Sapient Capital LLC acquired a new stake in shares of Phillips 66 (NYSE:PSX – Free Report) in the fourth quarter, HoldingsChannel.com reports. The fund acquired 2,019 shares of the oil and gas company’s stock, valued at approximately $269,000. Several other hedge funds and other institutional ...

Sapient Capital LLC Makes New Investment in Phillips 66 (NYSE:PSX) - Defense World

UK owner also says LNG ship-to-ship transfers saw strong demand

James Fisher pushes up tanker earnings as Phillips 66 agrees ‘biggest’ renewal | TradeWinds

Visit TradeWinds for the best news, insight and opinion covering the global shipping business.

James Fisher pushes up tanker earnings as Phillips 66 agrees 'biggest' 
renewal

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Phillips 66 stock outperforms market despite losses on the day

Aristotle Capital Management, LLC, an investment management company, released its “Value Equity Strategy” first quarter 2024 investor letter. A copy of the same can be downloaded here. The composite returned 7.63% gross of fees (7.56% net of fees) in the first quarter trailing the 8.99% ...

Here’s Why Aristotle Capital’s Value Equity Composite Sold Phillips 66 (PSX)

Phillips 66 (NYSE:PSX – Get Free Report) had its price target dropped by Mizuho from $167.00 to $162.00 in a research note issued on Friday, Benzinga reports. The brokerage presently has a “neutral” rating on the oil and gas company’s stock. Mizuho’s price target would suggest a potential ...

Phillips 66 (NYSE:PSX) PT Lowered to $162.00 - Defense World

Phillips 66 stock falls Friday, underperforms market

A man is dead and a private security guard is recovering after shots were fired at a Phillips 66 gas station in Philadelphia&#x27;s Fairmount neighborhood Tuesday night, according to police.

Man dead, security guard injured after shooting at Phillips 66 gas station in Philadelphia's Fairmount section: police - CBS Philadelphia

Fintel reports that on October 31, 2023, Wolfe Research upgraded their outlook for Phillips 66 (NYSE:PSX) from Peer Perform to Outperform .  Analyst Price Forecast Suggests 19.29% Upside  As of October 5,

Wolfe Research Upgrades Phillips 66 (PSX)

PIQUA — The chairman of a notable Piqua manufacturing company has died. Dan French, chairman of the board of French Oil Mill Machinery Company, died on Oct. 13 at the age of 79, according to his ...

Chairman of Piqua manufacturing company dead at 79

Phillips 66

phillips66

Matthew J Phillips is an entrepreneur from South Wales, U.K, and the creator of the Organic Client's program. Matthew has achieved feats such as closing multiple 4-figure clients weekly for his coaching business. He's also helped other coaches, consultants and mentors do the same. Some weeks ago, I sat down with Matthew and spoke on certain issues regarding his company and how he plans to move forward during these times. You'll also find this insightful. Let's have a look at what he had to say; So Matthew, why is now the time for your company to exist? More and more people are looking for a way to produce income in the chaos of COVID-19. We work with coaches, consultants, and mentors or those interested in becoming one. It seems to be a popular industry as there’s a low barrier to entry and many who are already coaches are in person trying to transition online. We supply the demand. What do you love about your team, and why are you the ones to solve this problem? We’re all results-oriented. We don’t care about flash, about the talk and all that jazz. We all care about the results. Every coach, consultant, or mentor we work worth our sole priority is ensuring they see success and changing their life. We show them how to create mouth-watering offers, generate streams of organic leads, and close their leads over the phone as high-paying clients. The three most common struggles you see and the only three things you need to master to have a thriving coaching, consulting, or mentorship business. If you weren’t building your startup, what would you be doing? I would be fitness coaching and continue to build my brand in the fitness industry like I was at 16 years old. I grew a following of 50K on Instagram and was sponsored by supplement companies etc. At the moment, how do you measure success? What are your metrics? Income. We care about helping coaches increase their clients to produce more income. For example, one of my clients from Montreal, Canada made $11,000 in under a month with his coaching business. Another, zero to $7,000 made in 5 days and a consultant from India closed 6 deals at $1,500 each totalling out to a total of $9,000 immediately upon implementation of my 1-1 program. In a few sentences, what do you offer to whom? We show coaches, consultants, and mentors how to create mouth-watering offers, generate streams of organic leads/sales appointments  - using inbound and outbound strategies for us then to show them how to close their leads as high-paying clients over the phone. We provide this service 1-1 through our main program The Organic Clients Program or we can breakdown that program  to fit precisely what they need. Help with content creation that drives leads, handling objections, etc. What’s most exciting about your traction to date? Our success stories and growth. My audience on Facebook is growing rapidly along with my FB group and we have new success stories pouring in daily along with interview and feature offers. Where do you think your growth will be next year? In the multiple 6 figure range along with branching out into other industries, for example, going back to my roots of fitness coaching where it all started for me before helping other coaches and starting a gym franchise/supplement company of my own. What’s your biggest threat? Hunger. As a young entrepreneur, 19 years old from the UK, South Wales, Cardiff to be specific. I’m hungry and I have options, now this helped me get to where I am although sometimes you can bite off too much than you can chew so I need to stay patient and not get off track with too many ventures and goals.

