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Humans are social beings, and most of our decisions are influenced by considerations of how others will respond. Whether in poker or political negotiations, the riskiness of a decision is often determined by the variance of the other party's possible responses. We use chess as a lens through which we can study human risk-taking behavior in adversarial decision making.
We develop a novel algorithm for calculating the riskiness of each move in a chess game, and apply it to data from over 1 billion online chess games. We find that players not only exhibit state-dependent risk preferences, but also change their risk-taking strategy depending on their opponent, and that this effect differs in experts and novices.
This is a snapshot of the beginning of a directed, acyclic graph that condenses billions of chess moves into hundreds of thousands of connected nodes. Our novel algorithm computes the riskiness of a move using the variance of possible response moves the opponent could make.
