Jeffrey K. MacKie-Mason

Automated Markets and Trading Agents (Download full paper)

Jeffrey K. MacKie-Mason and Michael P. Wellman

Published on: April, 2005

Abstract: 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.

Exploring bidding strategies for market-based scheduling (Download full paper)

Daniel M. Reeves, Michael. P. Wellman, Jeffrey K. MacKie-Mason, and Anna Osepayshvili

Published on: March, 2005

Abstract: A market-based scheduling mechanism allocates resources indexed by time to alternative uses based on the bids of participating agents. Agents are typically interested in multiple time slots of the schedulable resource, with value determined by the earliest deadline by which they can complete their corresponding tasks. Despite the strong complementarity among slots induced by such preferences, it is often infeasible to deploy a mechanism that coordinates allocation across all time slots. We explore the case of separate, simultaneous markets for individual time slots, and the strategic problem it poses for bidding agents. Investigation of the straightforward bidding policy and its variants indicates that the efficacy of particular strategies depends critically on preferences and strategies of other agents, and that the strategy space is far too complex to yield to general game-theoretic analysis. For particular environments, however, it is often possible to derive constrained equilibria through evolutionary search methods.

Price Prediction Strategies for Market-Based Scheduling (Download full paper)

MacKie-Mason, Jeffrey K. Osepayshvili, Anna Reeves, Daniel M. Wellman, Michael P.

Published on: June, 2004

Abstract: In a market-based scheduling mechanism, the allocation of time-specific resources to tasks is governed by a competitive bidding process. Agents bidding for multiple, separately allocated time slots face the risk that they will succeed in obtaining only part of their requirement, incurring expenses for potentially worthless slots. We investigate the use of price prediction strategies to manage such risk. Given an uncertain price forecast, agents follow simple rules for choosing whether and on which time slots to bid. We find that employing price predictions can indeed improve performance over a straightforward baseline in some settings. Using an empirical game-theoretic methodology, we establish Nash equilibrium profiles for restricted strategy sets. This allows us to con- firm the stability of price-predicting strategies, and measure overall efficiency. We further experiment with variant strategies to analyze the source of prediction's power, demonstrate the existence of self-confirming predictions, and compare the performance of alternative prediction methods.

Auction Protocols for Decentralized Scheduling (Download full paper)

Wellman, M. P., W. E. Walsh, P. R. Wurman and Jeffrey K. MacKie-Mason

Published on: January, 2001

Abstract: Scheduling is the problem of allocating resources to alternate possible uses over designated periods of time. Several have proposed (and some have tried) market-based approaches to decentralized versions of the problem, where the competing uses are represented by autonomous agents. Market mechanisms use prices derived through distributed bidding protocols to determine an allocation, and thus solve the scheduling problem. To analyze the behavior of market schemes, we formalize decentralized scheduling as a discrete resource allocation problem, and bring to bear some relevant economic concepts. Drawing on results from the literature, we discuss the existence of equilibrium prices for some general classes of scheduling problems, and the quality of equilibrium solutions. To remedy the potential nonexistence of price equilibria due to complementarity in preference, we introduce additional markets in combinations of basic goods. We present some auction mechanisms and bidding protocols corresponding to the two market structures, and analyze their computational and economic properties. Finally, we consider direct revelation mechanisms, and compare to the market-based approach.

A Report on the PEAK Experiment: Context and Design (Download full paper)

MacKie-Mason, Jeffrey K. Bonn, Maria S. Lougee, Wendy P. Riveros, Juan F.

Published on: June, 1999

The PEAK Experiment: Usage and Economic Behavior (Download full paper)

MacKie-Mason, Jeffrey K. Riveros, Juan F. Bonn, Maria S. Lougee, Wendy P.

Published on: January, 1999

'Bundling' y el Acceso Electronico a la Informacion Academica: El Projecto PEAK (Download full paper)

MacKie-Mason, Jeffrey K. Riveros, Juan F.

Published on: December, 1998

Some Economics of Market-Based Distributed Scheduling (Download full paper)

MacKie-Mason, Jeffrey K. Walsh, William E. Wellman, Michael P. Wurman, Peter

Published on: May, 1998

Abstract: Market mechanisms solve distributed scheduling problems by allocating the scheduled resources according to market prices. We model distributed scheduling as a discrete resource allocation problem, and demonstrate the applicability of economic analysis to this framework. Drawing on results from the literature, we discuss the existence of equilibrium prices for some general classes of scheduling problems, and the quality of equilibrium solutions. We then present two protocols for implementing market solutions, and analyze their computational and economic properties.

