Hybrid Model Reveals People Act Less Rationally In Complex Games, More Predictably In Simple Ones
alternative_right shares a report from Phys.org: Researchers at Princeton University, Boston University and other institutes used machine learning to predict the strategic decisions of humans in various games. Their paper, published in Nature Human Behavior, shows that a deep neural network trained on human decisions could predict the strategic choices of players with high levels of accuracy. [...] Essentially, the team suggests that people behave more rationally while playing games that they perceive as easier. In contrast, when they are playing more complex games, people's choices could be influenced by various other factors, thus the "noise" affecting their behavior would increase. As part of their future studies, the researchers would also like to shed more light on what makes a game "complex" or "easy." This could be achieved using the context-dependent noise parameter that they integrated into their model as a signature of "perceived difficulty." "Our analysis provides a robust model comparison across a wide range of candidate models of decision-making," said [Jian-Qiao Zhu, first author of the paper]. "We now have strong evidence that introducing context-dependence into the quantal response model significantly improves its ability to capture human strategic behavior. More specifically, we identified key factors in the game matrix that shape game complexity: considerations of efficiency, the arithmetic difficulty of computing payoff differences, and the depth of reasoning required to arrive at a rational solution." The findings gathered as part of this recent study also highlight the "lightness" with which many people approach strategic decisions, which could make them vulnerable to parties looking to sway them towards making irrational decisions. Once they gather more insight into what factors make games and decision-making scenarios more challenging for people, Zhu and his colleagues hope to start devising new behavioral science interventions aimed at prompting people to make more rational decisions.
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