The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective

• URL: http://arxiv.org/abs/2312.15524v2
• Date: Tue, 21 Jan 2025 21:34:52 GMT
• Title: The Challenge of Using LLMs to Simulate Human Behavior: A Causal Inference Perspective
• Authors: George Gui, Olivier Toubia,
• Abstract summary: Large Language Models (LLMs) have shown impressive potential to simulate human behavior. We identify a fundamental challenge in using them to simulate experiments. When LLM-simulated subjects are blind to the experimental design, variations in treatment systematically affect unspecified variables.
• Score: 0.27624021966289597
• License:
• Abstract: Large Language Models (LLMs) have shown impressive potential to simulate human behavior. We identify a fundamental challenge in using them to simulate experiments: when LLM-simulated subjects are blind to the experimental design (as is standard practice with human subjects), variations in treatment systematically affect unspecified variables that should remain constant, violating the unconfoundedness assumption. Using demand estimation as a context and an actual experiment as a benchmark, we show this can lead to implausible results. While confounding may in principle be addressed by controlling for covariates, this can compromise ecological validity in the context of LLM simulations: controlled covariates become artificially salient in the simulated decision process, which introduces focalism. This trade-off between unconfoundedness and ecological validity is usually absent in traditional experimental design and represents a unique challenge in LLM simulations. We formalize this challenge theoretically, showing it stems from ambiguous prompting strategies, and hence cannot be fully addressed by improving training data or by fine-tuning. Alternative approaches that unblind the experimental design to the LLM show promise. Our findings suggest that effectively leveraging LLMs for experimental simulations requires fundamentally rethinking established experimental design practices rather than simply adapting protocols developed for human subjects.

Related papers

• Model-free Methods for Event History Analysis and Efficient Adjustment (PhD Thesis) [55.2480439325792]
  This thesis is a series of independent contributions to statistics unified by a model-free perspective. The first chapter elaborates on how a model-free perspective can be used to formulate flexible methods that leverage prediction techniques from machine learning. The second chapter studies the concept of local independence, which describes whether the evolution of one process is directly influenced by another.
  arXiv  Detail & Related papers  (2025-02-11T19:24:09Z)
• Causal Lifting of Neural Representations: Zero-Shot Generalization for Causal Inferences [56.23412698865433]
  We focus on causal inferences on a target experiment with unlabeled factual outcomes, retrieved by a predictive model fine-tuned on a labeled similar experiment. First, we show that factual outcome estimation via Empirical Risk Minimization (ERM) may fail to yield valid causal inferences on the target population. We propose Deconfounded Empirical Risk Minimization (DERM), a new simple learning procedure minimizing the risk over a fictitious target population.
  arXiv  Detail & Related papers  (2025-02-10T10:52:17Z)
• Can LLMs Reliably Simulate Human Learner Actions? A Simulation Authoring Framework for Open-Ended Learning Environments [1.4999444543328293]
  Simulating learner actions helps stress-test open-ended interactive learning environments and prototype new adaptations before deployment. We propose Hyp-Mix, a simulation authoring framework that allows experts to develop and evaluate simulations by combining testable hypotheses about learner behavior.
  arXiv  Detail & Related papers  (2024-10-03T00:25:40Z)
• Simulating Field Experiments with Large Language Models [0.6144680854063939]
  This paper pioneers the utilization of large language models (LLMs) for simulating field experiments. By introducing two novel prompting strategies, observer and participant modes, we demonstrate the ability of LLMs to both predict outcomes and replicate participant responses within complex field settings. Our findings indicate a promising alignment with actual experimental results in certain scenarios, achieving a stimulation accuracy of 66% in observer mode.
  arXiv  Detail & Related papers  (2024-08-19T03:41:43Z)
• LLM and Simulation as Bilevel Optimizers: A New Paradigm to Advance Physical Scientific Discovery [141.39722070734737]
  We propose to enhance the knowledge-driven, abstract reasoning abilities of Large Language Models with the computational strength of simulations. We introduce Scientific Generative Agent (SGA), a bilevel optimization framework. We conduct experiments to demonstrate our framework's efficacy in law discovery and molecular design.
  arXiv  Detail & Related papers  (2024-05-16T03:04:10Z)
• LLM-driven Imitation of Subrational Behavior : Illusion or Reality? [3.2365468114603937]
  Existing work highlights the ability of Large Language Models to address complex reasoning tasks and mimic human communication. We propose to investigate the use of LLMs to generate synthetic human demonstrations, which are then used to learn subrational agent policies. We experimentally evaluate the ability of our framework to model sub-rationality through four simple scenarios.
  arXiv  Detail & Related papers  (2024-02-13T19:46:39Z)
• Online simulator-based experimental design for cognitive model selection [74.76661199843284]
  We propose BOSMOS: an approach to experimental design that can select between computational models without tractable likelihoods. In simulated experiments, we demonstrate that the proposed BOSMOS technique can accurately select models in up to 2 orders of magnitude less time than existing LFI alternatives.
  arXiv  Detail & Related papers  (2023-03-03T21:41:01Z)
• Sequential Causal Imitation Learning with Unobserved Confounders [82.22545916247269]
  "Monkey see monkey do" is an age-old adage, referring to na"ive imitation without a deep understanding of a system's underlying mechanics. This paper investigates the problem of causal imitation learning in sequential settings, where the imitator must make multiple decisions per episode.
  arXiv  Detail & Related papers  (2022-08-12T13:53:23Z)
• On the Importance of Application-Grounded Experimental Design for Evaluating Explainable ML Methods [20.2027063607352]
  We present an experimental study extending a prior explainable ML evaluation experiment and bringing the setup closer to the deployment setting. Our empirical study draws dramatically different conclusions than the prior work, highlighting how seemingly trivial experimental design choices can yield misleading results. We believe this work holds lessons about the necessity of situating the evaluation of any ML method and choosing appropriate tasks, data, users, and metrics to match the intended deployment contexts.
  arXiv  Detail & Related papers  (2022-06-24T14:46:19Z)
• Likelihood-Free Inference in State-Space Models with Unknown Dynamics [71.94716503075645]
  We introduce a method for inferring and predicting latent states in state-space models where observations can only be simulated, and transition dynamics are unknown. We propose a way of doing likelihood-free inference (LFI) of states and state prediction with a limited number of simulations.
  arXiv  Detail & Related papers  (2021-11-02T12:33:42Z)
• Simulation as Experiment: An Empirical Critique of Simulation Research on Recommender Systems [4.006331916849688]
  We argue that simulation studies of recommender system (RS) evolution are conceptually similar to empirical experimental approaches. By adopting standards and practices common in empirical disciplines, simulation researchers can mitigate many of these weaknesses.
  arXiv  Detail & Related papers  (2021-07-29T21:05:01Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.