Hügga

hgga

Get Rich Building Verticalized AI Wrappers (Even If You Don’t Code)

রিচ বিল্ডিং ভার্টিক্যালাইজড এআই র‍্যাপার পান (এমনকি যদি আপনি কোড না করেন)

Werden Sie reich durch die Entwicklung vertikaler KI-Wrapper (auch wenn Sie nicht programmieren können)

Hazte rico creando contenedores de IA verticalizados (aunque no sepas programar)

Devenez riche en créant des wrappers d’IA verticalisés (même si vous ne codez pas)

वर्टिकलाइज़्ड AI रैपर बनाकर अमीर बनें (भले ही आप कोड न जानते हों)

Menjadi Kaya dengan Membangun Pembungkus AI Vertikal (Bahkan Jika Anda Tidak Membuat Kode)

垂直統合型 AI ラッパーを構築して金持ちになる (コードを書かなくても)

Rich Building вертикалдандырылған AI орауыштарын алыңыз (тіпті кодтамасаңыз да)

수직화된 AI 래퍼를 구축하여 부자가 되세요(코딩하지 않아도)

Баян барилгын босоо AI боодолтой бол (та код бичээгүй байсан ч)

Fique rico construindo wrappers de IA verticalizados (mesmo que você não codifique)

Разбогатейте, создавая вертикальные оболочки ИИ (даже если вы не пишете код)

Hel dhismo qani ah oo toosan AI ku duub (Xitaa Haddi aadan Codayn)

Get Rich Building Verticalized AI Wrappers (Kahit Hindi Ka Mag-code)

Dikey AI Sarmalayıcıları Oluşturarak Zengin Olun (Kodlamasanız Bile)

رچ بلڈنگ ورٹیکلائزڈ AI ریپر حاصل کریں (چاہے آپ کوڈ نہ بھی کریں)

Làm giàu bằng cách xây dựng các trình bao bọc AI theo chiều dọc (ngay cả khi bạn không viết mã)

通过构建垂直化 AI 包装器致富（即使你不会编码）

Table of Links Abstract


Introduction
Background
Reinforcement Learning
Similar Work


Methodology
DRLAgent Design


Training Procedures


Testing Procedures


Results


SABR Experiments


Conclusions


Appendix A


References REFERENCES Assa, H., C. Kenyon, and H. Zhang. 2021. “Assessing Reinforcement Delta Hedging.” SSRN SSRN 3918375. http://dx.doi.org/10.2139/ssrn.3918375. Black, F., and M. Scholes. 1973. “The Pricing of Options and Corporate Liabilities” 81 (3): 637– 54. https://doi.org/10.1086/260062. Cao, Jay, Jacky Chen, Soroush Farghadani, John Hull, Zissis Poulos, Zeyu Wang, and Jun Yuan. 2023. “Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning.” Frontiers in Artificial Intelligence 6. https://www.frontiersin.org/articles/10.3389/frai.2023.1129370. Cao, Jay, Jacky Chen, John Hull, and Zissis Poulos. 2021. “Deep Hedging of Derivatives Using Reinforcement Learning.” The Journal of Financial Data Science 3 (1): 10–27. https://doi.org/10.3905/jfds.2020.1.052. Chebyshev, P. 1864. “Sur l’interpolation.” Zapiski Akademii Nauk 1 (4): 539–60. Du, Jiayi, Muyang Jin, Petter N. Kolm, Gordon Ritter, Yixuan Wang, and Bofei Zhang. 2020. “Deep Reinforcement Learning for Option Replication and Hedging.” The Journal of Financial Data Science 2 (4): 44–57. https://doi.org/10.3905/jfds.2020.1.045. Fathi, A., and B. Hientzsch. 2023. “A Comparison of Reinforcement Learning and Deep Trajectory Based Stochastic Control Agents for Stepwise Mean-Variance Hedging.” arXiv arXiv:2302.07996. Florescu, Ionuţ, and Cristian Gabriel Pãsãricã. 2009. “A Study about the Existence of the Leverage Effect in Stochastic Volatility Models.” Physica A: Statistical Mechanics and Its Applications 388 (4): 419–32. Giurca, B., and S. Borovkova. 2021. “Delta Hedging of Derivatives Using Deep Reinforcement Learning.” SSRN SSRN 3847272. https://ssrn.com/abstract=3847272. Glau, Kathrin, Micro Mahlstedt, and Christian Potz. 2018. “A New Approach for American Option Pricing: The Dynamic Chebyshev Method.” arXiv. https://doi.org/10.48550/arXiv.1806.05579. Hagan, Patrick, Deep Kumar, Andrew Lesniewski, and Diana Woodward. 2002. “Managing Smile Risk.” Wilmott Magazine 1 (January): 84–108. Halperin, Igor. 2017. “QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds.” The Journal of Derivatives 28 (1): 99–122. https://doi.org/10.3905/jod.2020.1.108. Heston, Steven L. 1993. “A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options.” The Review of Financial Studies 6 (2): 327–43. Hull, John, 1946-. 2012. Options, Futures, and Other Derivatives. Eighth edition. Boston : Prentice Hall, [2012] ©2012. https://search.library.wisc.edu/catalog/9910112878402121. Kolm, Petter N., and Gordon Ritter. 2019. “Dynamic Replication and Hedging: A Reinforcement Learning Approach.” The Journal of Financial Data Science 1 (1): 159–71. https://doi.org/10.3905/jfds.2019.1.1.159. Lillicrap, Timothy, Johnathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. “Continuous Control with Deep Reinforcement Learning.” arXiv arXiv:1509.02971. https://doi.org/10.48550/arXiv.1509.02971. Lin, Long-Ji. 1992. “Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching.” Machine Learning 8 (3): 293–321. https://doi.org/10.1007/BF00992699. Liu, Peng. 2023. “A Review on Derivative Hedging Using Reinforcement Learning.” The Journal of Financial Data Science. https://doi.org/10.3905/jfds.2023.1.124. Longstaff, Francis A., and Eduardo S. Schwartz. 2001. “Valuing American Options by Simulation: A Simple Least-Squares Approach.” UCLA: Finance. https://escholarship.org/uc/item/43n1k4jb. Mikkilä, Oskari, and Juho Kanniainen. 2023. “Empirical Deep Hedging.” Quantitative Finance 23 (1): 111–22. https://doi.org/10.1080/14697688.2022.2136037. Mnih, V., K. Kavukcuoglu, David Silver, A. Graves, I. Antonoglou, Daan Wierstra, and M. Riedmiller. 2013. “Playing Atari with Deep Reinforcement Learning.” arXiv arXiv:1312.5602. https://doi.org/10.48550/arXiv.1312.5602. Murray, Phillip, B. Wood, H. Buehler, Magnus Wiese, and Mikko Pakkanen. 2022. “Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions.” In ICAIF ’22: Proceedings of the Third ACM International Conference on AI in Finance, 361–68. Association for Computing Machinery. https://doi.org/10.1145/3533271.3561731. Pham, Uyen, Quoc Luu, and Hien Tran. 2021. “Multi-Agent Reinforcement Learning Approach for Hedging Portfolio Problem.” Soft Computing 25 (12): 7877–85. https://doi.org/10.1007/s00500-021-05801-6. Pickard, Reilly, and Yuri Lawryshyn. 2023. “Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review.” Mathematics 11 (24). https://doi.org/10.3390/math11244943. Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, et al. 2016. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529 (7587): 484–89. https://doi.org/10.1038/nature16961. Smith, Leslie N. 2018. “A Disciplined Approach to Neural Network Hyper-Parameters: Part 1 -- Learning Rate, Batch Size, Momentum, and Weight Decay.” https://doi.org/10.48550/arXiv.1803.09820. Sutton, Richard S., and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. A Bradford Book. Watkins, Christopher John Cornish Hellaby. 1989. “Learning from Delayed Rewards.” King’s College. https://www.cs.rhul.ac.uk/~chrisw/new_thesis.pdf. Xiao, B., W. Yao, and X. Zhou. 2021. “Optimal Option Hedging with Policy Gradient.” In 2021 International Conference on Data Mining Workshops (ICDMW), 1112–19. IEEE. https://doi.org/10.1109/ICDMW53433.2021.00145. Xu, Wei, and Bing Dai. 2022. “Delta-Gamma–Like Hedging with Transaction Cost under Reinforcement Learning Technique.” The Journal of Derivatives 29 (5): 60–82. https://doi.org/10.3905/jod.2022.1.156. Zheng, C., J. He, and C. Yang. 2023. “Option Dynamic Hedging Using Reinforcement Learning.” arXiv arXiv:2306.10743. Authors:
