Social cycling and conditional responses in the Rock-Paper-Scissors game
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Abstract (arXiv)
How humans make decisions in non-cooperative strategic interactions is a challenging question. For the fundamental model system of Rock-Paper-Scissors (RPS) game, classic game theory of infinite rationality predicts the Nash equilibrium (NE) state with every player randomizing her choices to avoid being exploited, while evolutionary game theory of bounded rationality in general predicts persistent cyclic motions, especially for finite populations. However, as empirical studies on human subjects have been relatively sparse, it is still a controversial issue as to which theoretical framework is more appropriate to describe decision making of human subjects. Here we observe population-level cyclic motions in a laboratory experiment of the discrete-time iterated RPS game under the traditional random pairwise-matching protocol. The cycling direction and frequency are not sensitive to the payoff parameter a. This collective behavior contradicts with the NE theory but it is quantitatively explained by a microscopic model of win-lose-tie conditional response without any adjustable parameter. Our theoretical calculations reveal that this new strategy may offer higher payoffs to individual players in comparison with the NE mixed strategy, suggesting that high social efficiency is achievable through optimized conditional response.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 21 Apr 2014.
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Citing papers per year: 2023: 1; 2024: 0; 2025: 1; 2026: 1.
3 papers.
- 2025Beyond Nash Equilibrium: Bounded Rationality of LLMs and humans in Strategic Decision-makingcited by 8
- 2023Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learningcited by 5
- 2026Discovering Differences in Strategic Behavior Between Humans and LLMscited by 2
- 2026Discovering Differences in Strategic Behavior Between Humans and LLMs10 Feb 2026
- 2025Beyond Nash Equilibrium: Bounded Rationality of LLMs and humans in Strategic Decision-making11 Jun 2025
- 2023Population-based Evaluation in Repeated Rock-Paper-Scissors as a Benchmark for Multiagent Reinforcement Learning2 Mar 2023