Reinforcement Learning Dynamics in Social Dilemmas

Segismundo Izquierdo (), Luis Izquierdo and Nicholas M. Gotts ()
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Nicholas M. Gotts: http://nickgotts.weebly.com

Journal of Artificial Societies and Social Simulation, 2008, vol. 11, issue 2, 1

Abstract: In this paper we replicate and advance Macy and Flache's (2002; Proc. Natl. Acad. Sci. USA, 99, 7229–7236) work on the dynamics of reinforcement learning in 2×2 (2-player 2-strategy) social dilemmas. In particular, we provide further insight into the solution concepts that they describe, illustrate some recent analytical results on the dynamics of their model, and discuss the robustness of such results to occasional mistakes made by players in choosing their actions (i.e. trembling hands). It is shown here that the dynamics of their model are strongly dependent on the speed at which players learn. With high learning rates the system quickly reaches its asymptotic behaviour; on the other hand, when learning rates are low, two distinctively different transient regimes can be clearly observed. It is shown that the inclusion of small quantities of randomness in players' decisions can change the dynamics of the model dramatically.

Keywords: Reinforcement Learning; Replication; Game Theory; Social Dilemmas; Agent-Based; Slow Learning (search for similar items in EconPapers)
Date: 2008-03-31
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:jas:jasssj:2007-11-2

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