(1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (reilly.pickard@mail.utoronto.ca);
(2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. Table of Links Abstract Introduction
Background
Reinforcement Learning
Similar Work Methodology
DRLAgent Design Training Procedures Testing Procedures Results SABR Experiments Conclusions Appendix A References Abstract Abstract Abstract Introduction Background Reinforcement Learning Similar Work Introduction Background Background Reinforcement Learning Reinforcement Learning Similar Work Similar Work Methodology DRLAgent Design Methodology Methodology DRLAgent Design DRLAgent Design Training Procedures Training Procedures Training Procedures Testing Procedures Testing Procedures Testing Procedures Results Results Results SABR Experiments SABR Experiments SABR Experiments Conclusions Conclusions Conclusions Appendix A Appendix A Appendix A References References References REFERENCES Assa, H., C. Kenyon, and H. Zhang. 2021. “Assessing Reinforcement Delta Hedging.” SSRN SSRN 3918375. http://dx.doi.org/10.2139/ssrn.3918375. Black, F., and M. Scholes. 1973. “The Pricing of Options and Corporate Liabilities” 81 (3): 637– 54. https://doi.org/10.1086/260062. Cao, Jay, Jacky Chen, Soroush Farghadani, John Hull, Zissis Poulos, Zeyu Wang, and Jun Yuan. 2023. “Gamma and Vega Hedging Using Deep Distributional Reinforcement Learning.” Frontiers in Artificial Intelligence 6. https://www.frontiersin.org/articles/10.3389/frai.2023.1129370. Cao, Jay, Jacky Chen, John Hull, and Zissis Poulos. 2021. “Deep Hedging of Derivatives Using Reinforcement Learning.” The Journal of Financial Data Science 3 (1): 10–27. https://doi.org/10.3905/jfds.2020.1.052. Chebyshev, P. 1864. “Sur l’interpolation.” Zapiski Akademii Nauk 1 (4): 539–60. Du, Jiayi, Muyang Jin, Petter N. Kolm, Gordon Ritter, Yixuan Wang, and Bofei Zhang. 2020. “Deep Reinforcement Learning for Option Replication and Hedging.” The Journal of Financial Data Science 2 (4): 44–57. https://doi.org/10.3905/jfds.2020.1.045. Fathi, A., and B. Hientzsch. 2023. “A Comparison of Reinforcement Learning and Deep Trajectory Based Stochastic Control Agents for Stepwise Mean-Variance Hedging.” arXiv arXiv:2302.07996. Florescu, Ionuţ, and Cristian Gabriel Pãsãricã. 2009. “A Study about the Existence of the Leverage Effect in Stochastic Volatility Models.” Physica A: Statistical Mechanics and Its Applications 388 (4): 419–32. Giurca, B., and S. Borovkova. 2021. “Delta Hedging of Derivatives Using Deep Reinforcement Learning.” SSRN SSRN 3847272. https://ssrn.com/abstract=3847272. Glau, Kathrin, Micro Mahlstedt, and Christian Potz. 2018. “A New Approach for American Option Pricing: The Dynamic Chebyshev Method.” arXiv. https://doi.org/10.48550/arXiv.1806.05579. Hagan, Patrick, Deep Kumar, Andrew Lesniewski, and Diana Woodward. 2002. “Managing Smile Risk.” Wilmott Magazine 1 (January): 84–108. Halperin, Igor. 2017. “QLBS: Q-Learner in the Black-Scholes(-Merton) Worlds.” The Journal of Derivatives 28 (1): 99–122. https://doi.org/10.3905/jod.2020.1.108. Heston, Steven L. 1993. “A Closed-Form Solution for Options with Stochastic Volatility with Applications to Bond and Currency Options.” The Review of Financial Studies 6 (2): 327–43. Hull, John, 1946-. 2012. Options, Futures, and Other Derivatives. Eighth edition. Boston : Prentice Hall, [2012] ©2012. https://search.library.wisc.edu/catalog/9910112878402121. Kolm, Petter N., and Gordon Ritter. 2019. “Dynamic Replication and Hedging: A Reinforcement Learning Approach.” The Journal of Financial Data Science 1 (1): 159–71. https://doi.org/10.3905/jfds.2019.1.1.159. Lillicrap, Timothy, Johnathan J. Hunt, Alexander Pritzel, Nicolas Heess, Tom Erez, Yuval Tassa, David Silver, and Daan Wierstra. 2015. “Continuous Control with Deep Reinforcement Learning.” arXiv arXiv:1509.02971. https://doi.org/10.48550/arXiv.1509.02971. Lin, Long-Ji. 1992. “Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching.” Machine Learning 8 (3): 293–321. https://doi.org/10.1007/BF00992699. Liu, Peng. 2023. “A Review on Derivative Hedging Using Reinforcement Learning.” The Journal of Financial Data Science. https://doi.org/10.3905/jfds.2023.1.124. Longstaff, Francis A., and Eduardo S. Schwartz. 2001. “Valuing American Options by Simulation: A Simple Least-Squares Approach.” UCLA: Finance. https://escholarship.org/uc/item/43n1k4jb. Mikkilä, Oskari, and Juho Kanniainen. 2023. “Empirical Deep Hedging.” Quantitative Finance 23 (1): 111–22. https://doi.org/10.1080/14697688.2022.2136037. Mnih, V., K. Kavukcuoglu, David Silver, A. Graves, I. Antonoglou, Daan Wierstra, and M. Riedmiller. 2013. “Playing Atari with Deep Reinforcement Learning.” arXiv arXiv:1312.5602. https://doi.org/10.48550/arXiv.1312.5602. Murray, Phillip, B. Wood, H. Buehler, Magnus Wiese, and Mikko Pakkanen. 2022. “Deep Hedging: Continuous Reinforcement Learning for Hedging of General Portfolios across Multiple Risk Aversions.” In ICAIF ’22: Proceedings of the Third ACM International Conference on AI in Finance, 361–68. Association for Computing Machinery. https://doi.org/10.1145/3533271.3561731. Pham, Uyen, Quoc Luu, and Hien Tran. 2021. “Multi-Agent Reinforcement Learning Approach for Hedging Portfolio Problem.” Soft Computing 25 (12): 7877–85. https://doi.org/10.1007/s00500-021-05801-6. Pickard, Reilly, and Yuri Lawryshyn. 2023. “Deep Reinforcement Learning for Dynamic Stock Option Hedging: A Review.” Mathematics 11 (24). https://doi.org/10.3390/math11244943. Silver, David, Aja Huang, Chris J. Maddison, Arthur Guez, Laurent Sifre, George van den Driessche, Julian Schrittwieser, et al. 2016. “Mastering the Game of Go with Deep Neural Networks and Tree Search.” Nature 529 (7587): 484–89. https://doi.org/10.1038/nature16961. Smith, Leslie N. 2018. “A Disciplined Approach to Neural Network Hyper-Parameters: Part 1 -- Learning Rate, Batch Size, Momentum, and Weight Decay.” https://doi.org/10.48550/arXiv.1803.09820. Sutton, Richard S., and Andrew G. Barto. 2018. Reinforcement Learning: An Introduction. A Bradford Book. Watkins, Christopher John Cornish Hellaby. 1989. “Learning from Delayed Rewards.” King’s College. https://www.cs.rhul.ac.uk/~chrisw/new_thesis.pdf. Xiao, B., W. Yao, and X. Zhou. 2021. “Optimal Option Hedging with Policy Gradient.” In 2021 International Conference on Data Mining Workshops (ICDMW), 1112–19. IEEE. https://doi.org/10.1109/ICDMW53433.2021.00145. Xu, Wei, and Bing Dai. 2022. “Delta-Gamma–Like Hedging with Transaction Cost under Reinforcement Learning Technique.” The Journal of Derivatives 29 (5): 60–82. https://doi.org/10.3905/jod.2022.1.156. Zheng, C., J. He, and C. Yang. 2023. “Option Dynamic Hedging Using Reinforcement Learning.” arXiv arXiv:2306.10743. Authors: (1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (reilly.pickard@mail.utoronto.ca); (2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. Authors: Authors: (1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada ( reilly.pickard@mail.utoronto.ca ); reilly.pickard@mail.utoronto.ca (2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. available on arxiv available on arxiv

