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Related Experiment Video

Updated: Jun 6, 2026

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats

Published on: January 18, 2013

Population-dependent agent performance in non-transitive games: a multi-agent rock-paper-scissors benchmark.

Ou Deng¹, Jianting Xu², Shoji Nishimura³

¹Graduate School of Human Sciences, Waseda University, Tokorozawa, Saitama, 359-1192, Japan. dengou@toki.waseda.jp.

Scientific Reports

|June 4, 2026

Summary

This summary is machine-generated.

In non-transitive games like rock-paper-scissors, recurrent predictors often perform best, but strategy rankings change significantly with the opponent pool. This benchmark highlights the complexity of evaluating agents in dynamic, population-dependent environments.

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Last Updated: Jun 6, 2026

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats

Peering into the Dynamics of Social Interactions: Measuring Play Fighting in Rats

Published on: January 18, 2013

Area of Science:

Artificial Intelligence
Game Theory
Computational Neuroscience

Background:

Non-transitive environments, such as iterated rock-paper-scissors, challenge the concept of a universally "best" strategy.
Agent performance is highly dependent on the specific opponent population and evaluation protocol.
Existing benchmarks often lack reproducibility and robust methods for evaluating strategy dynamics.

Purpose of the Study:

To introduce a reproducible multi-agent benchmark for iterated rock-paper-scissors.
To evaluate a diverse set of 54 agents across 18 archetypes, including deep learning and classical methods.
To develop and apply a simple regret certificate for online auditability and performance gap analysis.

Main Methods:

Conducted 500-round double round-robin tournaments with 54 agents across 10 random seeds.
Implemented a novel regret certificate based on Lipschitz-type inequality and action distribution discrepancy.
Analyzed agent performance, strategy rankings, and meta-game non-transitivity using statistical metrics.

Main Results:

Recurrent predictors achieved the highest and most stable scores, particularly against predictable opponents.
Agent rankings exhibited significant shifts across different opponent pools (Spearman rho), with top methods varying.
The meta-game displayed substantial non-transitivity, evidenced by 134 detected three-cycles.

Conclusions:

Recurrent agents show strong performance in short-horizon adversarial play within this benchmark.
Transformer agents were competitive but did not consistently outperform tuned recurrent baselines.
The developed benchmark and analysis pipeline offer a flexible platform for studying population-dependent learning and evaluation in non-transitive games.