not all explorations ended badly. Sometimes, they found new, nourishing foods. These foods became staples in their diet. It makes me wonder about the first person who discovered fire—what an intense moment that must have been. Anyway, just some food for thought on your own terms. Fast forward to today. This system still urges us to push boundaries and explore new possibilities. This is true even when we’re safe from saber-toothed tigers. This legacy influences everything. It affects how we approach challenges. It explains why a simple "Do not push" button becomes almost irresistible. It explains why you felt compelled to open this letter even when explicitly told not to. Understanding these impulses allows us to channel them well. We can use them to motivate ourselves, manage others, and craft messages that resonate on a gut level. By acknowledging and embracing this innate curiosity, we can turn a simple urge into a powerful tool for engagement. 3. Defying Conventions: My Path to Independence Thinking back on where I am today, a lot of it came from just not fitting the mold. I wanted to go to an architecture school, but never could. Why? Well, I just didn't vibe with the whole school scene. My grades weren't great—not because I couldn't handle the work of because or below average intelligence. It was because I couldn't stand the way they taught things. So, I'd end up doodling and distracting others instead of paying attention. My jobs? I've always had a bit of a tough time with authority. Starting my company was my way out. I did not like having to obey my superiors at work simply because they were my superiors. When they were wrong, I made sure they knew. They did not like it, and I rarely kept a job for more than 6 months. Looking back, each of those defiant steps led me right here, to doing my own thing my own way. This journey isn't just mine. It's a testament to the power of questioning norms. This power can redefine personal paths and how we approach marketing and social engagement. Speaking of Marketing.. 4. Impact on Marketing and Social Behavior My journey away from traditional roles mirrors the tactics businesses use to captivate consumers. Here's the thing about marketing—it thrives on our natural urge to reach for the forbidden. Take "limited edition" products or "members-only" sales. This is about scarcity, but not only. It’s makes us feel like we're part of an exclusive club, stepping past the rope where others can't go. In video games, they hide Easter eggs you can only find by not following the usual paths. It turns players into insiders, sharing secrets that feel almost rebellious. Fashion plays with this, too. Look at those unannounced drops—they create a buzz by being elusive, sparking a mad dash to grab something unique before it disappears. This marketing savvy taps directly into our love for the elusive and the exclusive, turning simple buying decisions into moments of personal triumph against the norm. After stepping back into line at the airport, smirking from my small act of rebellion, I realized something crucial: the same thrill of defiance that fuels our personal choices can powerfully drive our business strategies. This realization isn't just about me—it's about how any business can tap into these universal instincts to craft compelling marketing strategies and create unforgettable customer experiences. Let's explore how this instinct for defiance can be strategically harnessed in your business to captivate and engage your audience. 5. How to Use This in Your Business - Framework and ideas Understanding Reactance in Marketing Let's talk about using reactance in a way that really resonates with people. How about we make our marketing a bit rebellious? Imagine you own a boutique coffee shop that prides itself on unique, ethically sourced beans. To use reactance, you could launch a campaign called "Not your grandma’s Bustelo." It would say that your coffee is for those who dare to taste the unique and bold. This resonates with consumers' desire to rebel. Each purchase is an act of defiance. Scarcity and Exclusivity Ever noticed how a 'limited-time offer' makes us want to buy something right now? That's scarcity at work. It's like telling people, "Hey, these gems won’t last." It makes whatever you're selling seem more valuable. It has everyone rushing to make sure they don’t miss out. Imagine this. You are a business coach. You're offering an elite retreat named " " to your top-tier clients. Figure It Out, or Die It starts with a tough, three-day jungle trek. The trek pushes clients to their physical and mental limits. This adventure sets the stage for the climax. It's a coaching session in total darkness, deep in a secluded jungle cabin. There are no distractions. This compels clients to rely only on their sharp instincts and introspective insights. Limit it to just three clients annually. This exclusive setup not only ensures a transformative experience but also plays on the scarcity effect. How much would a thrill-seeking CEO pay for such a unique challenge if marketed just right? We offer products for a limited time. This taps into a consumer's urge to defy the fear of missing out. It turns the purchase into a rebellion against the market's scarcity. Storytelling Techniques Craft your brand's story to resonate with the spirit of independence. Narratives that highlight overcoming adversity or defying odds can inspire customers to align with your brand as a statement of their values. You are the founder and can’t find a story? Call the business coach above and attend his retreat. You’ll have one 🙂 Such stories can inspire customers. They show them how to align with your brand as a statement of their values. : Contrarian Content Engage your audience with content that challenges mainstream ideas. This could be through thought leadership pieces. They take a new stance on common issues. This attracts those who appreciate a fresh perspective. Do you run a financial advisory firm? Do you want to challenge normal retirement planning? You could create a series of blog posts or videos titled “ ”. You Might Lose It All, But Your Grandkids Will Be Proud Ok, that might be a little bold. But you get the idea. And you’ll certainly get their attention. Our content challenges mainstream thoughts. It appeals to those who pride themselves on questioning the norm and valuing independent thought. In the end, whether it's stepping over a line of cones at a Polynesian airport or defying conventional marketing tactics, the allure of the forbidden runs deep within us. We have primal instincts for survival. They drive our decisions and actions. From them to the strategies of modern businesses, the urge to challenge boundaries shapes us. As you reflect on this newsletter (that you were kindly asked not to open), remember this. The most intriguing adventures start with a simple act of defiance. Embracing the spirit of rebellion can lead to unexpected discoveries and transformative experiences. As you navigate your own journey, may you find joy in pushing boundaries. Until next time, keep defying expectations and embracing the thrill of the forbidden. Strategizingly yours, Ben

