Relevance insensitivity: A new look at some old biases

Christopher K. Hsee, Yang Yang and Xilin Li

Organizational Behavior and Human Decision Processes, 2019, vol. 153, issue C, 13-26

Abstract: People show systematic biases in judgment and decision making. We propose that many seemingly disparate biases reflect a common underlying mechanism—insensitivity to the relevance of some given information—and that manipulating the relevance of the information can eliminate or even reverse the original bias. We test our theory in four experiments, each focusing on a classic bias—the sunk cost fallacy, non-regressive prediction, anchoring bias, and base rate neglect, and show that people over-rely on a given piece of information when it is irrelevant, thus exhibiting one bias, and under-rely on the same piece of information when it is highly relevant, thus showing a reverse bias. For example, when a past cost is irrecoverable and hence irrelevant to future cost, people over-rely on it when making a decision for the future, thus exhibiting the classic sunk cost fallacy, but when the past cost is fully recoverable and hence highly relevant to future cost, people under-rely on it, thus showing the reverse of the sunk cost fallacy. We also find that when people are made sensitive to the relevance of the information, both the original biases and their reverse biases are attenuated. This research offers a new look at these “old” biases, suggesting that each individual bias is not general because it can be reversed, but collectively, these biases are general because they all reflect relevance insensitivity.

Keywords: Behavior decision theory; Heuristics and biases; Integrative theory; Noise model; Overgeneralization; Anchoring; Sunk cost; Regression toward the mean; Base rate neglect; Overconfidence (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:jobhdp:v:153:y:2019:i:c:p:13-26

DOI: 10.1016/j.obhdp.2019.05.002

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