We describe one such competition, the Trading Agent Competition for Supply Chain Management (TAC SCM). We discuss its significance in the context of today's global market economy as well as AI research, the ways in which it breaks away from limiting assumptions made in prior work, and some of the advances it has engendered over the past six years. TAC SCM requires autonomous supply chain entities, modeled as agents, to coordinate their internal operations while concurrently trading in multiple dynamic and highly competitive markets. Since its introduction in 2003, the competition has attracted more than 150 entries and brought together researchers from AI and beyond in the form of 75 competing teams from 25 different countries. Yet the real-time demands of many domains do not lend themselves to traditional assumptions of rationality (Simon 1979, Wellman 1996).

Quantitative prediction problems involving both spatial and temporal components have appeared prominently in several disparate research areas including finance, supply chain management, and civil engineering. Unfortunately, either the spatial or temporal aspect tends to dominate the other in many prediction formulations. We briefly examine the underlying formulations used in spatial and temporal prediction. Then, we outline a method that combines these approaches and improves prediction results in high-dimensional economic domains by integrating multivariate and time series techniques which require minimal tuning but achieve superior performance compared to previous methods. We present preliminary results in the context of the Trading Agent Competition for Supply Chain Management.

Over the years, competitions have been important catalysts for progress in Artificial Intelligence. We describe one such competition, the Trading Agent Competition for Supply Chain Management (TAC SCM). We discuss its significance in the context of today’s global market economy as well as AI research, the ways in which it breaks away from limiting assumptions made in prior work, and some of the advances it has engendered over the past six years. TAC SCM requires autonomous supply chain entities, modeled as agents, to coordinate their internal operations while concurrently trading in multiple dynamic and highly competitive markets. Since its introduction in 2003, the competition has attracted over 150 entries and brought together researchers from AI and beyond in the form of 75 competing teams from 25 different countries.