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ONE of the most prominent firms in crypto recently sent shockwaves through the industry by announcing it is suing the U.S. Securities and Exchange Commission (SEC). On April 25th, in a bitterly worded lawsuit filed in a Texas federal court, crypto giant Consensys accused the SEC of “regulatory overreach” in its bid to bring to heel the $380 billion cryptocurrency Ether (commonly referred to as ETH). The complaint is based on the grounds that “ETH bears none of the attributes of a security.” Consensys are wrong, and this lawsuit is doomed to fail. The reasons why stretch back nearly ten years to when Ethereum was first founded. But in order to understand them, we first need some context on what, exactly, Consensys does. Consensys is a major backer of the Ethereum blockchain; a “Layer 1” blockchain ledger, like Bitcoin, but on top of which other protocols and applications can be built. Ethereum users transact on the network using ETH, which acts as a form of payment made by users of the network to the individuals around the world who run it (known as “miners” - more on them shortly). Consensys has contributed significantly to Ethereum’s back-end infrastructure, as well as funding a lot of smaller projects within the ecosystem. However it’s best known as the developer of MetaMask; by far the most popular wallet used in the crypto industry, with around 30 million monthly active users as of February this year. The level of the firm’s contribution to the Ethereum ecosystem, and the huge adoption its products have seen, have enabled Consensys to become a colossus in the crypto world; in November 2021, it raised $200 million at a $3.2 billion valuation. In March 2022, it raised a further $450 million; this time, with a valuation of $7 billion. The success of Consensys is intrinsically tied to the success of the Ethereum network which it backs, however, the latter has been mired in controversy and plagued by regulatory scrutiny since its inception, on account of its token ETH. Specifically, the manner in which ETH was first launched onto the market. ETH was launched nearly ten years ago, in July 2014, through a process known as an Initial Coin Offering (ICO); essentially, the crypto industry’s version of an IPO, in which tokens are first sold to the public. In their heyday, ICOs were tremendously popular, with participation in them reaching a zenith in 2016-17; a period now referred to in the industry as the “ICO era.” Such was the exuberance around these sales that one token, EOS, raised almost $4.1 billion through a year-long ICO; more than the three biggest equity raises that year – Uber, Epic Games, and Juul – combined. With so much money flooding into ICOs, they soon caught the attention, and the ire, of regulators. The SEC produced an Investigative Report in July 2017, declaring that “offers and sales of digital assets by ‘virtual’ organizations are subject to the requirements of the federal securities laws.” Cryptocurrencies, the agency decided, were securities and were to be regulated as such. After issuing this decree, the regulators set about ardently pursuing and prosecuting ICOs that did not adhere to securities law. Which, unsurprisingly, was pretty much all of them. This brought about an abrupt end to the ICO mania, with token sales drying up virtually overnight. However, while the SEC was hell-bent on throwing the book at new ICOs, all the tokens that launched before the July 2017 report were effectively given a free pass. None of these token issuers were prosecuted because their sales had been conducted before the decree went out that ICOs fall within the boundaries of securities laws. This meant a lucky escape for ETH; had it launched after the report, then its developers would have been prosecuted. No registrations for the sale were filed with the regulator, meaning the ICO did not comply with the SEC’s securities laws. Yet, despite not actively pursuing ETH for securities violations, the agency resolved to keep both the network, which was skyrocketing in popularity, and the coin, which was skyrocketing in price, firmly on their radar. For the next five or so years, while some debate persisted around whether the SEC should in fact be going after ETH, the regulators’ main focus was to clamp down on the . many scams and frauds being conducted throughout the space All this was to change in 2022, however, when Ethereum made a major change to the way the network was run. The details around this change are quite technical, but essentially, Ethereum introduced a system called “proof-of-stake,” in which the network would operate by the aforementioned miners locking up (or “staking”) their ETH in exchange for the ability to receive payments from the network’s users. While this may sound like an innocuous technical update, comprehensible and interesting to only a handful of basement-dwelling obsessives, the switch to proof-of-stake changed everything for Ethereum. The network was now firmly within the SEC’s crosshairs. And