Cognitive Constraints on Decision Making under Uncertainty

Abstract

Models of decision making under uncertainty should be grounded in general cognitive processes reflecting pervasive constraints from the nature of our environment. Developing integrated models applicable across different tasks provides converging constraints that increase the predictiveness of models to new situations. Decision making is such a rich discipline that it is often considered in relative isolation, leading to entire fields devoted to specialized aspects and domains. Decision making under uncertainty can be better understood through the prism of general theories of cognition, constrained by representations and mechanisms developed to account for the much broader range of human activities (Anderson and Lebiere, 2003). This argument is an elaboration of Simon's bounded rationality (e.g., Simon, 1957) approach to constraining the rationality of optimal decision making by the cognitive limitations of the decision maker. Further, those cognitive limitations, and more generally the entire decision making process, should be modeled in a computational framework that captures in detail not only the cognitive mechanisms and representations involved (Newell, 1990) but also motivational processes (Kruglanski et al., 2007; Kruglanski and Gigerenzer, 2011) as well as perceptual (especially attentional) and motor processes (Card et al., 1983) to reflect the constraints of the task environment. Finally, decision making involves not simply raw cognitive processes but also knowledge and strategies on how to approach the problem (Gigerenzer et al., 1999). Fortunately, unified theories of cognition enable the representation of declarative and procedural knowledge constructs in a way that interacts with the constraints of the cognitive and perceptual processes to provide a rich account of performance in the task. Providing a detailed but unified computational account of those factors and their interaction across a wide range of tasks is essential for a deeper understanding of human decision making under uncertainty, as it involves general cognitive processes that are not limited to specific paradigms but take place across all human activities. We illustrate those points by briefly describing a number of instances of our recent line of research in models of decision making. In particular, we want to highlight the importance of applying the same modeling approach to widely different paradigms of decision making (including domains that are not usually considered part of decision making) in order to bring the maximum force of converging constraints onto the problem. Indeed, the main issue with many decision making tasks is not that they are too difficult to model, but instead that too many distinct models provide roughly equivalent accounts of the data, making it hard to determine which provide a fundamental understanding of human decision making processes and which are merely well-fitted parametric descriptions of human performance.