Theories of Learning in Games and Heterogeneity Bias

By Nathaniel T. Wilcox. 2006

109 citations recorded by the source corpus.

Subfields: General Decision Sciences, Safety Research

View Paper: https://www.semanticscholar.org/paper/967081b4fa04d476b052545d17947eb025d15e51

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Citations in the corpus

Neural correlates of mentalizing-related computations during strategic interactions in humans (2008, background): “Although the behavioral game theory literature has demonstrated that humans think strategically in one-shot games [they consider what the opponent could possibly believe (17, 18)], the updating of beliefs about the beliefs of the opponent has rarely been incorporated in the analysis of learning in r”
NEUROECONOMICS: A CRITICAL RECONSIDERATION (2008, background): “Years of scholarship in models of learning in games has reminded us of the dangers of confounding substantive models with simplistic statistical modelling when we have panels of time-series, as sharply demonstrated by Wilcox (2006) for the “reinforcement learning model”.”
Learning and the Economics of Small Decisions (2013, background): “, Camerer and Ho, 1999) can be useful but insights tend to be sensitive to the assumption that the underlying model is well calibrated (see Feltovich, 2000; Salmon, 2001; Wilcox, 2006; Erev & Haruvy 2005).”
Risk and Rationality: Uncovering Heterogeneity in Probability Distortion (2009, background): “If there is heterogeneity of a substantive kind, as the data suggest, basing predictions on a single preference theory is inappropriate and may lead to biased results (Wilcox (2006)).”
Dissociable neural representations of reinforcement and belief prediction errors underlie strategic learning (2012, background): “This paradigm has three key features that build on insights from previous experimental and theoretical studies on learning models that together help to computationally characterize behavior and, statistically, minimize collinearity in the model outputs (13, 14).”
Learning and Equilibrium (2009, result): “…discount factor, it is not worth sacrificing current payoff to influence this opponent's future play. discriminating between alternative learning models; this is supported by Wilcox (2006)’s finding that the assumption of a representative agent can drive some of the conclusions of this literature.”
Mixture models of choice under risk (2011, unknown): “The dangers of not so doing are well illustrated by Wilcox (2006), who shows that serious distortions in the econometric results may well be the consequence.”