Hedging American Put Options with Deep Reinforcement Learning: References

Table of Links Abstract


Introduction
Background
Reinforcement Learning
Similar Work


Methodology
DRLAgent Design


Training Procedures


Testing Procedures


Results


SABR Experiments


Conclusions


Appendix A


References SIMILAR WORK Pioneering works in the field of RL hedging are given by Halperin (2017) and Kolm and Ritter (2019). Halperin (2017) approximates a parametrized optimal 𝑄-function with fitted 𝑄- learning, and Kolm and Ritter (2019) use a function-approximation SARSA method by fitting a non-linear function of the form 𝑦 = 𝑄(𝑋) to approximate the 𝑄-function, where 𝑋 is the current state-action pair (𝑠, 𝑎). In terms of those who use DRL to solve the option hedging problem, Du et al. (2020) and Giurca and Borovkova (2021) use DQN to approximate the optimal 𝑄-function. Studies employing the policy method of DDPG include Cao et al. (2021), Assa, Kenyon, and Zhang (2021), Xu and Dai (2022), and Fathi and Hientzsch (2023). Cao et al. (2023) extend DDPG in their DRL hedging study, as instead of estimating the 𝑄-value, the critic network uses quantile regression (QR) to estimate the distribution of discrete fixed-point rewards. In the DRL hedging literature, most studies include the current asset price, time-to-maturity, and current holding in the state-space (Pickard and Lawryshyn 2023). Vittori et al. (2020), Giurca and Borovkova (2021), Xu and Dai (2022), Fathi and Hientzch (2023), and Zheng, He, and Yang (2023) include the BS Delta in the state-space for their study, while several papers explicitly explain that the BS Delta can be deduced from the option and stock prices and its inclusion only serves to unnecessarily augment the state (Cao et al. (2021), Kolm and Ritter (2019), Du et al. (2020)). As for the reward function, the mean-variance reward formulation is commonly used to provide feedback to the agent at each time step. A generalized version of the mean-variance reward for a single time-step 𝑡 is given as Given a defined state-space, action-space, and reward, a DRL agent needs experience in the form of data to learn optimal policies and value functions. In the dynamic hedging environment, the data may be simulated or empirical, i.e., market data. In a simulated case, the underlying asset follows a stochastic process such as GBM or a stochastic volatility model such as the SABR (Stochastic Alpha, Beta, Rho) (Hagan et al. 2002) or Heston (1993) models. Monte Carlo simulations of the GBM process are employed for data generation in most studies in the RL hedging literature (Pickard and Lawryshyn 2023). To train DRL agents for an environment with stochastic volatility, Cao et al. (2021) use a SABR model, and the Heston model is used to generate experimental data in Mikkilä and Kanniainen (2023), Murray et al. (2022), Xiao, Yao, and Zhou (2021), and Giurca and Borovkova (2021). It is noted that no papers in the literature calibrate the stochastic volatility model parameters to market data. Consistent results emerge across the DRL hedging literature when training and testing on simulated data in that DRL agents trained with a transaction cost penalty outperform the traditional BS Delta hedging strategy in frictional markets (Pickard and Lawryshyn 2023). While Giurca and Borovkova (2021), Xiao, Yao, and Zhou (2021), Zheng, He, and Yang (2023), and Mikkilä and Kanniainen (2023) train their agents on simulated data, each of these studies also performs a sim-to-real test in which they test the simulation trained DRL agent on empirical data. Notably, Xiao, Yao, and Zhou (2021), Mikkilä and Kanniainen (2023), and Zheng, He, and Yang (2023) achieve desirable results in the sim-to-real test, while Giurca and Borovkova (2021) do not. In addition to using empirical data for a sim-to-real test, Mikkilä & Kanniainen (2023) train their DRL agent using the empirical dataset. Others who train a DRL agent with empirical data are Pham, Luu, and Tran (2021) and Xu and Dai (2022). Pham, Luu, and Tran (2021) show that their DRL agent has a profit higher than the market return when testing with empirical prices. Of further note, Xu and Dai (2022) train and test their DRL agent with empirical American option data, which is not seen elsewhere in the DRL hedging literature. However, Xu and Dai (2022) do not change their DRL approach in any manner when considering American versus European options. There have been no encountered studies that train DRL agents specifically to hedge American options by incorporating exercise boundaries into the training and testing process. This observation that there is an absence of literature on DRL for hedging American options is supported by two literature reviews that analyze the use of DRL for dynamic option hedging (Pickard and Lawryshyn 2023, Liu 2023). Authors:
(1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (reilly.pickard@mail.utoronto.ca);
(2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada;
(5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. Table of Links Abstract Introduction
Background
Reinforcement Learning
Similar Work Methodology
DRLAgent Design Training Procedures Testing Procedures Results SABR Experiments Conclusions Appendix A References Abstract Abstract Abstract Introduction Background Reinforcement Learning Similar Work Introduction Background Background Reinforcement Learning Reinforcement Learning Similar Work Similar Work Methodology DRLAgent Design Methodology Methodology DRLAgent Design DRLAgent Design Training Procedures Training Procedures Training Procedures Testing Procedures Testing Procedures Testing Procedures Results Results Results SABR Experiments SABR Experiments SABR Experiments Conclusions Conclusions Conclusions Appendix A Appendix A Appendix A References References References SIMILAR WORK Pioneering works in the field of RL hedging are given by Halperin (2017) and Kolm and Ritter (2019). Halperin (2017) approximates a parametrized optimal 𝑄-function with fitted 𝑄- learning, and Kolm and Ritter (2019) use a function-approximation SARSA method by fitting a non-linear function of the form 𝑦 = 𝑄(𝑋) to approximate the 𝑄-function, where 𝑋 is the current state-action pair (𝑠, 𝑎). In terms of those who use DRL to solve the option hedging problem, Du et al. (2020) and Giurca and Borovkova (2021) use DQN to approximate the optimal 𝑄-function. Studies employing the policy method of DDPG include Cao et al. (2021), Assa, Kenyon, and Zhang (2021), Xu and Dai (2022), and Fathi and Hientzsch (2023). Cao et al. (2023) extend DDPG in their DRL hedging study, as instead of estimating the 𝑄-value, the critic network uses quantile regression (QR) to estimate the distribution of discrete fixed-point rewards. In the DRL hedging literature, most studies include the current asset price, time-to-maturity, and current holding in the state-space (Pickard and Lawryshyn 2023). Vittori et al. (2020), Giurca and Borovkova (2021), Xu and Dai (2022), Fathi and Hientzch (2023), and Zheng, He, and Yang (2023) include the BS Delta in the state-space for their study, while several papers explicitly explain that the BS Delta can be deduced from the option and stock prices and its inclusion only serves to unnecessarily augment the state (Cao et al. (2021), Kolm and Ritter (2019), Du et al. (2020)). As for the reward function, the mean-variance reward formulation is commonly used to provide feedback to the agent at each time step. A generalized version of the mean-variance reward for a single time-step 𝑡 is given as Given a defined state-space, action-space, and reward, a DRL agent needs experience in the form of data to learn optimal policies and value functions. In the dynamic hedging environment, the data may be simulated or empirical, i.e., market data. In a simulated case, the underlying asset follows a stochastic process such as GBM or a stochastic volatility model such as the SABR (Stochastic Alpha, Beta, Rho) (Hagan et al. 2002) or Heston (1993) models. Monte Carlo simulations of the GBM process are employed for data generation in most studies in the RL hedging literature (Pickard and Lawryshyn 2023). To train DRL agents for an environment with stochastic volatility, Cao et al. (2021) use a SABR model, and the Heston model is used to generate experimental data in Mikkilä and Kanniainen (2023), Murray et al. (2022), Xiao, Yao, and Zhou (2021), and Giurca and Borovkova (2021). It is noted that no papers in the literature calibrate the stochastic volatility model parameters to market data. Consistent results emerge across the DRL hedging literature when training and testing on simulated data in that DRL agents trained with a transaction cost penalty outperform the traditional BS Delta hedging strategy in frictional markets (Pickard and Lawryshyn 2023). While Giurca and Borovkova (2021), Xiao, Yao, and Zhou (2021), Zheng, He, and Yang (2023), and Mikkilä and Kanniainen (2023) train their agents on simulated data, each of these studies also performs a sim-to-real test in which they test the simulation trained DRL agent on empirical data. Notably, Xiao, Yao, and Zhou (2021), Mikkilä and Kanniainen (2023), and Zheng, He, and Yang (2023) achieve desirable results in the sim-to-real test, while Giurca and Borovkova (2021) do not. In addition to using empirical data for a sim-to-real test, Mikkilä & Kanniainen (2023) train their DRL agent using the empirical dataset. Others who train a DRL agent with empirical data are Pham, Luu, and Tran (2021) and Xu and Dai (2022). Pham, Luu, and Tran (2021) show that their DRL agent has a profit higher than the market return when testing with empirical prices. Of further note, Xu and Dai (2022) train and test their DRL agent with empirical American option data, which is not seen elsewhere in the DRL hedging literature. However, Xu and Dai (2022) do not change their DRL approach in any manner when considering American versus European options. There have been no encountered studies that train DRL agents specifically to hedge American options by incorporating exercise boundaries into the training and testing process. This observation that there is an absence of literature on DRL for hedging American options is supported by two literature reviews that analyze the use of DRL for dynamic option hedging (Pickard and Lawryshyn 2023, Liu 2023). Authors: (1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada (reilly.pickard@mail.utoronto.ca); (2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. Authors: Authors: (1) Reilly Pickard, Department of Mechanical and Industrial Engineering, University of Toronto, Toronto, ON M5S 3G8, Canada ( reilly.pickard@mail.utoronto.ca ); reilly.pickard@mail.utoronto.ca (2) Finn Wredenhagen, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (3) Julio DeJesus, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (4) Mario Schlener, Ernst & Young LLP, Toronto, ON, M5H 0B3, Canada; (5) Yuri Lawryshyn, Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, ON M5S 3E5, Canada. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. This paper is available on arxiv under CC BY-NC-SA 4.0 Deed (Attribution-Noncommercial-Sharelike 4.0 International) license. available on arxiv available on arxiv

What Previous Research Says About Using Reinforcement Learning for Hedging Options

