Strategic Interactions in a Supply Chain Game

Abstract

The TAC 2003 supply-chain game presented automated trading agents with a challenging strategic problem. Embedded within a high-dimensional stochastic environment was a pivotal strategic decision about initial procurement of components. Early evidence suggested that the entrant field was headed toward a self-destructive, mutually unprofitable equilibrium. Our agent, Deep Maize, introduced a preemptive strategy designed to neutralize aggressive procurement, perturbing the field to a more profitable equilibrium; it worked. Not only did preemption improve Deep Maize's profitability, it improved profitability for the whole field. Whereas it is perhaps counterintuitive that action designed to prevent others from achieving their goals actually helps them, strategic analysis employing an empirical game-theoretic methodology verifies and provides insight about this outcome.

Designing a successful trading agent for supply chain management

“…Here, it had the second highest score, in its semi-final it had the highest score, and in the final, it was the runner-up (to TacTex-05 [5]). 7 …”

“…In so doing, our agent wins a greater proportion of the orders than the other agents, but 7 TacTex-05 is believed to have won by learning between games, and improving its buying strategy over the course of the final. 8 In order to analyse a representative game, we select randomly from those games in the final that did not experience particurly low customer demand where all players ended the game year in debt.…”

“…In 2004, it procured most of its components by placing a large order on day 0 (due to the fact that the suppliers have their full capacity available on day 0 and thus offer the lowest possible price [3]). However, in 2005, the game rules were changed in order to reduce the lottery effect in the supplier interactions, to discourage the use of a day 0 procurement strategy [7], and to deter agents from requesting large quantities of components without intending to order them [1]. This means that the component agent used in 2004 is no longer effective, and thus, we present our updated component agent here.…”

Noncooperatively Optimized Tolerance: Decentralized Strategic Optimization in Complex Systems

“…Specifically, we use the following procedure for approximating Nash equilibria, building on previous methodological work in simulation-based game theoretic analysis [13,14,15,16,17,18] ii. With probability p, computeŝ i ← OPT(s −i ), the optimal decision of player i given s −i ; with probability 1 − p,…”

Pushing the Limits of Rational Agents: The Trading Agent Competition for Supply Chain Management

“…Empirical evidence based on the many games played over the years by agents developed by 75 different teams suggests that opportunities for strategic manipulation that have the potential to subvert the purpose of the game have been eliminated (Ketter et al, 2004;Wellman et al, 2005). Agents must manage their reputations with respect to each supplier; this discourages agents from creating false demand by making large requests and then turning down the resulting offers, thereby inflating prices.…”