Between Implementation and Outcomes, Growth Matters: Validating an Agent-Based Modeling Approach for Understanding Collaboration Process Management

Increasingly, innovative collaborative partnerships are adopted in setting that distributed organizations, groups, and individuals work together toward solving problems or projects that are too big or complex for single investigators. We look at how the growth process, a largely overlooked aspect of collaboration affects stakeholders’ expectation of each other’s contribution to a civic program and subsequently influences collaboration outcomes. Based on experimental economics and complexity theory, strategic behaviors of stakeholders in collaborations are formalized as a minimum-effort coordination game with Pareto ranked equilibria. We implement the minimum-effort coordination game in a multi-agent simulation. A series of simulated experiments are conducted to gain a fine-grained understanding of how the growth process can engender and reinforce positive expectation among stakeholders, minimize uncertainty, and aid the coordination of stakeholders’ behaviors in collaborative partnership. Additional experiments explore under what circumstances are the mechanisms more or less effective. Findings from this study not only help stakeholders in the collaborative partnership to understand and adopt appropriate growth mechanisms and achieve optimal outcomes; they also inform researchers and practitioner the important role agent-based modeling could play as a facilitative tool. The theoretical framework and the multi-agent method of this study help us to delve into the “black box” of collaborative processes and better explain the successful factors in policy implementation.