Division of the Humanities and Social Sciences California Institute of Technology Pasadena, California 91125 Categorical Cognition: a Psychological Model of Categories and Identification in Decision Making

Categorical Cognition: A Psychological Model of Categories and Identification in Decision Making

This paper introduces a psychological notion of categorization into economics and derives its implications for economic decision making. We show, using a tractable model of social cognition, that a decision maker in (efficiently) assigning past experiences to categories, will sort experiences of interaction with larger (majority) groups more finely than experiences with smaller (minority) group...

Division of the Humanities and Social Sciences California Institute of Technology Pasadena, California 91125 on Incentives and Updating in Agent Based Models

Division of the Humanities and Social Sciences California Institute of Technology Pasadena, California 91125 Bounded Rationality in Individual Decision Making

My goals in this paper are: (i) To give a pithy, opinionated summary of what has been learned about bounded rationality in individual decision making from experiments in economics and psychology (drawing on my 1995 Handbook of Experimental Economics chapter); and (ii) mention some promising new directions for research which would be included if that chapter were

Behavioral contagion during learning about another agent’s risk-preferences acts on the neural representation of decision-risk

Division of the Humanities and Social Sciences, California Institute of Technology, Pasadena, CA 91125; Faculty of Business and Economics, The University of Melbourne, Carlton, VIC 3010, Australia; Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Parkville, VIC 3052, Australia; and Computation and Neural Systems, California Institute of Technology, Pasadena, CA 9...

Nber Working Paper Series Experimenting with Measurement Error: Techniques with Applications to the Caltech Cohort Study

Measurement error is ubiquitous in experimental work. It leads to imperfect statistical controls, attenuated estimated effects of elicited behaviors, and biased correlations between characteristics. We develop simple statistical techniques for dealing with experimental measurement error. These techniques are applied to data from the Caltech Cohort Study, which conducts repeated incentivized sur...