PolicySimEval: A Benchmark for Evaluating Policy Outcomes through Agent-Based Simulation

• URL: http://arxiv.org/abs/2502.07853v1
• Date: Tue, 11 Feb 2025 12:36:00 GMT
• Title: PolicySimEval: A Benchmark for Evaluating Policy Outcomes through Agent-Based Simulation
• Abstract summary: We introduce PolicySimEval, the first benchmark designed to evaluate the capability of agent-based simulations in policy assessment tasks.<n>PolicySimEval aims to reflect the real-world complexities faced by social scientists and policymakers.
• Score: 2.826457831133425
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the growing adoption of agent-based models in policy evaluation, a pressing question arises: Can such systems effectively simulate and analyze complex social scenarios to inform policy decisions? Addressing this challenge could significantly enhance the policy-making process, offering researchers and practitioners a systematic way to validate, explore, and refine policy outcomes. To advance this goal, we introduce PolicySimEval, the first benchmark designed to evaluate the capability of agent-based simulations in policy assessment tasks. PolicySimEval aims to reflect the real-world complexities faced by social scientists and policymakers. The benchmark is composed of three categories of evaluation tasks: (1) 20 comprehensive scenarios that replicate end-to-end policy modeling challenges, complete with annotated expert solutions; (2) 65 targeted sub-tasks that address specific aspects of agent-based simulation (e.g., agent behavior calibration); and (3) 200 auto-generated tasks to enable large-scale evaluation and method development. Experiments show that current state-of-the-art frameworks struggle to tackle these tasks effectively, with the highest-performing system achieving only 24.5\% coverage rate on comprehensive scenarios, 15.04\% on sub-tasks, and 14.5\% on auto-generated tasks. These results highlight the difficulty of the task and the gap between current capabilities and the requirements for real-world policy evaluation.

