Large Language Models as Voting Mechanisms

Raša Karapandža and Yaw Nyarko

No 35839, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: Large language models (LLMs) increasingly provide economic and financial advice. We develop a microeconomic framework for understanding this role. We prove that an LLM acts like a voting rule: each input text is an election, possible continuations are candidates, and output probabilities are vote shares derived from the training corpus. Such rules can generate Condorcet cycles, implying intransitive pairwise recommendations and potential money-pump exploitation. Using ChatGPT-4o, we identify millions of cycles in pairwise investment choices among S&P 100 firms. Our theorems establish the voting interpretation on the training data and support its use off-sample. We also show that correcting cycles ex post is generally infeasible. Finally, temperature changes the voting rule: at T = 1, outputs are sampled in proportion to vote shares; lower temperatures favor the plurality winner, whereas higher temperatures flatten probabilities toward uniformity.

JEL-codes: D81 D83 D91 G41 (search for similar items in EconPapers)
Date: 2026-10
New Economics Papers: this item is included in nep-ain, nep-cdm, nep-cmp and nep-pol
Note: AP POL PR
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