the regulator, long since exasperated by the sheer number of scams unfolding in the cryptosphere, had the perfect excuse to go after the network that it saw as integral to the facilitation of so much fraudulent activity. Because to the SEC, staking constitutes an investment contract and falls firmly within the realm of securities law. Investment contracts are classified as such if they pass the “Howey Test”. Developed by the US Supreme Court in a landmark case in 1946, the Howey Test asserts that an investment contract is “a contract, transaction, or scheme whereby a person invests their money in a common enterprise and is led to expect profits solely from the efforts of the promoter or a third party.” Staking arrangements involve individual investors contributing crypto to staking pools with the expectation of receiving a reward while relying on the efforts and work of the team managing and operating the staking platform. A court using the Howey Test, therefore, would find staking does indeed constitute an investment contract, and therefore, falls under securities law. Since Ethereum’s move to proof-of-stake, the SEC has been vigorously and ruthlessly clamping down on the network and the companies within its ecosystem, unleashing a deluge of regulatory action against many of the major players in the space. In its lawsuit filing, Consensys even refer to the head of the agency, Gary Gensler, as “the crusading SEC Chair.” And, indeed, buried within that same lawsuit a diligent reader will discover, on reaching the 68th paragraph, that Consensys itself was recently served a Wells Notice by the SEC; a letter that informs firms about impending legal action, following the results of an investigation which the agency has conducted. It turns out, then, that prior to filing its lawsuit Consensys was staring down the barrel of imminent prosecution. The SEC’s grounds for this were that, amongst other things, the MetaMask wallet which Consensys built offers staking services within its product. With all of this considered, a very different picture begins to emerge around Consensys’ decision to sue the SEC, and its lawsuit starts to look very much like a preemptive blow. Knowing that prosecution was imminent, the firm decided to go on the offensive and sue the SEC before the SEC sued them. The problem is, however, that Consensys stands almost no chance of winning. Despite what Consensys may argue, ETH bears all the hallmarks of a security. Not only because of the staking mechanism which is central to the way the network operates, but also because the token was issued through an ICO; a process which the SEC has declared falls under securities laws. ETH simply got lucky and was given a pass because its ICO happened to have taken place prior to the declaration. It was good timing which prevented prosecution, and nothing else. And while the SEC can’t go after the token for the way in which it was launched onto the market, the incorporation of staking as a central feature of the network has given them all the ammo they need to initiate legal proceedings and take down ETH from a different angle. And, over and above the sheer weight of evidence against its case, Consensys also has the SEC’s track record of prosecuting crypto companies to contend with. The SEC website lists 238 lawsuits the agency has filed against crypto firms, and to date, it has never really lost a case that involves unregistered securities. Ripple, another cryptocurrency firm whose token , made a lot of noise around a purported court victory in July last year. However, this is misleading; XRP was deemed not to be a security when offered to the public in secondary sales on exchanges, but the court found it is indeed a security when sold to hedge funds and other institutions. At a push, it was a partial victory for Ripple, and certainly not a loss for the SEC. XRP the SEC claimed was a security With all of this in view, therefore, the decision by Consensys to sue the SEC over its alleged witch-hunt of the Ethereum ecosystem can best be regarded as something of a Hail Mary play; the firm knew it was going to get prosecuted, and likely very soon, so it decided to roll the dice and take on the SEC in court over a separate issue, perhaps in an attempt to shift attention away from itself and onto the legal uncertainties surrounding Ethereum and its token. In doing so, it’s also fast-tracking the resolution of one of the biggest open questions in crypto, which has long been looming over the industry: whether or not ETH is officially, and legally, a security. Up against such astronomically unlikely odds of victory, it seems the best Consensys can hope to achieve is to drag the court case on for as long as possible. The firm has vast financial resources and can afford to hire legions of the best lawyers in the business. In doing so, it’ll be able to keep the party going for a little longer. But sooner or later the court will, inevitably, come to a decision. And when that happens, it’ll be lights out for both ETH and Consensys.

