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on Neuroeconomics |
| By: | Zhongjie Jiang |
| Abstract: | Although synthetic data is widely promoted as a remedy, its prevailing production paradigm -- one optimizing for statistical smoothness -- systematically removes the long-tail, cognitively grounded irregularities that characterize human text. Prolonged training on such statistically optimal but cognitively impoverished data accelerates model collapse. This paper proposes a paradigm shift: instead of imitating the surface properties of data, we simulate the cognitive processes that generate human text. We introduce the Prompt-driven Cognitive Computing Framework (PMCSF), whose core consists of a Cognitive State Decoder (CSD) that reverse-engineers unstructured text into structured cognitive vectors, and a Cognitive Text Encoder (CTE) that re-materializes these states into text enriched with human-typical imperfections via mathematically defined Cognitive Perturbation Operators. The framework is validated through a two-stage objective evaluation pipeline. First, in cognitive codec verification, CTE text yields a Jensen-Shannon divergence of 0.0614 from human text (vs. 0.4431 for standard LLM output), passes double-blind professional media review, and achieves an intraclass correlation coefficient ICC > 0.9 for cognitive profile alignment across heterogeneous models. Second, in functional gain evaluation, isomorphic stress tests in the A-share market show that strategies incorporating CTE-generated data reduce maximum drawdown by 47.4% during the 2015 crash and deliver 8.6% Defensive Alpha, exceeding transaction costs by a factor of 33. Our findings demonstrate that modelling human cognitive limitations -- not copying surface data -- enables synthetic data with genuine functional gain, offering a viable technical pathway toward resolving the AI data-collapse crisis. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.01354 |
| By: | Maria Bigoni; Andrea Ichino; Aldo Rustichini; Giulio Zanella |
| Abstract: | Machines have at times equalized physical strength by substituting for human effort, and at other times amplified these differences. Artificial intelligence (AI) may likewise narrow or widen disparities in cognitive ability. Recent evidence from the Information and Communication Technology (ICT) revolution suggests that computers increased inequality by education but reduced it by cognitive ability. Early research on generative AI shows larger productivity gains for less-skilled than for high-skilled workers. Whether AI ultimately acts as an equalizer or an amplifier of human cognitive differences is especially crucial for education systems, which must decide whether -- and how -- to allow students to use AI in coursework and exams. This decision is urgent because employers value workers who can leverage AI effectively rather than operate independently of it. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2512.03902 |
| By: | Shohei Yamamoto; Rebecca McDonald; Daniel Read |
| Abstract: | This paper examines how outcome modality in intertemporal choice influences time preferences and whether the process differs across cultures, specifically Japan and the United States. Uni-modal choices are those when the outcomes being compared over time are very similar, and cross-modal choices are those when the outcomes are very different. The cross-modal effect, previously shown in the U.S., is that there is greater patience in cross-modal decisions. In Experiment 1, we employed a between-participants design, in which participants either made uni-modal or cross-modal decisions. In Experiment 2, we employed a within-participants design in which everyone made both types of decision. In both Experiments we replicated the cross-modal effect. Moreover, the magnitude of the effect did not vary with factors known to relate to time preference, such as cognitive ability and social status, and it did not differ across cultures, even though Japanese participants were much more patient than American ones. The effect was stronger in the between- than within-participants experiment. These results strengthen the conclusion that the cross-modal effect is universal and strengthens the argument that it is due to the fundamental process of attentional dilution. |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2511.23126 |