Deadpool and Wolverine is currently taking the world by storm. It has officially surpassed the $1 billion mark and has dethroned Joker to become the highest-grossing rated R film of all time. Everyone’s talking about it, and the one thing that people are talking about the most is all the cameos. MCU movies, especially now that they’re in their Multiverse phase, are filled with a litany of cameos and easter eggs, and Deadpool and Wolverine is no exception. With so many appearing throughout the film, even the most eagle-eyed MCU fan would find it hard to spot them all out. So, to help you out, here are all the Deadpool and Wolverine Cameos in order. Want to share your own thoughts on popular media? Start publishing on HackerNoon today! All the Deadpool and Wolverine Cameos Happy Hogan One of the O.G.s of the MCU, Happy Hogan has been a part of the universe since the very first Iron Man. His character has evolved throughout the years, and in Deadpool and Wolverine, he is seen managing the Avengers. Thor The God of Thunder makes a brief appearance where he’s seen comforting Deadpool on one of the TVA’s monitors. Whether this was just a one-off joke or indicative of a future storyline remains to be seen. Wolverine Variants in Deadpool and Wolverine As Deadpool goes around the multiverse looking for a Wolverine to help him, he comes across several unique-looking ones, including: Patch Wolverine’s spy alter-ego makes a quick cameo during the variants montage. Sporting his iconic white suit, he makes quick work of Deadpool. Old Man Logan We’ve already seen an adaptation of Old Man Logan before (from the Logan film), but now, we get to see a more comic-accurate version of the character. Callverine The (former) Superman actor makes a quick cameo as Wolverine which causes Deadpool to have a nerdgasm. Weapon Omega Wolverine Even with one hand, he still manages to beat the brakes off Deadpool. Uncanny X-Men Wolverine I have no idea how Wolverine got himself into this situation, but to be completely honest, it might be better not knowing. First-Appearance Wolverine And speaking of the first-appearance variant of the Wolverine, another MCU hero is also shown in that scene… The Hulk The jolly green giant can be seen fighting Wolverine and smacking the hell out of Deadpool. The Human Torch Chris Evans returns to the MCU to reprise a superhero role, and it’s not Captain America. Years before he picked up the iconic shield, Chris Evans played the Human Torch in The Fantastic Four and Fantastic Four: Rise of the Silver Surfer. Sabretooth Tyler Mane played Logan’s brother in the very first X-Men movie. Now, 24 years later, he once again returns to play Sabretooth in a very brief but enjoyable cameo. Pyro Another X-Men villain alumni who appeared in the film was Pyro from the original X-Men film trilogy. Pyro surprisingly gets more screen time than his contemporaries, but this was a welcomed surprise as he was great in the role. And Pyro and Sabretooth aren’t the only X-men villains who make it into the film, there are plenty more who show up, including: Toad The green and gross character has made two appearances in live-action movies before: the first time was in X-Men (2000), and the second time was in X-Men: Days of Future Past. Azazel The resident teleporter of the Hellfire Club, Azazel returns in Deadpool and Wolverine as one of Cassandra’s goons. Lady Deathstrike Lady Deathstrike was last seen battling Wolverine to death in X2. Spoiler alert, but she lost. Like in a gruesome way. However, it would be nice to see the character fight Wolverine or even X-23 in the future. Psylocke The sword-wielding mutant was last seen as one of Apocalypse’s horsewomen in X-Men: Apocalypse. Random side note: I had no idea the character also made an appearance in X-Men: The Last Stand, and I’ve seen that movie numerous times. Callisto Callisto had a pretty substantial role in X-Men: The Last Stand as one of Magneto’s supporters and foil to Storm. Blob To be honest, I had no idea he was in the movie until I saw a professional wrestler do an interview where he talked about his experience being the Blob in Deadpool and Wolverine. Might have to do a rewatch to see if I can spot him in the background 👀 Juggernaut Although the Juggernaut gets no lines in the movie, he does play a pretty big part in it. Or at least, his helmet does. The Russian Apart from the X-Men bad guys, another villain who returns to the big screen is The Russian from The Punisher (2004). Alioth The unstoppable force known as Alioth returns to the MCU. He was first seen in the Disney+ show Loki. Now, this movie has had a ton of bad guys, but they’ve also brought back old heroes (including a brand new one). Blade Blade wasn’t Marvel’s first film, but it was one of its first hits. The Blade film predates the X-Men and Spider-Man films, and it’s nice to see the character and the actor (Wesley Snipes) get the recognition they deserve. Elektra Jennifer Gardner’s Elektra didn’t have a ton of success. She was introduced in Daredevil (2003), a movie that most people would say is just okay. Then, 2 years later, she starred in her own film, which most people would say is downright bad. However, she finally gets her redemption in Deadpool and Wolverine. Gambit Channing Tatum’s Gambit isn’t a returning hero. This was the first time the actor portrayed the character. He was supposed to star in his own movie many years ago, but after roadblock after roadblock, it never materialized. Now, Channing Tatum’s dream finally comes true, and I would say that his performance stole the show (or the movie). X-23 The breakout star of the film Logan returns in Deadpool and Wolverine as one of the survivors in the Void. After the touching but tragic ending of Logan, it’s nice to see X-23 and Wolverine together again. Deadpool Corps Actors At the climax of the film, we see the Deadpool Corps. A group of Deadpools pulled together from different universes. And you won’t believe the actors that played these characters. Matthew McConaughey plays Cowboypool
Nathan Fillion plays Headpool
Football player Paul Mullin plays Welshpool
Blake Lively plays Ladypool
Blake Lively and Ryan Reynolds’ kids play Babypool and Kidpool And that’s a wrap for the Deadpool and Wolverine cameo guide. If you think there’s a cameo or special appearance that I missed, let me know in the comments below! Read More Now That the Dust Has Settled, Is 'Deadpool and Wolverine' Actually Good?
The X-Men Movies in Order: How to Watch Them By Release Date
How to Watch the Harry Potter Movies in Order Feature image source Deadpool and Wolverine is currently taking the world by storm. It has officially surpassed the $1 billion mark and has dethroned Joker to become the highest-grossing rated R film of all time. Everyone’s talking about it, and the one thing that people are talking about the most is all the cameos. $1 billion mark MCU movies, especially now that they’re in their Multiverse phase, are filled with a litany of cameos and easter eggs, and Deadpool and Wolverine is no exception. With so many appearing throughout the film, even the most eagle-eyed MCU fan would find it hard to spot them all out. So, to help you out, here are all the Deadpool and Wolverine Cameos in order. Want to share your own thoughts on popular media? Start publishing on HackerNoon today! Want to share your own thoughts on popular media? Start publishing on HackerNoon today! Want to share your own thoughts on popular media? Start publishing on HackerNoon today! Want to share your own thoughts on popular media? Start publishing on HackerNoon today! publishing All the Deadpool and Wolverine Cameos Happy Hogan One of the O.G.s of the MCU, Happy Hogan has been a part of the universe since the very first Iron Man. His character has evolved throughout the years, and in Deadpool and Wolverine, he is seen managing the Avengers. Thor The God of Thunder makes a brief appearance where he’s seen comforting Deadpool on one of the TVA’s monitors. Whether this was just a one-off joke or indicative of a future storyline remains to be seen. Wolverine Variants in Deadpool and Wolverine As Deadpool goes around the multiverse looking for a Wolverine to help him, he comes across several unique-looking ones, including: Patch Wolverine’s spy alter-ego makes a quick cameo during the variants montage. Sporting his iconic white suit, he makes quick work of Deadpool. Old Man Logan We’ve already seen an adaptation of Old Man Logan before (from the Logan film), but now, we get to see a more comic-accurate version of the character. Callverine The (former) Superman actor makes a quick cameo as Wolverine which causes Deadpool to have a nerdgasm. Weapon Omega Wolverine Even with one hand, he still manages to beat the brakes off Deadpool. Uncanny X-Men Wolverine I have no idea how Wolverine got himself into this situation, but to be completely honest, it might be better not knowing. First-Appearance Wolverine And speaking of the first-appearance variant of the Wolverine, another MCU hero is also shown in that scene… The Hulk The jolly green giant can be seen fighting Wolverine and smacking the hell out of Deadpool. The Human Torch Chris Evans returns to the MCU to reprise a superhero role, and it’s not Captain America. Years before he picked up the iconic shield, Chris Evans played the Human Torch in The Fantastic Four and Fantastic Four: Rise of the Silver Surfer. Sabretooth Tyler Mane played Logan’s brother in the very first X-Men movie. Now, 24 years later, he once again returns to play Sabretooth in a very brief but enjoyable cameo. Pyro Another X-Men villain alumni who appeared in the film was Pyro from the original X-Men film trilogy. Pyro surprisingly gets more screen time than his contemporaries, but this was a welcomed surprise as he was great in the role. And Pyro and Sabretooth aren’t the only X-men villains who make it into the film, there are plenty more who show up, including: Toad The green and gross character has made two appearances in live-action movies before: the first time was in X-Men (2000), and the second time was in X-Men: Days of Future Past. Azazel The resident teleporter of the Hellfire Club, Azazel returns in Deadpool and Wolverine as one of Cassandra’s goons. Lady Deathstrike Lady Deathstrike was last seen battling Wolverine to death in X2. Spoiler alert, but she lost. Like in a gruesome way. However, it would be nice to see the character fight Wolverine or even X-23 in the future. Psylocke The sword-wielding mutant was last seen as one of Apocalypse’s horsewomen in X-Men: Apocalypse. Random side note: I had no idea the character also made an appearance in X-Men: The Last Stand, and I’ve seen that movie numerous times. Callisto Callisto had a pretty substantial role in X-Men: The Last Stand as one of Magneto’s supporters and foil to Storm. Blob To be honest, I had no idea he was in the movie until I saw a professional wrestler do an interview where he talked about his experience being the Blob in Deadpool and Wolverine. Might have to do a rewatch to see if I can spot him in the background 👀 interview Juggernaut Although the Juggernaut gets no lines in the movie, he does play a pretty big part in it. Or at least, his helmet does. The Russian Apart from the X-Men bad guys, another villain who returns to the big screen is The Russian from The Punisher (2004). Alioth The unstoppable force known as Alioth returns to the MCU. He was first seen in the Disney+ show Loki. Now, this movie has had a ton of bad guys, but they’ve also brought back old heroes (including a brand new one). Blade Blade wasn’t Marvel’s first film, but it was one of its first hits. The Blade film predates the X-Men and Spider-Man films, and it’s nice to see the character and the actor (Wesley Snipes) get the recognition they deserve. Elektra Jennifer Gardner’s Elektra didn’t have a ton of success. She was introduced in Daredevil (2003), a movie that most people would say is just okay. Then, 2 years later, she starred in her own film, which most people would say is downright bad. However, she finally gets her redemption in Deadpool and Wolverine. Gambit Channing Tatum’s Gambit isn’t a returning hero. This was the first time the actor portrayed the character. He was supposed to star in his own movie many years ago, but after roadblock after roadblock, it never materialized. Now, Channing Tatum’s dream finally comes true, and I would say that his performance stole the show (or the movie). X-23 The breakout star of the film Logan returns in Deadpool and Wolverine as one of the survivors in the Void. After the touching but tragic ending of Logan, it’s nice to see X-23 and Wolverine together again. Deadpool Corps Actors At the climax of the film, we see the Deadpool Corps. A group of Deadpools pulled together from different universes. And you won’t believe the actors that played these characters. Matthew McConaughey plays Cowboypool Nathan Fillion plays Headpool Football player Paul Mullin plays Welshpool Blake Lively plays Ladypool Blake Lively and Ryan Reynolds’ kids play Babypool and Kidpool Matthew McConaughey plays Cowboypool Nathan Fillion plays Headpool Football player Paul Mullin plays Welshpool Blake Lively plays Ladypool Blake Lively and Ryan Reynolds’ kids play Babypool and Kidpool And that’s a wrap for the Deadpool and Wolverine cameo guide. If you think there’s a cameo or special appearance that I missed, let me know in the comments below! Read More Now That the Dust Has Settled, Is 'Deadpool and Wolverine' Actually Good? The X-Men Movies in Order: How to Watch Them By Release Date How to Watch the Harry Potter Movies in Order Now That the Dust Has Settled, Is 'Deadpool and Wolverine' Actually Good? Now That the Dust Has Settled, Is 'Deadpool and Wolverine' Actually Good? Now That the Dust Has Settled, Is 'Deadpool and Wolverine' Actually Good? The X-Men Movies in Order: How to Watch Them By Release Date The X-Men Movies in Order: How to Watch Them By Release Date The X-Men Movies in Order: How to Watch Them By Release Date How to Watch the Harry Potter Movies in Order How to Watch the Harry Potter Movies in Order How to Watch the Harry Potter Movies in Order Feature image source Feature image source source

