Commitment, Learning, and Alliance Performance: A Formal Analysis Using an Agent-Based Network

Current theoretical arguments highlight a dilemma faced by actors who either adopt a weak or strong commitment strategy for managing their alliances and partnerships. Actors who pursue a weak commitment strategy—i.e. immediately abandon current partners when a more profitable alternative is presented—are more likely to identify the most rewarding alliances. On the other hand, actors who enact a strong commitment approach are more likely to take advantage of whatever opportunities can be found in existing partnerships. Using agent-based modeling, we show that actors who adopt a moderate commitment strategy overcome this dilemma and outperform actors who adopt either weak or strong commitment approaches. We also show that avoiding this dilemma rests on experiencing a related tradeoff: moderately-committed actors sacrifice short-term performance for the superior knowledge and information that allows them to eventually do better.