Collective dynamics emerge from countless individual decisions. Yet, we poorly understand the processes governing dynamically-interacting individuals in human collectives under realistic conditions. We present a naturalistic immersive-reality experiment where groups of participants searched for rewards in different environments, studying how individuals weigh personal and social information and how this shapes individual and collective outcomes. Capturing high-resolution visual-spatial data, behavioral analyses revealed individual-level gains—but group-level losses—of high social information use and spatial proximity in environments with concentrated (vs. distributed) resources. Incentivizing participants at the group (vs. individual) level facilitated adaptation to concentrated environments, buffering apparently excessive scrounging. To infer discrete choices from unconstrained interactions and uncover the underlying decision mechanisms, we developed an unsupervised Social Hidden Markov Decision model. Computational results showed that participants were more sensitive to social information in concentrated environments frequently switching to a social relocation state where they approach successful group members. Group-level incentives reduced participants’ overall responsiveness to social information and promoted higher selectivity over time. Finally, mapping group-level spatio-temporal dynamics through time-lagged regressions revealed a collective exploration-exploitation trade-off across different timescales. Our study unravels the processes linking individual-level strategies to emerging collective dynamics, and provides tools to investigate decision-making in freely-interacting collectives.

集体动力源自无数个人的决定。然而，我们对现实条件下人类集体中动态互动个体的管理过程知之甚少。我们提出了一个自然主义的沉浸式现实实验，参与者小组在不同的环境中寻找奖励，研究个人如何权衡个人和社会信息以及这如何影响个人和集体的结果。通过捕获高分辨率的视觉空间数据，行为分析揭示了在资源集中（相对于分布式）的环境中，高社会信息使用和空间接近度带来了个人层面的收益，但群体层面的损失。在团体（相对于个人）层面激励参与者有助于适应集中环境，缓冲明显的过度搜寻。为了从不受约束的交互中推断离散选择并揭示潜在的决策机制，我们开发了一种无监督的社会隐马尔可夫决策模型。计算结果表明，在集中环境中，参与者对社交信息更加敏感，经常切换到社交重新定位状态，接触成功的群体成员。群体层面的激励措施降低了参与者对社会信息的整体反应，并随着时间的推移提高了选择性。最后，通过时滞回归绘制群体层面的时空动态，揭示了不同时间尺度上的集体探索-利用权衡。我们的研究揭示了将个人层面的策略与新兴集体动态联系起来的过程，并提供了研究自由互动的集体决策的工具。