Decision making under risk and uncertainty.

Decision making is studied from a number of different theoretical approaches. Normative theories focus on how to make the best decisions by deriving algebraic representations of preference from idealized behavioral axioms. Descriptive theories adopt this algebraic representation, but incorporate known limitations of human behavior. Computational approaches start from a different set of assumptions altogether, focusing instead on the underlying cognitive and emotional processes that result in the selection of one option over the other. This review comprehensively but concisely describes and contrasts three approaches in terms of their theoretical assumptions and their ability to account for behavioral and neurophysiological evidence from experimental research. Although each approach contributes substantially to our understanding of human decision making, we argue that the computational approach is more fruitful and parsimonious for describing and predicting choices in both laboratory and applied settings and for understanding the neurophysiological substrates of decision making.