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on Neuroeconomics |
| By: | Burak Uras (Williams College); Niccolo Zaccaria (Tilburg University); Sigrid Suetens (Tilburg University); |
| Abstract: | "This paper investigates the impact of misconfidence on price stickiness in markets charac- terized by strategic complementarities. Misconfidence denotes the tendency of cognitively able individuals to underestimate the cognitive ability of others. In an experiment, we first measure cognitive ability and misconfidence, and then have participants make price choices in a market. We find that prices in markets with at least one misconfident participant de- viate significantly more from equilibrium levels than in markets composed exclusively of confident, cognitively able agents. Importantly, misconfident individuals are no more prone to self-assessment errors than others, indicating that misconfidence constitutes a distinct cognitive bias—fundamentally different from conventional forms of overconfidence or un- derconfidence." |
| Keywords: | Price stickiness, strategic complementarity, controlled experiments, beliefs |
| JEL: | E71 |
| Date: | 2026–02–13 |
| URL: | https://d.repec.org/n?u=RePEc:wil:wileco:2026_103 |
| By: | Conti, Alice; Di Matteo, Fabio Lokwani; Candeloro, Giulia; Lancisi, Lorenzo; Sacco, Pier Luigi |
| Abstract: | Most economic models of decision-making assume that individuals maintain comprehensive mental representations of possible world states and compute expected utilities across these representations. The cognitive demands of such exhaustive state-space evaluation appear difficult to reconcile with known constraints on working memory, attention, and neural computation, motivating alternative accounts of how humans navigate complex choice environments. This review examines the hypothesis that neural systems supporting narrative processing contribute substantially to real-world decision-making. We synthesize evidence from three converging lines of research: hierarchical temporal processing in the cortex, the integrative functions of the default mode network, and inter-brain synchronization during naturalistic communication. We acknowledge the ongoing debates about the interpretation of these findings, but we argue that the neural architecture supporting narrative comprehension and generation offers cognitive efficiency advantages relevant to decision-making, including dimensionality reduction through causal structuring, integration of emotional and contextual information, and facilitation of social coordination through shared mental models. Rather than claiming that narrative processing constitutes the exclusive mechanism for decision-making, we propose that it represents one important component of a broader cognitive toolkit that also includes heuristic strategies, model-based planning, and habitual responses. We examine how this perspective relates to phenomena in cognitive economics, including context-dependent preferences, framing effects, and the propagation of economic beliefs through populations. By integrating findings from cognitive neuroscience with decision science, we aim to contribute toward a more biologically informed understanding of human choice behavior and identify key questions for future empirical investigation. |
| Date: | 2026–02–28 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:zxcvg_v1 |
| By: | Epper, Thomas (CNRS, IESEG School of Management, Univ. Lille, UMR 9221 – LEM – Lille Economie Management, F-59000 Lille, France); Ibsen, Kristoffer (Aarhus University); Koch, Alexander (Aarhus University); Nafziger, Julia (Aarhus University) |
| Abstract: | University dropout is costly, making it a policy priority to identify factors that predict dropout. Using a survey experiment with incoming first-year students linked to long-run administrative outcomes, we assess which information improves dropout prediction beyond standard university records. A small number of targeted, study-specific survey items - especially motivation and expectations about degree completion - substantially improve predictive performance. By contrast, widely used measures of general preferences and traits (such as grit and self-control) add little incremental value - a result that we qualitatively replicate in a large population. Our findings suggest inexpensive, scalable ways to improve dropout predictions. |
| Keywords: | dropout, non-cognitive skills, motivation, economic preferences, beliefs, education, machine learning |
| JEL: | I23 D91 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:iza:izadps:dp18439 |