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on Cognitive and Behavioural Economics |
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Issue of 2026–01–26
six papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Ciril Bosch-Rosa; Muhammed Bulutay; Bernhard Kassner |
| Abstract: | This paper integrates overprecision—a form of overconfidence where individuals overestimate the accuracy of their beliefs—into a canonical rational inattention model. We show that overprecision distorts belief updating directly by biasing the perceived value of new information and indirectly by amplifying the impact of attention costs. We test the model’s predictions in a pre-registered 2 x 2 belief-updating experiment that manipulates overprecision and information costs. The results confirm that overprecision reduces updating and that higher information costs lead to lower responsiveness to signals. While our pre-registered analysis finds no support for the predicted interaction between overprecision and attention costs, a more granular specification shows that the effect of information costs on belief updating is stronger for overprecise participants. These findings suggest that what appears as rational inattention partly reflects irrational inattention arising from misperceived prior accuracy, underscoring the need to distinguish informational frictions from cognitive biases when modeling attention. |
| Keywords: | Overconfidence, Rational inattention, Belief updating, Overprecision |
| JEL: | C83 D91 G41 |
| Date: | 2026–01–16 |
| URL: | https://d.repec.org/n?u=RePEc:bdp:dpaper:0086 |
| By: | Felix Chopra (Frankfurt School of Finance & Management, CESifo); Ingar Haaland (NHH Norwegian School of Economics, FAIR, CEPR, NTNU); Nicolas Roever (University of Cologne); Christopher Roth (University of Cologne and ECONtribute, Max Planck Institute for Behavioral Economics, CEPR, NHH) |
| Abstract: | We test the effectiveness of different AI-delivered conversation protocols to increase people’ motivation for change. In a large-scale experiment with 2, 719 social media users, we randomly assign participants to a control conversation or one of three treatment arms: two Motivational Interviewing protocols promoting self-persuasion (change focus or decisional balance) and a direct persuasion protocol providing unsolicited advice and information. All conversations are led by an AI interviewer, enabling standardized delivery of each protocol at scale. Our results show that all three interventions significantly increase motivation for change and the perceived costs of social media use, with change-focused self-persuasion yielding the largest effects. These effects persist and translate into self-reported reductions in social media use more than two weeks after the intervention. Our findings illustrate how AI-led conversations can serve as a scalable platform both for delivering behavioral interventions and for testing what makes them effective by systematically varying how conversations are conducted. |
| Keywords: | AI interviews, Scaling, Motivation, Persuasion, Social Media, Beliefs |
| JEL: | C90 D83 D91 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:385 |
| By: | Nattanicha Chairassamee; Kanokwan Chancharoenchai; Pattrapa Tangtatswas |
| Abstract: | The growing financial fraud issue has negatively impacted the psychological well-being of the general public, particularly those who have fallen victim to such scams. This study aims to collect data to examine and understand the factors influencing decision-making and victimization in various types of online financial fraud in Thailand. By using the framing effect through greedy emotions and time pressure, our results indicate that the emotions experienced during scam encounters play a significant role in determining online financial fraud victimization. Since emotions directly influence System 1 decision-making, our study suggests that merely educating and building public awareness may not be effective in preventing long-term online scam victimization. |
| Keywords: | Emotion; Financial decision; Online financial scam; Personality traits |
| JEL: | D91 G41 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:pui:dpaper:245 |
| By: | Rainer Michael Rilke (WHU - Otto Beisheim School of Management); Dirk Sliwka (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: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:384 |
| By: | Giuseppe Attanasi; Giuseppe Ciccarone; Giovanni Di Bartolomeo |
| Abstract: | We propose a simple model that connects creativity to rational inattention, introducing a new formal channel through which imprecise information generates creative benefits. While imprecision usually entails costs, it can also make creativity a complementary dimension of information acquisition, reshaping the trade-off between attention and decision quality. Our main result is that creativity reduces the effective cost of information processing. |
| Keywords: | Selective Attention; Information Processing Costs; Cognitive Constraints; Innovation |
| JEL: | D90 O31 D80 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:sap:wpaper:wp268 |
| By: | Avner Seror |
| Abstract: | We study choice among lotteries in which the decision maker chooses from a small library of decision rules. At each menu, the applied rule must make the realized choice a strict improvement under a dominance benchmark on perceived lotteries. We characterize the maximal Herfindahl-Hirschman concentration of rule shares over all locally admissible assignments, and diagnostics that distinguish rules that unify behavior across many menus from rules that mainly act as substitutes. We provide a MIQP formulation, a scalable heuristic, and a finite-sample permutation test of excess concentration relative to a menu-independent random-choice benchmark. Applied to the CPC18 dataset (N=686 subjects, each making 500-700 repeated binary lottery choices), the mean MRCI is 0.545, and 64.1% of subjects reject random choice at the 1% level. Concentration gains are primarily driven by modal-payoff focusing, salience-thinking, and regret-based comparisons. |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2601.02964 |