Every Deadpool and Wolverine Cameo in Order

প্রতিটি ডেডপুল এবং উলভারিন ক্যামিও অর্ডারে

Alle Cameo-Auftritte von Deadpool und Wolverine in der richtigen Reihenfolge

Cada cameo de Deadpool y Wolverine en orden

Chaque caméo de Deadpool et Wolverine dans l'ordre

डेडपूल और वूल्वरिन का हर कैमियो क्रम में

デッドプールとウルヴァリンのカメオ出演の順番

모든 데드풀과 울버린 카메오 순서대로

Todas as participações especiais de Deadpool e Wolverine em ordem

Все камео Дэдпула и Росомахи по порядку

Sırayla Her Deadpool ve Wolverine Cameo'su

Mọi cảnh quay của Deadpool và Wolverine theo thứ tự

死侍和金刚狼的客串顺序

My Little Journey Constructing LOTUS Website!

Authors:
(1) Wahei Hara;
(2) Yuki Hirano. Table of Links Abstract and Intro
Exchanges and Mutations of modifying modules
Quasi-symmetric representation and GIT quotient
Main results
Applications to Calabi-Yau complete intersections
Appendix A. Matrix factorizations
Appendix B. List of Notation
References References [BFK1] M. Ballard, D. Favero and L. Katzarkov, A category of kernels for equivariant factorizations and its implications for Hodge theory. Publ. Math. Inst. Hautes Etudes Sci. ´ 120, 1–111 (2014). 36, 38 [BFK2] M. Ballard, D. Favero and L. Katzarkov, Variation of geometric invariant theory quotients and derived categories, J. Reine Angew. Math. 746, 235–303 (2019). 32, 35 [BDFIK] M. Ballard, D. Deliu, D. Favero, M. U. Isik, and L. Katzarkov, Resolutions in factorization categories. Adv. Math. 295, 195–249 (2016). 34, 36 [BLS] D. Bergh, V. A. Lunts, O. M. Schn¨urer1, Geometricity for derived categories of algebraic stacks, Selecta Math. (N.S.) 22 (2016), no. 4, 2535–2568. 9 [Bri] T. Bridgeland, Flops and derived categories, Invent. Math. 147 (2002), no. 3, 613–632. 1 [BH] W. Bruns and J. Herzog, Cohen-Macaulay rings. Cambridge Studies in Advanced Mathematics, 39. 7, 12 [Che] J.-C. Chen, Flops and equivalences of derived categories for threefolds with only terminal Gorenstein singularities, J. Differential Geom. 61 (2002), no. 2, 227–261. 1 [Har1] W. Hara, Non-commutative crepant resolution of minimal nilpotent orbit closures of type A and Mukai flops. Adv. Math.318(2017), 355–410. 2, 4, 31 [Har2] W. Hara, On derived equivalence for Abuaf flop: mutation of non-commutative crepant resolutions and spherical twists. Matematiche (Catania) 77(2022), no.2, 329–371. 2, 4, 10, 30, 31 [Hal] D. Halpern-Leistner, The derived category of a GIT quotient. J. Amer. Math. Soc. 28 (2015), no. 3, 871–912. 23 [HSa] D. Halpern-Leistner and S. V. Sam, Combinatorial constructions of derived equivalences. J. Amer. Math. Soc. 33, no. 3, 871–912 (2020). 1, 2, 3, 12, 13, 14, 16, 21, 31, 32, 33 [HSh] D. Halpern-Leistner and I. Shipman, Autoequivalences of derived categories via geometric invariant theory. Adv. Math. 303, 1264–1299 (2016). 34, 35 [HN] A. Higashitani and Y. Nakajima, Conic divisorial ideals of Hibi rings and their applications to non-commutative crepant resolutions. Selecta Math. (N.S.) 25(2019), no.5, Paper No. 78, 25 pp. 2, 4 [Hir1] Y. Hirano, Equivalences of derived factorization categories of gauged Landau-Ginzburg models. Adv. Math. 306, 200–278 (2017). 36 [Hir2] Y. Hirano, Derived Kn¨orrer periodicity and Orlov’s theorem for gauged Landau-Ginzburg models. Compos. Math. 153, no. 5, 973–1007 (2017). 32 [Hir3] Y. Hirano, Equivariant Tilting Modules, Pfaffian Varieties and Noncommutative Matrix Factorizations . SIGMA Symmetry Integrability Geom. Methods Appl. 17, Paper No. 055, 43 pp (2021). 5, 36, 37, 38 [HW1] Y. Hirano and M. Wemyss, Faithful actions from hyperplane arrangements, Geom. Topol. 22 (2018), no. 6, 3395–3433. 1 [HW2] Y. Hirano and M. Wemyss, Stability conditions for 3-fold flops, arXiv:1907.09742. 1, 3 [HR] J. Hall, D. Rydh, Perfect complexes on algebraic stacks, Compos. Math. 153 (2017), no. 11, 2318–2367. 9 [Isi] M. U. Isik, Equivalence of the derived category of a variety with a singularity category, Int. Math. Res. Not. IMRN (2013), no. 12, 2787–2808. 32 [IR] O. Iyama and I. Reiten, Fomin-Zelevinsky mutation and tilting modules over Calabi-Yau algebras. Am. J. Math. 130 (4), 1087–1149 (2008). 5 [IW1] O. Iyama and M. Wemyss, Maximal modifications and Auslander-Reiten duality for non-isolated singularities. Invent. Math. 197 (2014), no. 3, 521–586. 1, 2, 6, 7, 8, 11 [IW2] O. Iyama and M. Wemyss, Tits cones intersections and applications, preprint. 3 [Kaw] Y. Kawamata, Flops connect minimal models, Publ. RIMS, 44, 419–423 (2008). 1 [KO] N. Koseki and G. Ouchi, Perverse schobers and Orlov equivalences, Eur. J. Math. 9, no. 2, Paper No. 32, 38 pp (2023). 36 [Nak] Y. Nakajima, Mutations of splitting maximal modifying modules: the case of reflexive polygons. Int. Math. Res. Not. IMRN(2019), no.2, 470–550. 2 [OT] C. Okonek and A. Teleman, Graded tilting for gauged Landau-Ginzubrg models and geometric applications. arXiv:1907.10099. 5, 37 [Pos] L. Positselski, Two kinds of derived categories, Koszul duality, and comodule-contramodule correspondence. Mem. Amer. Math. Soc. 212 (2011), no. 966. 36 [Shi] I. Shipman, A geometric approach to Orlov’s theorem, Compos. Math. 148, no. 5, 1365-1389 (2012). 32 [SV1] S. ˇ Spenko and M. Van den Bergh, ˇ Non-commutative resolutions of quotient singularities for reductive groups. Invent. Math. 210, no. 1, 3–67 (2017). 1, 12, 21, 22, 25 [SV2] S. ˇ Spenko and M. Van den Bergh, ˇ Non-commutative crepant resolutions for some toric singularities I. Int. Math. Res. Not. IMRN(2020), no.21, 8120–8138. 10 [SV3] S. ˇ Spenko and M. Van den Bergh, ˇ Non-commutative crepant resolutions for some toric singularities. II. J. Noncommut. Geom. 14 (2020), no. 1, 73–103. 9 [SV4] S. ˇ Spenko and M. Van den Bergh, ˇ Tilting bundles on hypertoric varieties. Int. Math. Res. Not. IMRN(2021), no.2, 1034–1042. 31 [SV5] S. ˇ Spenko and M. Van den Bergh, J.-P. Bell, ˇ On the noncommutative Bondal-Orlov conjecture for some toric varieties. Math. Z. 300(2022), no.1, 1055–1068. 2 [Sta] The Stacks Project Authors, Stacks Project. https://stacks.math.columbia.edu 12 [Tel] C. Teleman, The quantization conjecture revisited. Ann. of Math. (2) 152 (2000), no. 1, 1–43. 23, 24 [Van1] M. Van den Bergh, Three-dimensional flops and noncommutative rings, Duke Math. J. 122 (2004), no. 3, 423–455. 1 [Van2] M. Van den Bergh, Non-commutative crepant resolutions. The Legacy of Niels Henrik Abel, pp. 749–770. Springer, Berlin (2004) 1, 2 [Van3] M. Van den Bergh, Non-commutative crepant resolutions, an overview. arXiv:2207.09703. 1, 6, 26 [Wem] M. Wemyss, Flops and Clusters in the Homological Minimal Model Program, Invent. Math. 211 (2018), no. 2, 435–521. 