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on Cognitive and Behavioural Economics |
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Issue of 2026–02–09
three papers chosen by Marco Novarese, Università degli Studi del Piemonte Orientale |
| By: | Pietro Bini; Lin William Cong; Xing Huang; Lawrence J. Jin |
| Abstract: | Do generative AI models, particularly large language models (LLMs), exhibit systematic behavioral biases in economic and financial decisions? If so, how can these biases be mitigated? Drawing on the cognitive psychology and experimental economics literatures, we conduct the most comprehensive set of experiments to date—originally designed to document human biases—on prominent LLM families across model versions and scales. We document systematic patterns in LLM behavior. In preference-based tasks, responses become more human-like as models become more advanced or larger, while in belief-based tasks, advanced large-scale models frequently generate rational responses. Prompting LLMs to make rational decisions reduces biases. |
| JEL: | D03 G02 G11 G4 G40 G41 |
| Date: | 2026–01 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:34745 |
| By: | Simon Gaechter (University of Nottingham); Dominik Suri (University of Bonn); Sebastian Kube (University of Bonn) |
| Abstract: | AI-driven systems are rapidly moving from decision support to directing human behavior through rules, recommendations, and compliance requests. This shift expands everyday human–AI interaction and raises the possibility that AI may function as an authority figure. However, the behavioral consequences of AI as an authority figure remain poorly understood. We investigate whether individuals differ in their willingness to comply with arbitrary rules depending on whether these rules are attributed to an AI agent (ChatGPT) or to a fellow human. In a between-subject design, 977 US Prolific users completed the coins task: they could earn a monetary payoff by stopping the disappearance of coins at any time, but a rule instructed them to wait for a signal before doing so. There are no conventional reasons to follow this rule: complying is costly and nobody is harmed by non-compliance. Despite this, we find high rule-following rates: 64.3% followed the rule set by ChatGPT and 63.9% complied with the human-set rule. Descriptive and normative beliefs about rule following, aswell as compliance conditional on these beliefs, are also largely unaffected by the rule’s origin. However, subjective social closeness to the rule setter significantly predicts how participants condition their behavior on social expectations: when participants perceive the rule setter as subjectively closer, conditional compliance is higher and associated beliefs are stronger, irrespective of whether the rule setter is human or AI. |
| Keywords: | artificial intelligence; AI-human interaction; ChatGPT; rule-following; coins task; CRISP framework; social expectations; conditional rule conformity; social closeness; IOS11; online experiments |
| Date: | 2026–02 |
| URL: | https://d.repec.org/n?u=RePEc:not:notcdx:2026-02 |
| By: | TOLEDO, RALPH RENDELL (Government Procurement Policy Board-Technical Support Office) |
| Abstract: | Public administration systems have intensified accountability and oversight to address corruption, inefficiency, and misuse of public resources. These developments have reinforced an audit society in which verification and performance control increasingly shape everyday administrative routines. Although scholarship widely documents proceduralism, risk aversion, and compliance-oriented behavior under audit-intensive regimes, it has paid limited attention to the behavioral mechanism through which these outcomes are systematically produced. This paper develops the Fear-Based Decision-Making Framework, positioning fear as an institutionalized mechanism linking audit culture and asymmetric accountability to conservative administrative behavior. Using theory-driven conceptual synthesis across Audit Culture Theory, Risk Aversion Theory, and Behavioral Public Administration, the analysis integrates evidence from peer-reviewed literature and institutional reports, with comparative attention to Western and ASEAN governance contexts. The synthesis shows that expanding audit regimes tend to redefine accountability around documentation and procedural defensibility, encouraging anticipatory compliance and narrowing discretionary space. Conservative administrative behavior commonly manifests as rigid rule adherence, documentation inflation, defensive standardization, and discretion avoidance, reflecting loss-avoidance incentives under conditions of retrospective scrutiny. The framework specifies five-part typology of fear: anticipatory, sanction, reputational, interpretive, and institutionalized, and explains how ASEAN scholarship often masks fear-driven behavior through proxy framings such as capacity and compliance narratives. By making fear analytically explicit, the paper reframes conservative administration as an accountability design problem and provides a basis for developing testable propositions and governance reforms that protect integrity without systematically constraining discretion and judgment. |
| Date: | 2026–02–05 |
| URL: | https://d.repec.org/n?u=RePEc:osf:socarx:udrav_v1 |