An Interview With Mathew J Phillips

ADA: A Powerful Data Augmentation Technique for Improved Regression Robustness

In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold. In the dynamic political environment of the United States, the rapidly approaching 2024 presidential election is becoming the arena for a new kind of battle—one against the insidious rise of AI-generated deepfakes. These sophisticated synthetic constructs, capable of distorting reality, have started a legislative marathon across the nation, as state leaders press forward with decisive measures to uphold the integrity of voter information. There is no federal law that deals with deepfakes, leaving it up to individual states to legislate. The top official at the Federal Election Commission (FEC) has signaled expectations for the issue to reach a resolution later this year. This forecast hints at a significant stretch of the 2024 election period potentially unfolding with this brand of misinformation remaining unchecked. Recently, bridging the partisan divide with a shared vision for election integrity, Sens. Amy Klobuchar of Minnesota and Lisa Murkowski of Alaska joined forces. Their collaborative legislative endeavor presents a forward-thinking bill aimed squarely at the digital frontier of political advertising. This proposed legislation would bring much-needed clarity to the murky waters of AI-generated political ads by enforcing disclaimers. But it doesn't stop there; the bill also empowers the Federal Election Commission (FEC) to step up as the digital watchdog, ensuring that any infractions don't go unnoticed or unaddressed. It's a bold stride towards safeguarding transparency as we sail into the next wave of electoral campaigns. What is going to happen during this election year if, as expected, there will be a slew of politically-motivated deepfakes? How can people be protected from being influenced? There is no easy answer. Deepfakes Decoded: Understanding the Synthetic Threat Deepfakes—a term blending 'deep learning' with 'fake'—refer to artificially generated audio, images, or videos designed to replicate a person's likeness with unnerving accuracy. February's New Hampshire incident serves as a harrowing example: an AI-generated voice, eerily mimicking President Joe Biden, misled thousands with false voting information. This event underscores the pressing need for informed and proactive legislation. "In a surprising turn of events this Tuesday, a voice on the airwaves was heard declaring, 'Your vote today is a step towards bringing Donald Trump back into office,'" recounted an associate from Rep. Dean Phillips' team, D-Minn., during an interview with NBC News. This individual, working for an underdog in the race against Biden, revealed that he had orchestrated the ad on his own accord, without the campaign's knowledge or approval. Shifting to the campaign trail on the Republican side, a striking audio piecesurfaced from the Trump camp. The clip was artfully edited to give the illusion that Florida's Gov. Ron DeSantis was in dialogue with none other than Adolf Hitler. In a retaliatory move, the DeSantis campaign then distributed a doctored photopresenting Trump in a warm embrace with Anthony Fauci, sending ripples through social media platforms. The Legislative Surge: Over 100 Bills to Protect Voters As guardians of electoral integrity, the Voting Rights Lab vigilantly surveys an ever-growing wave of legislative initiatives. In a landscape where over 100 bills have sprung up across 40 states, the commitment to combating AI-generated election disinformation appears as unwavering. With each bill introduced or passed, there's an unmistakable sense of urgency among legislators to reinforce the foundations of democracy. It's a legislative crusade that's rapidly gaining traction, showcasing the collective resolve to shield our democratic processes from the deceptive shadows cast by digital manipulations. Emerging Legislative Frameworks In the wake of the robocall incident, a flurry of legislative activity ensued. States like Arizona, Florida, and Wisconsin are now at the forefront, each showcasing a different regulatory trend that epitomizes the varied approaches being adopted to combat these technological ploys. Wisconsin recently enacted a law requiring political affiliates to disclose AI-generated content, while Florida awaits the governor's signature on a similar bill. Arizona, on the other hand, is threading the needle between regulation and constitutional rights, crafting bills that could classify the omission of labels on AI political media as a felony under certain conditions. Another bill in Arizona under consideration opens the door for individuals who have been wronged to initiate civil litigation against the creators of the content, providing them with the potential to obtain monetary compensation. Since 2019, both California and Texas have established laws in place. In Indiana, a bill that has garnered bipartisan backing is making its way to the governor's office. Should it pass, it would mandate that advertisements employing deepfake technology must clearly state, "Parts of this media have been digitally modified or synthetically produced." This directive would extend across images, videos, and audio content. Candidates at the state and federal level who find themselves impacted by such content would have the legal standing to pursue compensation through civil litigation. Drawing inspiration from the deceptive robocall incident mimicking President Biden, a bipartisan effort happened in Kansas. Spearheaded by Republican State Representative Pat Proctor alongside a Democratic leader, they pushed forward a legislative proposal that's tackling the deepfake dilemma head-on. This bill not only called for mandatory disclosures on deepfake advertisements but also proposed stringent penalties for those impersonating election officials, like secretaries of state, with the malicious aim of dissuading voters from casting their ballots. Currently, the bill is dead, but according to Proctor, can be brought back to life. Tackling the Disinformation Threat Beyond Legislation Legislation is just one facet of the multi-pronged strategy needed to address AI-generated deepfakes. Entities like NewDeal are spearheading initiatives that equip election administrators with tools to counter disinformation campaigns effectively. Incident response preparation and public awareness campaigns, such as those launched by secretaries of state in New Mexico and Michigan, are vital components in educating voters and reinforcing the democratic infrastructure. Big Tech Transparency Moves Major tech companies are proactively setting new standards for authenticity in online content, with a sharp focus on advertisements influenced by AI. Meta, the umbrella company for social platforms like Facebook, Instagram, and Threads, is diligently removing any advertisements that are misleading or lack context, particularly those that could potentially undermine the electoral process or jump the gun on election results. All AI-generated ads are subject to this rigorous scrutiny. In sync with these efforts, Google has made a declaration that transparency must accompany any AI-enhanced images, videos, or audio used in election advertising. As of November, such content will require explicit disclosures. YouTube, under Google's wing, is committing to transparency by ensuring its users can recognize when content has been digitally manipulated. These strategic moves by Meta and Google signal a pivotal shift toward clarity and honesty in the digital content we consume, especially in the political sphere. As we navigate the complexities of an election year increasingly influenced by AI technology, the question remains: Are state laws sufficiently robust to control the spread of deepfakes? With the FEC indicating that national resolutions may not arrive until later in the year, the responsibility falls on state legislatures, tech companies, and the public to build a bulwark against these digital deceptions. The legislative efforts, both proposed and enacted across the states, represent a patchwork of defenses, with some offering a glimpse of the rigorous measures needed to ensure fair play in our political arenas. The commitment to clarity and integrity from tech companies, too, adds a layer of much-needed oversight. Yet, as sophisticated as these measures may be, they are not impermeable. There will always be new challenges and loopholes to close, especially as technology advances at a breakneck pace. In this environment, voter education and awareness become as crucial as the laws themselves. The dissemination of information on how to discern real from synthetic media, alongside a discerning eye for what we consume and share, is paramount. It's a collective effort—a blend of legal, corporate, and individual actions—that shapes our resilience against the rising tide of AI-generated misinformation. As the 2024 election approaches, it's evident that there is no panacea for the deepfake phenomenon. Yet, the burgeoning legislative efforts, combined with proactive measures from tech companies and the vigilant participation of the public, set the stage for a robust defense. These are the foundations upon which we can hope to preserve the sanctity of our elections, fostering a well-informed electorate that is equipped to navigate the murky waters of digital propaganda. In conclusion, while the legislative landscape is still forming and the digital battleground is ever-changing, our collective vigilance and adaptability are key. The actions taken today by lawmakers, technologists, and every voter will determine the efficacy of our defense against deepfakes. In this election year and beyond, the safeguarding of truth remains a shared responsibility—one that we must continuously strive to uphold.

Can The Government, The Technology Platforms, or Anyone Else Control the Spread of Deepfakes?

Können die Regierung, die Technologieplattformen oder sonst jemand die Verbreitung von Deepfakes kontrollieren?

¿Pueden el gobierno, las plataformas tecnológicas o cualquier otra persona controlar la propagación de deepfakes?

آیا دولت، پلتفرم‌های فناوری یا هر کس دیگری می‌توانند گسترش دیپ‌فیک را کنترل کنند؟

Poden o goberno, as plataformas tecnolóxicas ou calquera outra persoa controlar a propagación dos Deepfakes?

