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on Cognitive and Behavioural Economics |
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Issue of 2026–03–09
four papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Alexander Bertermann; Hannah Schildberg-Hörisch |
| Abstract: | This paper provides the first evidence that children’s economic preferences vary systematically with parental mental health. Using experimentally elicited measures of economic preferences from more than 4, 500 children in Bangladesh, we document that children of parents with indications of mental illness are less prosocial but more patient than their peers with mentally healthy parents. Attitudes toward risk remain unchanged. We discuss potential pathways through which parental mental health may influence the formation of children’s preferences, documenting that children of parents with indication of mental illness assume greater responsibilities within the family, experience less parental involvement, and are exposed to a more adverse home environment. |
| Keywords: | mental health, social preferences, risk preferences, patience, origins of preferences, experiments with children, Bangladesh |
| JEL: | C91 D01 D10 I10 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12441 |
| By: | Giuseppe Ciccarone; Giovanni Di Bartolomeo; Valentina Peruzzi; Maria Luisa 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: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:sap:wpaper:wp272 |
| By: | Rilke, Rainer (WHU - Otto Beisheim School of Management); Sliwka, Dirk (University of Cologne) |
| Abstract: | A large body of research across management, psychology, accounting, and economics shows that subjective performance evaluations are systematically biased: ratings cluster near the midpoint of scales and are often excessively lenient. As organizations increasingly adopt large language models (LLMs) for evaluative tasks, little is known about how these systems perform when assessing human performance. We document that, in the absence of clear objective standards and when individuals are rated independently, LLMs reproduce the familiar patterns of human raters. However, LLMs generate greater dispersion and accuracy when evaluating multiple individuals simultaneously. With noisy but objective performance signals, LLMs provide substantially more accurate evaluations than human raters, as they (i) are less subject to biases arising from concern for the evaluated employee and (ii) make fewer mistakes in information processing closely approximating rational Bayesian benchmarks. |
| Keywords: | performance evaluation, large language models, signal objectivity, algorithmic judgment, Gen-AI |
| JEL: | J24 J28 M12 M53 |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18371 |
| By: | Douglas K.G. Araujo (Banco Central do Brasil); Harald Uhlig (University of Chicago, CEPR and NBER) |
| Abstract: | As Large Language Models (LLMs) are increasingly tasked with autonomous decisionmaking, understanding their behavior in strategic settings is crucial. We investigate the choices of various LLMs in the Ultimatum Game, a setting where human behavior notably deviates from theoretical rationality. We conduct experiments varying the stake size and the nature of the opponent (Human vs. AI) across both Proposer and Responder roles. Three key results emerge. First, LLM behavior is heterogeneous but predictable when conditioning on stake size and player types. Second, while some models approximate the rational benchmark and others mimic human social preferences, a distinct “altruistic†mode emerges where LLMs propose hyper-fair distributions (greater than 50%). Third, LLM Proposers forgo a large share of total payoff, and an even larger share when the Responder is human. These findings highlight the need for careful testing before deploying AI agents in economic settings. |
| Keywords: | Ultimatum Game, LLM, AI Agents, Behavioral Economics, Algorithmic Decision Making |
| JEL: | C70 C90 D91 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:bfi:wpaper:2026-29 |