22 research outputs found
Price Prediction in a Trading Agent Competition
The 2002 Trading Agent Competition (TAC) presented a challenging market game
in the domain of travel shopping. One of the pivotal issues in this domain is
uncertainty about hotel prices, which have a significant influence on the
relative cost of alternative trip schedules. Thus, virtually all participants
employ some method for predicting hotel prices. We survey approaches employed
in the tournament, finding that agents apply an interesting diversity of
techniques, taking into account differing sources of evidence bearing on
prices. Based on data provided by entrants on their agents' actual predictions
in the TAC-02 finals and semifinals, we analyze the relative efficacy of these
approaches. The results show that taking into account game-specific information
about flight prices is a major distinguishing factor. Machine learning methods
effectively induce the relationship between flight and hotel prices from game
data, and a purely analytical approach based on competitive equilibrium
analysis achieves equal accuracy with no historical data. Employing a new
measure of prediction quality, we relate absolute accuracy to bottom-line
performance in the game
Walverine: A Walrasian Trading Agent
TAC-02 was the third in a series of Trading Agent Competition events fostering research in automating trading strategies by showcasing alternate approaches in an open-invitation market game. TAC presents a challenging travel-shopping scenario where agents must satisfy client preferences for complementary and substitutable goods by interacting through a variety of market types. Michigan's entry, Walverine, bases its decisions on a competitive (Walrasian) analysis of the TAC travel economy. Using this Walrasian model, we construct a decision-theoretic formulation of the optimal bidding problem, which Walverine solves in each round of bidding for each good. Walverine's optimal bidding approach, as well as several other features of its overall strategy, are potentially applicable in a broad class of trading environments.trading agent, trading competition, tatonnement, competitive equilibrium
Decision-theoretic bidding in online-auctions
With the increasing role of electronic commerce in business applications, much attention is paid to online-auctions. As auctions become more and more popular in electronic commerce, agents face the problem of participating in multiple independent auctions simultaneously or in sequence. Decision making of agents becomes difficult when they have to buy bundles of goods. In this case the agents have to cope with substitutable or complementary effects between the single goods. In this paper we analyse existing approaches of tackling the problem of decision making in multiple, heterogeneous auctions and develop a flexible Dynamic Programming-based decision-making framework for agents, participating in multiple auctions. This work extends existing Dynamic Programming-approaches in this field
Fostering efficiency of computational resource allocation - Integrating information services into markets
The application of market mechanisms for the allocation of computing services is a demanding task,
which requires bridging economic and associated technical challenges. Even if the market-based
approach promises an efficient allocation of computing services, the wide heterogeneity of consumer
requirements and the diversity of computational services on provider side are challenging the
processes of finding, allocating, and using an appropriate service in an autonomous way. The focus of
the most papers is mainly devoted to the optimization embedded in the allocation process itself.
However, we think that the optimization process starts much earlier and contains the information
gathering until the final market-based resource allocations.
In this paper we introduce an integrated framework for market-based allocation of computing
services, integrating information retrieval of market information, prediction models, bidding
strategies and marked mechanisms. As proof-of-concept, we implemented a first prototype of the
framework. Furthermore, we propose a methodology for evaluating strategic behavior in market
mechanisms with bidding strategies using market information and statistical prediction techniques.
First simulation results show strategic behavior in selected market mechanisms by applying the
proposed techniques
Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges
We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation 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 to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management.dynamic pricing;machine learning;market forecasting;Trading agents
Detecting and Forecasting Economic Regimes in Multi-Agent Automated Exchanges
We show how an autonomous agent can use observable market conditions to characterize the microeconomic situation 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 to learn dominant market conditions, such as over-supply or scarcity, from historical data using Gaussian mixture models to construct price density functions. We discuss how this model can be combined with real-time observable information to identify the current dominant market condition and to forecast market changes over a planning horizon. We forecast market changes via both a Markov correction-prediction process and an exponential smoother. Empirical analysis shows that the exponential smoother yields more accurate predictions for the current and the next day (supporting tactical decisions), while the Markov correction-prediction process is better for longer term predictions (supporting strategic decisions). Our approach offers more flexibility than traditional regression based approaches, since it does not assume a fixed functional relationship between dependent and independent variables. We validate our methods by presenting experimental results in a case study, the Trading Agent Competition for Supply Chain Management