क्या सरकार, प्रौद्योगिकी प्लेटफॉर्म या कोई अन्य डीपफेक के प्रसार को नियंत्रित कर सकता है?

政府、テクノロジープラットフォーム、あるいは他の誰かがディープフェイクの拡散を制御できるのでしょうか?

Afaka mifehy ny fiparitahan'ny Deepfakes ve ny governemanta, ny sehatra teknolojia, na olon-kafa?

Дали владата, технолошките платформи или некој друг може да го контролира ширењето на Deepfakes?

के सरकार, टेक्नोलोजी प्लेटफर्म, वा अरू कसैले Deepfakes को फैलावट नियन्त्रण गर्न सक्छ?

Czy rząd, platformy technologiczne lub ktokolwiek inny może kontrolować rozprzestrzenianie się deepfake'ów?

Dawladdu, Platform-yada Tignoolajiyada, ama cid kale ma xakamayn kartaa fiditaanka been-abuurka?

Чи може уряд, технологічні платформи чи будь-хто інший контролювати поширення Deepfakes?

Holistic Evaluation of Text-to-Image Models: Author contributions, Acknowledgments and References

The Greedy Miner’s Dilemma: Flaws in DAG-Based Consensus Protocols

AI in Education: Aiding Educators or Replacing Them? Expert Views

Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Table of Links Abstract and Introduction Theory coherent TVP-VAR Forecasting with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Conclusion and References A Appendix 5 Conclusion TVP-VAR are flexible statistical models used both for prediction and policy analysis in macroeconomics. Despite their flexibility allows to capture changes in the dynamic relationship among the macroeconomic variables, these models can easily become too flexible with the risk of over fitting the data. This translate into poor forecasting performances and imprecise inference on the time-varying parameters and of typical objects of interests such as the impulse responses. On the other side, models from the economic theory typically provide a more tightly parameterized representation of the macroeconomy and therefore have the opposite tendency of fitting the data rather poorly. This paper exploits the restrictions implied by economic theory to formulate a prior for the parameters of TVP-VARs so as to enhance inference within this class of models. It does so by introducing a shrinkage prior that centers the time varying coefficients at each time period on the cross equation restrictions implied by an underlying economic theory about the variables in the system. The paper shows that “economic shrinkage” can be successfully used to obtain more accurate forecasts and more precise estimates of typical objects of interests such as the impulse responses. Future research For future research the prior proposed in this paper could be modified to accommodate for multiple competing theories `a la Loria et al. (2022). As well, the model could be extended to embed stochastic volatility, adapting the algorithm in Bognanni (2018). More in general, restrictions from economic theory could successfully be used to reduce overfitting and sharpen inference in non-parametric VARs and Gaussian Procesess VARs (Hauzenberger et al. 2022). References Ascari, Guido, Paolo Bonomolo, and Qazi Haque. 2023. “The long-run phillips curve is... a curve.” Ascari, Guido, and Argia M. Sbordone. 2014. “The Macroeconomics of Trend Inflation.” Journal of Economic Literature 52 (3): 679–739. Belmonte, Miguel A.G., Gary Koop, and Dimitris Korobilis. 2014. “Hierarchical Shrinkage in Time-Varying Parameter Models.” Journal of Forecasting 33 (1): 80–94. Benati, Luca, and Thomas A. Lubik. 2023. Impulse Response Analysis at the Zero Lower Bound. Diskussionsschriften dp2306. Universitaet Bern, Departement Volkswirtschaft, June. Bitto, Angela, and Sylvia Fr¨uhwirth-Schnatter. 2019. “Achieving shrinkage in a time-varying parameter model framework.” Annals Issue in Honor of John Geweke “Complexity and Big Data in Economics and Finance: Recent Developments from a Bayesian Perspective”, Journal of Econometrics 210 (1): 75–97. Bognanni, Mark. 2018. A Class of Time-Varying Parameter Structural VARs for Inference under Exact or Set Identification. Working Papers (Old Series) 1811. Federal Reserve Bank of Cleveland. Cagliarini, Adam, and Mariano Kulish. 2013. “Solving Linear Rational Expectations Models with Predictable Structural Changes.” The Review of Economics and Statistics 95, no. 1 (March): 328–336. Carriero, Andrea, Todd E. Clark, and Massimiliano Marcellino. 2021. “No-arbitrage priors, drifting volatilities, and the term structure of interest rates.” Journal of Applied Econometrics 36 (5): 495–516. Carter, C. K., and R. Kohn. 1994. “On Gibbs Sampling for State Space Models.” Biometrika 81 (3): 541–553. Chan, Joshua C.C., Eric Eisenstat, and Rodney W. Strachan. 2020. “Reducing the state space dimension in a large TVP-VAR.” Journal of Econometrics 218 (1): 105–118. Chan, Joshua CC, and Ivan Jeliazkov. 2009. “Efficient simulation and integrated likelihood estimation in state space models.” International Journal of Mathematical Modelling and Numerical Optimisation 1 (1-2): 101–120. Chen, Han, Vasco C´urdia, and Andrea Ferrero. 2012. “The Macroeconomic Effects of Large-scale Asset Purchase Programmes*.” The Economic Journal 122 (564): F289–F315. Cogley, Timothy, and Thomas Sargent. 2002. “Evolving Post-World War II US Inflation Dynamics.” In NBER Macroeconomics Annual 2001, Volume 16, 331–388. National Bureau of Economic Research, Inc. Cogley, Timothy, and Thomas J. Sargent. 2005. “Drifts and volatilities: monetary policies and outcomes in the post WWII US.” Monetary Policy and Learning, Review of Economic Dynamics 8 (2): 262–302. Cogley, Timothy, and Argia M. Sbordone. 2008. “Trend Inflation, Indexation, and Inflation Persistence in the New Keynesian Phillips Curve.” American Economic Review 98, no. 5 (December): 2101–26. Coulombe, Philippe Goulet. 2021. Time-Varying Parameters as Ridge Regressions. D’Agostino, Antonello, Luca Gambetti, and Domenico Giannone. 2013. “Macroeconomic forecasting and structural change.” Journal of Applied Econometrics 28 (1): 82–101. Debortoli, Davide, Jordi Gal´ı, and Luca Gambetti. 2019. “On the Empirical (Ir)relevance of the Zero Lower Bound Constraint.” In NBER Macroeconomics Annual 2019, volume 34, 141–170. NBER Chapters. National Bureau of Economic Research, Inc, October. Del Negro, Marco, Marc P. Giannoni, and Frank Schorfheide. 