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on Cognitive and Behavioural Economics |
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Issue of 2026–07–20
four papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Ennio Bilancini; Leonardo Boncinelli; Pablo Marcos-Prieto; Chiara Nardi |
| Abstract: | Using a one-shot Hawk–Dove game, we experimentally investigate the effect of different cognitive modes—intuitive (induced by Time Pressure), deliberative (by Time Delay), and motivated deliberative (by Time Delay combined with a written motivation)—on the propensity to behave hostilely (i.e., to play Hawk). We also examine whether cognitive modes affect responsiveness to payoff incentives by varying the harshness of conflict. Our results show that intuition significantly increases the likelihood of hostile behavior, while motivated deliberation reduces it. The harshness of conflict does not significantly affect behavior, and we find no evidence that its effect differs across cognitive manipulations. However, when restricting attention to subjects in the pooled delay conditions, the effect of harshness becomes statistically significant, indicating that responsiveness to payoff incentives may require deliberation. Consistently, we find that deliberation increases the likelihood that subjects best respond to their own beliefs |
| JEL: | C72 C90 D91 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:dis:wpaper:dis2603 |
| By: | Ryota IWAMOTO; Takunori ISHIHARA; Takanori IDA |
| Abstract: | This study empirically investigates the differences in risk preferences and reference dependence between humans and generative AI. We conduct a nationwide online survey of 4, 838 individuals and generate AI responses under identical conditions by using personas constructed from demographic attributes. The results show that in gain domains, both humans and the AI select risk-averse options and exhibit similar preference patterns. However, in loss domains, AI shows a stronger risk-loving tendency and responds more sharply to individual attributes such as gender, age, and income. We retrain the AI by fine-tuning it based on human choice data. After fine-tuning, the AI’s preference distribution moves closer to that of humans, with loss-related decisions showing the greatest improvement. Using the Wasserstein distance, we also confirm that fine-tuning reduces the behavioral gap between AI and humans. |
| Keywords: | bias, risk preference, reference dependence, generative AI, persona, fine-tuning, Wasserstein distance |
| JEL: | D91 C91 |
| Date: | 2026–07 |
| URL: | https://d.repec.org/n?u=RePEc:kue:epaper:e-25-006-v2 |
| By: | Stephen M. Nei (Department of Economics, University of Exeter); Pauline Vorjohann (Department of Economics, University of Exeter) |
| Abstract: | In the presence of communication frictions, individuals must draw inferences about the information they receive from others. Building on a simple theoretical model we experimentally investigate strategic information exchange in the presence of costly and coarse communication. While we find support for the model's prediction that individuals respond to the instrumental value of information, there is significant over-participation in costly information exchange. Moreover, participants fail to account for the strategic behavior of others, in line with the literature on selection neglect and failures of contingent reasoning. Failures occur in both directions, with uninformed agents refusing to use valuable information and well-informed agents blindly "following the crowd". In a subsequent online experiment, we consider potential drivers of the gap between theoretical predictions and experimental results, varying the complexity of the information updating task and introducing a non-social source of additional information. Our results suggest that people fail to use contextual information when interpreting the information they receive from peers, highlighting a way that the "wisdom of the crowd" can fail. |
| Keywords: | information exchange, self-selection, social information, wisdom of the crowd |
| JEL: | D83 D90 |
| Date: | 2026–07–10 |
| URL: | https://d.repec.org/n?u=RePEc:exe:wpaper:2609 |
| By: | Charles N. Noussair; Tauhidur Rahman |
| Abstract: | Burnout is a phenomenon that has received significant attention in the past several decades, with meta-analyses emphasizing its adverse consequences for performance. However, a major limitation of the current literature is that it is exclusively correlational. In this study, we conduct a laboratory experiment to acquire the first causal evidence regarding the effect of burnout on performance. We study the performance of students on a standardized test. In the treatment group, burnout is induced with a recall task, while in the control group it is not. The data show that inducing burnout improves performance on the test. At the same time, we do replicate the commonly observed negative correlation between burnout and performance. Together, these results suggest that the observed correlation is driven by the effect of poor performance on feelings of burnout. We conjecture that feelings of burnout are a reaction that serves to partially offset poor performance. |
| Keywords: | Burnout, performance, experiment |
| JEL: | C9 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:exc:wpaper:2026-02 |