Yongren Shi | Searcharxiv

arXiv2026

When Bots Join the Team: Bot Adoption and the Institutional Fabric of Open-Source Software Projects

AI agents are joining human teams, raising a basic question: when an automated agent becomes a regular participant, does group organization strengthen or weaken? We study this question in open-source software, where bots open pull requests, review code, and merge changes alongside people, leaving a public record of every interaction. Treating bots as participants rather than tools, we examine 2,991 GitHub projects for two years before and after each adopted its first bot. We measure three capabilities that institutional theory links to durable coordination - repeated engagement, social memory, and role differentiation - and two outcomes: conflict cascades and output distinctiveness. Bot adoption is followed by more repeated collaboration, greater recognition of specific bots in discussion, fewer conflict cascades, and more distinctive outputs. These changes cluster around adoption rather than accumulating gradually. Because we lack an untreated comparison group, we interpret the results as precisely timed associations, not causal effects. Two patterns are difficult for alternative explanations to account for: capabilities predict outcomes according to their function - coordination versus differentiation - rather than whether humans or bots provide them, and human-side capabilities account for the bot-conflict association but not the bot-distinctiveness association. The findings are consistent with a specific interpretation: predictable, rule-based agents can become part of a community's social infrastructure. The bot is the occasion; social organization is the mechanism.

cs.AI↗

arXiv2025

Multiplex Networks Provide Structural Pathways for Social Contagion in Rural Social Networks

Human social networks are inherently multiplex, comprising overlapping layers of relationships. Different layers may have distinct structural properties and interpersonal dynamics, but also may interact to form complex interdependent pathways for social contagion. This poses a fundamental problem in understanding behavioral diffusion and in devising effective network-based interventions. Here, we introduce a new conceptualization of how much each network layer contributes to critical contagion pathways and quantify it using a novel metric, network torque. We exploit data regarding sociocentric maps of 110 rural Honduran communities using a battery of 11 name generators and an experiment involving an exogenous intervention. Using a novel statistical framework, we assess the extent to which specific network layers alter global connectivity and support the spread of three experimentally introduced health practices. The results show that specific relationship types - such as close friendships - particularly enable non-overlapping diffusion pathways, amplifying behavioral change at the village level. For instance, non-redundant pathways enabled by closest friends can increase the adoption of correct knowledge about feeding newborns inappropriate chupones and enhance attitudes regarding fathers' involvement in postpartum care. Non-overlapping multiplex social ties are relevant to social contagion and social coherence in traditionally organized social systems.

cs.SI↗