York Hagmayer and David Lagnado (2012)

Causal models in judgment and decision making.

In: False, ed. Dhami, Mandeep K. and Schlottmann, Anne and Waldmann, Michael R. and Dhami, Mandeep K. (Ed) and Schlottmann, Anne (Ed) and Waldmann, Michael R. (Ed). Cambridge University Press

Probabilistic models have dominated judgment and decision making (JDM) research, both in terms of the normative theories that people should conform to, and the mental models that people use to reason and decide under uncertainty. This is perfectly natural—what else could (or should) lie at the center of our capacity to reason about uncertainty? In this chapter, however, the authors argue that this focus on probabilistic models has obscured and sidelined an equally fundamental concept—causality. Moreover, shifting the focus onto causal models gives us a better understanding of how people make judgments and decisions under uncertainty. This thesis is not entirely new, but recent work in causal inference, both theoretical and empirical, has paved the way for a more formal and in-depth exposition. The authors will present a sampling of this work, and link this with questions traditionally addressed by JDM research. (PsycINFO Database Record (c) 2016 APA, all rights reserved)

Accession Number: 2012-08875-007. Partial author list: First Author & Affiliation: Hagmayer, York; Department of Psychology, University of Göttingen, Göttingen, Germany. Release Date: 20120611. Publication Type: Book (0200), Edited Book (0280). Format Covered: Print. Document Type: Chapter. ISBN: 978-0-521-76781-1, Hardcover. Language: English. Major Descriptor: Causality; Decision Making; Judgment; Models. Minor Descriptor: Empirical Methods; Experimentation; Inference. Classification: Cognitive Processes (2340). Population: Human (10); Male (30); Female (40). Intended Audience: Psychology: Professional & Research (PS). References Available: Y. Page Count: 29.