Human Decision Making with Bounded Rationality

In critical environments that require a high accuracy of decisions, utilizing human cognitive strengths and expertise in addition to machine observations is advantageous to improve decision quality and enhance situational awareness. While the current literature on human decision making is primarily based on the paradigm of perfect rationality, humans are subject to decision noise and employ stochastic choice rules. Human decision making under such realistic environments needs to be further studied. In this paper, instead of assuming that a human selects the optimal action with probability one, we employ a bounded rationality choice model where all the actions are candidates for selection, but better options are chosen with higher probabilities. In a Bayesian hypothesis testing framework, we evaluate the individual decision making performance when humans have different degrees of bounded rationality. Furthermore, we analyze the decision fusion rule for a team of two human agents and characterize the asymptotic performance of collaborative decision making as the number of human participants becomes large.