2015. “Inflation in the Great Recession and New Keynesian Models.” American Economic Journal: Macroeconomics 7, no. 1 (January): 168–96. Del Negro, Marco, and Frank Schorfheide. 2004. “Priors from General Equilibrium Models for VARS*.” International Economic Review 45 (2): 643–673. Farmer, Roger E.A., Daniel F. Waggoner, and Tao Zha. 2009. “Understanding Markov-switching rational expectations models.” Journal of Economic Theory 144 (5): 1849–1867. Fessler, Pirmin, and Maximilian Kasy. 2019. “How to Use Economic Theory to Improve Estimators: Shrinking Toward Theoretical Restrictions.” The Review of Economics and Statistics 101, no. 4 (October): 681–698. Fr¨uhwirth-Schnatter, Sylvia, and Helga Wagner. 2010. “Stochastic model specification search for Gaussian and partial non-Gaussian state space models.” Journal of Econometrics 154 (1): 85–100. Gallant, A. Ronald, and Robert E. McCulloch. 2009. “On the Determination of General Scientific Models With Application to Asset Pricing.” Journal of the American Statistical Association 104 (485): 117–131. Giacomini, Raffaella, and Giuseppe Ragusa. 2014. “Theory-coherent forecasting.” Causality, Prediction, and Specification Analysis: Recent Advances and Future Directions, Journal of Econometrics 182 (1): 145–155. Giannone, Domenico, Michele Lenza, and Giorgio E. Primiceri. 2015. “Prior Selection for Vector Autoregressions.” The Review of Economics and Statistics 97, no. 2 (May): 436–451. Hauzenberger, Niko, Florian Huber, Massimiliano Marcellino, and Nico Petz. 2022. Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty. Ingram, Beth F., and Charles H. Whiteman. 1994. “Supplanting the ’Minnesota’ prior: Forecasting macroeconomic time series using real business cycle model priors.” Journal of Monetary Economics 34, no. 3 (December): 497–510. Kalli, Maria, and Jim E. Griffin. 2014. “Time-varying sparsity in dynamic regression models.” Journal of Econometrics 178 (2): 779–793. Loria, Francesca, Christian Matthes, and Mu-Chun Wang. 2022. “Economic theories and macroeconomic reality.” Journal of Monetary Economics 126:105–117. Milani, Fabio. 2007. “Expectations, learning and macroeconomic persistence.” Journal of Monetary Economics 54 (7): 2065–2082. Primiceri, Giorgio E. 2005. “Time varying structural vector autoregressions and monetary policy.” The Review of Economic Studies 72 (3): 821–852. Sims, Christopher. 2002. “Solving Linear Rational Expectations Models.” Computational Economics 20 (1-2): 1–20. Sims, Christopher A. 1980. “Macroeconomics and Reality.” Econometrica 48 (1): 1–48. Smets, Frank, and Rafael Wouters. 2007. “Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach.” American Economic Review 97, no. 3 (June): 586–606. Smith Jr., A. A. 1993. “Estimating nonlinear time-series models using simulated vector autoregressions.” Journal of Applied Econometrics 8 (S1): S63–S84. Stevanovic, Dalibor. 2016. “Common time variation of parameters in reduced-form macroeconomic models.” Studies in Nonlinear Dynamics & Econometrics 20, no. 2 (April): 159– 183. Stock, James H., and Mark W. Watson. 2001. “Vector Autoregressions.” Journal of Economic Perspectives 15, no. 4 (December): 101–115. Wind, Joris de, and Luca Gambetti. 2014. Reduced-rank time-varying vector autoregressions. CPB Discussion Paper 270. CPB Netherlands Bureau for Economic Policy Analysis, March. Woodford, Michael. 2003. Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press. This paper is available on arxiv under CC 4.0 license. Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Table of Links Abstract and Introduction Abstract and Introduction Theory coherent TVP-VAR Theory coherent TVP-VAR Forecasting with the TC-TVP-VAR Forecasting with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Conclusion and References Conclusion and References A Appendix A Appendix 5 Conclusion TVP-VAR are flexible statistical models used both for prediction and policy analysis in macroeconomics. Despite their flexibility allows to capture changes in the dynamic relationship among the macroeconomic variables, these models can easily become too flexible with the risk of over fitting the data. This translate into poor forecasting performances and imprecise inference on the time-varying parameters and of typical objects of interests such as the impulse responses. On the other side, models from the economic theory typically provide a more tightly parameterized representation of the macroeconomy and therefore have the opposite tendency of fitting the data rather poorly. This paper exploits the restrictions implied by economic theory to formulate a prior for the parameters of TVP-VARs so as to enhance inference within this class of models. It does so by introducing a shrinkage prior that centers the time varying coefficients at each time period on the cross equation restrictions implied by an underlying economic theory about the variables in the system. The paper shows that “economic shrinkage” can be successfully used to obtain more accurate forecasts and more precise estimates of typical objects of interests such as the impulse responses. Future research For future research the prior proposed in this paper could be modified to accommodate for multiple competing theories `a la Loria et al. (2022). As well, the model could be extended to embed stochastic volatility, adapting the algorithm in Bognanni (2018). More in general, restrictions from economic theory could successfully be used to reduce overfitting and sharpen inference in non-parametric VARs and Gaussian Procesess VARs (Hauzenberger et al. 2022). Future research References Ascari, Guido, Paolo Bonomolo, and Qazi Haque. 2023. “The long-run phillips curve is... a curve.” Ascari, Guido, and Argia M. Sbordone. 2014. “The Macroeconomics of Trend Inflation.” Journal of Economic Literature 52 (3): 679–739. Belmonte, Miguel A.G., Gary Koop, and Dimitris Korobilis. 2014. “Hierarchical Shrinkage in Time-Varying Parameter Models.” Journal of Forecasting 33 (1): 80–94. Benati, Luca, and Thomas A. Lubik. 2023. Impulse Response Analysis at the Zero Lower Bound. Diskussionsschriften dp2306. Universitaet Bern, Departement Volkswirtschaft, June. Bitto, Angela, and Sylvia Fr¨uhwirth-Schnatter. 