Browsing by Author "Gupta, Alok"

Now showing 1 - 2 of 2
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    Detecting and Forecasting Economic Regimes in Automated Exchanges
    (2007-03-05) Ketter, Wolfgang; Collins, John; Gini, Maria; Gupta, Alok; Schrater, Paul
    We present basic building blocks of an agent that can use observable market conditions to characterize the microeconomic conditions of the market and predict future market trends. The agent can use this information to make both tactical decisions such as pricing and strategic decisions such as product mix and production planning. We develop methods that can learn dominant market conditions, such as over-supply or scarcity, from historical data using computational methods to construct price density functions. We discuss how this knowledge can be used, together with real-time observable information, to identify the current dominant market condition and to forecast market changes over a planning horizon. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.
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    Tactical and Strategic Sales Management for Intelligent Agents Guided By Economic Regimes
    (2008-10-20) Ketter, Wolfgang; Collins, John; Gini, Maria; Gupta, Alok; Schrater, Paul
    We present a computational approach that autonomous software agents can adopt to make tactical decisions, such as product pricing, and strategic decisions, such as product mix and production planning, to maximize profit in markets with supply and demand uncertainties. Using a combination of machine learning and optimization techniques, the agent is able to characterize economic regimes, which are historical microeconomic conditions reflecting situations such as over-supply and scarcity. We assume an agent is capable of using real-time observable information to identify the current dominant market condition and we show how it can forecast regime changes over a planning horizon. We demonstrate how the agent can then use regime characterization to predict prices, price trends, and the probability of receiving a customer order in a dynamic supply chain environment. We validate our methods by presenting experimental results from a testbed derived from the Trading Agent Competition for Supply Chain Management (TAC SCM). The results show that our agent outperforms traditional short- and long-term predictive methodologies (such as exponential smoothing) significantly, resulting in accurate prediction of customer order probabilities, and competitive market prices. This, in turn, has the potential to produce higher profits. We also demonstrate the versatility of our computational approach by applying the methodology to prediction of stock price trends.