Related papers

• Active Real-World Factor-Based Evaluation for Generalist Robot Policies [13.386170952445868]
  Generalist robot manipulation policies have shown remarkable promise across a wide range of tasks.<n>We propose an active evaluation framework that treats policy evaluation as a sequential experimental design problem.<n>We conduct 2331 real-world evaluations across 3 tasks with 3 factor variations and find that our approach typically saves the evaluator at least 20-40% of trials.
  arXiv  Detail & Related papers  (2026-07-16T00:21:54Z)
• EvoPolicyGym: Evaluating Autonomous Policy Evolution in Interactive Environments [64.33452656100393]
  We introduce Autonomous Policy Evolution, a controlled evaluation setting in which a harness-model agent repeatedly edits an executable policy system under a fixed interaction budget.<n>We instantiate this setting in EvoPolicyGym, a benchmark built from compact interactive RL environments.<n>On the EvoPolicyGym suite, GPT-5.5 achieves the strongest aggregate rank score and top-two performance on all 16 environments.
  arXiv  Detail & Related papers  (2026-07-02T17:10:13Z)
• PolicyLLM: Towards Excellent Comprehension of Public Policy for Large Language Models [56.37206340175191]
  Large Language Models (LLMs) are increasingly integrated into real-world decision-making.<n>Yet, their ability to comprehend and reason about policy-related content remains underexplored.<n>We present textbftextitPolicyBench, the first large-scale cross-system benchmark (US-China) evaluating policy comprehension.
  arXiv  Detail & Related papers  (2026-04-14T17:27:50Z)
• Empowering Real-World: A Survey on the Technology, Practice, and Evaluation of LLM-driven Industry Agents [63.03252293761656]
  This paper systematically reviews the technologies, applications, and evaluation methods of industry agents based on large language models (LLMs)<n>We examine the three key technological pillars that support the advancement of agent capabilities: Memory, Planning, and Tool Use.<n>We provide an overview of the application of industry agents in real-world domains such as digital engineering, scientific discovery, embodied intelligence, collaborative business execution, and complex system simulation.
  arXiv  Detail & Related papers  (2025-10-20T12:46:55Z)
• Reliable and Scalable Robot Policy Evaluation with Imperfect Simulators [9.868826622074899]
  SureSim is a framework to augment large-scale simulation with relatively small-scale real-world testing.<n>We leverage non-asymptotic mean estimation algorithms to provide confidence intervals on mean policy performance.<n>Our approach saves over (20-25%) of hardware evaluation effort to achieve similar bounds on policy performance.
  arXiv  Detail & Related papers  (2025-10-05T20:37:53Z)
• Reinforcement Learning with Continuous Actions Under Unmeasured Confounding [14.510042451844766]
  This paper addresses the challenge of offline policy learning in reinforcement learning with continuous action spaces. We develop a minimax estimator and introduce a policy-gradient-based algorithm to identify the in-class optimal policy. We provide theoretical results regarding the consistency, finite-sample error bound, and regret bound of the resulting optimal policy.
  arXiv  Detail & Related papers  (2025-05-01T04:55:29Z)
• Survey on Evaluation of LLM-based Agents [28.91672694491855]
  The emergence of LLM-based agents represents a paradigm shift in AI. This paper provides the first comprehensive survey of evaluation methodologies for these increasingly capable agents.
  arXiv  Detail & Related papers  (2025-03-20T17:59:23Z)
• Evaluating Real-World Robot Manipulation Policies in Simulation [91.55267186958892]
  Control and visual disparities between real and simulated environments are key challenges for reliable simulated evaluation. We propose approaches for mitigating these gaps without needing to craft full-fidelity digital twins of real-world environments. We create SIMPLER, a collection of simulated environments for manipulation policy evaluation on common real robot setups.
  arXiv  Detail & Related papers  (2024-05-09T17:30:16Z)
• How Generalizable Is My Behavior Cloning Policy? A Statistical Approach to Trustworthy Performance Evaluation [17.638831964639834]
  Behavior cloning policies are increasingly successful at solving complex tasks by learning from human demonstrations. We present a framework that provides a tight lower-bound on robot performance in an arbitrary environment. In experiments we evaluate policies for visuomotor manipulation in both simulation and hardware.
  arXiv  Detail & Related papers  (2024-05-08T22:00:35Z)
• Evaluating General-Purpose AI with Psychometrics [43.85432514910491]
  We discuss the need for a comprehensive and accurate evaluation of general-purpose AI systems such as large language models. Current evaluation methodology, mostly based on benchmarks of specific tasks, falls short of adequately assessing these versatile AI systems. To tackle these challenges, we suggest transitioning from task-oriented evaluation to construct-oriented evaluation.
  arXiv  Detail & Related papers  (2023-10-25T05:38:38Z)
• On the Value of Myopic Behavior in Policy Reuse [67.37788288093299]
  Leveraging learned strategies in unfamiliar scenarios is fundamental to human intelligence. In this work, we present a framework called Selective Myopic bEhavior Control(SMEC) SMEC adaptively aggregates the sharable short-term behaviors of prior policies and the long-term behaviors of the task policy, leading to coordinated decisions.
  arXiv  Detail & Related papers  (2023-05-28T03:59:37Z)
• Policy Learning with Competing Agents [2.972870935419738]
  Decision makers often aim to learn a treatment assignment policy under a capacity constraint on the number of agents that they can treat. In this paper, we study capacity-constrained treatment assignment in the presence of such interference.
  arXiv  Detail & Related papers  (2022-04-04T23:15:00Z)
• Fast Model-based Policy Search for Universal Policy Networks [45.44896435487879]
  Adapting an agent's behaviour to new environments has been one of the primary focus areas of physics based reinforcement learning. We propose a Gaussian Process-based prior learned in simulation, that captures the likely performance of a policy when transferred to a previously unseen environment. We integrate this prior with a Bayesian optimisation-based policy search process to improve the efficiency of identifying the most appropriate policy from the universal policy network.
  arXiv  Detail & Related papers  (2022-02-11T18:08:02Z)
• Building a Foundation for Data-Driven, Interpretable, and Robust Policy Design using the AI Economist [67.08543240320756]
  We show that the AI Economist framework enables effective, flexible, and interpretable policy design using two-level reinforcement learning and data-driven simulations. We find that log-linear policies trained using RL significantly improve social welfare, based on both public health and economic outcomes, compared to past outcomes.
  arXiv  Detail & Related papers  (2021-08-06T01:30:41Z)
• Reliable Off-policy Evaluation for Reinforcement Learning [53.486680020852724]
  In a sequential decision-making problem, off-policy evaluation estimates the expected cumulative reward of a target policy. We propose a novel framework that provides robust and optimistic cumulative reward estimates using one or multiple logged data.
  arXiv  Detail & Related papers  (2020-11-08T23:16:19Z)
• Learning Goal-oriented Dialogue Policy with Opposite Agent Awareness [116.804536884437]
  We propose an opposite behavior aware framework for policy learning in goal-oriented dialogues. We estimate the opposite agent's policy from its behavior and use this estimation to improve the target agent by regarding it as part of the target policy.
  arXiv  Detail & Related papers  (2020-04-21T03:13:44Z)

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.