Research Handbook on Behavioral Law and Economics, Edward Elgar Publishing, Joshua C. Teitelbaum & Kathryn Zeiler, eds., 2017, Forthcoming
29 Pages Posted: 30 Jan 2014 Last revised: 21 Feb 2017
Date Written: August 1, 2016
Throughout their long history, humans have worked hard to tame chance. They adapted to their uncertain physical and social environments by using the method of trial and error. This evolutionary process made humans reason about uncertain facts the way they do. Behavioral economists argue that humans’ natural selection of their prevalent mode of reasoning wasn’t wise. They censure this mode of reasoning for violating the canons of mathematical probability that a rational person must obey.
This chapter challenges both parts of this ambitious claim. Based on the insights from probability theory and the philosophy of induction, I argue that a rational person need not apply mathematical probability in making decisions about individual causes and effects. Instead, she should be free to use common sense reasoning that generally aligns with causative probability. I also show that behavioral experiments uniformly miss their target when they ask reasoners to extract probability from information that combines causal evidence with statistical data. Because it is perfectly rational for a person focusing on a specific event to prefer causal evidence to general statistics, those experiments establish no deviations from rational reasoning. Those experiments are also flawed in that they do not separate the reasoners’ unreflective beliefs from rule-driven acceptances. The behavioral economists’ claim that people are probabilistically challenged consequently remains unproven.
Keywords: probability, behavioral economics, heuristics and biases, Kahneman, Tversky
JEL Classification: A10, K10
Suggested Citation: Suggested Citation
Stein, Alex, Behavioral Probability (August 1, 2016). Research Handbook on Behavioral Law and Economics, Edward Elgar Publishing, Joshua C. Teitelbaum & Kathryn Zeiler, eds., 2017, Forthcoming. Available at SSRN: https://ssrn.com/abstract=2387269