2019. “Achieving shrinkage in a time-varying parameter model framework.” Annals Issue in Honor of John Geweke “Complexity and Big Data in Economics and Finance: Recent Developments from a Bayesian Perspective”, Journal of Econometrics 210 (1): 75–97. Bognanni, Mark. 2018. A Class of Time-Varying Parameter Structural VARs for Inference under Exact or Set Identification. Working Papers (Old Series) 1811. Federal Reserve Bank of Cleveland. Cagliarini, Adam, and Mariano Kulish. 2013. “Solving Linear Rational Expectations Models with Predictable Structural Changes.” The Review of Economics and Statistics 95, no. 1 (March): 328–336. Carriero, Andrea, Todd E. Clark, and Massimiliano Marcellino. 2021. “No-arbitrage priors, drifting volatilities, and the term structure of interest rates.” Journal of Applied Econometrics 36 (5): 495–516. Carter, C. K., and R. Kohn. 1994. “On Gibbs Sampling for State Space Models.” Biometrika 81 (3): 541–553. Chan, Joshua C.C., Eric Eisenstat, and Rodney W. Strachan. 2020. “Reducing the state space dimension in a large TVP-VAR.” Journal of Econometrics 218 (1): 105–118. Chan, Joshua CC, and Ivan Jeliazkov. 2009. “Efficient simulation and integrated likelihood estimation in state space models.” International Journal of Mathematical Modelling and Numerical Optimisation 1 (1-2): 101–120. Chen, Han, Vasco C´urdia, and Andrea Ferrero. 2012. “The Macroeconomic Effects of Large-scale Asset Purchase Programmes*.” The Economic Journal 122 (564): F289–F315. Cogley, Timothy, and Thomas Sargent. 2002. “Evolving Post-World War II US Inflation Dynamics.” In NBER Macroeconomics Annual 2001, Volume 16, 331–388. National Bureau of Economic Research, Inc. Cogley, Timothy, and Thomas J. Sargent. 2005. “Drifts and volatilities: monetary policies and outcomes in the post WWII US.” Monetary Policy and Learning, Review of Economic Dynamics 8 (2): 262–302. Cogley, Timothy, and Argia M. Sbordone. 2008. “Trend Inflation, Indexation, and Inflation Persistence in the New Keynesian Phillips Curve.” American Economic Review 98, no. 5 (December): 2101–26. Coulombe, Philippe Goulet. 2021. Time-Varying Parameters as Ridge Regressions. D’Agostino, Antonello, Luca Gambetti, and Domenico Giannone. 2013. “Macroeconomic forecasting and structural change.” Journal of Applied Econometrics 28 (1): 82–101. Debortoli, Davide, Jordi Gal´ı, and Luca Gambetti. 2019. “On the Empirical (Ir)relevance of the Zero Lower Bound Constraint.” In NBER Macroeconomics Annual 2019, volume 34, 141–170. NBER Chapters. National Bureau of Economic Research, Inc, October. Del Negro, Marco, Marc P. Giannoni, and Frank Schorfheide. 2015. “Inflation in the Great Recession and New Keynesian Models.” American Economic Journal: Macroeconomics 7, no. 1 (January): 168–96. Del Negro, Marco, and Frank Schorfheide. 2004. “Priors from General Equilibrium Models for VARS*.” International Economic Review 45 (2): 643–673. Farmer, Roger E.A., Daniel F. Waggoner, and Tao Zha. 2009. “Understanding Markov-switching rational expectations models.” Journal of Economic Theory 144 (5): 1849–1867. Fessler, Pirmin, and Maximilian Kasy. 2019. “How to Use Economic Theory to Improve Estimators: Shrinking Toward Theoretical Restrictions.” The Review of Economics and Statistics 101, no. 4 (October): 681–698. Fr¨uhwirth-Schnatter, Sylvia, and Helga Wagner. 2010. “Stochastic model specification search for Gaussian and partial non-Gaussian state space models.” Journal of Econometrics 154 (1): 85–100. Gallant, A. Ronald, and Robert E. McCulloch. 2009. “On the Determination of General Scientific Models With Application to Asset Pricing.” Journal of the American Statistical Association 104 (485): 117–131. Giacomini, Raffaella, and Giuseppe Ragusa. 2014. “Theory-coherent forecasting.” Causality, Prediction, and Specification Analysis: Recent Advances and Future Directions, Journal of Econometrics 182 (1): 145–155. Giannone, Domenico, Michele Lenza, and Giorgio E. Primiceri. 2015. “Prior Selection for Vector Autoregressions.” The Review of Economics and Statistics 97, no. 2 (May): 436–451. Hauzenberger, Niko, Florian Huber, Massimiliano Marcellino, and Nico Petz. 2022. Gaussian Process Vector Autoregressions and Macroeconomic Uncertainty. Ingram, Beth F., and Charles H. Whiteman. 1994. “Supplanting the ’Minnesota’ prior: Forecasting macroeconomic time series using real business cycle model priors.” Journal of Monetary Economics 34, no. 3 (December): 497–510. Kalli, Maria, and Jim E. Griffin. 2014. “Time-varying sparsity in dynamic regression models.” Journal of Econometrics 178 (2): 779–793. Loria, Francesca, Christian Matthes, and Mu-Chun Wang. 2022. “Economic theories and macroeconomic reality.” Journal of Monetary Economics 126:105–117. Milani, Fabio. 2007. “Expectations, learning and macroeconomic persistence.” Journal of Monetary Economics 54 (7): 2065–2082. Primiceri, Giorgio E. 2005. “Time varying structural vector autoregressions and monetary policy.” The Review of Economic Studies 72 (3): 821–852. Sims, Christopher. 2002. “Solving Linear Rational Expectations Models.” Computational Economics 20 (1-2): 1–20. Sims, Christopher A. 1980. “Macroeconomics and Reality.” Econometrica 48 (1): 1–48. Smets, Frank, and Rafael Wouters. 2007. “Shocks and Frictions in US Business Cycles: A Bayesian DSGE Approach.” American Economic Review 97, no. 3 (June): 586–606. Smith Jr., A. A. 1993. “Estimating nonlinear time-series models using simulated vector autoregressions.” Journal of Applied Econometrics 8 (S1): S63–S84. Stevanovic, Dalibor. 2016. “Common time variation of parameters in reduced-form macroeconomic models.” Studies in Nonlinear Dynamics & Econometrics 20, no. 2 (April): 159– 183. Stock, James H., and Mark W. Watson. 2001. “Vector Autoregressions.” Journal of Economic Perspectives 15, no. 4 (December): 101–115. Wind, Joris de, and Luca Gambetti. 2014. Reduced-rank time-varying vector autoregressions. CPB Discussion Paper 270. CPB Netherlands Bureau for Economic Policy Analysis, March. Woodford, Michael. 2003. Interest and Prices: Foundations of a Theory of Monetary Policy. Princeton University Press. 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

