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on Cognitive and Behavioural Economics |
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Issue of 2026–02–23
four papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Thomas Dohmen (University of Bonn); Frauke Meyer (Eilert-Academy, Berlin); Gari Walkowitz (Technical University Bergakademie Freiberg, Technical University of Munich) |
| Abstract: | Thomas Dohmen, Frauke Meyer, Gari Walkowitz |
| Keywords: | Basic Needs, Redistribution, Distributional Motives, Maximin, Public Policy, Field Experiment, Laboratory Experiment |
| JEL: | D31 D63 H23 C93 C91 D01 D91 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:389 |
| By: | Luca Corazzini (University of Milan-Bicocca); Marco Diamante (Ca’ Foscari University of Venice); Valeria Maggian (Ca’ Foscari University of Venice) |
| Abstract: | Assessing the causal impact of narratives on beliefs and behaviors remains an empirical challenge for social scientists, largely due to endogeneity and cultural factors. To address these limitations, we present the results of a novel, content-neutral laboratory experiment. In this experiment, participants (i) engage in a zero-sum game against a non-strategic robot, where the final outcome is determined with equal probability either by their choices or by randomness, and (ii) are exposed to either hopeful or passive narratives. These narratives differ in how ambiguous evidence is presented, suggesting whether or not participants can actively determine the final outcome of the game through their choices. Our findings reveal that, regardless of the narrative they are exposed to, participants consistently form beliefs and make choices under the illusion that they can influence the final outcomes. When provided with unambiguous evidence disproving this illusion, participants adjust their beliefs accordingly, although their choices take longer to align with these updated beliefs. Furthermore, exposure to the passive narrative reduces the inconsistency between beliefs and choices when participants mistakenly believe their choices determine the final outcome. Finally, presenting unambiguous evidence that contradicts the narrative's content increases the proportion of random and unpredictable choices. |
| Keywords: | Narratives, polarization, illusion of control, lab experiment |
| JEL: | C91 C70 D91 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2026:02 |
| By: | Daniele Caliari; Valentino Dardanoni; Carla Guerriero; Paola Manzini; Marco Mariotti |
| Abstract: | We study experimentally how children’s ability to avoid choice errors develops over time, focusing on both riskless and risky decisions among primary school children. We identify four types of rationality violations: cycles and menu effects in the riskless domain; and dominance and framing effects compatible with correlation neglect in the risky domain. We find that types of violations are correlated within domains but broadly independent across domains. To interpret our results we build and estimate a structural model of limited consideration. We identify an index of error avoidance and study how it develops with age and socioeconomic background, providing a new tool to understand the development of choice errors. |
| Date: | 2026–01–30 |
| URL: | https://d.repec.org/n?u=RePEc:bri:uobdis:26/821 |
| By: | Bossaerts, P. |
| Abstract: | This review summarizes research over the last two decades on human attitudes towards computationally "hard" problems. The focus is on the nature of uncertainty that computational complexity generates because humans do not have the cognitive capacity or the resources (time) to fully resolve the problems they are dealing with. Although decision theorists have traditionally labeled this type of uncertainty as ambiguity, behavior under computational complexity shows that humans neither deal with it as prescribed in rational decision theory nor simply avoid it as in traditional accounts of ambiguity aversion. Instead, behavior (effort applied and performance reached) exhibits distinct features that can be rationalized using the theory of computational complexity, originally developed for electronic computers. Although the theory cannot decisively tell us which problems are most difficult, it does provide classifications that allow one to predict human performance and effort. The theory also identifies which instances of a problem are more difficult, and human performance and effort appear to align with this identification. Evidence is discussed that humans do not appear to allocate cognitive effort ex ante when faced with a "hard" choice. Absence of correlation between early neural signals and ex ante metrics of instance difficulty corroborate this finding. Finally, the heterogeneity in ways humans approach "hard" problems suggests that, collectively, much can be gained from incentive mechanisms that promote communication. Particular market designs appear to be extremely effective in helping participants make "hard" choices. |
| Keywords: | Computational Complexity, NP-Hard, Uncertainty, Ambiguity, Decision-Making, Rationality, Opportunism, Algorithms, Expected Utility, Neuroeconomics, Cognitive Foundations of Decision-Making |
| Date: | 2026–01–31 |
| URL: | https://d.repec.org/n?u=RePEc:cam:camdae:2611 |