An Evolutionarily Motivated Model of Decision-Making Under Uncertainty

There has been a recent shift in decision theory research from attempting to fix classical compensatory models towards adopting non-compensatory models of decision-making. This shift has arisen largely because of the inability of compensatory expected-utility based approaches to explain a large number of cognitive biases reliably observed in human subjects on experimental decision tasks. We show, using a jointly evolutionary and information-theoretic argument, that these so-called biases are, in fact, completely rational, if rationality is defined as minimizing the subject's cognitive effort in making a satisfactorily accurate decision. In this paper, we formalize this intuition in the form of a compensatory model of decision-making, and show that this extremely simple and interpretable model can generatively replicate three classic experimental studies spanning distinctive families of cognitive biases, viz. probabilistic sub-additivity leading to a fourfold pattern of risk aversion, confirmatory positive hypothesis selection and serial ordering effects. We suggest that this unified explanation for hitherto unconnected cognitive phenomena provides evidence for the existence of a fundamental information-theoretic optimality principle in the nature of human intelligence