Theory Coherent Shrinkage of Time Varying Parameters in VARs: Conclusion and References

Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Table of Links Abstract and Introduction Theory coherent TVP-VAR Forecasting with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Conclusion and References A Appendix 3 Forecasting with the TC-TVP-VAR In this section I consider the problem of forecasting the rate of growth of GDP, the inflation rate and the Fed Fund rate using a trivariate TVP-VAR model. In the specific, I estimate a trivariate TVP-VAR for the US economy using data from 1970 up to 2019 and I compare the forecast accuracy of a standard TVP-VAR model to the forecasts from a TC-TVP-VAR. 3.1 Small scale New Keynesian model for the US economy In the TC-TVP-VAR I exploit the New Keynesian model in Del Negro et al. (2004) to parametrize the theory coherent prior. The conceptual framework commonly denoted as the 3-equation New Keynesian model constitutes the nucleus of Michael Woodford’s book “Interest and Prices” (Woodford 2003) and underpins most of modern monetary macroeconomics models.[21] More specifically, the structural model is composed by an IS curve (31), a New Keynesian Phillips curve (32), a monetary policy rule (33) and two equations that describe the dynamics for the log-deviation from the steady state of technological process (34) and government spending (35), namely The population moments needed to parametrize the prior are derived from the state-space representation of the New Keynesian model obtained by solving the system of non-linear rational expectation equations. In particular, the non-linear rational expectation equations are solved using the method based on matrix eigenvalue decomposition by Sims (2002) leading to a solution which has the form 3.2 Forecast comparison Table 1 shows the comparison of the forecasts from a standard TVP-VAR model and the TCTVP-VAR model. The forecasting exercise is designed such that I compute the recursive one quarter, two quarters, and one year ahead forecasts starting from 1985-Q1 up to 2019-Q4.22 To compare relative point forecast accuracy, in the table I report the Root Mean Squared Error (RMSE) while for evaluating density forecast accuracy I report the average Cumulative Ranked Probability Scores (CRPS). In the table I also include the results concerning the forecasts from a constant parameters VAR with flat prior and a constant parameters Bayesian VAR (BVAR) with Minnesota type of prior.23 Overall, the TC-TVP-VAR provides the most accurate point and density forecasts for both output growth and inflation rate, outperforming the standard TVP-VAR model at all the horizons considered. For forecasting output growth, the standard TVP-VAR performs poorly relative to the TC-TVP-VAR, but also to the constant parameters BVAR with Minnesota prior, suggesting that the model tends to fit some noise in the time series of output growth. In line with the previous forecasting literature, allowing for time variation of the parameters of the VAR is important for obtaining accurate forecasts of the inflation rate, as the standard TVP-VAR outperforms both the VAR with flat prior and the BVAR with the Minnesota prior. However, economic shrinkage is helpful to obtain more reliable point and density forecasts when modelling the time variation of the coefficients. Indeed the TC-TVP-VAR outperforms the standard TVP-VAR at all the horizons. As a caveat, the standard Minnesota prior centering the autoregressive coefficients on a random walk process outperforms the other competitors, including the TC-TVP-VAR, for forecasting the Fed Fund Rate. This result is consistent with the results of the forecasting exercise in Del Negro et al. (2004) which use the same small scale NK model to parametrize a prior for a constant parameters VAR. This paper is available on arxiv under CC 4.0 license. [21.] See Del Negro et al. (2004) for the more in depth details on the New Keynesian Model. [22.] Details on the data can be found in the appendix A.2.1 [23.] Appendix A.2.2 the reports details on the competing forecasting models Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Andrea Renzetti, Department of Economics, Alma Mater Studiorium Universit`a di Bologna, Piazza Scaravilli 2, 40126 Bologna, Italy. Table of Links Abstract and Introduction Abstract and Introduction Theory coherent TVP-VAR Theory coherent TVP-VAR Forecasting with the TC-TVP-VAR Forecasting with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Response analysis at the ZLB with the TC-TVP-VAR Conclusion and References Conclusion and References A Appendix A Appendix 3 Forecasting with the TC-TVP-VAR In this section I consider the problem of forecasting the rate of growth of GDP, the inflation rate and the Fed Fund rate using a trivariate TVP-VAR model. In the specific, I estimate a trivariate TVP-VAR for the US economy using data from 1970 up to 2019 and I compare the forecast accuracy of a standard TVP-VAR model to the forecasts from a TC-TVP-VAR. 3.1 Small scale New Keynesian model for the US economy In the TC-TVP-VAR I exploit the New Keynesian model in Del Negro et al. (2004) to parametrize the theory coherent prior. The conceptual framework commonly denoted as the 3-equation New Keynesian model constitutes the nucleus of Michael Woodford’s book “Interest and Prices” (Woodford 2003) and underpins most of modern monetary macroeconomics models.[21] More specifically, the structural model is composed by an IS curve (31), a New Keynesian Phillips curve (32), a monetary policy rule (33) and two equations that describe the dynamics for the log-deviation from the steady state of technological process (34) and government spending (35), namely The population moments needed to parametrize the prior are derived from the state-space representation of the New Keynesian model obtained by solving the system of non-linear rational expectation equations. In particular, the non-linear rational expectation equations are solved using the method based on matrix eigenvalue decomposition by Sims (2002) leading to a solution which has the form 3.2 Forecast comparison Table 1 shows the comparison of the forecasts from a standard TVP-VAR model and the TCTVP-VAR model. The forecasting exercise is designed such that I compute the recursive one quarter, two quarters, and one year ahead forecasts starting from 1985-Q1 up to 2019-Q4.22 To compare relative point forecast accuracy, in the table I report the Root Mean Squared Error (RMSE) while for evaluating density forecast accuracy I report the average Cumulative Ranked Probability Scores (CRPS). In the table I also include the results concerning the forecasts from a constant parameters VAR with flat prior and a constant parameters Bayesian VAR (BVAR) with Minnesota type of prior.23 Overall, the TC-TVP-VAR provides the most accurate point and density forecasts for both output growth and inflation rate, outperforming the standard TVP-VAR model at all the horizons considered. For forecasting output growth, the standard TVP-VAR performs poorly relative to the TC-TVP-VAR, but also to the constant parameters BVAR with Minnesota prior, suggesting that the model tends to fit some noise in the time series of output growth. In line with the previous forecasting literature, allowing for time variation of the parameters of the VAR is important for obtaining accurate forecasts of the inflation rate, as the standard TVP-VAR outperforms both the VAR with flat prior and the BVAR with the Minnesota prior. However, economic shrinkage is helpful to obtain more reliable point and density forecasts when modelling the time variation of the coefficients. Indeed the TC-TVP-VAR outperforms the standard TVP-VAR at all the horizons. As a caveat, the standard Minnesota prior centering the autoregressive coefficients on a random walk process outperforms the other competitors, including the TC-TVP-VAR, for forecasting the Fed Fund Rate. This result is consistent with the results of the forecasting exercise in Del Negro et al. (2004) which use the same small scale NK model to parametrize a prior for a constant parameters VAR. 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 [21.] See Del Negro et al. (2004) for the more in depth details on the New Keynesian Model. [22.] Details on the data can be found in the appendix A.2.1 [23.] Appendix A.2.2 the reports details on the competing forecasting models

