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

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

An agent for online auctions: bidding and bundling goods for multiple clients.

by Chi-Lun Chau.Thesis (M.Phil.)--Chinese University of Hong Kong, 2003.Includes bibliographical references (leaves 91-93).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.ivTable of Contents --- p.vList of Figures --- p.viiiDefinitions --- p.1Chapter Chapter 1 - --- Introduction --- p.2Chapter 1.1 --- Background --- p.2Chapter 1.2 --- Testing Environment --- p.4Chapter 1.2.1 --- Game Overview --- p.4Chapter 1.2.2 --- Auctions --- p.5Chapter 1.2.3 --- Utility and Scores --- p.8Chapter 1.3 --- Thesis Contribution and Organization --- p.10Chapter Chapter 2 - --- Relatfd Work --- p.12Chapter 2.1 --- Traditional auction theory --- p.12Chapter 2.2 --- Technologies related to online auctions --- p.13Chapter 2.3 --- Recent researches on online auctions --- p.14Chapter 2.3.1 --- Priceline (proposed by Amy Greenwald) --- p.16Chapter 2.3.2 --- ATTac: Integer Linear Programming (ILP) --- p.17Chapter 2.3.3 --- RoxyBot: Beam search --- p.19Chapter Chapter 3 - --- Theoretical model for agents in online auctions --- p.20Chapter 3.1 --- High-level planning --- p.20Chapter 3.2 --- Mathematical model --- p.21Chapter Chapter 4 - --- Agent Architecture and Mechanisms --- p.26Chapter 4.1 --- Architecture --- p.26Chapter 4.2 --- Cost Estimator (CE) --- p.29Chapter 4.2.1 --- Closed auction --- p.29Chapter 4.2.2 --- "Open ""take-it or leave-it"" market" --- p.30Chapter 4.2.3 --- Open continuous double auction (CDA) --- p.31Chapter 4.2.4 --- Open multi-unit ascending auction --- p.33Chapter 4.4.2.1 --- Historical clearing prices --- p.33Chapter 4.4.2.2 --- Increasing marginal costs --- p.35Chapter 4.4.2.3 --- Bid winning probability --- p.37Chapter 4.3 --- Allocation and Acquisition Solver (AAS) --- p.39Chapter 4.3.1 --- Un-coordinated VS coordinated aspiration --- p.39Chapter 4.3.2 --- Optimal VS heuristic approach --- p.40Chapter 4.3.3 --- An greedy approach with coordinated aspiration --- p.41Chapter 4.4 --- The Bidders --- p.44Chapter 4.4.1 --- """Take-it or leave-it"" market" --- p.44Chapter 4.4.2 --- Multi-unit ascending auction --- p.46Chapter 4.4.2.1 --- Budget bidding --- p.47Chapter 4.4.2.2 --- Low price bidding --- p.49Chapter 4.4.3 --- Continuous double auction (CDA) --- p.51Chapter 4.4.3.1 --- Review of fuzzy reasoning mechanism --- p.52Chapter 4.4.3.2 --- Fuzzy Reasoning in FL-strategy --- p.54Chapter 4.4.3.3 --- Adaptive Risk Attitude --- p.59Chapter Chapter 5 - --- Results --- p.61Chapter 5.1 --- TAC '02 Competition --- p.62Chapter 5.1.1 --- Tournament result of our working agent --- p.63Chapter 5.1.2 --- "Comparisons between CUHK, ATTac and Roxybot" --- p.65Chapter 5.1.3 --- Low-price Bidding --- p.66Chapter 5.2 --- Controlled Environment --- p.67Chapter 5.2.1 --- Software platform --- p.67Chapter 5.2.2 --- Aggressive agent vs. Adaptive agent --- p.68L-agent (aggressive agent) --- p.68S-agent (adaptive agent) --- p.69Experimental Setting --- p.70Experimental Results --- p.71The Hawk-Dove Game --- p.72Chapter 5.2.3 --- Our agent model --- p.73Experimental Setting --- p.73Experimental Results --- p.74Chapter 5.2.4 --- Historical clearing price --- p.75Experimental Setting --- p.76Experimental Result --- p.76Comparisons among different approaches --- p.77Chapter 5.2.5 --- Increasing marginal cost --- p.79Experimental Setting --- p.79Experimental Result --- p.79Chapter 5.2.6 --- Bid winning probability --- p.81Experimental Setting --- p.81Experimental Result --- p.81Chapter 5.2.7 --- FL-strategy --- p.82A-strategy --- p.83Experimental Setting --- p.84Experimental Result --- p.85Chapter Chapter 6 - --- Conclusion and Future work --- p.87Reference --- p.9

