# Behavioral History Outperforms Descriptions of the Person for LLM Synthetic Personas

arXiv: https://arxiv.org/abs/2610.03998 · HTML: https://mlsanity.com/paper/2610.03998/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 33 of its 61 references that are in the map.
- First posted on arXiv 2 Oct 2026.

## Builds on (most cited first, 30 of 33)

- [Whose Opinions Do Language Models Reflect?](https://mlsanity.com/paper/2303.17548/) (2023)
- [Out of One, Many: Using Language Models to Simulate Human Samples](https://mlsanity.com/paper/2209.06899/) (2022)
- [Using Large Language Models to Simulate Multiple Humans and Replicate Human Subject Studies](https://mlsanity.com/paper/2208.10264/) (2022)
- [LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individuals](https://mlsanity.com/paper/2411.10109/) (2024)
- [Bias Runs Deep: Implicit Reasoning Biases in Persona-Assigned LLMs](https://mlsanity.com/paper/2311.04892/) (2023)
- [Quantifying the Persona Effect in LLM Simulations](https://mlsanity.com/paper/2402.10811/) (2024)
- [Large language models that replace human participants can harmfully misportray and flatten identity groups](https://mlsanity.com/paper/2402.01908/) (2024)
- [LLM Generated Persona is a Promise with a Catch](https://mlsanity.com/paper/2503.16527/) (2025)
- [Questioning the Survey Responses of Large Language Models](https://mlsanity.com/paper/2306.07951/) (2023)
- [The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective](https://mlsanity.com/paper/2312.15524/) (2023)
- [Large Language Models Assume People are More Rational than We Really are](https://mlsanity.com/paper/2406.17055/) (2024)
- [SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors](https://mlsanity.com/paper/2510.17516/) (2025)
- [LLM economicus? Mapping the Behavioral Biases of LLMs via Utility Theory](https://mlsanity.com/paper/2408.02784/) (2024)
- [Diminished Diversity-of-Thought in a Standard Large Language Model](https://mlsanity.com/paper/2302.07267/) (2023)
- [Large Language Models: An Applied Econometric Framework](https://mlsanity.com/paper/2412.07031/) (2024)
- [Evaluating language models as risk scores](https://mlsanity.com/paper/2407.14614/) (2024)
- [Language Models Trained to do Arithmetic Predict Human Risky and Intertemporal Choice](https://mlsanity.com/paper/2405.19313/) (2024)
- [Assessing the Reliability of Persona-Conditioned LLMs as Synthetic Survey Respondents](https://mlsanity.com/paper/2602.18462/) (2026)
- [How Personality Traits Shape LLM Risk-Taking Behaviour](https://mlsanity.com/paper/2503.04735/) (2025)
- [Missing the Margins: A Systematic Literature Review on the Demographic Representativeness of LLMs](https://mlsanity.com/paper/2511.01864/) (2025)
- [Behavioral Economics of AI: LLM Biases and Corrections](https://mlsanity.com/paper/2602.09362/) (2026)
- [Evaluating and Aligning Human Economic Risk Preferences in LLMs](https://mlsanity.com/paper/2503.06646/) (2025)
- [The Chameleon's Limit: Investigating Persona Collapse and Homogenization in Large Language Models](https://mlsanity.com/paper/2604.24698/) (2026)
- [Plausible but Not Valid: A Psychometric Audit of LLMs as Synthetic Survey Respondents](https://mlsanity.com/paper/2608.14606/) (2026)
- [When Synthetic Users Fail: A Cross-Domain Benchmark of LLM-Simulated Human Survey Responses](https://mlsanity.com/paper/2607.26348/) (2026)
- [When Can Digital Personas Reliably Approximate Human Survey Findings?](https://mlsanity.com/paper/2605.10659/) (2026)
- [Pay What LLM Wants: Can LLM Simulate Economics Experiment with 522 Real-human Persona?](https://mlsanity.com/paper/2508.03262/) (2025)
- [The Collapse of Heterogeneity in Silicon Philosophers](https://mlsanity.com/paper/2604.23575/) (2026)
- [From Demographics to Survey Anchors: Evaluating LLM Agents for Modeling Retirement Attitudes](https://mlsanity.com/paper/2605.16303/) (2026)
- [Synthetic Personalities: How Well Can LLMs Mimic Individual Respondents Using Socio-Economic Microdata?](https://mlsanity.com/paper/2606.04592/) (2026)

## Abstract (arXiv)

Large language models (LLMs) are increasingly used as synthetic personas representing survey respondents. Their validity as substitutes for particular respondents depends on whether they reproduce individuals' decisions. We examine what information helps synthetic respondents predict each individual's later choices, using five conditions that add progressively richer information: no personal information, demographics, personality traits, cognitive scores, and finally the respondent's earlier survey choices as behavioral history. We use a two-wave panel of 845 US adults who completed measures of 14 behavioral biases (spanning risk, time preferences, overconfidence, and reasoning), so each respondent's earlier answers provide a human test-retest benchmark; in the behavioral-history condition, all items that score the target bias are withheld. At the population level, the average number of biases per respondent in every condition is close to the human average (7.1-8.1 biases, against 7.1 for humans). This aggregate similarity masks differences in variance: persona descriptions recover only 53-67% of human between-person variation, whereas adding behavioral history restores it to approximately the human level. At the individual level, description-based personas achieve only 7-12% of the informedness observed in human test-retest responses, while adding behavioral history raises this to 28%. The condition including behavioral history has the highest estimated informedness in all 17 demographic groups, whereas description-based conditions provide little or no information for some groups. Synthetic responses also exhibit stronger education- and income-related differences than human responses. For LLM synthetic personas, a respondent's past answers add more to individual-level prediction than a description of who they are.