Here's Why Consensys' Lawsuit Against the SEC Is Doomed

এসইসির বিরুদ্ধে কনসেনসিসের মামলা কেন ধ্বংস হয়ে গেছে তা এখানে

Darum ist die Klage von Consensys gegen die SEC zum Scheitern verurteilt

He aquí por qué la demanda de Consensys contra la SEC está condenada al fracaso

Voici pourquoi le procès de Consensys contre la SEC est voué à l'échec

यहाँ बताया गया है कि क्यों एसईसी के खिलाफ कंसेन्सिस का मुकदमा असफल हो गया है

Consensys の SEC に対する訴訟が失敗に終わる理由はここにある

SEC에 대한 Consensys의 소송이 실패한 이유는 다음과 같습니다.

Veja por que o processo da Consensys contra a SEC está condenado

Вот почему иск Consensys против SEC обречен

İşte Consensys'in SEC'e Karşı Açtığı Dava Neden Sonuçsuz?

Đây là lý do tại sao vụ kiện của Consensys chống lại SEC lại thất bại

这就是为什么 Consensys 对 SEC 的诉讼注定失败

Loss of Amazon Rainforest Resilience: Abstract and Introduction

Population Stability Index (PSI) is a statistical measure commonly used in credit risk modeling and other fields to assess the stability of a population across different time periods or segments. It's particularly valuable in scenarios where changes in the population distribution could impact the effectiveness of a model. It is a fundamental assumption that the population on which a statistical model is applied to is similar to the population on which the model is developed at. The model performance relies on the closeness between the model development sample and the production data. Therefore, there is an intrinsic need to compare the two samples (development vs production) and assess whether a model needs to be recalibrated. The population change may occur for several reasons – change in the business strategies and policies or changes in external factors such as the political, economic and social environment. The model performance is impacted because of a change in the joint distribution p(y, X), where y is the model outcome and X is the design matrix that comprises the observations of the model features. Therefore, the entire model fit, i.e., the estimates of the model coefficients and the inherent functions get impacted. If this happens, then a decision must be made towards the recalibration or redevelopment of the model.  Before we dive deep into the PSI mathematical expression, we start with some key concepts on Entropy and KL divergence. Entropy and KL Divergence Entropy is a measure of uncertainty or randomness in a set of data. In machine learning, particularly in decision trees and information theory, entropy is used to quantify the impurity or disorder in a dataset. For instance, the splits of a decision tree for a classification problem are created to minimize the entropy. Entropy is used to determine the best attribute to split the data at each node of the decision tree. In the above definition of entropy, the negative sign ensures that Entropy is positive (because log of the probability will be negative as the probability will be < 1). Higher (lower) the value of P(X = X_i) lower (higher) is the entropy or randomness or uncertainty about X. Thus, ‘skewed’ distributions have lower entropy but ‘balanced’ distributions have higher entropy. The objective of splitting a variable at a node for a binary classification problem is to create more skewed distribution such that we can discriminate between an outcome value y = 1 and outcome value y = 0. Therefore, we move from a high entropy stage before splitting the variable to a lower entropy stage after splitting the variable.
If Entropy H(X) is high, then the uncertainty before knowing X is high. So after knowing X we have gained more information.
If Entropy H(X) is low, then the uncertainty before knowing X is low. So after knowing X we have gained less information.
