The predictive power of Cumulative Prospect Theory (CPT) and Expected Utility Theory (EUT) is typically compared using decisions from description (DfD), wherein lotteries' outcome values and probabilities are explicitly stated. In decisions from experience (DfE), individuals sample (without...

Learning the probabilities of multiple events from the environment is an important core competency of any organism. In our within‐participant experiment, participants experienced samples from two distributions, or prospects, each comprised of two to four events, and were required to provide...

This paper aspires to fill a conspicuous gap in the existing literature on learning in games, namely the absence of any empirical verification of learning rules involving pattern recognition. An extension of weighted fictitious play is proposed both obeying cognitive laws of subjective...

This paper addresses how neural networks learn to play one-shot normal form games through experience in an environment of randomly generated game payoffs and randomly selected opponents. This agent based computational approach allows the modeling of learning all strategic types of normal form...

Bayesian model averaging is applied to robustly ascertain the determinants of various output volatility measures, including the downside semideviation of growth rates. Financial sophis- tication variables are found to have qualitatively different effects on volatility. The ratio of govern- ment...

The purpose of this paper is to reexamine the seminal belief elicitation experiment by Nyarko and Schotter (2002) under the prism of pattern recognition. Instead of modeling elicited beliefs by a standard weighted fictitious play model this paper proposes a generalized variant of fictitious...