This research was supported in part by a grant from the Defense Threat Reduction Agency (DTRA) grant number: HDTRA1-09-1-0053 to Cleotilde Gonzalez and Christian Lebiere. We thank Hau-yu Wong for editing this manuscript and Christian Lebiere and Ion Juvina for discussions that contributed to ideas in this article.
Special Issue: New Theoretical Perspectives on Conflict and Negotiation
Scaling up Instance-Based Learning Theory to Account for Social Interactions
Article first published online: 31 MAR 2011
© 2011 International Association for Conflict Management and Wiley Periodicals, Inc.
Negotiation and Conflict Management Research
Volume 4, Issue 2, pages 110–128, May 2011
How to Cite
Gonzalez, C. and Martin, J. M. (2011), Scaling up Instance-Based Learning Theory to Account for Social Interactions. Negotiation and Conflict Management Research, 4: 110–128. doi: 10.1111/j.1750-4716.2011.00075.x
- Issue published online: 31 MAR 2011
- Article first published online: 31 MAR 2011
- social information;
- instance-based learning theory;
- Prisoner’s Dilemma
Traditional economic theory often describes real-world social dilemmas as abstract games where an individual’s goal is to maximize economic benefit by cooperating or competing with others. Despite extensive empirical work, descriptive models of human behavior in social dilemmas are lacking in both cognitive realism and predictive power. This article addresses a central challenge arising from the success of modeling individuals making decisions from experience: our ability to scale these models up to explain social interactions. We propose that models based on the instance-based learning theory (IBLT) will help us to understand how conflictual social interactions are influenced by prior experiences of involved individuals and by information available to them during the course of interaction. We present mechanisms by which IBLT might capture the effects of social interaction with different levels of information without assuming predefined interaction strategies, but rather by assuming learning from experience.