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on Cognitive and Behavioural Economics |
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Issue of 2026–10–05
five papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Bugarčić, Milica; Hanić, Hasan |
| Abstract: | The paper presents the results of a meta-analysis of empirical literature focused on the study of behavioral biases in financial decision-making. The findings indicate that biases such as loss aversion, present bias, status quo bias, overconfidence, and herding systematically shape both individual and collective financial behavior. Their influence is evident across diverse institutional and cultural contexts, confirming that behavioral deviations are structural rather than incidental features of economic behavior. The study highlights the need to integrate longitudinal and neuroeconomic approaches to achieve a deeper understanding of the interaction between cognitive, emotional, and contextual factors in decision-making processes. By incorporating psychological dimensions into economic theory, behavioral economics provides a more comprehensive and realistic framework for interpreting market dynamics and enhancing the quality of financial decision-making. |
| Keywords: | behavioral economics, behavioral finance, financial decion-making, cognitive biases |
| JEL: | D14 G53 I22 G41 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:esconf:343874 |
| By: | Malte Baader; Kai Barron; Anna Hochleitner; Jonas P. Kaiser |
| Abstract: | In two studies using AI-assisted qualitative interviews, we ask a representative sample of the German electorate (N = 1, 013) to reflect on the most important societal events of the last 15 years. We document that far-right supporters construct systematically different narratives about the recent past. First, they recall different events—with a strong focus on migration-related issues. Second, even when they do consider the same events, they are more likely to blame the establishment and describe events more negatively and with greater emotional intensity. We further document that these narratives are associated with policy preferences, and that farright supporters are more inclined than supporters of other parties to prioritize cultural over economic issues. Together, our findings reveal that partisan divides extend to how citizens remember and interpret shared national experiences. |
| Keywords: | Polarization, Narratives, AI-Assisted Qualitative Interviews, Explanations, Mental Models |
| JEL: | D01 C90 D84 |
| Date: | 2026–09–22 |
| URL: | https://d.repec.org/n?u=RePEc:bdp:dpaper:0111 |
| By: | Malte Baader (Norwegian School of Economics, FAIR); Kai Barron (ESCP Business School, WZB Berlin); Anna Hochleitner (Norwegian School of Economics, FAIR, SNF); Jonas P. Kaiser (TU Berlin, Berlin School of Economics) |
| Abstract: | In two studies using AI-assisted qualitative interviews, we ask a representative sample of the German electorate (N = 1, 013) to reflect on the most important societal events of the last 15 years. We document that far-right supporters construct systematically different narratives about the recent past. First, they recall different events—with a strong focus on migration-related issues. Second, even when they do consider the same events, they are more likely to blame the establishment and describe events more negatively and with greater emotional intensity. We further document that these narratives are associated with policy preferences, and that far right supporters are more inclined than supporters of other parties to prioritize cultural over economic issues. Together, our findings reveal that partisan divides extend to how citizens remember and interpret shared national experiences. |
| Keywords: | polarization; narratives; ai-assisted qualitative interviews; explanations; mental models; |
| JEL: | D01 C90 D84 |
| Date: | 2026–09–18 |
| URL: | https://d.repec.org/n?u=RePEc:rco:dpaper:586 |
| By: | Zhen Ren; Yinxing Li |
| Abstract: | Whether success comes from talent or from luck is an old debate. The Talent-versus-Luck (TvL) model drew wide attention with a blunt answer: luck decides who gets rich, and the richest are rarely the most talented. But its agents are passive.They stay put and take whatever luck reaches them, while real people move toward chances and away from trouble. To our knowledge we are the first to let TvL agents act and learn, using Double DQN, in a two-by-two design that crosses learning (off or on) with the kind of luck (asymmetric or symmetric). Two results follow. The new one is behavioral. Under TvL’s original luck, where a chance is more likely to cost than to pay, the agents learn on their own to avoid opportunity altogether, a model version of “lying flat”. It is a rational choice rather than laziness, and the proof is that the same reward makes them seek events once luck is made fair. The learning also gives the shape of that retreat, which the arithmetic does not: the agents keep moving, meet almost nothing, and talent stops leaving any mark on wealth. The second result is that TvL’s core findings survive. The richest are still the luckiest rather than the most talented, and talent predicts wealth only once luck exposure is held fixed, because talent is simply the rate at which luck is turned into gains. Within the bounded agency modeled here, acting protects what an agent starts with but cannot make effort beat luck. |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:toh:dssraa:154 |
| By: | Isaac Cheah; Ethan Pancer; Jianping Huang; Aimee Pink; Béatrice Parguel (DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique); Fong Keng-Highberger |
| Abstract: | Research examining artificial intelligence's (AI) role in food choice largely asks whether AI helps people make better decisions, treating it as an information source that improves the accuracy or personalization of recommendations. We suggest that this framing captures one point on a broader continuum of how AI impacts food decisions. AI can act, often simultaneously, as an advisor consulted for information, a decision partner that actively shares in constructing the choice, or an ambient influence that shapes what appears desirable and available before deliberate choice begins. As AI becomes more conversational and more embedded across the food environment, this continuum is increasingly populated in less visible regions, yet the field remains concentrated on the advisor role. We focus on the decision-partner role, where AI participates in value construction, goal clarification, attribute weighting, and choice justification, raising questions about agency, responsibility, and delegation. We articulate four mechanisms that describe this participation, namely preference offloading, responsibility reallocation, confidence calibration, and food-decision skill erosion. Because food choice is frequent, often habitual, and commonly involves trade-offs among hedonic and utilitarian goals, it is an ideal domain in which these process-level effects can compound. We draw on evidence from consumer psychology, automation research, and cognitive science, noting where boundary conditions remain untested in food contexts, and close with a research agenda organized around agency, reliance, responsibility, confidence, and cognitive effort. When AI shares in constructing a food choice, the values being optimized may no longer be entirely the consumer's own. |
| Keywords: | Artificial intelligence, Food choice, Decision-making, Consumer psychology, Algorithm reliance |
| Date: | 2027–01 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05752256 |