Belief Updating Beyond the Two-State Setting

Abstract

Heuristics and biases in probabilistic belief updating have typically been examined in simple two-state experimental settings. We argue that the two-state setting has probabilistic properties that do not extend to settings with more states. With three states, we find that individuals apply similar heuristics, such as representativeness and anchoring, when providing posterior probability distributions. However, because of the different normative benchmark, the use of these heuristics results in different biases for point estimates. In particular, we demonstrate that the well-known finding of stronger underinference for larger signal sets does not translate from the two-state to the three-state setting. Our findings caution against an indiscriminate transfer of updating biases observed in two-state settings to a broad set of real-world applications. This paper was accepted by Manel Baucells, behavioral economics and decision analysis. Funding: This work was supported by the Fritz Thyssen Stiftung [Grant 20.21.0.023WW]. Supplemental Material: The data and online appendix are available at https://doi.org/10.1287/mnsc.2022.00513 .