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on Cognitive and Behavioural Economics |
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Issue of 2026–08–31
six papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Gary Charness (University of California); Francesco Feri (Royal Holloway University of London); Matthew O. Jackson (Stanford University & Santa Fe Institute); Miguel A. Meléndez-Jiménez (Universidad de Málaga); Matthias Sutter (Max Planck Institute for Behavioral Economics, University of Cologne & University of Innsbruck) |
| Abstract: | We provide a first causal analysis of the behavioral consequences of the friendship paradox—the fact that people’s friends in a network have more connections than average. We find that people’s behavior is biased by their network position: they do not best respond to what they should infer the average behavior of the population to be, but instead simply to the average behavior of their friends. Moreover, we find that they fail to learn to overcome such a bias when relocated within the network, varying their observational environment. In these games of complements, the friendship paradox generates a systematic upward distortion in actions, increases behavioral dispersion, and persists despite learning opportunities. |
| Keywords: | Friendship paradox, networks, learning, experiment |
| JEL: | C91 D01 D85 D90 |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:ajk:ajkdps:426 |
| By: | Holden, Stein T. (Centre for Land Tenure Studies, Norwegian University of Life Sciences) |
| Abstract: | This paper examines how decision imprecision affects the measurement of risk and time preferences in Multiple Price List (MPL) tasks. Using experimental data with alternative elicitation procedures and repeated binary choices, we document systematic relationships between inconsistency in choices and the distribution of elicited switching behavior.<p> We show that lower decision precision compresses switching behavior toward intermediate rows, reducing the prevalence of corner outcomes. In the risk domain, this compression shifts choices relative to the risk-neutral threshold, increasing the likelihood that observations are classified as risk-loving in low-probability environments. Consistent with this, decision precision affects both the level and probability sensitivity of inferred risk premia. <p>In the time domain, lower precision is associated with lower estimated discount rates, implying that less precise individuals appear more patient in elicitation data. <p>Across domains, these results indicate that commonly used measures such as risk premia and discount rates reflect not only underlying preferences but also variation in decision precision and the structure of elicitation tasks. We therefore interpret these measures as reduced-form summaries of switching behavior rather than structural preference parameters. The evidence is based on a large population-based field experiment in a developing country context, providing external validity to recent findings on cognitive imprecision that have primarily been established in smaller laboratory samples. |
| Keywords: | Multiple price lists; Decision precision; Stochastic choice; Risk preferences; Time preferences |
| JEL: | C91 C93 D81 D91 |
| Date: | 2026–08–05 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:nlsclt:2026_006 |
| By: | Gary Charness; Francesco Feri; Matthew O. Jackson; Miguel A. Melendez-Jimenez; Matthias Sutter |
| Abstract: | We provide a first causal analysis of the behavioral consequences of the friendship paradox-the fact that people's friends in a network have more connections than average. We find that people's behavior is biased by their network position: they do not best respond to what they should infer the average behavior of the population to be, but instead simply to the average behavior of their friends. Moreover, we find that they fail to learn to overcome such a bias when relocated within the network, varying their observational environment. In these games of complements, the friendship paradox generates a systematic upward distortion in actions, increases behavioral dispersion, and persists despite learning opportunities. |
| Date: | 2026–08 |
| URL: | https://d.repec.org/n?u=RePEc:arx:papers:2608.07819 |
| By: | Joshua Greubel; Henrik Guhling; Fabian Herweg |
| Abstract: | In an online experiment, we study how generative AI steers individuals toward dominated choices and, conversely, helps them avoid such mistakes. Participants choose between two virtual index funds, one of which strictly dominates the other. An AI chatbot increases optimal choices by over 20 percentage points when promoting the optimal fund but reduces them by almost 30 points when promoting the dominated fund. Its influence is undiminished when framed as bank-provided despite a disclosed interest. Incentivized human advice steers choices toward the promoted fund but is significantly less effective. Transcript analyses tentatively suggest that AI's advantage reflects more persuasive argumentation. |
| Keywords: | AI chatbots, conflicts of interest, dominated choices, financial advice, generative AI, persuasion, retail investors |
| JEL: | C91 D14 G11 |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:ces:ceswps:_12925 |
| By: | Holden, Stein T. (Centre for Land Tenure Studies, Norwegian University of Life Sciences); Tione, Sarah (Centre for Land Tenure Studies, Norwegian University of Life Sciences) |
| Abstract: | We study how trust and trustworthiness respond to social distance and how beliefs, norms, and cognitive precision shape social exchange in a field experiment with irrigation farmers. Using a standard trust game with within-subject variation in social-distance framing, we distinguish interactions with an anonymous partner from the same irrigation block from interactions with a partner from another scheme within the same district. In addition to trust and reciprocal behavior, we elicit beliefs about expected returns, stated moral obligations to reciprocate, risk preferences, and decision precision measured through consistency in multiple price list choices. <p> The results reveal a clear asymmetry between trust and trustworthiness. Changes in trust across social-distance framings are primarily driven by changes in beliefs, whereas trustworthiness is more strongly associated with normative commitments and exhibits greater behavioral stability across contexts. Cognitive precision is positively related to baseline levels of trust but is associated with smaller framing-induced changes in trustworthiness, consistent with an interpretation of precision as stabilizing behavior rather than uniformly increasing prosociality. Risk preferences play a limited role once beliefs are taken into account.<p>By separating trust from trustworthiness and by distinguishing between behavioral levels and framing-induced changes, the paper provides new field evidence on the mechanisms underlying social exchange. The findings highlight the importance of beliefs for trusting behavior, norms for reciprocal behavior, and decision precision for the consistency of social decisions across contexts. |
| Keywords: | Social distance; Beliefs; Norms; Decision precision; Trust; Trustworthiness |
| JEL: | C92 C93 D01 D91 |
| Date: | 2026–08–05 |
| URL: | https://d.repec.org/n?u=RePEc:hhs:nlsclt:2026_007 |
| By: | Elia Antoniou |
| Abstract: | This paper examines the source of algorithmic aversion, defined as the unwillingness to accept advice or decisions made by algorithms. Algorithmic aversion is conceptualized as a form of individual partiality, driven either by belief-based factors attributed to differences in perceived ability, or by preference-based factors which reflect a disamenity associated with selecting algorithms. To empirically test the predictions of the model, a preregistered online experiment was conducted, where participants evaluated answers to objective and subjective economic questions, with varying information on whether the source was human or algorithm. The results provide no evidence of algorithmic aversion in the evaluation task: participants did not systematically favor human-generated answers over algorithm-generated ones. These results suggest that algorithmic aversion may not be as robust or uniform as previously assumed. |
| Keywords: | Algorithmic Aversion; Human-AI interaction; Experimental Economics. |
| JEL: | D83 D90 C91 |
| Date: | 2025–11–25 |
| URL: | https://d.repec.org/n?u=RePEc:ucy:cypeua:05-2025 |