PEAK: Pricing Electronic Access to Knowledge (Download full paper)

MacKie-Mason, Jeffrey K. Jankovich, Alexandra

Published on: January, 1997

Improving Learning Performance by Applying Economic Knowledge (Download full paper)

Christopher H. Brooks, Robert S. Gazzale, Jeffrey K. MacKie-Mason, and Edmund H. Durfee

Abstract: Digital information economies require information goods producers to learn how to position themselves within a potentially vast product space. Further, the topography of this space is often nonstationary, due to the interactive dynamics of multiple producers changing their positions as they try to learn the distribution of consumer preferences and other features of the problem's economic structure. This presents a producer or its agent with a difficult learning problem: how to locate profitable niches in a very large space. In this paper, we present a model of an information goods duopoly and show that, under complete information, producers would prefer not to compete, instead acting as local monopolists and targeting separate niches in the consumer population. However, when producers have no information about the problem they are solving, it can be quite difficult for them to converge on this solution. We show how a modest amount of economic knowledge about the problem can make it much easier, either by reducing the search space, starting in a useful area of the space, or by introducing a gradient. These experiments support the hypothesis that a producer using some knowledge of a problem's (economic) structure can outperform a producer that is performing a naive, knowledge-free form of learning.

Model Selection in an Information Economy: Choosing what to Learn (Download full paper)

Christopher H. Brooks, Robert S. Gazzale, Rajarshi Das, Jeffrey O. Kephart, Jeffrey K. MacKie-Mason, and Edmund H. Durfee

Abstract: In an economy in which a producer must learn the preferences of a consumer population, it is faced with a classic decision problem: when to explore and when to exploit. If the producer has a limited number of chances to experiment, it must explicitly consider the cost of learning (in terms of foregone profit) against the value of the information acquired. Information goods add an additional dimension to this problem; due to their flexibility, they can be bundled and priced according to a number of different price schedules. An optimizing producer should consider the profit each price schedule can extract, as well as the difficulty of learning of this schedule. In this paper, we demonstrate the tradeoff between complexity and profitability for a number of common price schedules. We begin with a one-shot decision as to which schedule to learn. Schedules with moderate complexity are preferred in the short and medium term, as they are learned quickly, yet extract a significant fraction of the available profit. We then turn to the repeated version of this one-shot decision and show that moderate complexity schedules, in particular two-part tariff, perform well when the producer must adapt to nonstationarity in the consumer population. When a producer can dynamically change schedules as it learns, it can use an explicit decision-theoretic formulation to greedily select the schedule which appears to yield the greatest profit in the next period.

Pricing Information Bundles in a Dynamic Environment (Download full paper)

Jeffrey O. Kephart, Rajarshi Das, Christopher H. Brooks, Edmund H. Durfee, Robert S. Gazzale and Jeffrey K. MacKie-Mason

Abstract: We explore a scenario in which a monopolist producer of information goods seeks to maximize its profits in a market where consumer demand shifts frequently and unpredictably. The producer is free to set an arbitrarily complex price schedule-a function that maps the set of purchased items to a price-but without direct knowledge of consumer demand it cannot compute the optimal schedule. Instead, it must employ a form of optimization based on trial and error. By means of a simple model of consumer demand and a modified version of a simple nonlinear optimization routine, we study a variety of parameterizations of the price schedule and quantity some of the relationships among learnability, complexity, and profitability. In particular, we show that fixed pricing or simple two-parameter dynamic pricing schedules are preferred when consumer demand shifts frequently, but that dynamic pricing based on more complex schedules tends to be most profitable when consumer demand shifts very infrequently.

Endogenous Differentiation of Information Goods Under Uncertainty (Download full paper)

Robert S. Gazzale and Jeffrey K. MacKie-Mason

Abstract: Information goods can be reconfigured at low cost. Therefore, firms can choose how to differentiate their products at a frequency comparable to price changes. However, doing so effectively is complicated by uncertainty about customer preferences, compounded by the fact that the search for a good product niche is carried out in competition with other searching firms. We study two firms that differentiate their information goods. The firms simultaneously compete in product configuration and price. We assume a non-uniform distribution of consumers: the largest number prefer a product located at a ``sweet spot,'' but the rate at which the customer density falls off away from this product configuration is unknown. Our characterization reflects the standard tradeoff between exploitation (current profit) and exploration (learning to enhance future profit). In our model firms balance current profits from competing for a mass and a niche market, while learning about the profitability of these alternative strategies. We show that the amount of learning that firms will undertake depends on the convexity or concavity of the profit function in the rate of demand fall-off. In our model firms have an incentive to learn, and can use both price and product configuration in order to explore. We show that the ability to explore in product characteristic space leads to a previously unidentified consequence of learning: attenuation of competition. The incentive to learn induces firms to differentiate their products more than they would if the value of learning were ignored. This leads to decreased direct competition with rivals, and thus higher prices and profits than if the firms were acting myopically. Thus, we might expect that when firms are not well informed about consumer preferences for information goods --- as might be especially true in new markets for innovative products --- product diversity will be higher and direct competition will be smaller than might otherwise be expected.

Information Bundling in a Dynamic Environment (Download full paper)

Christopher H. Brooks, Rajarshi Das, Jeffrey O. Kephart, Jeffrey K. MacKie-Mason, Robert S. Gazzale, and Edmund H. Durfee

Abstract: Markets for digital information goods provide the possibility of exploring new and more complex pricing schemes, due to information goods' flexibility and negligible marginal cost. In this paper we compare the dynamic performance of price schedules of varying complexity under two different specifications of consumer demand shifts.

