Sampling in Judgment and Decision Making

An exploration of how statistical sampling principles impose theoretical constraints and enable novel insights on judgments and decisions.

Klaus Fiedler (Edited by), Peter Juslin (Edited by), Jerker Denrell (Edited by)

9781009009867, Cambridge University Press

Paperback / softback, published 15 June 2023

520 pages
22.8 x 15.1 x 2.9 cm, 0.81 kg

'This book makes a compelling case that we can greatly enrich our understanding of human judgment and decision making if we focus on how we sample information from environments that often seem deliberately designed to confuse or mislead us.' Barbara Mellers, University of Pennsylvania, USA

Sampling approaches to judgment and decision making are distinct from traditional accounts in psychology and neuroscience. While these traditional accounts focus on limitations of the human mind as a major source of bounded rationality, the sampling approach originates in a broader cognitive-ecological perspective. It starts from the fundamental assumption that in order to understand intra-psychic cognitive processes one first has to understand the distributions of, and the biases built into, the environmental information that provides input to all cognitive processes. Both the biases and restriction, but also the assets and capacities, of the human mind often reflect, to a considerable degree, the irrational and rational features of the information environment and its manifestations in the literature, the Internet, and collective memory. Sampling approaches to judgment and decision making constitute a prime example of theory-driven research that promises to help behavioral scientists cope with the challenges of replicability and practical usefulness.

1. The theoretical beauty and fertility of sampling approaches: a historical and meta-theoretical review Klaus Fiedler, Peter Juslin and Jerker Denrell
2. Homo ordinalus and sampling models: the past, present, and future decision by sampling Gordon D. A. Brown and Lukasz Walasek
3. In decisions from experience what you see is up to your sampling of the world Timothy J. Pleskac and Ralph Hertwig
4. The hot stove effect Jerker Denrell and Gaël Le Mens
5. The J/DM separation paradox and the reliance on small samples hypothesis Ido Erev and Ori Plonsky
6. Sampling as preparedness in evaluative learning Mandy Hütter and Zachary Adolph Niese
7. The dog that didn't bark: Bayesian approaches to reasoning Brett K. Hayes, Saoirse Connor Desai, Keith Ransom and Charles Kemp
8. Unpacking intuitive and analytica memory sampling in multiple-cue judgment August Collsiöö, Joakim Sundh and Peter Juslin
9. Biased preferences through exploitation Chris Harris and Ruud Custers
10. Evaluative consequences of sampling distinct information Hans Alves, Alex Koch, and Christian Unkelbach
11. Information sampling in contingency learning: sampling strategies and their consequences for (pseudo-)contingency Franziska M. Bott and Thorsten Meiser
12. The collective hot stove effect Gaël Le Mens, Balázs Kovác, Judith Avrahami, and Yaakov Kareev
13. Sequential decisions from sampling: inductive generation of stopping decisions using instance-based learning theory Cleotilde Gonzales and Palvi Aggarwal
14. Thurstonian uncertainty in self-determined judgment and decision making Johannes Prager, Klaus Fiedler, and Linda McCaughey
15. The information cost-benefit trade-off as a sampling problem in information search Linda McCaughey, Johannes Prager, and Klaus Fiedler
16. Heuristic social sampling Thorsten Pachur and Christin Schulze
17. Social sampling for judgments and predictions of societal trends Henrik Olsson, Mirta Galesic and Wändi Bruine de Bruin
18. Group-motivated sampling: from skewed experiences to biased evaluations Yrian Derreumaux, Robin Bergh, Marcus Lindskog and Brent Hughes
19. Opinion homogenization and polarization: three sampling models Elizaveta Konovalova and Gaël Le Mens
20. An introduction to psychologically plausible sampling schemes for approximating Bayesian inference Jian-Qiao Zhu, Nick Chater, Pablo León-Villagrá, Jake Spicer, Joakim Sundh and Adam Sanborn
21. Approximating Bayesian inference through internal sampling Joakim Sundh, Adam Sanborn, Jian-Qiao Zhu, Jake Spicer, Pablo León-Villagrá and Nick Chater
22. Sampling data, beliefs, and actions Erik Brockbank, Cameron Holdaway, Daniel Acosta-Kane, and Edward Vul.

Subject Areas: Cognition & cognitive psychology [JMR], Psychology [JM]