What We Know About Heuristics and Biases Learning, Work, and Everyday Life

People often rely on mental shortcuts, known as heuristics, to help them make judgments and decisions quickly and efficiently. While, generally, these heuristics lead to accurate judgments, in certain circumstances, heuristics can bias problem-solving and decision-making producing errors with serious consequences. What We Know About Heuristics and Biases introduces the literature on heuristics and provides an assessment tool designed to obviate these problems. Measuring six cognitive and social biases—confirmation bias, fundamental attribution error, the bias blind spot, anchoring bias, representativeness bias, and projection bias–this tool provides an innovate and cutting edge method for assessment professionals and researchers in measurement.

1. Introduction. 2. Research on Heuristics and Biases and Individual Differences. a. Relationship Between Heuristics and Biases and Individual Differences. b. Previous Attempts to Develop Standardized Measures of Judgment and Decision-Making Bias. c. Bias Mitigation. d. Overview of the Current Book. 3. Confirmation Bias. a. Illustrative Examples. b. Causes of Confirmation Bias. c. Measurement of Confirmation Bias. d. Mitigation of Confirmation Bias. e. Education and Workforce Implications/Applications. 4. The Fundamental Attribution Error. a. Illustrative Examples. b. Causes of the Fundamental Attribution Error. c. Measurement of the Fundamental Attribution Error. d. Mitigation of the Fundamental Attribution Error. e. Education and Workforce Implications/Applications. 5. Bias Blind Spot. a. Illustrative Examples. b. Causes of Bias Blind Spot. c. Measurement of Bias Blind Spot. d. Mitigation of Bias Blind Spot. e. Education and Workforce Implications/Applications. 6. Anchoring Bias. a. Illustrative Examples. b. Causes of Anchoring Bias. c. Measurement of Anchoring Bias. d. Mitigation of Anchoring Bias. e. Education and Workforce Implications/Applications. 7. Representativeness Bias. a. Illustrative Examples. b. Causes of Representativeness Bias. c. Measurement of Representativeness Bias. d. Mitigation of Representativeness Bias. e. Education and Workforce Implications/Applications. 8. Projection Bias. a. Illustrative Examples. b. Causes of Projection Bias. c. Measurement of Projection Bias. d. Mitigation of Projection Bias. e. Education and Workforce Implications/Applications. 9. General Discussion. 10. Appendices. a. Assessment of Biases in Cognition (ABC). i. Design of ABC. ii. Illustrative Tasks and/or Links to Online Assessments.

Franklin Zaromb is Research Scientist in the Research and Development Division at Educational Testing Service. Abigail Gertner is Principal AI Engineer, Data Analytics, at The MITRE Corporation. Robert Schneider is President at Research & Assessment Solutions, Ltd. Jeremy Burrus is Principal Research Scientist at ACT. Rebecca Rhodes is a PhD Candidate at the University of Michigan. Gerald Matthews is Associate Research Professor in the Institute for Simulation and Training at University of Central Florida. Jonathon Kopecky is Cognitive Engineer at Johns Hopkins University Applied Physics Laboratory. Richard Roberts is Vice President and Chief Scientist at Professional Examination Service in New York City, where he directs their Center for Innovative Assessments.