Theory Coherent Shrinkage of Time Varying Parameters in VARs: Forecasting with the TC-TVP-VAR

It doesn’t require the perspicacity of Sherlock Holmes to realize that the internet has altered our notions of space and time. However, highlighting how this change has affected the scene is essential. In time, we live in the shadow of the immediate and the interminable; in space, we are faced with an approach that separates the real from the virtual. This division between the virtual and the real was useful during the early days of the internet to help understand the emerging reality. However, as time progresses, this distinction makes less sense. Disrupting the notion that we are in two distinct environments is important for starting the conversation about digital habits. Manuel Castells, already in the early 2000s [1], questioned this distinction by proposing the term “real virtuality.” There would be no opposition between the real and the virtual, as virtuality would already be part of our realities, and it would be impossible to think about our lives in society without considering this dimension [1]. More recently, the Italian Stefano Quintarelli [2] has named a "non-material dimension" or "immaterial reality," a dimension in which "people engage in economic and social relationships through digital technologies." According to Quintarelli, the immaterial reality actually emerged at the beginning of 2001. Alongside this divided approach, when considering the effects of technologies—perhaps to accommodate the discomfort we feel in the face of a complex scenario involving the pace of technological development, economic changes, and the limits of our institutions—we tend to adopt perspectives where the focus is on the technologies themselves. Thus, we reduce the discussion to classifying a particular technology, whether devices, social networks, or even the internet, as either good or bad. It is not uncommon to see nostalgic discourses evoked, as if things were better in the past, as if all communication and interaction were "real" before. This is an illusion, because our communication is, to some extent, always of the virtual order, even offline. “It is estimated that by around 2030 there will be 500 billion devices connected to the Internet” [2]. And with the Internet of Things (IoT), “devices” refers to a multitude of objects: from household appliances and cars to wearables like smart pacemakers and ingestible sensors. In this complex and sometimes anxiety-inducing scenario, it becomes necessary, though somewhat late, to factor in our digital habits or the way we use technology. Why should the main focus of these discussions be our digital habits and the way we use technology? Usage is related both to the development of technologies and to their effects and how they impact us. People and organizations that design or develop new technologies try to anticipate specific uses and, in some cases, even the emotions and feelings involved. This is because most information technologies rely heavily on interactivity, engagement, and high immersion, as their "popularity" depends on user attention. Some authors already use the term "attention economy" in these contexts. Alongside information, user attention is what holds the most value in the immaterial dimension [2]. The term persuasive computing [3] [4] refers to how software design, through the experience it provides—including the emotions and feelings it evokes—can influence users to adopt certain behaviors. However, design cannot predict all possible uses. In different contexts, with the presence of new variables or changes in interaction methods, other responses may emerge. Thus, developers of any product, whether technological or not, when designing, can and should aim to offer benefits and improvements to users. On the other hand, even if they strive to design with the intention of providing well-being and benefits, the way we use these products can alter the impacts and effects, whether emotional or otherwise. When using most digital products or services, our configuration preferences and the way we interact with these services or products are included in our data. This information becomes available to the provider of the product or service. Based on this data, new versions can be optimized, features added or removed, and improvements implemented. [2] This is why some features are first tested in certain countries. The choice may be based on the behavior of the majority of users in that region. A recent example is Twitter's Fleets, a kind of stories feature, which was initially tested in Brazil, followed by Italy, and last month became available for testing to users in India as well. “Since Brazil is one of the countries where people converse the most on Twitter, with many of you passionate about talking and following other Brazilians on the platform, we are excited to test the new feature here.” [Twitter Blog] That said, in addition to requiring more engaged stances regarding corporate accountability and ethics in product development and privacy aspects—which is increasingly necessary—discussions should also include how we use certain technologies. This perspective holds some potential for change. Just as developers and designers think before creating, we users can also rethink how we use technologies, thereby making choices that contribute to our own well-being and, in the case of specific products, influence service providers or producers, leading to modifications or even the development of positive technologies. I do not have the naivety to believe that service providers or producers will implement changes solely because it could offer well-being to us users. However, if our attention is the currency in this dimension—and it is—facing the possibility of losing user attention, they will at least consider the possibility of making changes. This is what happened with the supposed “disappearance” of Instagram likes—supposed because the information is still visible, but it is no longer displayed all the time in the app as it was before. With criticisms backed by some research indicating that the count of likes was contributing to users' discomfort, and to ensure a positive image of the organization and avoid losing these users, the information was hidden. The development of technologies occurs in a social context where consumption and an ideal of happiness based on consumption still dominate: “(...) the consumer society educates us in the virtues and pleasures of knowing ourselves and understanding what we desire (to know ourselves simply means to know what we want to have).” Adam Phillips [5]. The way we use technology still serves these discourses, and the discomfort we often feel may also be related to the possibility that technologies allow us to confront the harsher expression of this logic, which we might take longer to notice under different circumstances. Technologies can influence changes in behavior; however, the way we use them—which can reflect the discourses that shape us—is an important component and perhaps the one closest to us. “‘Machines’ offer the chance to penetrate information into the most incomparable curves of space-time ever imagined. Use these machines in the way things should be 'used,' critically; otherwise, they will use you. The difference lies in the approach, and sometimes it’s just a matter of method; but I hope you are able to distance yourselves from the facts, reflect on information, and interrupt the flow whenever you need to regain your own sense of the world.” Stefano Quintarelli [2] Originally written in Brazilian Portuguese References [1] Castells, M. (2002) A sociedade em rede. 6ª edição. Editora Paz e Terra. [2] Quintarelli, S. (2019) Instruções para um futuro imaterial. Editora Elefante. [3] Fogg, B. J. (1998). Persuasive computers: perspectives and research directions. Proceedings of CHI 1998, ACM Press, 225–232 . [4]Thompson, Clive. (2019). Coders: The Making of a New Tribe and the Remaking of the World. Penguin Books. [5] Phillips, Adam. (2013). O Que Você É e o Que Você Quer Ser. Editora Benvirá. It doesn’t require the perspicacity of Sherlock Holmes to realize that the internet has altered our notions of space and time. However, highlighting how this change has affected the scene is essential. In time, we live in the shadow of the immediate and the interminable; in space, we are faced with an approach that separates the real from the virtual. This division between the virtual and the real was useful during the early days of the internet to help understand the emerging reality. However, as time progresses, this distinction makes less sense. Disrupting the notion that we are in two distinct environments is important for starting the conversation about digital habits. Disrupting the notion that we are in two distinct environments is important for starting the conversation about digital habits. Manuel Castells, already in the early 2000s [1], questioned this distinction by proposing the term “real virtuality.” There would be no opposition between the real and the