The Determinants of Response Time in a Repeated Constant-Sum Game: A Robust Bayesian Hierarchical Dual-Process Model

Cognition, 172, 107-123 doi: 10.1016/j.cognition.2017.11.006

39 Pages Posted: 6 Mar 2016 Last revised: 4 Jun 2018

See all articles by Leonidas Spiliopoulos

Leonidas Spiliopoulos

Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Human Development

Date Written: March 15, 2018

Abstract

The investigation of response time and behavior has a long tradition in cognitive psychology, particularly for non-strategic decision-making. Recently, experimental economists have also studied response time in strategic interactions, but within an emphasis on either one-shot games or repeated social-dilemmas. I investigate the determinants of response time in a repeated (pure-conflict) game, admitting a unique mixed strategy Nash equilibrium, with fixed partner matching. Response times depend upon the interaction of two decision models embedded in a dual-process framework (Achtziger and Alós-Ferrer, 2014; Alós-Ferrer, 2016). The first decision model is the commonly used win-stay/lose-shift heuristic and the second the pattern-detecting reinforcement learning model in Spiliopoulos (2013). The former is less complex and can be executed more quickly than the latter. As predicted, conflict between these two models (i.e., each one recommending a different course of action) led to longer response times than cases without conflict. The dual-process framework makes other qualitative response time predictions arising from the interaction between the existence (or not) of conflict and which one of the two decision models the chosen action is consistent with—these were broadly verified by the data. Other determinants of RT were hypothesized on the basis of existing theory and tested empirically. Response times were strongly dependent on the actions chosen by both players in the previous rounds and the resulting outcomes. Specifically, response time was shortest after a win in the previous round where the maximum possible payoff was obtained; response time after losses was significantly longer. Strongly auto-correlated behavior (regardless of its sign) was also associated with longer response times. I conclude that, similar to other tasks, there is a strong coupling in repeated games between behavior and RT, which can be exploited to further our understanding of decision making.

Keywords: response time, dual-process models, pattern recognition, reinforcement learning, win-stay lose shift heuristic, Bayesian hierarchical modeling, robust inference, non-cooperative game theory, constant-sum games, experimental economics

JEL Classification: C11, C72, C91

Suggested Citation

Spiliopoulos, Leonidas, The Determinants of Response Time in a Repeated Constant-Sum Game: A Robust Bayesian Hierarchical Dual-Process Model (March 15, 2018). Cognition, 172, 107-123 doi: 10.1016/j.cognition.2017.11.006, Available at SSRN: https://ssrn.com/abstract=2740989 or http://dx.doi.org/10.2139/ssrn.2740989

Leonidas Spiliopoulos (Contact Author)

Max Planck Society for the Advancement of the Sciences - Max Planck Institute for Human Development ( email )

Lentzeallee 94
D-14195 Berlin, 14195
Germany

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
53
Abstract Views
513
rank
505,848
PlumX Metrics