Article

Large Language Models as Synthetic Respondents for Economic Preferences: A Global Audit

2026-02-16

Abstract excerpt

<title>Abstract</title> <p>Large-scale surveys are central to the study of economic preferences but are costly and slow to conduct. Recent advances in large language models (LLMs) raise the question of whether synthetic respondents can substitute for human participants. We address this question by running a virtual version of the Global Preference Survey (GPS), a large cross-national instrument that elicits six e...

Topics

Open a Topic to create a Post that cites this publication.

Identifiers and source

Literature Corpus work: 2b3c446f-4713-5c05-b5f2-d414167c3457
DOI: 10.21203/rs.3.rs-8775326/v1

Open publication

Related research

Semantic proximity does not establish scientific evidence.

Click a neighbor to travelStep 1 · 12 closest

Large Language Models as Synthetic Respondents for Economic Preferences: A Global AuditDOI 10.21203/rs.3.rs-8775326/v1Read

Synthetic Policymaking: Using LLM-Generated Personas to Predict Public Views on Existing and Prospective Policies in non-WEIRD CountriesSemantic proximity Read

Synthetic survey participants cannot substitute for sample diversity in policySemantic proximity Read

Inferring the Public Mind: Accuracy and Biases in Out-of-Sample Public Opinion Estimation with Large Language ModelsSemantic proximity Read

Evaluating Large Language Models in Psychological Research: A Guide for ReviewersSemantic proximity Read

Synthetic Participants Generated by Large Language Models: A Systematic Literature ReviewSemantic proximity Read

Designing, Evaluating, and Using Synthetic Participants in Behavioural Public Policy: The PATH ChecklistSemantic proximity Read

The Potential of Synthetic Twins for Personalized Behavioural Interventions at ScaleSemantic proximity Read

AI as a Research Proxy: Navigating the New Frontier of Social Science Inquiry through Language ModelsSemantic proximity Read

Simulating Public Opinion: Comparing Distributional and Individual-Level Predictions from LLMs and Random ForestsSemantic proximity Read