Simulating Organized Group Behavior: New Framework, Benchmark, and Analysis

TL;DR

This paper introduces a new framework and benchmark for simulating organized group decision-making, providing tools for understanding and analyzing collective behaviors with real-world data.

Contribution

It formalizes the problem of group behavior simulation, offers a comprehensive benchmark, and develops an analytical framework with novel mechanisms for interpretability and transfer learning.

Findings

The benchmark covers 44 entities with over 8,000 context-decision pairs.

The proposed framework outperforms baseline models in interpretability and prediction accuracy.

Temporal behavioral drift and cross-group similarities are effectively captured and analyzed.

Abstract

Simulating how organized groups (e.g., corporations) make decisions (e.g., responding to a competitor's move) is essential for understanding real-world dynamics and could benefit relevant applications (e.g., market prediction). In this paper, we formalize this problem as a concrete research platform for group behavior understanding, providing: (1) a task definition with benchmark and evaluation criteria, (2) a structured analytical framework with a corresponding algorithm, and (3) detailed temporal and cross-group analysis. Specifically, we propose Organized Group Behavior Simulation, a task that models organized groups as collective entities from a practical perspective: given a group facing a particular situation (e.g., AI Boom), predict the decision it would take. To support this task, we present GROVE (GRoup Organizational BehaVior Evaluation), a benchmark covering 44 entities with 8,052 real-world context-decision pairs collected from Wikipedia and TechCrunch across 9 domains, with an end-to-end evaluation protocol assessing consistency, initiative, scope, magnitude, and horizon. Beyond straightforward prompting pipelines, we propose a structured analytical framework that converts collective decision-making events into an interpretable, adaptive, and traceable behavioral model, achieving stronger performance than summarization- and retrieval-based baselines. It further introduces an adapter mechanism for time-aware evolution and group-aware transfer, and traceable evidence nodes grounding each decision rule in originating historical events. Our analysis reveals temporal behavioral drift within individual groups, which the time-aware adapter effectively captures for stronger prediction, and structured cross-group similarity that enables knowledge transfer for data-scarce organizations.