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on Neuroeconomics |
| By: | Ali Moghaddasi Kelishomi (Loughborough University); Daniel Sgroi (University of Warwick); Andis Sofianos (Durham University) |
| Abstract: | How does rationality shape cooperation in strategic settings? We study this question in a laboratory experiment that links individual rationality, measured by consistency with the generalized axiom of revealed preference, to behaviour in an indefinitely repeated Prisoner’s Dilemma. Participants are grouped by pre-measured rationality before interacting repeatedly. We find that higher rationality substantially increases cooperation and payoffs. This effect operates through a novel mechanism: more rational individuals make fewer implementation errors when executing their intended strategies, thereby sustaining cooperative outcomes. By contrast, higher cognitive ability also promotes cooperation and higher payoffs, but through a distinct channel—reducing strategic errors in responding optimally to others’ actions. Our results provide the first experimental evidence linking rationality to cooperation via decision-making errors, and clarify the distinct roles of rationality and intelligence in shaping strategic behaviour. Together, the findings offer a unified account of how cognitive constraints affect cooperation in repeated games |
| Keywords: | Repeated Prisoners Dilemma, Cooperation, Rationality, Intelligence, Learning, Strategy Errors JEL codes: C73, C91, C92, D83 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:wrk:warwec:1630 |
| By: | Rai, Sudhanshu (None) |
| Abstract: | A growing empirical literature uses pre-built "AI occupational exposure" scores (most prominently the AI Occupational Exposure (AIOE) index of Felten, Raj, and Seamans (2021) and the GPT-4 task-exposure measure of Eloundou et al. (2024)) as occupation-level treatments or predictors for AI's labor-market effects. We show that two of the most-cited scores, AIOE and Eloundou's GPT-4 measure, substantially re-label cognitive task content rather than capturing AI-specific exposure, a construct-validity problem that does not extend to a third, differently-built score (Webb 2020, patent-based). This note horse-races these three measures against transparent cognitive/manual task-content indices and the established Autor–Dorn Routine Task Intensity (RTI) measure. Across 773 occupations: (i) the ten-plus AI "applications" underlying AIOE collapse to a single factor (first principal component ≈ 88%); (ii) AIOE and Eloundou each correlate strongly with a cognitive-ability index (+0.85 / +0.70) and negatively with a manual-ability index (−0.91 / −0.83), correlate 0.86 with each other, but only moderately with RTI (−0.33 / −0.30): the confound is specifically cognitive ability level, not the classic routine-task polarization axis; (iii) each score's positive wage association reverses sign controlling for cognitive content but is barely affected by controlling for RTI; and (iv) the much-cited pre-ChatGPT "AI foresight" wage-divergence pattern collapses to near-zero under the cognitive control (not under the RTI control). Critically, this collapse is not universal: Webb's patent-text-overlap score is essentially uncorrelated with AIOE (r=0.03) and Eloundou (r=−0.03), only weakly related to cognitive content (r=0.13), and its modest wage associations do not reverse under cognitive control. The cognitive-content collapse is a signature of how a score is built: subjective crowd-relatedness ratings (AIOE) or LLM/human task judgments (Eloundou), not an inherent property of occupational AI-exposure measurement. Studies using relatedness- or judgment-based exposure scores should control for cognitive content and re-interpret accordingly; a patent-based measure is not shown here to have the same problem, though its own construct validity is untested. |
| Date: | 2026–09–16 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:47gfn_v1 |
| By: | Kim, MinSub; Park, Yoonsoo |
| Abstract: | Strengthening workforce competencies and labor productivity has become increasingly important amid rapid advances in automation and industrial transformation. However, international comparative findings show that the cognitive skills of Korean workers decline steeply with age, raising concerns about labor productivity. While multiple factors contribute to this decline, the primary driver appears to be a wage structure that provides insufficient incentives for skills development. To promote labor productivity growth, wage systems based on skills and performance need to be expanded, alongside the provision of broader opportunities for upskilling and reskilling. |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:kdifoc:343515 |
| By: | Kern, Christoph |
| Abstract: | Why do we use models to predict the most likely outcome? Integrating model predictions into high-stakes decision-making pipelines can amplify biases and introduce flawed decisions when human caseworkers and models compete over the same prediction target. Learning to anticipate the most likely outcome risks reproducing biases embedded in historical data, while delivering such predictions in human-AI decision-making raises issues of cognitive biases and algorithmic aversion versus overreliance. In this perspective, I argue for Antagonistic Predictions, a structurally different paradigm where models are tasked to act as sparring partners that challenge human pre-conceptions and show reachable futures rather than anticipating the most common pattern. I envision instantiations of Antagonistic Predictions that can counteract historical disadvantages, social stereotyping, and cognitive biases and sketch potential implementations in predictive ML and generative AI settings. |
| Date: | 2026–09–25 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:nbmq8_v1 |
| By: | Philipp Hansmeier (Paderborn University) |
| Abstract: | Nowadays, users tend to misperceive the ontological nature of generative AI as quasi-social, driven by AI’s agency and humanlike behavior. However, such experience seems “simulated”—a cognitive bias that attributes authenticity to interactions that never truly occurred—and remains insufficiently explored in current experience research. To address this issue, we conduct a theoretical literature review across information systems, marketing, and service disciplines, complemented by mini case studies drawing on a qualitative analysis of 2, 466 user reviews of Google’s Gemini and OpenAI’s ChatGPT. Our findings indicate that generative AI distorts affective, relational, symbolic, and cognitive experience dimensions, whereas sensorial and physical dimensions seem less affected. Theoretically, we contribute an initial framework that distinguishes authentic from “simulated” experience and treats “simulated” experience as biasing mechanism in the human mind. Our groundwork supports future studies in measuring experience while controlling this cognitive bias. Moreover, this study helps practitioners stay vigilant while designing GenAI-experience. |
| Keywords: | Generative artificial intelligence, Customer experience, Case study, Theory building |
| JEL: | L86 |
| Date: | 2026–09 |
| URL: | https://d.repec.org/n?u=RePEc:pdn:dispap:184 |