1, 2, 30, 31 Kavli Institute for the Physics and Mathematics of the Universe (WPI), University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-8583, Japan Email address: wahei.hara@ipmu.jp Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo 184-8588, Japan Email address: hirano@go.tuat.ac.jp This paper is available on arxiv under CC0 1.0 DEED license. Authors: (1) Wahei Hara; (2) Yuki Hirano. Authors: Authors: (1) Wahei Hara; (2) Yuki Hirano. Table of Links Abstract and Intro Exchanges and Mutations of modifying modules Quasi-symmetric representation and GIT quotient Main results Applications to Calabi-Yau complete intersections Appendix A. Matrix factorizations Appendix B. List of Notation References Abstract and Intro Abstract and Intro Exchanges and Mutations of modifying modules Exchanges and Mutations of modifying modules Quasi-symmetric representation and GIT quotient Quasi-symmetric representation and GIT quotient Main results Main results Applications to Calabi-Yau complete intersections Applications to Calabi-Yau complete intersections Appendix A. Matrix factorizations Appendix A. Matrix factorizations Appendix B. List of Notation Appendix B. List of Notation References References References [BFK1] M. Ballard, D. Favero and L. Katzarkov, A category of kernels for equivariant factorizations and its implications for Hodge theory. Publ. Math. Inst. Hautes Etudes Sci. ´ 120, 1–111 (2014). 36, 38 [BFK2] M. Ballard, D. Favero and L. Katzarkov, Variation of geometric invariant theory quotients and derived categories, J. Reine Angew. Math. 746, 235–303 (2019). 32, 35 [BDFIK] M. Ballard, D. Deliu, D. Favero, M. U. Isik, and L. Katzarkov, Resolutions in factorization categories. Adv. Math. 295, 195–249 (2016). 34, 36 [BLS] D. Bergh, V. A. Lunts, O. M. Schn¨urer1, Geometricity for derived categories of algebraic stacks, Selecta Math. (N.S.) 22 (2016), no. 4, 2535–2568. 9 [Bri] T. Bridgeland, Flops and derived categories, Invent. Math. 147 (2002), no. 3, 613–632. 1 [BH] W. Bruns and J. Herzog, Cohen-Macaulay rings. Cambridge Studies in Advanced Mathematics, 39. 7, 12 [Che] J.-C. Chen, Flops and equivalences of derived categories for threefolds with only terminal Gorenstein singularities, J. Differential Geom. 61 (2002), no. 2, 227–261. 1 [Har1] W. Hara, Non-commutative crepant resolution of minimal nilpotent orbit closures of type A and Mukai flops. Adv. Math.318(2017), 355–410. 2, 4, 31 [Har2] W. Hara, On derived equivalence for Abuaf flop: mutation of non-commutative crepant resolutions and spherical twists. Matematiche (Catania) 77(2022), no.2, 329–371. 2, 4, 10, 30, 31 [Hal] D. Halpern-Leistner, The derived category of a GIT quotient. J. Amer. Math. Soc. 28 (2015), no. 3, 871–912. 23 [HSa] D. Halpern-Leistner and S. V. Sam, Combinatorial constructions of derived equivalences. J. Amer. Math. Soc. 33, no. 3, 871–912 (2020). 1, 2, 3, 12, 13, 14, 16, 21, 31, 32, 33 [HSh] D. Halpern-Leistner and I. Shipman, Autoequivalences of derived categories via geometric invariant theory. Adv. Math. 303, 1264–1299 (2016). 34, 35 [HN] A. Higashitani and Y. Nakajima, Conic divisorial ideals of Hibi rings and their applications to non-commutative crepant resolutions. Selecta Math. (N.S.) 25(2019), no.5, Paper No. 78, 25 pp. 2, 4 [Hir1] Y. Hirano, Equivalences of derived factorization categories of gauged Landau-Ginzburg models. Adv. Math. 306, 200–278 (2017). 36 [Hir2] Y. Hirano, Derived Kn¨orrer periodicity and Orlov’s theorem for gauged Landau-Ginzburg models. Compos. Math. 153, no. 5, 973–1007 (2017). 32 [Hir3] Y. Hirano, Equivariant Tilting Modules, Pfaffian Varieties and Noncommutative Matrix Factorizations . SIGMA Symmetry Integrability Geom. Methods Appl. 17, Paper No. 055, 43 pp (2021). 5, 36, 37, 38 [HW1] Y. Hirano and M. Wemyss, Faithful actions from hyperplane arrangements, Geom. Topol. 22 (2018), no. 6, 3395–3433. 1 [HW2] Y. Hirano and M. Wemyss, Stability conditions for 3-fold flops, arXiv:1907.09742. 1, 3 [HR] J. Hall, D. Rydh, Perfect complexes on algebraic stacks, Compos. Math. 153 (2017), no. 11, 2318–2367. 9 [Isi] M. U. Isik, Equivalence of the derived category of a variety with a singularity category, Int. Math. Res. Not. IMRN (2013), no. 12, 2787–2808. 32 [IR] O. Iyama and I. Reiten, Fomin-Zelevinsky mutation and tilting modules over Calabi-Yau algebras. Am. J. Math. 130 (4), 1087–1149 (2008). 5 [IW1] O. Iyama and M. Wemyss, Maximal modifications and Auslander-Reiten duality for non-isolated singularities. Invent. Math. 197 (2014), no. 3, 521–586. 1, 2, 6, 7, 8, 11 [IW2] O. Iyama and M. Wemyss, Tits cones intersections and applications, preprint. 3 [Kaw] Y. Kawamata, Flops connect minimal models, Publ. RIMS, 44, 419–423 (2008). 1 [KO] N. Koseki and G. Ouchi, Perverse schobers and Orlov equivalences, Eur. J. Math. 9, no. 2, Paper No. 32, 38 pp (2023). 36 [Nak] Y. Nakajima, Mutations of splitting maximal modifying modules: the case of reflexive polygons. Int. Math. Res. Not. IMRN(2019), no.2, 470–550. 2 [OT] C. Okonek and A. Teleman, Graded tilting for gauged Landau-Ginzubrg models and geometric applications. arXiv:1907.10099. 5, 37 [Pos] L. Positselski, Two kinds of derived categories, Koszul duality, and comodule-contramodule correspondence. Mem. Amer. Math. Soc. 212 (2011), no. 966. 36 [Shi] I. Shipman, A geometric approach to Orlov’s theorem, Compos. Math. 148, no. 5, 1365-1389 (2012). 32 [SV1] S. ˇ Spenko and M. Van den Bergh, ˇ Non-commutative resolutions of quotient singularities for reductive groups. Invent. Math. 210, no. 1, 3–67 (2017). 1, 12, 21, 22, 25 [SV2] S. ˇ Spenko and M. Van den Bergh, ˇ Non-commutative crepant resolutions for some toric singularities I. Int. Math. Res. Not. IMRN(2020), no.21, 8120–8138. 10 [SV3] S. ˇ Spenko and M. Van den Bergh, ˇ Non-commutative crepant resolutions for some toric singularities. II. J. Noncommut. Geom. 14 (2020), no. 1, 73–103. 9 [SV4] S. ˇ Spenko and M. Van den Bergh, ˇ Tilting bundles on hypertoric varieties. Int. Math. Res. Not. IMRN(2021), no.2, 1034–1042. 31 [SV5] S. ˇ Spenko and M. Van den Bergh, J.-P. Bell, ˇ On the noncommutative Bondal-Orlov conjecture for some toric varieties. Math. Z. 300(2022), no.1, 1055–1068. 2 [Sta] The Stacks Project Authors, Stacks Project. https://stacks.math.columbia.edu 12 [Tel] C. Teleman, The quantization conjecture revisited. Ann. of Math. (2) 152 (2000), no. 1, 1–43. 23, 24 [Van1] M. Van den Bergh, Three-dimensional flops and noncommutative rings, Duke Math. J. 122 (2004), no. 3, 423–455. 1 [Van2] M. Van den Bergh, Non-commutative crepant resolutions. The Legacy of Niels Henrik Abel, pp. 749–770. Springer, Berlin (2004) 1, 2 [Van3] M. Van den Bergh, Non-commutative crepant resolutions, an overview. arXiv:2207.09703. 1, 6, 26 [Wem] M. Wemyss, Flops and Clusters in the Homological Minimal Model Program, Invent. Math. 211 (2018), no. 2, 435–521. 1, 2, 30, 31 Kavli Institute for the Physics and Mathematics of the Universe (WPI), University of Tokyo, 5-1-5 Kashiwanoha, Kashiwa, 277-8583, Japan Email address: wahei.hara@ipmu.jp Email address: wahei.hara@ipmu.jp Tokyo University of Agriculture and Technology, 2-24-16 Nakacho, Koganei, Tokyo 184-8588, Japan Email address: hirano@go.tuat.ac.jp Email address: hirano@go.tuat.ac.jp This paper is available on arxiv under CC0 1.0 DEED license. This paper is available on arxiv under CC0 1.0 DEED license. available on arxiv

Mutations of noncommutative crepant resolutions: References

নন-কমিউটেটিভ ক্রেপ্যান্ট রেজোলিউশনের মিউটেশন: রেফারেন্স

Mutationen nichtkommutativer Crepant-Auflösungen: Referenzen

Mutaciones de resoluciones crepantes no conmutativas: referencias

Mutations de résolutions crépantes non commutatives : références

गैर-विनिमेय क्रेपेंट संकल्पों के उत्परिवर्तन: संदर्भ

非可換クレパント解決の突然変異: 参考文献

비가환적 크레판트 해상도의 돌연변이: 참고자료

Mutações de resoluções crepantes não comutativas: Referências

Мутации некоммутативных крепантных резолюций: Литература.

