A Resource‐Rational, Process‐Level Account of the St. Petersburg Paradox. (3rd January 2020)

• Record Type: Journal Article
• Authors: Nobandegani, Ardavan S.; Shultz, Thomas R.
• Is Part Of: Topics in cognitive science. Volume 12:Number 1(2020)
• Page Start: 417
• Page End: 432
• Publication Date: 2020-01-03
• Subjects: St. Petersburg paradox -- Bounded rationality -- Rational process models -- Expected utility theory -- Inference by sampling -- Sample‐based expected utility model; Cognitive science -- Periodicals; Cognitive Science -- Periodicals; 153.05
• DOI: 10.1111/tops.12486
• Languages: English
• ISSNs: 1756-8757

Abstract

Abstract: The St. Petersburg paradox is a centuries‐old philosophical puzzle concerning a lottery with infinite expected payoff for which people are only willing to pay a small amount to play. Despite many attempts and several proposals, no generally accepted resolution is yet at hand. In this work, we present the first resource‐rational, process‐level explanation of this paradox, demonstrating that it can be accounted for by a variant of normative expected utility valuation which acknowledges cognitive limitations. Specifically, we show that Nobandegani et al.'s (2018) metacognitively rational model, sample‐based expected utility (SbEU), can account for major experimental findings on this paradox. Crucially, our resolution is consistent with two empirically well‐supported assumptions: (a) People use only a few samples in probabilistic judgments and decision‐making, and (b) people tend to overestimate the probability of extreme events in their judgment. Our work seeks to understand the St. Petersburg gamble as a particularly risky gamble whose process‐level explanation is consistent with a broader process‐level model of human decision‐making under risk. Abstract : How much would you pay to play a lottery with an "infinite expected payoff?" In the case of the century old, St. Petersburg Paradox, the answer is that the vast majority of people would only pay a small amount. The authors seek to understand this paradox by providing an explanation consistent with a broad, process‐level model of human decision‐making under risk.