Identification and prediction of economic regimes to guide decision making in multi-agent marketplaces

• Authors: Maria Gini;Wolfgang Ketter
• Venue: Identification and prediction of economic regimes to guide decision making in multi-agent marketplaces
• Year: 2007

Supply chain management is commonly employed by businesses to improve organizational processes by optimizing the transfer of goods, information, and services between buyers and suppliers. Traditionally, supply chains have been created and maintained through the interactions of human representatives of the various companies involved. However, the recent advent of autonomous software agents opens new possibilities for automating and coordinating the decision making processes between the various parties involved. Autonomous agents participating in supply chain management must typically make their decisions in environments of high complexity, high variability, and high uncertainty since only limited information is visible. We present an approach whereby an autonomous agent is able to make tactical decisions, such as product pricing, as well as strategic decisions, such as product mix and production planning, in order to maximize its profit despite the uncertainties in the market. The agent predicts future market conditions and adapts its decisions on procurement, production, and sales accordingly. Using a combination of machine learning and optimization techniques; the agent first characterizes the microeconomic conditions, such as over-supply or scarcity, of the market. These conditions are distinguishable statistical patterns that we call economic regimes. They are learned from historical data by using a Gaussian Mixture Model to model the price density of the different products and by clustering price distributions that recur across days. In real-time the agent identifies the current dominant market condition and forecasts market changes over a planning horizon. Methods for the identification of regimes are explored in detail, and three different algorithms are presented. One is based on exponential smoothing, the second on a Markov prediction process, and the third on a Markov correction-prediction process. We examine a wide range of tuning options for these algorithms, and show how they can be used to predict prices, price trends, and the probability of receiving a customer order. We validate our methods by presenting experimental results from the Trading Agent Competition for Supply Chain Management, an international competition of software agents that has provided inspiration for this work. We also show how the same approach can be applied to the stock market.