Değişmeyen crepant çözünürlüklerin mutasyonları: Referanslar

Đột biến của độ phân giải crepant không giao hoán: Tài liệu tham khảo

非交换 crepant 解析的突变：参考文献

Authors: (1) Renato P. dos Santos, CIAGE – Centre for Generative Artificial Intelligence in Cognition and Education. Table of Links Abstract and Introduction Materials And Methods Results and Analyses Prompts and generated texts Conceptualizing chemical reactions Deepening on understanding of chemical reactions Question about combustion Question about a graph of gases turning into water over time Question about the difference between atoms, molecules, and moles Deepening on the concept of mole Question about changing of state Question about an animated representation of water molecules undergoing phase changes Question about plasma, a state of matter Question about chemical bondings Question about illustration of chemical bonds Question about the essence of the type of chemical bonding Further analysis Conclusions Limitations of the study and possible future studies Author Contributions, Conflicts of interest, Acknowledgements, and References Limitations of the study and possible future studies Despite its inherent limitations, including its single case design and the potential for bias, the exploratory depth of the study uncovered hidden potential within these systems, even amid serious concerns about generalizability. Future research could include: • Refining the crafting of prompts. • Exploring new features of these and other GenAIbots being introduced with increasing frequency. • Establishing standardised benchmarks to evaluate and compare chatbots and AI systems' performance, accuracy, and reliability. • Conducting long-term studies to observe the evolution of chatbots' capabilities and their impact on user interactions over time. • Conducting research with real students in classroom settings and beyond to assess these AI systems' practical educational applications and challenges. • Investigating chatbots' learning and adaptation capabilities to individual user needs and preferences over time. • Researching the integration of multimodal inputs (e.g., text, voice, image) to enhance chatbot capabilities and user interaction experiences. When implementing GenAIbots in Chemistry learning, it is crucial to evaluate benefits and drawbacks judiciously, ensuring accurate information delivery and considering the implications of reduced human interaction. These concerns may be alleviated by integrating GenAIbots with other educational tools or activities promoting collaborative dialogue among learners. Author Contributions The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation. Conflicts of interest There are no conflicts to declare. Acknowledgements The authors warmly acknowledge Melanie Swan for her invaluable suggestion, which led to the transition from using the term 'objects-to-think-with' to 'agents-to-thinkwith'. References Adiguzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionising education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology, 15(3), ep429. https://doi.org/10.30935/cedtech/13152 Baidoo-Anu, D., & Owusu Ansah, L. (2023). Education in the Era of Generative Artificial Intelligence (AI): Understanding the Potential Benefits of ChatGPT in Promoting Teaching and Learning. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.4337484 Bardin, L. (1977). L’analyse de contenu. PUF - Presses Universitaires de France. Bitzenbauer, P. (2023). ChatGPT in physics education: A pilot study on easy-to-implement activities. Contemporary Educational Technology, 15(3), ep430. https://doi.org/10.30935/cedtech/13176 Brown, T. B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., Agarwal, S., Herbert-Voss, A., Krueger, G., Henighan, T., Child, R., Ramesh, A., Ziegler, D. M., Wu, J., Winter, C., … Amodei, D. (2020). Language Models are Few-Shot Learners. ArXiv, 2005.14165. http://arxiv.org/abs/2005.14165 Castro Nascimento, C. M., & Pimentel, A. S. (2023). Do Large Language Models Understand Chemistry? A Conversation with ChatGPT. Journal of Chemical Information and Modeling, 63(6), 1649–1655. https://doi.org/10.1021/acs.jcim.3c00285 David, E. (2023, September 21). Microsoft to add DALL-E 3 to Bing Chat. The Verge; Vox Media. https://www.theverge.com/2023/9/21/23873690/microsoft-new-ai-features-bingsearch-shopping-dall-e-3 Dewi, C. A., Pahriah, P., & Purmadi, A. (2021). The Urgency of Digital Literacy for Generation Z Students in Chemistry Learning. International Journal of Emerging Technologies in Learning, 16(11), 88–103. https://doi.org/10.3991/ijet.v16i11.19871 Dunlop, L., Hodgson, A., & Stubbs, J. E. (2020). Building capabilities in chemistry education: happiness and discomfort through philosophical dialogue in chemistry. Chemistry Education Research and Practice, 21(1), 438–451. https://doi.org/10.1039/C9RP00141G Flavell, J. H. (1976). Metacognitive aspects of problem-solving. In L. B. Resnick (Ed.), The Nature of Intelligence (pp. 231–236). Lawrence Erlbaum. Floridi, L., & Chiriatti, M. (2020). GPT-3: Its Nature, Scope, Limits, and Consequences. Minds and Machines, 30(4), 681–694. https://doi.org/10.1007/s11023-020-09548-1 Franciscu, S. (2023). ChatGPT: A Natural Language Generation Model for Chatbots. https://doi.org/10.13140/RG.2.2.24777.83044 Gregorcic, B., & Pendrill, A.-M. (2023). ChatGPT and the frustrated Socrates. Physics Education, 58(3), 035021. https://doi.org/10.1088/1361-6552/acc299 Latour, B. (1991). Nous n’avons jamais été modernes: Essai d’anthropologie symétrique. La Découverte. Leon, A. J., & Vidhani, D. (2023). ChatGPT Needs a Chemistry Tutor, Too. Journal of Chemical Education. https://doi.org/10.1021/acs.jchemed.3c00288 Liu, F., Budiu, R., Zhang, A., & Cionca, E. (2023, October 1). ChatGPT, Bard, or Bing Chat? Differences Among 3 Generative-AI Bots. https://www.nngroup.com/articles/ai-botcomparison/ Metz, C., & Hsu, T. (2023, September 20). ChatGPT Can Now Generate Images, Too. New York Times - Technology. https://www.nytimes.com/2023/09/20/technology/chatgpt-dalle3-images-openai.html Mishra, A., Soni, U., Arunkumar, A., Huang, J., Kwon, B. C., & Bryan, C. (2023). PromptAid: Prompt Exploration, Perturbation, Testing and Iteration using Visual Analytics for Large Language Models. ArXiv, 2304.01964. https://doi.org/10.48550/arXiv.2304.01964 Mollick, E. R. (2023, April 26). A guide to prompting AI (for what it is worth): A little bit of magic,but mostly just practice. One Useful Thing Blog. https://www.oneusefulthing.org/p/a-guidetoprompting-ai-for-what Okonkwo, C. W., & Ade-Ibijola, A. (2021). Chatbots applications in education: A systematic review. Computers and Education: Artificial Intelligence, 2, 100033. https://doi.org/10.1016/j.caeai.2021.100033 OpenAI. (2023). GPT-4 Technical Report. OpenAI. https://cdn.openai.com/papers/gpt-4.pdf Papert, S. A. (1980). Mindstorms - Children, Computers and Powerful Ideas. Basic Books. http://www.arvindguptatoys.com/arvindgupta/mindstorms.pdf Permatasari, M. B., Rahayu, S., & Dasna, W. (2022). Chemistry Learning Using Multiple Representations: A Systematic Literature Review. J.Sci.Learn.2022, 5(2), 334–341. https://doi.org/10.17509/jsl.v5i2.42656 Pimentel, A., Wagener, A., Silveira, E. F. da, Picciani, P., Salles, B., Follmer, C., & Jr., O. N. O. (2023). Challenging ChatGPT with Chemistry-Related Subjects. ChemRxiv. https://doi.org/10.26434/chemrxiv-2023-xl6w3 Schlosser, M. (2019). Agency. In The Stanford Encyclopedia of Philosophy. https://plato.stanford.edu/archives/win2019/entries/agency/ Swan, M. (2015, January 21). We Should Consider the Future World as one of Multi-Species Intelligence. Response to The Edge Question 2015: What Do You Think About Machines That Think?; Edge.org. http://edge.org/response-detail/26070 Taylor, C. A., Hogarth, H., Hacking, E. B., & Bastos, E. (2022). Posthuman Object Pedagogies: Thinking with Things to Think with Theory for Innovative Educational Research. Cultural and Pedagogical Inquiry, 14(1), 206–221. https://doi.org/10.18733/cpi29662 Timilsena, N. P., Maharjan, K. B., & Devkota, K. M. (2022). Teachers’ And Students’ Experiences In Chemistry Learning Difficulties. Journal of Positive School Psychology, 6(10), 2856–2867. https://www.journalppw.com/index.php/jpsp/article/view/13764 Tümay, H. (2016). Reconsidering learning difficulties and misconceptions in chemistry: emergence in chemistry and its implications for chemical education. Chemistry Education Research and Practice, 17(2), 229–245. https://doi.org/10.1039/C6RP00008H Turkle, S. (1984). The Second Self: Computers and the Human Spirit. Simon and Schuster. Yin, R. K. (2011). Applications of Case Study Research. SAGE Publications, Inc. This paper is under CC BY-SA 4.0 DEED license. available on arxiv

Overcoming Limitations in AI Chatbot Research for Future Educational Impact

ভবিষ্যতের শিক্ষাগত প্রভাবের জন্য এআই চ্যাটবট গবেষণায় সীমাবদ্ধতা অতিক্রম করা

Überwindung von Einschränkungen in der KI-Chatbot-Forschung für zukünftige Bildungseffekte

Superar las limitaciones en la investigación de chatbots de IA para lograr un impacto educativo futuro

Surmonter les limites de la recherche sur les chatbots IA pour un impact éducatif futur

भविष्य के शैक्षिक प्रभाव के लिए AI चैटबॉट अनुसंधान में सीमाओं पर काबू पाना

将来の教育への影響のために AI チャットボット研究の限界を克服する

미래 교육적 영향을 위한 AI 챗봇 연구의 한계 극복

Superando as limitações na pesquisa do AI Chatbot para um impacto educacional futuro

Преодоление ограничений в исследованиях чат-ботов с использованием искусственного интеллекта для будущего воздействия на образование