Automated Markets and Trading Agents

Computer automation has the potential, just starting to be realized, of transforming the design and operation of markets, and the behaviors of agents trading in them. We discuss the possibilities for automating markets, presenting a broad conceptual framework covering resource allocation as well as enabling marketplace services such as search and transaction execution. One of the most intriguing opportunities is provided by markets implementing computationally sophisticated negotiation mechanisms, for example combinatorial auctions. An important theme that emerges from the literature is the centrality of design decisions about matching the domain of goods over which a mechanism operates to the domain over which agents have preferences. When the match is imperfect (as is almost inevitable), the market game induced by the mechanism is analytically intractable, and the literature provides an incomplete characterization of rational bidding policies. A review of the literature suggests that much of our existing knowledge comes from computational simulations, including controlled studies of abstract market designs (e.g., simultaneous ascending auctions), and research tournaments comparing agent strategies in a variety of market scenarios. An empirical game-theoretic methodology combines the advantages of simulation, agent-based modeling, and statistical and game-theoretic analysis.http://deepblue.lib.umich.edu/bitstream/2027.42/49510/1/ace_galleys.pd

Practical Strategic Reasoning with Applications in Market Games.

Strategic reasoning is part of our everyday lives: we negotiate prices, bid in auctions, write contracts, and play games. We choose actions in these scenarios based on our preferences, and our beliefs about preferences of the other participants. Game theory provides a rich mathematical framework through which we can reason about the influence of these preferences. Clever abstractions allow us to predict the outcome of complex agent interactions, however, as the scenarios we model increase in complexity, the abstractions we use to enable classical game-theoretic analysis lose fidelity. In empirical game-theoretic analysis, we construct game models using empirical sources of knowledge—such as high-fidelity simulation. However, utilizing empirical knowledge introduces a host of different computational and statistical problems. I investigate five main research problems that focus on efficient selection, estimation, and analysis of empirical game models. I introduce a flexible modeling approach, where we may construct multiple game-theoretic models from the same set of observations. I propose a principled methodology for comparing empirical game models and a family of algorithms that select a model from a set of candidates. I develop algorithms for normal-form games that efficiently identify formations—sets of strategies that are closed under a (correlated) best-response correspondence. This aids in problems, such as finding Nash equilibria, that are key to analysis but hard to solve. I investigate policies for sequentially determining profiles to simulate, when constrained by a budget for simulation. Efficient policies allow modelers to analyze complex scenarios by evaluating a subset of the profiles. The policies I introduce outperform the existing policies in experiments. I establish a principled methodology for evaluating strategies given an empirical game model. I employ this methodology in two case studies of market scenarios: first, a case study in supply chain management from the perspective of a strategy designer; then, a case study in Internet ad auctions from the perspective of a mechanism designer. As part of the latter analysis, I develop an ad-auctions scenario that captures several key strategic issues in this domain for the first time.Ph.D.Computer Science & EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/75848/1/prjordan_1.pd

Toward Automating and Systematizing the Use of Domain Knowledge in Feature Selection

University of Minnesota Ph.D. dissertation. August 2015. Major: Computer Science. Advisor: Maria Gini. 1 computer file (PDF); xi, 185 pages.Constructing prediction models for real-world domains often involves practical complexities that must be addressed to achieve good prediction results. Often, there are too many sources of data (features). Limiting the set of features in the prediction model is essential for good performance, but prediction accuracy may be degraded by the inadvertent removal of relevant features. The problem is even more acute in situations where the number of training instances is limited, as limited sample size and domain complexity are often attributes of real-world problems. This thesis explores the practical challenges of building regression models in large multivariate time-series domains with known relationships between variables. Further, we explore the conventional wisdom related to preparing datasets for model calibration in machine learning, and discuss best practices for learning time-varying concepts from data. The core contribution of this work is a novel wrapper-based feature selection framework called Developer-Guided Feature Selection (DGFS). It systematically incorporates domain knowledge for domains characterized by a large number of observable features. The observable features may be related to each other by logical, temporal, or spatial relationships, some of which are known to the model developer a priori. The approach relies on limited domain-specific knowledge but can replace or improve upon more elaborate domain specific models and on fully automated feature selection for many applications. As a wrapper-based approach, DGFS can augment existing multivariate techniques used in high-dimensional domains to produce improved modeling results particularly in situations where the volume of training data is limited. We demonstrate the viability of our method in several complex domains (natural and synthetic) that have significant temporal aspects and many observable features