Behavioural and neural characterization of optimistic reinforcement learning
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DOI: 10.1038/s41562-017-0067
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- 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.
- 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.
- Riccardo Bruni & Alessandro Gioffré & Maria Marino, 2023. "In-Group Bias in Preferences for Redistribution: A Survey Experiment in Italy," CESifo Working Paper Series 10785, CESifo.
- 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.
- Chih-Chung Ting & Nahuel Salem-Garcia & Stefano Palminteri & Jan Engelmann & Maël Lebreton, 2023. "Neural and computational underpinnings of biased confidence in human reinforcement learning," PSE-Ecole d'économie de Paris (Postprint) halshs-04409145, HAL.
- Chih-Chung Ting & Nahuel Salem-Garcia & Stefano Palminteri & Jan Engelmann & Maël Lebreton, 2023. "Neural and computational underpinnings of biased confidence in human reinforcement learning," Post-Print halshs-04409145, HAL.
- 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.
- 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.
- 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," Post-Print hal-02356248, HAL.
- 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.
- 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.
- 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.
- Johann Lussange & Stefano Vrizzi & Stefano Palminteri & Boris Gutkin, 2024. "Modelling crypto markets by multi-agent reinforcement learning," Papers 2402.10803, arXiv.org.
- Daniel J. Benjamin, 2018.
"Errors in Probabilistic Reasoning and Judgment Biases,"
NBER Working Papers
25200, National Bureau of Economic Research, Inc.
- Daniel J. Benjamin, 2018. "Errors in Probabilistic Reasoning and Judgment Biases," GRU Working Paper Series GRU_2018_023, City University of Hong Kong, Department of Economics and Finance, Global Research Unit.
- Johann Lussange & Boris Gutkin, 2023. "Order book regulatory impact on stock market quality: a multi-agent reinforcement learning perspective," Papers 2302.04184, arXiv.org.
- 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.
- 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.
- repec:hal:journl:hal-04790290 is not listed on IDEAS
- Tapia, Carlos & Coulton, Jeff & Saydam, Serkan, 2020. "Using entropy to assess dynamic behaviour of long-term copper price," Resources Policy, Elsevier, vol. 66(C).
- 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.
- Johann Lussange & Stefano Vrizzi & Sacha Bourgeois-Gironde & Stefano Palminteri & Boris Gutkin, 2022. "Stock Price Formation: Precepts from a Multi-Agent Reinforcement Learning Model," Post-Print hal-03827363, HAL.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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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