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on Neuroeconomics |
| By: | Alina Machado (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Fedora Carbajal (Universidad de la República (Uruguay). Facultad de Ciencias Económicas y de Administración. Instituto de Economía); Lucía Alvarez Núñez (Universidad de la República (Uruguay). Facultad de Psicología. Instituto de Fundamentos y Métodos en Psicología); Cecilia Rodríguez Ingold (University of North Carolina at Chapel Hill); Alejandro Maiche (Universidad de la República (Uruguay). Facultad de Psicología); Alejandro Vásquez Echeverría (Universidad de la República (Uruguay). Facultad de Psicología) |
| Abstract: | Personality traits, other psychological factors, and cognitive abilities have consistently been related to academic performance. However, there is limited empirical evidence on how these factors jointly influence dropout decisions. This study examines the relationship between big-five personality traits, consideration of future consequences, and fluid intelligence on dropout decisions among second-year students in the Economics and Psychology colleges at Uruguay’s largest university. Using data from the 2018 student cohort and controlling for a range of sociodemographic and economic variables, we employed Probit and Multinomial Models to analyze dropout patterns. Our findings reveal that personality traits and fluid intelligence are significantly associated with dropout decisions, though their effects vary across different academic disciplines. Moreover, we identify distinct patterns in the influence of personality traits and cognitive abilities on instructional versus systemic dropout. These findings contribute to the growing literature on psychological determinants of educational outcomes and offer insights for higher education policy aimed at improving student retention. |
| Keywords: | higher education, instructional and systemic dropout, personality, fluid intelligence, academic trajectories |
| JEL: | I20 I21 I23 |
| Date: | 2025–04 |
| URL: | https://d.repec.org/n?u=RePEc:ulr:wpaper:dt-12-25 |
| By: | Pietro Bini; Lin William Cong; Xing Huang; Lawrence J. Jin |
| Abstract: | Do generative AI models, particularly large language models (LLMs), exhibit systematic behavioral biases in economic and financial decisions? If so, how can these biases be mitigated? Drawing on the cognitive psychology and experimental economics literatures, we conduct the most comprehensive set of experiments to date$-$originally designed to document human biases$-$on prominent LLM families across model versions and scales. We document systematic patterns in LLM behavior. In preference-based tasks, responses become more human-like as models become more advanced or larger, while in belief-based tasks, advanced large-scale models frequently generate rational responses. Prompting LLMs to make rational decisions reduces biases. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.09362 |
| By: | Raman Ebrahimi; Sepehr Ilami; Babak Heydari; Isabel Trevino; Massimo Franceschetti |
| Abstract: | Standard models of bounded rationality typically assume agents either possess accurate knowledge of the population's reasoning abilities (Cognitive Hierarchy) or hold dogmatic, degenerate beliefs (Level-$k$). We introduce the ``Connected Minds'' model, which unifies these frameworks by integrating iterative reasoning with a parameterized network bias. We posit that agents do not observe the global population; rather, they observe a sample biased by their network position, governed by a locality parameter $p$ representing algorithmic ranking, social homophily, or information disclosure. We show that this parameter acts as a continuous bridge: the model collapses to the myopic Level-$k$ recursion as networks become opaque ($p \to 0$) and recovers the standard Cognitive Hierarchy model under full transparency ($p=1$). Theoretically, we establish that network opacity induces a \emph{Sophisticated Bias}, causing agents to systematically overestimate the cognitive depth of their opponents while preserving the log-concavity of belief distributions. This makes $p$ an actionable lever: a planner or platform can tune transparency, globally or by segment (a personalized $p_k$), to shape equilibrium behavior. From a mechanism design perspective, we derive the \emph{Escalation Principle}: in games of strategic complements, restricting information can maximize aggregate effort by trapping agents in echo chambers where they compete against hallucinated, high-sophistication peers. Conversely, we identify a \emph{Transparency Reversal} for coordination games, where maximizing network visibility is required to minimize variance and stabilize outcomes. Our results suggest that network topology functions as a cognitive zoom lens, determining whether agents behave as local imitators or global optimizers. |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2602.10053 |