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on Cognitive and Behavioural Economics |
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Issue of 2025–12–08
six papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Charlotte Cordes; Jana Friedrichsen; Simeon Schudy |
| Abstract: | Experimental studies show that individuals update beliefs about ego-relevant information optimistically when they expect no resolution of uncertainty but neutrally when their ability is revealed immediately. This paper studies belief updating and the role of motivated memory when feedback is delayed but eventually disclosed. In a longitudinal experiment, participants receive noisy signals about their relative performance in a IQ-related task (Raven matrices) and learn their true rank four weeks later. Across subjects, belief updating is asymmetric: unfavorable signals are weighted less than favorable signals. Further, we identify motivated memory among participants who view the task as ego-relevant. |
| Keywords: | motivated beliefs, feedback, memory, Anticipatory utility, motivated cognition, uncertainty |
| JEL: | C91 D03 D81 D83 D84 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12286 |
| By: | Moritz Loewenfeld (Universität Wien = University of Vienna); Jiakun Zheng (AMSE - Aix-Marseille Sciences Economiques - EHESS - École des hautes études en sciences sociales - AMU - Aix Marseille Université - ECM - École Centrale de Marseille - CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Allowing risk preferences to depend on the correlation between lottery outcomes can explain behavioral anomalies, while empirical evidence is limited and mixed. Using the framework of correlation sensitivity, we classify preferences into three types and adapt a choice task to categorize subjects. Experiments show that aggregate choices exhibit correlation sensitivity opposite to regret and salience theory predictions. Clustering analysis reveals that a correlation-sensitive minority drives these patterns, while most subjects display no sensitivity. We further disentangle deliberate within-state comparisons from incidental payoff comparisons, finding that both contribute to correlation sensitivity, with deliberate comparisons exerting slightly stronger effects. |
| Keywords: | regret theory, salience theory, experiment, correlation effects, choice under risk |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05346525 |
| By: | Gangadharan, Lata (Monash University); Maitra, Pushkar (Monash University); Vecci, Joseph (University of Gothenburg); Veettil, Prakashan Chellattan; Villeval, Marie Claire (CNRS) |
| Abstract: | This study examines whether adherence to advice depends on an advisor’s identity and status beyond message content. Using a survey experiment with over 3000 farmers in India, we find that individuals are more likely to follow advice in a social dilemma game when it comes from high-status or in-group advisors, even when the advice diverges from prevailing norms. Admired role models can attenuate the influence of status and identity, though their beneficial effect is not universal. Our experimental findings align with evidence from an agricultural advisory program involving the same participant sample, highlighting the broader real-world relevance of these patterns. |
| Keywords: | group identity, status, social learning, advice, survey experiment |
| JEL: | C93 D83 D91 O13 Q16 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18280 |
| By: | Thomas R. Cook; Sophia Kazinnik; Zach Modig; Nathan M. Palmer |
| Abstract: | Large language models (LLMs) are now used for economic reasoning, but their implicit "preferences” are poorly understood. We study LLM preferences as revealed by their choices in simple allocation games and a job-search setting. Most models favor equal splits in dictator-style allocation games, consistent with inequality aversion. Structural estimates recover Fehr–Schmidt parameters that indicate inequality aversion is stronger than in similar experiments with human participants. However, we find these preferences are malleable: reframing (e.g., masking social context) and learned control vectors shift choices toward payoff-maximizing behavior, while personas move them less effectively. We then turn to a more complex economic scenario. Extending a McCall job search environment, we also recover effective discounting from accept/reject policies, but observe that model responses may not always be rationalizable, and in some cases suggest inconsistent preferences. Efforts to steer LLM responses in the McCall scenario are also less consistent. Together, our results suggest (i) LLMs exhibit latent preferences that may not perfectly align with typical human preferences and (ii) LLMs can be steered toward desired preferences, though this is more difficult with complex economic tasks. |
| Keywords: | large language models; Simulation modeling |
| JEL: | C63 C68 C61 D14 D83 D91 E20 E21 |
| Date: | 2025–11–25 |
| URL: | https://d.repec.org/n?u=RePEc:fip:fedkrw:102166 |
| By: | Giuseppe Ciccarone; Giovanni Di Bartolomeo; Valentina Peruzzi; Maria Luigia Signore |
| Abstract: | We model creativity as capital built by costly cognitive effort that complements social capital and is often accompanied by routines that economize attention and time. Higher effort costs deter entry into the creative state, while openness and trust increase the productivity of cognitive effort mainly through creative capital. Using lab-in-the-field data from an Italian music festival and a recursive bivariate probit, we find that costs depress creativity, whereas creativity strongly boosts festival collaboration, volunteering, and territorial cooperation. Consistent with a routinization perspective, the creativity–engagement link is stronger when participation occurs in more socially "structured" environments. To encourage creativity, policies should reduce cognitive frictions and improve the productivity of cognitive effort. |
| Keywords: | Creativity; cognitive effort; social capital; routinization; field experiment |
| JEL: | C93 C35 D01 Z13 O31 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:sap:wpaper:wp267 |
| By: | Halenta, David (Picyboo Cybernetics Inc., CA) |
| Abstract: | This paper proposes an integrative interdisciplinary framework to distinguish perceptual distortion from misconduct in legal evaluations of police culpability. The analysis examines how neurocognitive processes (brain-based cognitive functions) shape perception under high-stress conditions, particularly in police use-of-force incidents. Drawing on validated theories of predictive processing (the brain’s mechanism of anticipating sensory input based on prior experience) and evidence from perceptual neuroscience, the paper argues that some misconduct cases may involve genuine perceptual distortions (misinterpretations of sensory input caused by internal biases or stress-induced errors) rather than deliberate wrongdoing. It synthesizes research on the free-energy principle (a theory suggesting the brain reduces prediction error by either adjusting expectations or interpreting sensory input to fit those expectations), source monitoring theory (a model explaining how the brain identifies where a memory came from, and may confuse real sources or conflate internal and external origins), and emotional attention modulation (how emotional states influence what we notice, overlook, or prioritize in our environment) to explain how neurocognitive biases can produce vivid but erroneous threat perceptions. Rather than excusing harm, this approach aims to support more accurate allocation of responsibility between cognitive limitations and institutional failures in training, screening, and policy. The paper advocates for integrating neuroscientific insights into legal doctrine through reformed culpability standards that distinguish perceptual error from cognitive bias (systematic deviations from rational judgment caused by mental shortcuts or stress), while emphasizing accountability measures that reflect predictable human constraints. |
| Date: | 2025–09–24 |
| URL: | https://d.repec.org/n?u=RePEc:osf:lawarc:n32er_v1 |