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on Neuroeconomics |
| By: | Kikken, Max (Maastricht University); Künn, Steffen (Maastricht University) |
| Abstract: | This paper studies how short-term variations in fine particulate matter (PM2.5) pollution affects cognitive performance across tasks of varying complexity. While prior work shows that pollution impairs performance in highly demanding cognitive settings, it remains unclear whether these effects extend to simpler tasks. We examine this question using data from official Rubik’s Cube tournaments in the United States and India. Solving different cube sizes provides a natural proxy for task complexity, while solving time measures cognitive performance. To identify causal effects, we exploit exogenous variation in local PM2.5 generated by wind direction. We find that PM2.5 pollution has negligible effects on simple tasks but significantly slows performance on complex ones for tournaments in the United States. In India, where baseline PM2.5 levels are substantially higher, we find similar effect patterns but none of the effects are statistically significant. We show that this pattern is explained by diminishing marginal sensitivity to short-term PM2.5 shocks as baseline PM2.5 pollution levels increase. Our findings provide causal evidence that the cognitive costs of PM2.5 pollution depend critically on task complexity. |
| Keywords: | cognitive performance, air pollution, task complexity |
| JEL: | D64 D91 Z13 P16 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18656 |
| By: | Eunsik Chang; María Padilla-Romo; Cecilia Peluffo |
| Abstract: | This paper estimates the effects of early academic rank in elementary school on later cognitive and noncognitive outcomes in the context of Mexico. We use linked administrative records to compare students with similar third-grade achievement but different ordinal positions. These rank differences arise from idiosyncratic variation in the achievement distributions of elementary-school cohorts. We find that a higher third-grade rank increases performance on a high-stakes high school admission exam. Both broader school-cohort rank and classroom rank contribute to this achievement gain when estimated jointly. Higher rank leads to more selective high school choices and improves self-reported measures of self-perception, academic aspirations, classroom responsibility, learning strategies, and teamwork attitudes by the end of ninth grade. We also provide evidence that higher elementary school rank improves students' high school placement outcomes. |
| JEL: | I21 I25 J24 |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35267 |
| By: | Po Han Teo |
| Abstract: | Researchers have started using LLM agents in place of human subjects in behavioural and political-science experiments, often as a cheaper substitute for laboratory pools. The substitution does not hold up in strategic settings: humans and LLMs reliably make different choices, and neither fine-tuning on human response data nor persona conditioning has closed the gap. The behavioural-economics literature has, since Simon's introduction of bounded rationality, modelled human strategic behaviour as a classical baseline plus an additive correction term $\delta$. The framework proposed here reads $\delta$ as the mathematical signature of bounded computation: the gap between what an unboundedly-rational agent would compute and what a computationally bounded agent actually produces. For canonical games whose solutions are present in standard training corpora, LLMs retrieve and recombine corpus material, bypassing the bound that produces $\delta$ in humans. The framing extends to reasoning-distilled models through cognitive-hierarchy theory: their accessible level-$k$ strategic reasoning is bounded by compute budget and context length rather than by the cognitive constraints that bound humans, and the $\delta$ they produce, if any, carries different structural signatures. Four operational tests (conditional dependence, distributional asymmetry, path-dependence under repetition, and paraphrase-robustness) are proposed to discriminate human-shaped $\delta$ from LLM-shaped $\delta$. A moderator prediction is that $|\delta|$ scales with peer-signal individuation in the decision environment, with a quantitative bound of Cohen's $d \geq 0.5$ between named-opponent and aggregate-opponent settings. |
| Date: | 2026–05 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2605.26437 |
| By: | Bernard, Rochelle (NeuroSync LLC) |
| Abstract: | Emerging evidence in neurodevelopmental research increasingly indicates that individuals diagnosed with Autism Spectrum Disorder (ASD) and Attention-Deficit/Hyperactivity Disorder (ADHD) may demonstrate amplified pattern recognition capabilities as a core cognitive feature. Historically, these capacities have been framed as deficits within prevailing diagnostic and clinical traditions rather than as expressions of differentiated cognitive architecture. However, the field has yet to develop a framework that accounts for the distinct types of capabilities across these profiles or the developmental conditions that shape their expression. The present paper introduces the Neurodivergent Intelligence Framework (NIF), proposing that individuals within these diagnostic categories exhibit two broad pattern recognition orientations: one externally oriented, functioning primarily through real-time environmental input, and the other internally oriented, operating through subconscious and reflective processing. Within each orientation, the domain toward which pattern recognition is directed produces distinct processing combinations, yielding four total combinations with different functional strengths, neurochemical dependencies, and vulnerability profiles. These differences are proposed to produce differential responses to early developmental environments, with long-term risk emerging when those environments fail to meet the cognitive demands of the individual’s specific architecture. Beginning in early developmental windows, exposure to social dynamics, parenting styles, and institutional contexts may shape neuroplasticity in ways that either support or suppress these cognitive profiles, with suppression potentially producing neurochemical consequences across dopamine, serotonin, oxytocin, and cortisol systems. When this suppression becomes chronic across critical developmental periods, the framework proposes that resulting cognitive and neurochemical strain may increase risk for impulsivity-driven coping responses and psychiatric comorbidity. In cases where these suppressive patterns coincide with trauma exposure and prolonged environmental misalignment, the framework further proposes elevated risk for conditions including schizophrenia, OCD, and bipolar disorder. The NIF also identifies elevated substance use disorder risk as a downstream consequence of chronic architectural misidentification and unmet neurochemical demands. |
| Date: | 2026–06–04 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:hx72k_v1 |
| By: | Dr Kiran Mishra, Dr Kiran Mishra; Dr Rajkumari Ghosh, Dr Rajkumari Ghosh |
| Abstract: | For most of human history, output has been constrained by effort. More goods required more labor; more knowledge required more study; more creative work required more time. This relationship — effort as the binding input to production — has shaped economic theory, labor markets, and moral intuitions about value in equal measure. The emergence of capable generative AI systems introduces a structural break in this relationship, enabling the near-instantaneous production of text, code, imagery, analysis, and decision-support at costs that approach zero regardless of the complexity of the output.This paper introduces the concept of the zero-marginal-effort economy to describe an emerging productive regime in which the cognitive and creative labor traditionally required to generate a unit of output is largely, and in some domains wholly, displaced by machine inference. We argue that this decoupling is categorically distinct from prior waves of automation, which reduced physical effort while leaving cognitive effort largely intact. The current transition compresses both simultaneously, and does so across sectors previously considered immune to mechanization. We examine three consequences of this shift. First, the collapse of effort-based pricing and the pressure it places on professional service markets, creative industries, and knowledge work. Second, the redistribution of scarce inputs: as cognitive effort becomes abundant, human judgment, taste, accountability, and relational trust emerge as the binding constraints on value creation. Third, the challenge this transition poses to existing frameworks of economic distribution, which have historically relied on the scarcity of human labor as the primary mechanism through which individuals participate in market economies. |
| Keywords: | generative AI, labor economics, marginal cost, cognitive automation, productivity, economic distribution, future of work. |
| JEL: | O3 O31 O43 O47 |
| Date: | 2025–05–09 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128761 |