2026-05-21 マックス・プランク研究所
In some scenarios, being highly sensitive to short-term results may not lead to the best possible outcomes. On the contrary, being less sensitive to short-term gains – seemingly acting erratic – can render long-term advantages.© Marta C. Couto
＜関連情報＞
- https://www.mpg.de/26530016/when-noisy-decision-making-becomes-a-strategic-advantage
- https://www.pnas.org/doi/10.1073/pnas.2529959123
ゲームにおけるノイズ学習の進化 Evolution of noisy learning in games
Marta C. Couto, Fernando P. Santos, and Christian Hilbe
Proceedings of the National Academy of Sciences Published:May 12, 2026
DOI:https://doi.org/10.1073/pnas.2529959123
Significance
In strategic interactions, people can improve their performance by reasoning about their available strategies. This reasoning process can be captured with models of learning and evolutionary game theory. These models often contain a parameter that reflects how likely individuals are to switch to strategies they deem more attractive. This parameter has been referred to as an individual’s sensitivity, or as the strength of selection. The smaller this parameter, the noisier the learning process becomes. Herein, we study how this sensitivity itself may evolve over time. We find many scenarios where this sensitivity increases indefinitely. However, we also identify situations where it converges toward a finite value. These results help us understand how noisy strategy updating may result in long-term advantages.
Abstract
People make strategic decisions many times a day—during negotiations, when coordinating actions with others, or when choosing partners for cooperation. The resulting dynamics can be studied with learning theory and evolutionary game theory. These frameworks explore how people adapt their decisions over time, in light of how effective their strategies have been. The outcomes of such learning processes depend on how sensitive individuals are to the performance of their strategies. When they are more sensitive, they systematically favor strategies they deem more successful. When they are less sensitive, their learning process is noisier and more erratic. Traditionally, most models treat this sensitivity as a fixed parameter—like the “selection strength” parameter in evolutionary models. Instead, we study how strategies and sensitivities coevolve. We find that the coevolutionary endpoints depend on both the type of strategic interaction and the learning rule employed. In prisoner’s dilemmas, we often observe sensitivities to increase indefinitely. But in snowdrift and stag-hunt games, sensitivities often converge to a finite value, or we observe evolutionary branching altogether. These results shed light on how evolution might shape learning mechanisms for social behavior. They suggest that noisy learning does not need to be a by-product of cognitive constraints. Instead, it can serve as a means to gain strategic advantages.