We try to minimize entropy whilst building binary regression models. For example, we ‘bin’ or ‘discretize’ a model factor such the distribution of binary outcome becomes as skewed as possible across the different bins. Kullback-Liebler Divergence (KL Divergence) has been referred in several places in the literature (Lin, 2017), (Wu & Olson, 2010), (Li et al., 2008), etc. It measures the difference between two probability distributions p(x) and q(x). It quantifies how one probability distribution diverges from another. The distribution p(x) is considered the ‘true’ or ‘base’ distribution, and the distribution q(x) is considered the ‘untrue’ or ‘target’ distribution, so that KL Divergence represents some sort of loss due to using the ‘wrong’ distribution. KL divergence is non-negative and is zero if and only if p(x) and q(x) are identical, i.e., the same distribution. KL divergence measures the dissimilarity of the two probability distributions p(x) and q(x). If both the distributions are identical, KL Divergence is 0. We have a single random variable “X” and we have two different probability distributions for that variable. One is assumed to be the ‘true’ distribution and the other is the approximation of the ‘true’ distribution. So, we try “approximating” one distribution with the other and calculate the difference in the information about the event as a measure of the “divergence”. Note that the notation ‘P||Q’ implies P is approximated as Q.
Notice how the mathematical expressions for the individual terms of KL divergence are similar to that of Entropy or information which we introduced earlier. The second term inside the brackets is the information in p(x) about x. The first term inside the brackets is the information in an approximation distribution of p(x) as q(x) about x. That is if we approximate the target distribution p(x) by q(x); then the difference in the information between them is the relative entropy or KL Divergence index. The better our approximation the closer p(x) and q(x) and hence the KL divergence will be 0.
Even though KL Divergence measures divergence of q(x) from p(x), it is technically not a distance measure because the definition is not symmetric. Thus KL(P||Q) != KL(Q||P) Population Stability Index – Now that we have covered the mathematical expressions of Entropy and KL Divergence Index, we will see how they would help us in understanding the expression of Population Stability Index. Notice that we have added another term q(x) inside the brackets of KL divergence expression to get the mathematical expression for PSI. But the formulae look so similar that we can think of expressing PSI as a function of KL Divergence. Here is how we can do that. That is the PSI metric is actually the sum of the relative entropies of q(x) over p(x) and p(x) over q(x). In other words we are adding the information ‘gain’ by expressing p(x) using an approximation q(x) and vice-versa. The reason we do this is because we don’t know the ‘ground truth’. We assume that one of them is a ‘true’ distribution and the other is an ‘approximation’; and as we know that the KL divergence is not symmetric, we compute it from p(x) to q(x) and vice-versa and add them to get a final metric which can be judged to ascertain the difference between p(x) and q(x). The PSI calculation typically begins by dividing the data into bins or segments based on key variables used in the model.
PSI values close to zero indicate stability between the initial and new populations.
Higher PSI values suggest significant shifts in the population distribution, which may require further investigation or model recalibration. So how high should PSI be to make that judgement? The following table summarizes the PSI values which are ‘industry wide rules of thumb’. Interestingly, PSI is very similar to ‘Information Value’ used to measure the strength of the modelling variables in developing credit risk models. We will explore that soon in a different article. Population Stability Index (PSI) is a statistical measure commonly used in credit risk modeling and other fields to assess the stability of a population across different time periods or segments. It's particularly valuable in scenarios where changes in the population distribution could impact the effectiveness of a model. It is a fundamental assumption that the population on which a statistical model is applied to is similar to the population on which the model is developed at. The model performance relies on the closeness between the model development sample and the production data. Therefore, there is an intrinsic need to compare the two samples (development vs production) and assess whether a model needs to be recalibrated. The population change may occur for several reasons – change in the business strategies and policies or changes in external factors such as the political, economic and social environment. The model performance is impacted because of a change in the joint distribution p(y, X ), where y is the model outcome and X is the design matrix that comprises the observations of the model features. Therefore, the entire model fit, i.e., the estimates of the model coefficients and the inherent functions get impacted. If this happens, then a decision must be made towards the recalibration or redevelopment of the model.  