Hasil untuk "Economics"

M. Christie, Eduardo S Brondízio, R. Costanza et al.

Xavier Labandeira Villot, P. Linares

S. Durlauf, L. Blume

J. Lin, Gary S. Becker, Otaviano Canuto et al.

D. MacKenzie, Fabian Muniesa, Leung Sea Siu

J. R. Harris, N. Rosenberg

G. Hodgson

R. David, Shin-Kap Han

Christine Jolls, Christine Jolls, C. Sunstein et al.

Economic analysis of law usually proceeds under the assumptions of neoclassical economics. But empirical evidence gives much reason to doubt these assumptions; people exhibit bounded rationality, bounded self-interest, and bounded willpower. This article offers a broad vision of how law and economics analysis may be improved by increased attention to insights about actual human behavior. It considers specific topics in the economic analysis of law and proposes new models and approaches for addressing these topics. The analysis of the article is organized into three categories: positive, prescriptive, and normative. Positive analysis of law concerns how agents behave in response to legal rules and how legal rules are shaped. Prescriptive analysis concerns what rules should be adopted to advance specified ends. Normative analysis attempts to assess more broadly the ends of the legal system: Should the system always respect people's choices? By drawing attention to cognitive and motivational problems of both citizens and government, behavioral law and economics offers answers distinct from those offered by the standard analysis.

R. Lucas

P. Krugman, M. Obstfeld

David Besanko, D. Dranove, Mark T. Shanley et al.

R. Ydenberg, L. Dill

Simeon Djankov, E. Glaeser, R. Porta et al.

C. Bell

T. Stanley, Hristos Doucouliagos

S. Mathew

W. Bickel, Matthew W. Johnson, Mikhail N. Koffarnus et al.

G. Boyer

Pietro Bini, Lin William Cong, Xing Huang et al.

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.