Evaluating Binary Decision Biases in Large Language Models: Implications for Fair Agent-Based Financial Simulations

TL;DR

This study evaluates biases in large language models used for simulating decision-making in financial agent-based models, highlighting how sampling methods and model versions influence bias and distribution properties.

Contribution

It systematically compares GPT models and sampling approaches, revealing biases and proposing considerations for integrating LLMs into financial simulations.

Findings

GPT-4o-Mini-2024-07-18 shows less bias than GPT-4-0125-preview

Sampling method impacts output distribution significantly

Few-shot sampling can approximate uniform distributions under certain conditions

Abstract

Large Language Models (LLMs) are increasingly being used to simulate human-like decision making in agent-based financial market models (ABMs). As models become more powerful and accessible, researchers can now incorporate individual LLM decisions into ABM environments. However, integration may introduce inherent biases that need careful evaluation. In this paper we test three state-of-the-art GPT models for bias using two model sampling approaches: one-shot and few-shot API queries. We observe significant variations in distributions of outputs between specific models, and model sub versions, with GPT-4o-Mini-2024-07-18 showing notably better performance (32-43% yes responses) compared to GPT-4-0125-preview's extreme bias (98-99% yes responses). We show that sampling methods and model sub-versions significantly impact results: repeated independent API calls produce different distributions compared to batch sampling within a single call. While no current GPT model can simultaneously achieve a uniform distribution and Markovian properties in one-shot testing, few-shot sampling can approach uniform distributions under certain conditions. We explore the Temperature parameter, providing a definition and comparative results. We further compare our results to true random binary series and test specifically for the common human bias of Negative Recency - finding LLMs have a mixed ability to 'beat' humans in this one regard. These findings emphasise the critical importance of careful LLM integration into ABMs for financial markets and more broadly.