We examine behavior in an experimental collaboration game that incorporates endogenous network formation. The environment is modeled as a generalization of the voluntary contributions mechanism. By varying the information structure in a controlled laboratory experiment, we examine the underlying mechanisms of reciprocity that generate emergent patterns in linking and contribution decisions. Providing players more detailed information about the sharing behavior of others drastically increases efficiency, and positively affects a number of other key outcomes. To understand the driving causes of these changes in behavior we develop and estimate a structural model for actions and small network panels and identify how social preferences affect behavior. We find that the treatment reduces altruism but stimulates reciprocity, helping players coordinate to reach mutually beneficial outcomes. In a set of counterfactual simulations, we show that increasing trust in the community would encourage higher average contributions at the cost of mildly increased free-riding. Increasing overall reciprocity greatly increases collaborative behavior when there is limited information but can backfire in the treatment, suggesting that negative reciprocity and punishment can reduce efficiency. The largest returns would come from an intervention that drives players away from negative and toward positive reciprocity.

翻译：我们研究了一个包含内生网络形成的实验性协作博弈中的行为。该环境被建模为自愿贡献机制的一般化形式。通过在受控实验室实验中改变信息结构，我们探讨了互惠性的潜在机制，这些机制在连接和贡献决策中产生了涌现模式。向参与者提供关于他人分享行为的更详细信息显著提升了效率，并对其他若干关键结果产生了积极影响。为了理解这些行为变化的驱动原因，我们构建并估计了一个关于行动和小型网络面板的结构模型，并识别了社会偏好如何影响行为。我们发现，该处理降低了利他主义，但激发了互惠性，帮助参与者协调以实现互利结果。在一组反事实模拟中，我们表明，增加社区内的信任会鼓励更高的平均贡献，但代价是轻度增加搭便车行为。在信息有限的情况下，提升整体互惠性会大幅增强协作行为，但在处理中可能适得其反，这表明负向互惠和惩罚可能降低效率。最大的收益来自于引导参与者从负向互惠转向正向互惠的干预措施。