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@InProceedings{KetterPhDThesis,
  author =       "Wolfgang Ketter",
  title =        "Identification and Prediction of Economic Regimes to
                  Guide Decision Making in Multi-Agent Marketplaces",
  booktitle =    "PhD Thesis",
  pages =        {1--138},
  year =         "2007",
  abstract = "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 \emph{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.",
address = {University of Minnesota, Twin-Cities, USA},
month = {January},
bib2html_pubtype = {Thesis},
bib2html_rescat = {Trading Agents: Supply-Chain Management},
}