Agent learning in supplier selection models

We use agent-based modeling to study the performance of a supplier selection model, originally proposed by Croson and Jacobides, which displays a complicated reward and punishment profile under incomplete information. We document the dynamics and convergence to equilibrium of the interactions of a single buyer with a heterogeneous group of sellers, which results in both separation of sellers capable of producing high-quality goods from those incapable of doing so, and continuing incentives for high-quality-capable sellers to produce at the maximum quality possible. We model two methods of determining exploration reference points – an “auction-style” model focusing on probability of success and a “newsvendor-style” model focusing on profitability. Our simulation shows that (1) the tournament structure suffices to reach convergence at high-quality levels whenever the number of suppliers exceeds three, (2) punishment length and number of suppliers are substitutes, and (3) shorter punishments improve learning speed of convergence. Moreover, we show that it is strictly better for the buyer to transact with relatively few suppliers – a conclusion generated endogenously inside the model as a tradeoff between exploration and exploitation, rather than through assumptions that explicitly penalize supplier proliferation.