While previous models of economic decision-making offer de-scriptive accounts of behavior, they often overlook the com-putational complexity of estimating expected utility. Here,we seek to understand how both environmental and individualconstraints on cognition shape our daily decision. Informedby the predictions of a recently-proposed resource-rationalprocess model of risky choice, sample-based expected utility(SbEU; Nobandegani, da Silva Castanheira, Otto, & Shultz,2018), we reveal that both time pressure and individual dif-ferences in processing speed have a convergent effect on riskpreferences during a risky decision-making task. Under severetime constraints, participants’ risk preferences manifested astrong framing effect compared to little time pressure in whichchoice adhered to the classic fourfold pattern of risk prefer-ences. Similarly, individual differences in processing speed,measured using an established task, predicted similar effectsupon risk attitudes as extrinsic time pressure. These findingsreveal a converging contribution of environmental and individ-ual limitations on risky choice, and provide empirical supportfor SbEU as a resource-rational process model of risky deci-sion making. Notably, SbEU serves as a single-process modelof two well-established biases, and the transition between thetwo, in risky choice.

The Availability bias, manifested in the over-representation ofextreme eventualities, is a well-known cognitive bias, and isgenerally taken as evidence of human irrationality. In thiswork, we present the first rational, metacognitive account ofthe Availability bias, formally articulated at Marr’s algorith-mic level of analysis. Concretely, we present a normative,metacognitive model of how a cognitive system should over-represent extreme eventualities, depending on the amount oftime available for decision-making. Our Sample-based Ex-pected Utility model also accounts for two well-known fram-ing effects in human decision-making under risk—the fourfoldpattern of risk preferences in outcome probability (Tversky& Kahneman, 1992) and in outcome magnitude (Markovitz,1952)—thereby providing the first metacognitively-rationalbasis for the aforementioned effects. Empirical evidence con-firms an important prediction of our model. Surprisingly, ourmodel is strikingly robust with respect to its focal parameter.We discuss the implications of our work for studies on hu-man decision-making, and conclude by presenting a counter-intuitive prediction of our model, which, if confirmed, wouldhave intriguing implications for human decision-making un-der risk. To our knowledge, our model is the first metacog-nitive, resource-rational process model of cognitive biases indecision-making. Notably, our work also contributes to thefields of artificial intelligence and computational statistics, bypresenting a previously unknown proposal distribution, withfirm rational grounds, broadly applicable to the influential sub-field of importance sampling Monte Carlo methods.

The Availability bias, manifested in the over-representation ofextreme eventualities, is a well-known cognitive bias, and isgenerally taken as evidence of human irrationality. In thiswork, we present the first rational, metacognitive account ofthe Availability bias, formally articulated at Marr’s algorith-mic level of analysis. Concretely, we present a normative,metacognitive model of how a cognitive system should over-represent extreme eventualities, depending on the amount oftime available for decision-making. Our Sample-based Ex-pected Utility model also accounts for two well-known fram-ing effects in human decision-making under risk—the fourfoldpattern of risk preferences in outcome probability (Tversky& Kahneman, 1992) and in outcome magnitude (Markovitz,1952)—thereby providing the first metacognitively-rationalbasis for the aforementioned effects. Empirical evidence con-firms an important prediction of our model. Surprisingly, ourmodel is strikingly robust with respect to its focal parameter.We discuss the implications of our work for studies on hu-man decision-making, and conclude by presenting a counter-intuitive prediction of our model, which, if confirmed, wouldhave intriguing implications for human decision-making un-der risk. To our knowledge, our model is the first metacog-nitive, resource-rational process model of cognitive biases indecision-making. Notably, our work also contributes to thefields of artificial intelligence and computational statistics, bypresenting a previously unknown proposal distribution, withfirm rational grounds, broadly applicable to the influential sub-field of importance sampling Monte Carlo methods.

The St. Petersburg paradox is a centuries-old philosophicalpuzzle concerning a lottery with infinite expected payoff,on which people are, nevertheless, willing to place only asmall bid. Despite many attempts and several proposals, nogenerally-accepted resolution is yet at hand. In this work, wepresent the first resource-rational process-level explanation ofthis paradox, demonstrating that it can be accounted for by avariant of normative expected-utility-maximization which ac-knowledges cognitive limitations. Specifically, we show thatNobandegani et al.’s (2018) metacognitively-rational model,sample-based expected utility (SbEU), can account for majorexperimental findings on this paradox. Crucially, our resolu-tion is consistent with two empirically well-supported assump-tions: (1) people use only a few samples in probabilistic judg-ments and decision-making, and (2) people tend to overesti-mate the probability of extreme events in their judgment.

The concept of Nash equilibrium has played a profound rolein economics, and is widely accepted as a normative stance forhow people should choose their strategies in competitive envi-ronments. However, extensive empirical evidence shows thatpeople often systematically deviate from Nash equilibrium. Inthis work, we present the first resource-rational mechanisticapproach to one-shot, non-cooperative games (ONG), show-ing that a variant of normative expected-utility maximizationacknowledging cognitive limitations can account for impor-tant deviations from the prescriptions of Nash equilibrium inONGs. Concretely, we show that Nobandegani et al.’s (2018)metacognitively-rational model, sample-based expected util-ity, can account for purportedly irrational cooperation rates ob-served in one-shot, non-cooperative Prisoner’s Dilemma, andcan accurately explain how cooperation rate varies dependingon the parameterization of the game. Additionally, our workprovides a resource-rational explanation of why people withhigher general intelligence tend to cooperate less in OPDs, andserves as the first (Bayesian) rational, process-level explana-tion of a well-known violation of the law of total probability inOPDs, documented by Shafir and Tversky (1992), which hasresisted explanation by a model governed by classical proba-bility theory for nearly three decades. Surprisingly, our workdemonstrates that cooperation can arise from purely selfish,expected-utility maximization subject to cognitive limitations.