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Behavioural and neural characterization of optimistic reinforcement learning

Citations

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Cited by:

  1. Johann Lussange & Ivan Lazarevich & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2021. "Modelling Stock Markets by Multi-agent Reinforcement Learning," Computational Economics, Springer;Society for Computational Economics, vol. 57(1), pages 113-147, January.
  2. Riccardo Bruni & Alessandro Gioffré & Maria Marino, 2022. ""In-group bias in preferences for redistribution: a survey experiment in Italy"," IREA Working Papers 202223, University of Barcelona, Research Institute of Applied Economics, revised Nov 2023.
  3. Chih-Chung Ting & Nahuel Salem-Garcia & Stefano Palminteri & Jan B. Engelmann & Maël Lebreton, 2023. "Neural and computational underpinnings of biased confidence in human reinforcement learning," Nature Communications, Nature, vol. 14(1), pages 1-18, December.
  4. Simon Ciranka & Juan Linde-Domingo & Ivan Padezhki & Clara Wicharz & Charley M. Wu & Bernhard Spitzer, 2022. "Asymmetric reinforcement learning facilitates human inference of transitive relations," Nature Human Behaviour, Nature, vol. 6(4), pages 555-564, April.
  5. Aurélien Nioche & Basile Garcia & Germain Lefebvre & Thomas Boraud & Nicolas P. Rougier & Sacha Bourgeois-Gironde, 2019. "Coordination over a unique medium of exchange under information scarcity," Palgrave Communications, Palgrave Macmillan, vol. 5(1), pages 1-11, December.
  6. R Becket Ebitz & Brianna J Sleezer & Hank P Jedema & Charles W Bradberry & Benjamin Y Hayden, 2019. "Tonic exploration governs both flexibility and lapses," PLOS Computational Biology, Public Library of Science, vol. 15(11), pages 1-37, November.
  7. Nura Sidarus & Stefano Palminteri & Valérian Chambon, 2019. "Cost-benefit trade-offs in decision-making and learning," PLOS Computational Biology, Public Library of Science, vol. 15(9), pages 1-28, September.
  8. Stefano Palminteri & Germain Lefebvre & Emma J Kilford & Sarah-Jayne Blakemore, 2017. "Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing," PLOS Computational Biology, Public Library of Science, vol. 13(8), pages 1-22, August.
  9. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.
  10. Daniel J. Benjamin, 2018. "Errors in Probabilistic Reasoning and Judgment Biases," NBER Working Papers 25200, National Bureau of Economic Research, Inc.
  11. Johann Lussange & Boris Gutkin, 2023. "Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective," Papers 2302.04184, arXiv.org.
  12. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," PLOS ONE, Public Library of Science, vol. 19(4), pages 1-40, April.
  13. Sucharit Katyal & Quentin JM Huys & Raymond J. Dolan & Stephen M. Fleming, 2025. "Distorted learning from local metacognition supports transdiagnostic underconfidence," Nature Communications, Nature, vol. 16(1), pages 1-16, December.
  14. Shogo Homma & Masanori Takezawa, 2024. "Risk preference as an outcome of evolutionarily adaptive learning mechanisms: An evolutionary simulation under diverse risky environments," PLOS ONE, Public Library of Science, vol. 19(8), pages 1-22, August.
  15. Tapia Cortez, Carlos A. & Hitch, Michael & Sammut, Claude & Coulton, Jeff & Shishko, Robert & Saydam, Serkan, 2018. "Determining the embedding parameters governing long-term dynamics of copper prices," Chaos, Solitons & Fractals, Elsevier, vol. 111(C), pages 186-197.
  16. Toby Wise & Jochen Michely & Peter Dayan & Raymond J Dolan, 2019. "A computational account of threat-related attentional bias," PLOS Computational Biology, Public Library of Science, vol. 15(10), pages 1-21, October.
  17. Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Mesoscale effects of trader learning behaviors in financial markets: A multi-agent reinforcement learning study," Post-Print hal-04790290, HAL.
  18. Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
  19. Johann Lussange & Stefano Vrizzi & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2023. "Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model," Computational Economics, Springer;Society for Computational Economics, vol. 61(4), pages 1523-1544, April.
  20. Sudipta Mukherjee, 2022. "Consumer altruism and risk taking: why do altruistic consumers take more risks?," International Review on Public and Nonprofit Marketing, Springer;International Association of Public and Non-Profit Marketing, vol. 19(4), pages 781-803, December.
  21. C. A. Tapia Cortez & J. Coulton & C. Sammut & S. Saydam, 2018. "Determining the chaotic behaviour of copper prices in the long-term using annual price data," Palgrave Communications, Palgrave Macmillan, vol. 4(1), pages 1-13, December.
  22. Riccardo Bruni & Alessandro Gioffré & Maria Marino, 2025. "In‐group bias in preferences for redistribution: a survey experiment in Italy," Economica, London School of Economics and Political Science, vol. 92(367), pages 1009-1080, July.
  23. Hu Sun & Yun Wang, 2019. "Do On-lookers See Most of the Game? Evaluating Job-seekers' Competitiveness of Oneself versus of Others in a Labor Market Experiment," Working Papers 2019-07-11, Wang Yanan Institute for Studies in Economics (WISE), Xiamen University.
  24. Xinyi Li & Yinchuan Li & Yuancheng Zhan & Xiao-Yang Liu, 2019. "Optimistic Bull or Pessimistic Bear: Adaptive Deep Reinforcement Learning for Stock Portfolio Allocation," Papers 1907.01503, arXiv.org.
  25. Filip Gesiarz & Donal Cahill & Tali Sharot, 2019. "Evidence accumulation is biased by motivation: A computational account," PLOS Computational Biology, Public Library of Science, vol. 15(6), pages 1-15, June.
  26. Tsutomu Harada, 2021. "Three heads are better than two: Comparing learning properties and performances across individuals, dyads, and triads through a computational approach," PLOS ONE, Public Library of Science, vol. 16(6), pages 1-16, June.
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