Real-Time Strategy: Multi-Level Dynamics in an Uncertain Environment

Abstract

As an agent gathers information about its environment and monitors the decisions of other agents, its behavior may fluctuate adaptively over short time scales while still maintaining a long-term strategy. We designed a real-time virtual environment to experimentally investigate the relationship between the micro-level dynamics of dyadic behavior within single games and the macro-level dynamics of outcomes across iterated games. In one experiment, participants played a real-time game of "chicken," simultaneously guiding avatars toward high-payoff or low-payoff targets. If both participants reached a demarcated vicinity of a target at the same time, that target was destroyed. We recorded their trajectories, and induced uncertainty by adding noise to their movement speeds. At the macro-level, we found evidence of self-organized turn-taking across repeated games. At the micro-level, we found that even within a turn-taking equilibrium, both players competitively pursued the high payoff for a period of time before one of them diverted.


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