Partial Computing

Lecturas recomendadas

  1. Akerlof, G. A. (1970). The Market for “Lemons”: Quality Uncertainty and the Market Mechanism. The Quarterly Journal of Economics, 84(3).
  2. Alexander, S. (2014). Meditations on Moloch. Slate Star Codex.
  3. Aumann, R. J. (1976). Agreeing to Disagree. The Annals of Statistics, 4(6).
  4. Bakhtin, A., et al. (Meta FAIR Diplomacy Team) (2022). Human-level play in the game of Diplomacy by combining language models with strategic reasoning. Science, 378(6624).
  5. Baron, D. P., & Ferejohn, J. A. (1989). Bargaining in Legislatures. American Political Science Review, 83(4).
  6. Bowling, M., Burch, N., Johanson, M., & Tammelin, O. (2015). Heads-up limit hold’em poker is solved. Science, 347(6218).
  7. Brown, N., Lerer, A., Gross, S., & Sandholm, T. (2019). Deep Counterfactual Regret Minimization. Proceedings of the 36th International Conference on Machine Learning, PMLR 97.
  8. Brown, N., & Sandholm, T. (2018). Superhuman AI for heads-up no-limit poker: Libratus beats top professionals. Science, 359(6374).
  9. Brown, N., & Sandholm, T. (2019a). Facebook, Carnegie Mellon build first AI that beats pros in 6-player poker. Meta AI.
  10. Brown, N., & Sandholm, T. (2019b). Solving Imperfect-Information Games via Discounted Regret Minimization. Proceedings of the AAAI Conference on Artificial Intelligence, 33.
  11. Brown, N., & Sandholm, T. (2019c). Superhuman AI for multiplayer poker. Science, 365(6456).
  12. Camerer, C. F., Ho, T.-H., & Chong, J.-K. (2004). A Cognitive Hierarchy Model of Games. The Quarterly Journal of Economics, 119(3).
  13. Case, N. (2017). The Evolution of Trust. ncase.me.
  14. Crawford, V. P., & Sobel, J. (1982). Strategic Information Transmission. Econometrica, 50(6).
  15. Google DeepMind. (2022). Mastering Stratego, the classic game of imperfect information. Google DeepMind.
  16. Harsanyi, J. C. (1967). Games with Incomplete Information Played by “Bayesian” Players, I–III. Management Science, 14(3).
  17. Horton, J. J. (2023). Large Language Models as Simulated Economic Agents: What Can We Learn from Homo Silicus?. arXiv, 2301.07543.
  18. Jones, C. R., & Bergen, B. K. (2023). Does GPT-4 pass the Turing test?. arXiv, 2310.20216.
  19. Kamenica, E., & Gentzkow, M. (2011). Bayesian Persuasion. American Economic Review, 101(6).
  20. Moravčík, M., et al. (2017). DeepStack: Expert-level artificial intelligence in heads-up no-limit poker. Science, 356(6337).
  21. Park, J. S., et al. (2023). Generative Agents: Interactive Simulacra of Human Behavior. Proceedings of the 36th Annual ACM Symposium on User Interface Software and Technology.
  22. Perolat, J., et al. (2022). Mastering the game of Stratego with model-free multiagent reinforcement learning. Science, 378(6623).
  23. Rahwan, I., et al. (2019). Machine behaviour. Nature, 568(7753).
  24. Sanfey, A. G., Rilling, J. K., Aronson, J. A., Nystrom, L. E., & Cohen, J. D. (2003). The Neural Basis of Economic Decision-Making in the Ultimatum Game. Science, 300(5626).
  25. Schmid, M., et al. (2023). Student of Games: A unified learning algorithm for both perfect and imperfect information games. Science Advances, 9(46).
  26. Sutton, R. (2019). The Bitter Lesson. Incomplete Ideas.
  27. Tammelin, O. (2014). Solving Large Imperfect Information Games Using CFR+. arXiv, 1407.5042.
  28. Turing, A. M. (1950). Computing Machinery and Intelligence. Mind, 59(236).
  29. Vanderschraaf, P., & Sillari, G. (2022). Common Knowledge. Stanford Encyclopedia of Philosophy.
  30. Weng, L. (2023). LLM Powered Autonomous Agents. Lil’Log.
  31. Zinkevich, M., Johanson, M., Bowling, M., & Piccione, C. (2007). Regret Minimization in Games with Incomplete Information. Advances in Neural Information Processing Systems, 20.