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on Cognitive and Behavioural Economics |
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Issue of 2026–05–11
four papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Bertermann, Alexander; Schildberg-Hörisch, Hannah |
| 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:zbw:dicedp:340844 |
| By: | Benjamin Sheehan (IUJ Research Institute, International University of Japan); Pramodhya Dissanayake; Janani Kumarathunga |
| Abstract: | Prosocial behavior is essential for a functioning society. Despite increasing urbanization, the impact of population density on prosocial behavior remains unclear. Prior research offers conflicting predictions. Some research suggests that increased density might increase prosocial behavior via increased opportunities to help others. Contrasting accounts suggest that increased density decreases prosocial behavior via anonymity and competition for resources. Across two correlational designs (N = 400), the present research suggests a small, positive relationship between density and self-reported prosocial behavior. However, a third study (N = 482) which manipulates perceived density, alongside other relevant constructs, suggests that strong vs. weak social norms and high vs. low interdependence are more proximal influences of prosocial behavior, than population density itself. |
| Keywords: | Prosocial behavior, Population density, Social norms, Interdependence, Visibility |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:iuj:wpaper:ems_2026_06 |
| By: | Damm, Cara; Bauer, Kevin; Hett, Florian; Pelizzon, Loriana |
| Abstract: | The shift from information retrieval (keyword-based search engines) to information synthesis (generative AI) constitutes a fundamental change in how people inform themselves online. We investigate how this shift impacts investment behavior using an incentivized online experiment (N = 374), in which we vary whether participants have access to keyword-based search engines, an LLM-based chatbot, or no additional information source. We find that LLMs facilitate participation in the stock market. Participants with access to an LLM when making investment decisions are significantly more likely to enter the stock market and to remain invested compared to those with access to keyword-based search engines or no further information. Our experiment suggests that perceived difficulty of stock market participation decreases and confidence in these choices increases when using an LLM. However, we also document a substantial risk. Access to LLMs enables individuals to confirm and strengthen experimentally induced beliefs. Even when the chatbot itself is not biased, users can prompt the model to validate beliefs they want to hold. Overall, our findings suggest that while LLMs can reduce participation frictions and encourage stock market investments, their effectiveness in confirmation-seeking can also have detrimental consequences. Consequently, these results highlight the critical need for consumer protection frameworks and financial literacy programs that specifically address the unique dynamics of human-AI interaction in modern retail investing. |
| Keywords: | Large Language Models, Belief Formation, Motivated Reasoning, Financial Decision Making, Robo-Advisors, Stock Market Participation |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:safewp:340833 |
| By: | Federico Atzori (Sapienza University); Luca Corazzini (University of Milan - Bicocca); Valeria Maggian (Ca’ Foscari University of Venice); Filippo Pavesi (LIUC University); Massimo Scotti (LIUC University) |
| Abstract: | We investigate how generative AI shapes creative performance and human-AI interaction in an open-ended writing task that employs a laboratory experiment in which participants are randomly assigned to either receive access to a large language model (ChatGPT-4.2) or not. Creative performance is measured by the average score assigned by independent evaluators recruited through the Prolific platform, and detailed logs of human-AI interaction are analyzed to measure AI use, prompting intensity, ideation requests, and the textual overlap between AI outputs and participants' final writings. Three main results emerge. First, AI access increases performance, but the gain is entirely driven by active use: participants with access who do not submit queries perform no better than those without AI. Second, the relationship between interaction intensity and performance is concave, peaking at roughly eight queries, consistent with iterative exploration rather than mechanical copying. Third, structural mediation analyses show that ideation requests affect performance primarily indirectly, by increasing downstream incorporation of AI-generated language; the direct effect of requesting an idea from the AI is negligible once execution-stage reliance is accounted for. We further document heterogeneity in AI reliance: cultural capital (proxied by books owned) predicts lower AI use, while prior AI exposure predicts higher use. By contrast, incentive schemes have limited effects on both outcomes and AI-related behaviors. |
| Keywords: | Human-AI Interaction; Creativity; Generative AI; Laboratory Experiment |
| JEL: | C91 D83 J24 O33 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ven:wpaper:2026:16 |