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on Neuroeconomics |
| By: | Benjamin Enke; Thomas Graeber; Ryan Oprea; Jeffrey Yang |
| Abstract: | We report the results of over 30 experiments to study the elasticity of economic decisions with respect to fundamentals. Our experiments cover a broad range of domains, from choice and valuation to belief formation, from strategic games to generic optimization problems, involving investment, savings, effort supply, product demand, taxes, externalities, fairness, beauty contests, search, policy evaluation, forecasting and inference. We identify two general patterns. First, behavioral attenuation: in 93% of our experiments, the elasticity of decisions to variation in fundamentals decreases in subjects’ cognitive uncertainty about their best decision. Second, diminishing sensitivity: the elasticity of decisions decreases in the distance of the fundamental from ‘simple points’ at which the best decision is transparent, and this decrease in elasticities is again mirrored by an increase in cognitive uncertainty. These results suggest that cognitive uncertainty systematically predicts an attenuation of economic elasticities, and that there is less (or no) uncertainty and attenuation when problems are cognitively easy. We argue that attenuation links several known decision anomalies, and study its limits. |
| Keywords: | Behavioral attenuation, diminishing sensitivity, cognitive uncertainty, experiments |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:zur:econwp:487 |
| By: | David L. Dickinson |
| Abstract: | This paper introduces a novel Payoff-Trolley dilemma task, where other participantsÕ payoffs were at stake on the main or sidetrack in a trolley dilemma. Different scenarios varied the number of ÒothersÕ payoffsÓ on the sidetrack and main track in ways that helped identify more clearly immoral choices of commission and omission, and utilitarian choices. Study participants had also made hypothetical Trolley dilemma choices in a separate study 4-5 years prior, allowing for a direct comparison of hypothetical and consequential moral dilemma choices. One key finding is that past hypothetical choices are statistically significant predictors of present consequential choices in the Payoff-Trolley task. Also, we find that oneÕs degree of cognitive reflection is the most robust person-specific characteristic that predicts choicesÑhigher cognitive reflection predicts more utilitarian choices, a reduced likelihood of immoral acts of commission and omission, and it impacts oneÕs sensitivity to immoral choices for a given level of net-harm present in the scenario. These results hope to bridge a gap in our understanding of how choices in hypothetical moral dilemmas inform behaviors in consequential moral dilemmas. Key Words: Moral choice, experiments, Trolley dilemma, dark personality, cognitive reflection |
| JEL: | C9 D61 D91 I31 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:apl:wpaper:26-02 |
| By: | Marine Coupaud (ESSCA - Campus de Bordeaux - ESSCA - École Supérieure de Commerce d'Angers [Campus de Bordeaux] - ESSCA - ESSCA – École supérieure des sciences commerciales d'Angers = ESSCA Business School); Jean-Sébastien Lacam (ESSCA - Campus de Bordeaux - ESSCA - École Supérieure de Commerce d'Angers [Campus de Bordeaux] - ESSCA - ESSCA – École supérieure des sciences commerciales d'Angers = ESSCA Business School) |
| Abstract: | This study is the first to examine the impact of coopetition on workers' health. It uses the original Job Demands-Resources theoretical approach to show that coopetition may pose a health risk by generating burnout among workers due to high job demands. A parallel mediation model was used to investigate whether coopetition is related to emotional exhaustion via deterioration in working conditions. The quantitative analysis of data collected from 188 French workers confirms that the cognitive effort and emotional labor imposed on individuals to achieve coopetitive performance provokes emotional exhaustion. These results enrich the theoretical context of coopetition at the individual level: first, the performance of coopetition impacts the health of workers due to the efforts necessary to transform their work habits, reconfigure their resources and/or acquire new skills aligned with the strategy; secondly, workers must make an emotional regulation effort to align with relational strategies: if workers do not modify their authentic emotions, they prefer to engage more in the artificial presentation of emotions expected by stakeholders. Therefore, organizations engaged in coopetition must anticipate these additional cognitive and emotional efforts, such as through awareness, training and recovery programs for their managers and workers. |
| Keywords: | individual level, cognitive effort, emotional labor, emotional exhaustion, burnout, performance, Coopetition, Coopetition performance individual level cognitive effort emotional labor emotional exhaustion burnout |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:hal:journl:hal-05485967 |
| By: | Manuel A. Hidalgo-Pérez (Universidad Pablo de Olavide) |
| Abstract: | This paper models the impact of Generative AI on labor inequality by endogenizing technological complementarity (β) as a function of human capital (h) and supervision costs (σ). I introduce "Positional Capital" (W) -workers' material conditions'- as a key determinant of adaptation capacity, showing how precarity generates transition traps. The framework accounts for the "expert paradox", cognitive decapitalization through AI dependency, and the micro-macro productivity disconnect. Calibrated simulations indicate that the wage inequality ratio rises from 1.33x to 2.12x within a decade under current technological trajectories. |
| Keywords: | Generative AI, Technological Complementarity, Inequality, Positional Capital, Cognitive Decapitalization. |
| JEL: | J24 J31 O33 J62 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:pab:wpaper:26.01 |
| By: | Alan Piper; Min Zou; Ying Zhou |
| Abstract: | Using long running panel data spanning at least 15 years from Australia, Germany and the UK, this study investigates longitudinal age–wellbeing trajectories by the Big Five personality traits. We estimate within person (fixed effects) models separately for each country and for low/high trait subgroups, producing 30 distinct trajectories. Across all subgroups, we found the same ageing pattern: a decline in wellbeing into midlife, a clear midlife low and a subsequent recovery. However, the shape of this trajectory differs systematically across personality. Individuals high in conscientiousness, agreeableness, and emotional stability experience a steeper decline into midlife compared to those lower on these traits. In contrast, highly extraverted individuals show a more gradual early decline and a shallower midlife low, followed by a stronger recovery. These patterns are broadly consistent across the three countries. Openness, by comparison, is only weakly associated with well-being trajectories and exhibits inconsistent, country-specific patterns. |
| Keywords: | ageing, lifespan, personality traits, wellbeing |
| JEL: | I13 J14 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:diw:diwsop:diw_sp1236 |
| By: | Gunnar P. Epping; Andrew Caplin; Erik Duhaime; William R. Holmes; Daniel Martin; Jennifer S. Trueblood |
| Abstract: | Many operational AI systems depend on large-scale human annotation to detect rare but consequential events (e.g., fraud, defects, and medical abnormalities). When positives are rare, the prevalence effect induces systematic cognitive biases that inflate misses and can propagate through the AI lifecycle via biased training labels. We analyze prior experimental evidence and run a field experiment on DiagnosUs, a medical crowdsourcing platform, in which we hold the true prevalence in the unlabeled stream fixed (20% blasts) while varying (i) the prevalence of positives in the gold-standard feedback stream (20% vs. 50%) and (ii) the response interface (binary labels vs. elicited probabilities). We then post-process probabilistic labels using a linear-in-log-odds recalibration approach at the worker and crowd levels, and train convolutional neural networks on the resulting labels. Balanced feedback and probabilistic elicitation reduce rare-event misses, and pipeline-level recalibration substantially improves both classification performance and probabilistic calibration; these gains carry through to downstream CNN reliability out of sample. |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2603.11511 |