Before we dive deep into the PSI mathematical expression, we start with some key concepts on Entropy and KL divergence. X Entropy and KL Divergence Entropy and KL Divergence Entropy and KL Divergence Entropy is a measure of uncertainty or randomness in a set of data. In machine learning, particularly in decision trees and information theory, entropy is used to quantify the impurity or disorder in a dataset. For instance, the splits of a decision tree for a classification problem are created to minimize the entropy. Entropy is used to determine the best attribute to split the data at each node of the decision tree. In the above definition of entropy, the negative sign ensures that Entropy is positive (because log of the probability will be negative as the probability will be < 1). Higher (lower) the value of P(X = X_i) lower (higher) is the entropy or randomness or uncertainty about X. Thus, ‘skewed’ distributions have lower entropy but ‘balanced’ distributions have higher entropy. The objective of splitting a variable at a node for a binary classification problem is to create more skewed distribution such that we can discriminate between an outcome value y = 1 and outcome value y = 0. Therefore, we move from a high entropy stage before splitting the variable to a lower entropy stage after splitting the variable. If Entropy H(X) is high, then the uncertainty before knowing X is high. So after knowing X we have gained more information. If Entropy H(X) is low, then the uncertainty before knowing X is low. So after knowing X we have gained less information. We try to minimize entropy whilst building binary regression models. For example, we ‘bin’ or ‘discretize’ a model factor such the distribution of binary outcome becomes as skewed as possible across the different bins. Higher (lower) the value of P(X = X_i) lower (higher) is the entropy or randomness or uncertainty about X. Thus, ‘skewed’ distributions have lower entropy but ‘balanced’ distributions have higher entropy. The objective of splitting a variable at a node for a binary classification problem is to create more skewed distribution such that we can discriminate between an outcome value y = 1 and outcome value y = 0. Therefore, we move from a high entropy stage before splitting the variable to a lower entropy stage after splitting the variable. If Entropy H(X) is high, then the uncertainty before knowing X is high. So after knowing X we have gained more information. If Entropy H(X) is low, then the uncertainty before knowing X is low. So after knowing X we have gained less information. We try to minimize entropy whilst building binary regression models. For example, we ‘bin’ or ‘discretize’ a model factor such the distribution of binary outcome becomes as skewed as possible across the different bins. Kullback-Liebler Divergence (KL Divergence) has been referred in several places in the literature (Lin, 2017), (Wu & Olson, 2010), (Li et al., 2008), etc. It measures the difference between two probability distributions p(x) and q(x). It quantifies how one probability distribution diverges from another. The distribution p(x) is considered the ‘true’ or ‘base’ distribution, and the distribution q(x) is considered the ‘untrue’ or ‘target’ distribution, so that KL Divergence represents some sort of loss due to using the ‘wrong’ distribution. KL divergence is non-negative and is zero if and only if p(x) and q(x) are identical, i.e., the same distribution. KL divergence measures the dissimilarity of the two probability distributions p(x) and q(x). If both the distributions are identical, KL Divergence is 0. We have a single random variable “X” and we have two different probability distributions for that variable. One is assumed to be the ‘true’ distribution and the other is the approximation of the ‘true’ distribution. So, we try “approximating” one distribution with the other and calculate the difference in the information about the event