Gelecekteki Eğitim Etkisi için Yapay Zeka Chatbot Araştırmalarında Sınırlamaların Aşılması

Khắc phục những hạn chế trong nghiên cứu Chatbot AI để tạo ra tác động giáo dục trong tương lai

克服人工智能聊天机器人研究的局限性，为未来教育带来影响

What’s up Metaversians? Dr.One is back, and I’m here to let you know that we weren’t playing around when we announced that HackerNoon had joined the Blockchain Game Alliance. To show our love and appreciation for blockchain games and support developers who are building the future of the gaming industry, we have launched our own platform to track the world’s most-trending blockchain games. How Does Our Blockchain Games Platform Work? Every blockchain game listed on HackerNoon gets its own dedicated page that shows all the information a gamer needs to know about it. Most importantly, the page lists all the latest articles on HackerNoon that have mentioned the game. Furthermore, the page also lists all the latest YouTube videos that have mentioned the game, and in the rankings page a preview of the game’s trailer is shown. By calculating the traffic to each individual game page on HackerNoon, we determine how often people are searching for the blockchain game week-over-week. Based on that data, our rankings page will show the percentage rise or fall of interest in that game for that particular week. In the future, we plan to implement other data points, such as Token price fluctuations, overall mentions across the web, and more. Why Bother Tracking Public Interest in Blockchain Games? Our answer to that question depends on who is asking it. For Gamers: For those that just want to play the best games the metaverse has to offer, we want to provide an easy interface to see which games are the most popular week over week. Gamers will be able to click on any game in the rankings that interest them and see what they’re all about directly on HackerNoon. They can even view game trailers and YouTuber coverage on those games to see if it’s the type of game they’d be interested in trying out. For Investors: Before you buy a game’s token or NFT, it’s important to DYOR. We hope that our ‘trendingness’ ratings can help you see which games are rising and falling in the public eye. We don’t need to tell you that there are tons of scams out there. Being able to see the trending news around a particular blockchain game project can help you keep up to date on all the latest happenings in the gaming metaverse. For Game Devs: Social listening and community management is an important part of marketing and developing your games. Your blockchain game page on HackerNoon is an easy way to see what our writers are saying about you and see the trend of people searching for your game on HackerNoon and the rest of the world wide web. The Metaverse is Here to Stay Regardless of whether or not you believe in the play-to-earn or play-and-earn movement, one thing is certain; the metaverse isn’t going anywhere. We’ve been partnering with blockchain gaming companies such as The Sandbox, and Megafans, and the response has been phenomenal. The creative stories our contributors have come up with show just how much passion there is for the metaverse and the future it holds for us. HackerNoon wants to be part of building the metaverse, in whatever small or large way we can. We want to help game devs get their games the attention they deserve, and this new platform is just one way we’re going to do that! How Do I Get My Blockchain Game Listed on HackerNoon? Do you want to get your game listed on HackerNoon? Any blockchain game developer can get their game listed on our platform in 2 easy steps: Step 1. Fill out this form: https://hngamingpages.paperform.co/ Step 2. Contact our Sponsors and Partners Rep partners@hackernoon.com That’s it! We hope you enjoy this new release as much as we do. Until next time, keep gaming, and keep writing on HackerNoon. What is your favorite blockchain game? Which games have you invested in? Let us know in the comments below! What’s up Metaversians? What’s up Metaversians? Dr.One is back, and I’m here to let you know that we weren’t playing around when we announced that HackerNoon had joined the Blockchain Game Alliance. HackerNoon had joined the Blockchain Game Alliance. To show our love and appreciation for blockchain games and support developers who are building the future of the gaming industry, we have launched our own platform to track the world’s most-trending blockchain games . most-trending blockchain games How Does Our Blockchain Games Platform Work? Every blockchain game listed on HackerNoon gets its own dedicated page that shows all the information a gamer needs to know about it. Most importantly, the page lists all the latest articles on HackerNoon that have mentioned the game. Furthermore, the page also lists all the latest YouTube videos that have mentioned the game, and in the rankings page a preview of the game’s trailer is shown. By calculating the traffic to each individual game page on HackerNoon, we determine how often people are searching for the blockchain game week-over-week. Based on that data, our rankings page will show the percentage rise or fall of interest in that game for that particular week. In the future, we plan to implement other data points, such as Token price fluctuations, overall mentions across the web, and more. Why Bother Tracking Public Interest in Blockchain Games? Our answer to that question depends on who is asking it. For Gamers: For those that just want to play the best games the metaverse has to offer, we want to provide an easy interface to see which games are the most popular week over week. Gamers will be able to click on any game in the rankings that interest them and see what they’re all about directly on HackerNoon. They can even view game trailers and YouTuber coverage on those games to see if it’s the type of game they’d be interested in trying out. For Gamers: For Investors: Before you buy a game’s token or NFT, it’s important to DYOR . We hope that our ‘trendingness’ ratings can help you see which games are rising and falling in the public eye. We don’t need to tell you that there are tons of scams out there . Being able to see the trending news around a particular blockchain game project can help you keep up to date on all the latest happenings in the gaming metaverse. For Investors: DYOR tons of scams out there For Game Devs: Social listening and community management is an important part of marketing and developing your games. Your blockchain game page on HackerNoon is an easy way to see what our writers are saying about you and see the trend of people searching for your game on HackerNoon and the rest of the world wide web. For Game Devs: The Metaverse is Here to Stay Regardless of whether or not you believe in the play-to-earn or play-and-earn movement, one thing is certain; the metaverse isn’t going anywhere. We’ve been partnering with blockchain gaming companies such as The Sandbox , and Megafans , and the response has been phenomenal. The creative stories our contributors have come up with show just how much passion there is for the metaverse and the future it holds for us. The Sandbox Megafans HackerNoon wants to be part of building the metaverse, in whatever small or large way we can. We want to help game devs get their games the attention they deserve, and this new platform is just one way we’re going to do that! How Do I Get My Blockchain Game Listed on HackerNoon? Do you want to get your game listed on HackerNoon? Any blockchain game developer can get their game listed on our platform in 2 easy steps: Step 1. Fill out this form: https://hngamingpages.paperform.co/ Step 1. https://hngamingpages.paperform.co/ Step 2. Contact our Sponsors and Partners Rep partners@hackernoon.com Step 2. partners@hackernoon.com That’s it! We hope you enjoy this new release as much as we do. Until next time, keep gaming, and keep writing on HackerNoon. What is your favorite blockchain game? Which games have you invested in? Let us know in the comments below! What is your favorite blockchain game? Which games have you invested in? Let us know in the comments below!

Tracking The World's Top Trending Blockchain Games

Older and Younger Adults Are Influenced Differently by Dark Pattern Designs: Conclusion & References

Authors: (1) Dominic Petrak, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany; (2) Nafise Sadat Moosavi, Department of Computer Science, The University of Sheffield, United Kingdom; (3) Ye Tian, Wluper, London, United Kingdom; (4) Nikolai Rozanov, Wluper, London, United Kingdom; (5) Iryna Gurevych, UKP Lab, Department of Computer Science, Technical University of Darmstadt, Germany. Table of Links Abstract & Introduction Related Work Datasets Examined Manual Error Type Analysis and Taxonomies Automatic Filtering for Potentially Relevant Dialogs Statistical Analysis Evaluation and Experiments Discussion Conclusion, Limitation, Acknowledgments, and References A Integrated Error Taxonomy – Details B Error-Indicating Sentences And Phrases C Automatic Filtering – Implementation D Automatic Filtering – Sentence-Level Analysis E Task-Oriented Dialogs – Examples F Effectiveness Of Automatic Filtering – A Detailed Analysis G Inter-Annotator Agreement – Detailed Analysis H Annotation Guidelines I Hyperparameters and Baseline Experiments J Human-Human Dialogs – Examples G Inter-Annotator Agreement – Detailed Analysis This section gives more insights on the interannotator agreement. All additional annotators that participated in this study were non-native speakers. They were experts from our lab with sound English skills and NLP background. We did not select them based on specific criteria; they participated voluntarily. Accordingly, they were not paid extra for this, since they did the annotations during their working hours. For annotation, we did not use any specific tool. We provided the annotators with dialogs in json format and asked them to do the annotations directly in the respective files. See Section H for the annotation guidelines. Table 13 shows the inter-annotator agreement for each dataset using our modified Integrated Error Taxonomy. In the case of human-human dialogs, the overall agreement is rather low (except for Persona Chat (Zhang et al., 2018)). We find that errors are hard to identify in these dialogs, as humans rather suggest disagreements instead of accusing the partner of a mistake. This is also reflected in the user response type agreement since it depends on the error type annotation. However, Persona Chat seems to be different (according to Table 8). We attribute this to the dialog type, which is open-domain, where we find that humans react harshly and accusing to errors in system utterances, resulting in more direct feedback that is easier to identify. Table 14 shows the inter-annotator agreement for each dataset using the original Integrated Error Taxonomy as proposed by Higashinaka et al. (2021). Using this taxonomy deteriorates the interannotator agreement. This is most obvious in case of MWoZ (Budzianowski et al., 2018) and PC (Zhang et al., 2018), which are both humanhuman datasets. A detailed analysis revealed that this is mostly due to over-specialized error types which were merged in our modified taxonomy, such as ignore expectation and ignore request, I9 and I6 in the original taxonomy (Table 2). Another reason are the newly added error types, such as factually incorrect, E5 (Table 4), which were not covered in the original taxonomy, but occur in the dialogs. This paper is under CC BY-NC-SA 4.0 DEED license. available on arxiv

A Detailed Analysis of Inter-Annotator Agreement

Stories, News & Mentions about HGGA

Hgga’s Stories on HackerNoon