virtual, as virtuality would already be part of our realities, and it would be impossible to think about our lives in society without considering this dimension [1]. More recently, the Italian Stefano Quintarelli [2] has named a "non-material dimension " or " immaterial reality ," a dimension in which "people engage in economic and social relationships through digital technologies." According to Quintarelli, the immaterial reality actually emerged at the beginning of 2001. "non-material dimension immaterial reality Alongside this divided approach, when considering the effects of technologies—perhaps to accommodate the discomfort we feel in the face of a complex scenario involving the pace of technological development, economic changes, and the limits of our institutions—we tend to adopt perspectives where the focus is on the technologies themselves. Thus, we reduce the discussion to classifying a particular technology, whether devices, social networks, or even the internet, as either good or bad. It is not uncommon to see nostalgic discourses evoked, as if things were better in the past, as if all communication and interaction were "real" before. This is an illusion, because our communication is, to some extent, always of the virtual order, even offline. “It is estimated that by around 2030 there will be 500 billion devices connected to the Internet” [2]. And with the Internet of Things (IoT), “devices” refers to a multitude of objects: from household appliances and cars to wearables like smart pacemakers and ingestible sensors. In this complex and sometimes anxiety-inducing scenario, it becomes necessary, though somewhat late, to factor in our digital habits or the way we use technology. our digital habits or the way we use technology. Why should the main focus of these discussions be our digital habits and the way we use technology? Why should the main focus of these discussions be our digital habits and the way we use technology? Usage is related both to the development of technologies and to their effects and how they impact us. People and organizations that design or develop new technologies try to anticipate specific uses and, in some cases, even the emotions and feelings involved. This is because most information technologies rely heavily on interactivity, engagement, and high immersion , as their "popularity" depends on user attention . Some authors already use the term "attention economy" in these contexts. Alongside information, user attention is what holds the most value in the immaterial dimension [2]. most information technologies rely heavily on interactivity, engagement, and high immersion as their "popularity" depends on user attention The term persuasive computing [3] [4] refers to how software design, through the experience it provides—including the emotions and feelings it evokes—can influence users to adopt certain behaviors. However, design cannot predict all possible uses. In different contexts, with the presence of new variables or changes in interaction methods, other responses may emerge. persuasive computing Thus, developers of any product, whether technological or not, when designing, can and should aim to offer benefits and improvements to users. On the other hand, even if they strive to design with the intention of providing well-being and benefits, the way we use these products can alter the impacts and effects, whether emotional or otherwise. When using most digital products or services, our configuration preferences and the way we interact with these services or products are included in our data. This information becomes available to the provider of the product or service. Based on this data, new versions can be optimized, features added or removed, and improvements implemented. [2] This is why some features are first tested in certain countries. The choice may be based on the behavior of the majority of users in that region. A recent example is Twitter's Fleets, a kind of stories feature, which was initially tested in Brazil, followed by Italy, and last month became available for testing to users in India as well. “Since Brazil is one of the countries where people converse the most on Twitter, with many of you passionate about talking and following other Brazilians on the platform, we are excited to test the new feature here.” [Twitter Blog] “Since Brazil is one of the countries where people converse the most on Twitter, with many of you passionate about talking and following other Brazilians on the platform, we are excited to test the new feature here.” [Twitter Blog] [Twitter Blog] That said, in addition to requiring more engaged stances regarding corporate accountability and ethics in product development and privacy aspects —which is increasingly necessary—discussions should also include how we use certain technologies. more engaged stances regarding corporate accountability and ethics in product development and privacy aspects This perspective holds some potential for change. Just as developers and designers think before creating, we users can also rethink how we use technologies, thereby making choices that contribute to our own well-being and, in the case of specific products, influence service providers or producers, leading to modifications or even the development of positive technologies. I do not have the naivety to believe that service providers or producers will implement changes solely because it could offer well-being to us users. However, if our attention is the currency in this dimension—and it is—facing the possibility of losing user attention, they will at least consider the possibility of making changes. This is what happened with the supposed “disappearance” of Instagram likes—supposed because the information is still visible, but it is no longer displayed all the time in the app as it was before. With criticisms backed by some research indicating that the count of likes was contributing to users' discomfort, and to ensure a positive image of the organization and avoid losing these users, the information was hidden. The development of technologies occurs in a social context where consumption and an ideal of happiness based on consumption still dominate: The development of technologies occurs in a social context where consumption and an ideal of happiness based on consumption still dominate: “(...) the consumer society educates us in the virtues and pleasures of knowing ourselves and understanding what we desire (to know ourselves simply means to know what we want to have).” Adam Phillips [5]. “(...) the consumer society educates us in the virtues and pleasures of knowing ourselves and understanding what we desire (to know ourselves simply means to know what we want to have).” Adam Phillips [5]. The way we use technology still serves these discourses, and the discomfort we often feel may also be related to the possibility that technologies allow us to confront the harsher expression of this logic, which we might take longer to notice under different circumstances. The way we use technology still serves these discourses, and the discomfort we often feel may also be related to the possibility that technologies allow us to confront the harsher expression of this logic, which we might take longer to notice under different circumstances. Technologies can influence changes in behavior; however, the way we use them—which can reflect the discourses that shape us—is an important component and perhaps the one closest to us. “‘Machines’ offer the chance to penetrate information into the most incomparable curves of space-time ever imagined. Use these machines in the way things should be 'used,' critically; otherwise, they will use you. The difference lies in the approach, and sometimes it’s just a matter of method; but I hope you are able to distance yourselves from the facts, reflect on information, and interrupt the flow whenever you need to regain your own sense of the world.” Stefano Quintarelli [2] “‘Machines’ offer the chance to penetrate information into the most incomparable curves of space-time ever imagined. Use these machines in the way things should be 'used,' critically; otherwise, they will use you. The difference lies in the approach, and sometimes it’s just a matter of method; but I hope you are able to distance yourselves from the facts, reflect on information, and interrupt the flow whenever you need to regain your own sense of the world.” Stefano Quintarelli [2] Originally written in Brazilian Portuguese Originally written in Brazilian Portuguese References References [1] Castells, M. (2002) A sociedade em rede. 6ª edição. Editora Paz e Terra. [2] Quintarelli, S. (2019) Instruções para um futuro imaterial. Editora Elefante. [3] Fogg, B. J. (1998). Persuasive computers: perspectives and research directions. Proceedings of CHI 1998, ACM Press, 225–232 . [4]Thompson, Clive. (2019). Coders: The Making of a New Tribe and the Remaking of the World. Penguin Books. [5] Phillips, Adam. (2013). O Que Você É e o Que Você Quer Ser. Editora Benvirá.

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