as a measure of the “divergence”. Note that the notation ‘P||Q’ implies P is approximated as Q. Notice how the mathematical expressions for the individual terms of KL divergence are similar to that of Entropy or information which we introduced earlier. The second term inside the brackets is the information in p(x) about x. The first term inside the brackets is the information in an approximation distribution of p(x) as q(x) about x. That is if we approximate the target distribution p(x) by q(x); then the difference in the information between them is the relative entropy or KL Divergence index. The better our approximation the closer p(x) and q(x) and hence the KL divergence will be 0. Even though KL Divergence measures divergence of q(x) from p(x), it is technically not a distance measure because the definition is not symmetric. Thus KL(P||Q) != KL(Q||P) We have a single random variable “X” and we have two different probability distributions for that variable. One is assumed to be the ‘true’ distribution and the other is the approximation of the ‘true’ distribution. So, we try “approximating” one distribution with the other and calculate the difference in the information about the event as a measure of the “divergence”. Note that the notation ‘P||Q’ implies P is approximated as Q. Notice how the mathematical expressions for the individual terms of KL divergence are similar to that of Entropy or information which we introduced earlier. The second term inside the brackets is the information in p(x) about x. The first term inside the brackets is the information in an approximation distribution of p(x) as q(x) about x. That is if we approximate the target distribution p(x) by q(x); then the difference in the information between them is the relative entropy or KL Divergence index. The better our approximation the closer p(x) and q(x) and hence the KL divergence will be 0. Even though KL Divergence measures divergence of q(x) from p(x), it is technically not a distance measure because the definition is not symmetric. Thus KL(P||Q) != KL(Q||P) Population Stability Index – Population Stability Index Population Stability Index Now that we have covered the mathematical expressions of Entropy and KL Divergence Index, we will see how they would help us in understanding the expression of Population Stability Index. Notice that we have added another term q(x) inside the brackets of KL divergence expression to get the mathematical expression for PSI. But the formulae look so similar that we can think of expressing PSI as a function of KL Divergence. Here is how we can do that. That is the PSI metric is actually the sum of the relative entropies of q(x) over p(x) and p(x) over q(x). In other words we are adding the information ‘gain’ by expressing p(x) using an approximation q(x) and vice-versa. The reason we do this is because we don’t know the ‘ground truth’. We assume that one of them is a ‘true’ distribution and the other is an ‘approximation’; and as we know that the KL divergence is not symmetric, we compute it from p(x) to q(x) and vice-versa and add them to get a final metric which can be judged to ascertain the difference between p(x) and q(x). The PSI calculation typically begins by dividing the data into bins or segments based on key variables used in the model. PSI values close to zero indicate stability between the initial and new populations. Higher PSI values suggest significant shifts in the population distribution, which may require further investigation or model recalibration. The PSI calculation typically begins by dividing the data into bins or segments based on key variables used in the model. PSI values close to zero indicate stability between the initial and new populations. Higher PSI values suggest significant shifts in the population distribution, which may require further investigation or model recalibration. So how high should PSI be to make that judgement? The following table summarizes the PSI values which are ‘industry wide rules of thumb’. Interestingly, PSI is very similar to ‘Information Value’ used to measure the strength of the modelling variables in developing credit risk models. We will explore that soon in a different article.

Population Stability Index in Machine learning models

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