Pricing and Bundling Electronic Information Goods: Experimental Evidence (Download full paper)

MacKie-Mason, Jeffrey K., Juan F. Riveros and Robert S. Gazzale

Abstract: Dramatic increases in the capabilities and decreases in the costs of computers and communication networks have fomented revolutionary thoughts in the scholarly publishing community. In one dimension, traditional pricing schemes and product packages are being modified or replaced. We designed and undertook a large-scale field experiment in pricing and bundling for electronic access to scholarly journals: PEAK. We provided Internet-based delivery of content from 1200 Elsevier Science journals to users at multiple campuses and commercial facilities. Our primary research objective was to generate rich empirical evidence on user behavior when faced with various bundling schemes and price structures. In this article we report initial results. We found that although there is a steep initial learning curve, decision-makers rapidly comprehended our innovative pricing schemes. We also found that our novel and flexible "generalized subscription" was successful at balancing paid usage with easy access to a larger body of content than was previously available to participating institutions. Finally, we found that both monetary and non-monetary user costs have a significant impact on the demand for electronic access.

Two-sided Learning in an Agent Economy for Information Bundles (Download full paper)

Kephart, Jeffrey O., Rajarshi Das and Jeffrey K. MacKie-Mason

Abstract: Commerce in information goods is one of the earliest emerging applications for intelligent agents in commerce. However, the fundamental characteristics of information goods mean that they can and likely will be offered in widely varying configurations. Participating agents will need to deal with uncertainty about both prices and location in multi-dimensional product space. Thus, studying the behavior of learning agents is central to understanding and designing for agent-based information economies. Since uncertainty will exist on both sides of transactions, and interactions between learning agents that are negotiating and transacting with other learning agents may lead to unexpected dynamics, it is important to study two-sided learning. We present a simple but powerful model of an information bundling economy with a single producer and multiple consumer agents. We explore the pricing and purchasing behavior of these agents when articles can be bundled. In this initial exploration, we study the dynamics of this economy when consumer agents are uninformed about the distribution of article values. We discover that a reasonable albeit naive consumer learning strategy can lead to disastrous market behavior. We find a simple explanation for this market failure, and develop a simple improvement to the producer agent's strategy that largely ameliorates the problem. But in the process we learn an important lesson: dynamic market interactions when there is substantial uncertainty can lead to pathological outcomes if agents are designed with "reasonable" but not sufficiently adaptive strategies. Thus, in programmed agent environments it may be essential to dramatically increase our understanding of adaptivity and learning if we want to obtain good aggregate outcomes.

Automated Strategy Searches in an Electronic Goods Market: Learning and Complex Price Schedules (Download full paper)

Brooks, Christopher H., Scott Fay, Rajarshi Das, Jeffrey K. MacKie-Mason, Jeffrey O. Kephart and Edmund Durfee

Abstract: In an automated market for electronic goods new problems arise that have not been well studied previously. For example, information goods are very flexible. Marginal costs are negligible and nearly limitless bundling and unbundling of these items are possible, in contrast to physical goods. Consequently, producers can offer complex pricing schemes. However, the profit-maximizing design of a complex pricing schedule depends on a producer's knowledge of the distribution of consumer preferences for the available information goods. Preferences are private and can only be gradually uncovered through market experience. In this paper we compare dynamic performance across price schedules of varying complexity. We provide the producer with two machine learning method producer that is performing a naive, knowledge-free form of leanings (function approximation and hill-climbing) which implement a strategy that balances exploitation to maximize current profits against exploration of the profit landscape to improve future profits. We find that the tradeoff between exploitation and exploration is different depending on the learning algorithms employed, and in particular depending on the complexity of the price schedule that if offered. In general, simpler price schedules are more robust and give up less profit during the learning periods even though in our stationary environment learning eventually is complete and the more complex schedules have high long-run profits. These results hold for both learning methods, even though the relative performance of the methods is quite sensitive to choice of initial conditions and differences in the smoothness of the profit landscape for different price schedules. Our results have implications for automated learning and strategic pricing in non-stationary environments, which arise when the consumer population changes, individuals change their preferences, or competing firms change their strategies.

Evaluating and Selecting Digital Payment Mechanisms (Download full paper)

MacKie-Mason, Jeffrey K. and Kimberly White

Abstract: The Internet is growing rapidly as a marketplace for the exchange of both tangible and information goods and services. Numerous payment mechanisms suitable for use in this marketplace are in various stages of development. Because their development is so recent, it is difficult for potential participants in electronic commerce to evaluate and select payment mechanisms. We propose a systematic method for evaluating and selecting payment mechanisms. Our selection process typically leads to a solution in a few iterations or less; it is generalizable; and it requires relatively little information about each alternative, reducing the cost of evaluating and selecting payment mechanisms. Researchers and payment mechanism designers are guided on further development by the needs of users who desire particular bundles of characteristics. As a by-product of our analysis, we present a detailed matrix characterizing 10 leading payment systems according to 30 criteria.