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

• URL: http://arxiv.org/abs/2604.09874v1
• Date: Fri, 10 Apr 2026 20:04:33 GMT
• Title: Simulating Organized Group Behavior: New Framework, Benchmark, and Analysis
• Abstract summary: We propose a task that models organized groups as collective entities from a practical perspective.<n>We present GROVE, a benchmark covering 44 entities with 8,052 real-world context-decision pairs.<n>We also propose a structured analytical framework that converts collective decision-making events into an interpretable, adaptive, and traceable behavioral model.
• Score: 49.796228341744076
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

Related papers

• Imputation Meets Clustering: Exploiting Latent Subgroup Structure for Missing Data Recovery [52.29866804948133]
  We propose CAGI (Cluster-Aware Generative Imputation), a framework that reformulates clustering and imputation as a mutually reinforcing co-optimization process.<n>Experiments on 14 benchmark datasets with 15 representative baselines demonstrate the superiority of CAGI.
  arXiv  Detail & Related papers  (2026-07-08T02:50:02Z)
• SmartIterator: Visual Analytics Workflows for Supervising Unsupervised Data Grouping [0.0]
  emphSmartIterator(SI) is a visual analytics approach that treats the full sequence of grouping results as a first-class analytical object.<n>emphIteraScope(IS) is a coordinated visual display combining quality-metric charts with color encoding, a 1D group embedding with Sankey-style transition flows, and a 2D group embedding with HDBSCAN-detected recurrent archetypes.
  arXiv  Detail & Related papers  (2026-05-27T09:35:32Z)
• World Models as Group Actions [70.0506723637968]
  We argue that action faithfulness should be understood through the compositional structure of actions.<n>We formalize action-conditioned world modeling as realizing a group action on the state space.<n>Our method consistently improves both Group-Action Consistency (GAC) and Group-Action Robustness (GAR) in state-of-the-art video world models.
  arXiv  Detail & Related papers  (2026-05-23T13:42:35Z)
• AgentGR: Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendation [9.65582609558242]
  Group Recommendation (GR) aims to suggest items to a group of users, which has become a critical component of modern social platforms.<n>We propose AgentGR, a novel Semantic-aware Agentic Group Decision-Making Simulator for Group Recommendations.<n>It aims to jointly capture collaborative-semantic user preferences for member-role-playing and simulate dynamic group interactions to reflect real-world group decision-making processes.
  arXiv  Detail & Related papers  (2026-05-11T11:10:53Z)
• Cluster-R1: Large Reasoning Models Are Instruction-following Clustering Agents [81.79110139097297]
  General-purpose embedding models excel at recognizing semantic similarities but fail to capture the characteristics of texts specified by user instructions.<n>We reframe instruction-following clustering as a generative task and train large reasoning models as autonomous clustering agents.<n>Our reasoning-driven training pipeline enables LRMs to interpret high-level clustering instructions and then infer the corresponding latent groupings.
  arXiv  Detail & Related papers  (2026-03-06T10:15:54Z)
• CoT Referring: Improving Referring Expression Tasks with Grounded Reasoning [67.18702329644526]
  CoT Referring enhances model reasoning across modalities through a structured, chain-of-thought training data structure.<n>We restructure the training data to enforce a new output form, providing new annotations for existing datasets.<n>We also integrate detection and segmentation capabilities into a unified MLLM framework, training it with a novel adaptive weighted loss to optimize performance.
  arXiv  Detail & Related papers  (2025-10-03T08:50:21Z)
• Prompt-Guided Relational Reasoning for Social Behavior Understanding with Vision Foundation Models [8.36651942320007]
  Group Activity Detection (GAD) involves recognizing social groups and their collective behaviors in videos.<n>Vision Foundation Models (VFMs), like DinoV2, offer excellent features, but are pretrained primarily on object-centric data.<n>We introduce Prompt-driven Group Activity Detection (ProGraD) -- a method that bridges this gap through 1) learnable group prompts to guide the VFM attention toward social configurations.
  arXiv  Detail & Related papers  (2025-08-11T13:59:22Z)
• Redefining Event Types and Group Evolution in Temporal Data [0.16385815610837165]
  In temporal data, the predominant approach for characterizing group evolution has been through the identification of events" We think of events as archetypes" characterized by a unique combination of quantitative dimensions that we call facet extremities" We apply our framework to evolving groups from several face-to-face interaction datasets, showing it enables richer, more reliable characterization of group dynamics.
  arXiv  Detail & Related papers  (2024-03-11T14:39:24Z)
• A structured regression approach for evaluating model performance across intersectional subgroups [53.91682617836498]
  Disaggregated evaluation is a central task in AI fairness assessment, where the goal is to measure an AI system's performance across different subgroups. We introduce a structured regression approach to disaggregated evaluation that we demonstrate can yield reliable system performance estimates even for very small subgroups.
  arXiv  Detail & Related papers  (2024-01-26T14:21:45Z)
• Detecting Evidence of Organization in groups by Trajectories [0.0]
  We introduce two new approaches to detect network structure inferences based on agent trajectories. To evaluate the effectiveness of the new approaches, we conducted experiments using four scenario simulations based on the animal kingdom.
  arXiv  Detail & Related papers  (2023-08-31T23:57:02Z)
• An Operational Perspective to Fairness Interventions: Where and How to Intervene [9.833760837977222]
  We present a holistic framework for evaluating and contextualizing fairness interventions. We demonstrate our framework with a case study on predictive parity. We find predictive parity is difficult to achieve without using group data.
  arXiv  Detail & Related papers  (2023-02-03T07:04:33Z)
• Overcoming Data Sparsity in Group Recommendation [52.00998276970403]
  Group recommender systems should be able to accurately learn not only users' personal preferences but also preference aggregation strategy. In this paper, we take Bipartite Graphding Model (BGEM), the self-attention mechanism and Graph Convolutional Networks (GCNs) as basic building blocks to learn group and user representations in a unified way.
  arXiv  Detail & Related papers  (2020-10-02T07:11:19Z)

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.