Predicting How People Play Games: Reinforcement Learning in Experimental Games with Unique, Mixed Strategy Equilibria

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

The Economist as Engineer: Game Theory, Experimentation, and Computation as Tools for Design Economics (2002, unknown): “, Roth and Erev (1995) and Erev and Roth (1998) for one train of thought in that”
When good decisions have bad outcomes: The impact of affect on switching behavior (2005, background): “Reinforcement models of human learning indicate that people are more likely to repeat actions that have produced favorable outcomes in the past (Erev & Roth, 1998; Hammond, Summers, & Deane, 1973; Thorndike, 1898).”
Learning by Doing in the Newsvendor Problem: A Laboratory Investigation of the Role of Experience and Feedback (2008, background): “Erev and Roth’s (1998) adaptive learning model provides a theoretical explanation for these effects.”
Predicting In-Game Actions from Interviews of NBA Players (2019, background): “There are some interesting attempts at learning to better predict human action (Erev and Roth 1998; Wright and Leyton-Brown 2010; Hartford, Wright, and Leyton-Brown 2016), but the task at hand is usually addressed in lab conditions or using synthetic data.”
Small feedback‐based decisions and their limited correspondence to description‐based decisions (2003, background): “Finally, the study of choice behavior in repeated games reveals high sensitivity to recent feedback (see e.g. Erev & Roth, 1998; Cheung & Friedman, 1999; Camerer & Ho, 1999; Selten & Buchta, 1998; Daniel et al., 1998; Cooper et al., 1997; Chen & Tang, 1999).”