The Art of War: Beyond Memory-one Strategies in Population Games

Journal Article · Tue Mar 24 00:00:00 EDT 2015 · PLoS ONE

DOI:https://doi.org/10.1371/journal.pone.0120625· OSTI ID:1904728

Lee, Christopher ^[1]; Harper, Marc ^[2]; Fryer, Dashiell ^[3]

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1. University of California, Los Angeles, CA (United States); University of California, Los Angeles
2. University of California, Los Angeles, CA (United States)
3. Pomona College, Claremont, CA (United States)

We show that the history of play in a population game contains exploitable information that can be successfully used by sophisticated strategies to defeat memory-one opponents, including zero determinant strategies. The history allows a player to label opponents by their strategies, enabling a player to determine the population distribution and to act differentially based on the opponent’s strategy in each pairwise interaction. For the Prisoner’s Dilemma, these advantages lead to the natural formation of cooperative coalitions among similarly behaving players and eventually to unilateral defection against opposing player types. We show analytically and empirically that optimal play in population games depends strongly on the population distribution. For example, the optimal strategy for a minority player type against a resident TFT population is ALLC, while for a majority player type the optimal strategy versus TFT players is ALLD. Such behaviors are not accessible to memory-one strategies. Drawing inspiration from Sun Tzu’s the Art of War, we implemented a non-memory-one strategy for population games based on techniques from machine learning and statistical inference that can exploit the history of play in this manner. Via simulation we find that this strategy is essentially uninvadable and can successfully invade (significantly more likely than a neutral mutant) essentially all known memory-one strategies for the Prisoner’s Dilemma, including ALLC (always cooperate), ALLD (always defect), tit-for-tat (TFT), winstay-lose-shift (WSLS), and zero determinant (ZD) strategies, including extortionate and generous strategies.

• Research Organization: University of California, Los Angeles, CA (United States)
• Sponsoring Organization: USDOE Office of Science (SC), Biological and Environmental Research (BER)
• Grant/Contract Number: FC02-02ER63421
• OSTI ID: 1904728
• Journal Information: PLoS ONE, Journal Name: PLoS ONE Journal Issue: 3 Vol. 10; ISSN 1932-6203
• Publisher: Public Library of Science
• Country of Publication: United States
• Language: English

Related Subjects

60 APPLIED LIFE SCIENCES
97 MATHEMATICS AND COMPUTING
battles
evolutionary algorithms
evolutionary theory
game theory
games
natural selection
prisoner's dilemma
simulation and modeling