We Need Strong Preconditions For Using Simulations In Policy

• URL: http://arxiv.org/abs/2604.07838v1
• Date: Thu, 09 Apr 2026 05:39:35 GMT
• Title: We Need Strong Preconditions For Using Simulations In Policy
• Abstract summary: Two critical challenges remain understudied: the dual-use potential of accurate models of individual or population-level human behavior and the difficulty of validating simulation outputs.<n>We propose and discuss three preconditions for societal-scale LLM agent simulations.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Simulations, and more recently LLM agent simulations, have been adopted as useful tools for policymakers to explore interventions, rehearse potential scenarios, and forecast outcomes. While LLM simulations have enormous potential, two critical challenges remain understudied: the dual-use potential of accurate models of individual or population-level human behavior and the difficulty of validating simulation outputs. In light of these limitations, we must define boundaries for both simulation developers and decision-makers to ensure responsible development and ethical use. We propose and discuss three preconditions for societal-scale LLM agent simulations: 1) do not treat simulations of marginalized populations as neutral technical outputs, 2) do not simulate populations without their participation, and 3) do not simulate without accountability. We believe that these guardrails, combined with our call for simulation development and deployment reports, will help build trust among policymakers while promoting responsible development and use of societal-scale LLM agent simulations for the public benefit.

Related papers

• A Practical Recipe Towards Improving Sim-and-Real Correlation for VLA Evaluation [13.439555106885491]
  We study whether simulated evaluation preserves real-world conclusions in terms of policy ranking consistency, performance correlation, and perturbation-wise failure patterns.<n>This analysis allows us to characterize the limitations of existing simulators and identify what kinds of simulation signals are more aligned with real-world deployment.
  arXiv  Detail & Related papers  (2026-06-09T03:25:02Z)
• Mind the Sim2Real Gap in User Simulation for Agentic Tasks [101.69142591891234]
  We formalize the Sim2Real gap in user simulation and present the first study running the full $$-bench protocol with real humans.<n> Behaviorally, LLM simulators are excessively cooperative, stylistically uniform, and lack realistic frustration or ambiguity.<n>These findings highlight the importance of human validation when using LLM-based user simulators in the agent development cycle.
  arXiv  Detail & Related papers  (2026-03-11T19:12:31Z)
• Sim2Act: Robust Simulation-to-Decision Learning via Adversarial Calibration and Group-Relative Perturbation [54.29523408543184]
  Simulation-to-decision learning enables safe policy training in digital environments without risking real-world deployment.<n>Existing approaches either focus on improving average simulation fidelity or adopt conservative regularization.<n>We propose Sim2Act, a robust simulation-to-decision framework that addresses both simulator and policy robustness.
  arXiv  Detail & Related papers  (2026-03-10T00:51:47Z)
• SimBench: Benchmarking the Ability of Large Language Models to Simulate Human Behaviors [58.87134689752605]
  We introduce SimBench, the first large-scale, standardized benchmark for a robust, reproducible science of LLM simulation.<n>We show that even the best LLMs today have limited simulation ability (score: 40.80/100), performance scales log-linearly with model size.<n>We demonstrate that simulation ability correlates most strongly with deep, knowledge-intensive reasoning.
  arXiv  Detail & Related papers  (2025-10-20T13:14:38Z)
• LLM-Based Social Simulations Require a Boundary [3.351170542925928]
  This position paper argues that large language model (LLM)-based social simulations should establish clear boundaries.<n>We examine three key boundary problems: alignment (simulated behaviors matching real-world patterns), consistency (maintaining coherent agent behavior over time), and robustness.
  arXiv  Detail & Related papers  (2025-06-24T17:14:47Z)
• YuLan-OneSim: Towards the Next Generation of Social Simulator with Large Language Models [50.35333054932747]
  We introduce a novel social simulator called YuLan-OneSim.<n>Users can simply describe and refine their simulation scenarios through natural language interactions with our simulator.<n>We implement 50 default simulation scenarios spanning 8 domains, including economics, sociology, politics, psychology, organization, demographics, law, and communication.
  arXiv  Detail & Related papers  (2025-05-12T14:05:17Z)
• From ChatGPT to DeepSeek: Can LLMs Simulate Humanity? [32.93460040317926]
  Large Language Models (LLMs) have become a promising method for exploring complex human social behaviors.<n>Recent studies highlight discrepancies between simulated and real-world interactions.
  arXiv  Detail & Related papers  (2025-02-25T13:54:47Z)
• GenSim: A General Social Simulation Platform with Large Language Model based Agents [111.00666003559324]
  We propose a novel large language model (LLMs)-based simulation platform called textitGenSim.<n>Our platform supports one hundred thousand agents to better simulate large-scale populations in real-world contexts.<n>To our knowledge, GenSim represents an initial step toward a general, large-scale, and correctable social simulation platform.
  arXiv  Detail & Related papers  (2024-10-06T05:02:23Z)
• CoMPosT: Characterizing and Evaluating Caricature in LLM Simulations [61.9212914612875]
  We present a framework to characterize LLM simulations using four dimensions: Context, Model, Persona, and Topic. We use this framework to measure open-ended LLM simulations' susceptibility to caricature, defined via two criteria: individuation and exaggeration. We find that for GPT-4, simulations of certain demographics (political and marginalized groups) and topics (general, uncontroversial) are highly susceptible to caricature.
  arXiv  Detail & Related papers  (2023-10-17T18:00:25Z)

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.