Jeffrey K. MacKie-Mason

Auction Protocols for Decentralized Scheduling

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 complementarities 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.

Exploring bidding strategies for market-based scheduling

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 complementarities 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.

Generalized Vickrey Auctions

We describe a generalization of the Vickrey auction. Our mechanism extends the auction to implement efficient allocations for problems with more than one good, multiple units for the goods, and externalities. The primary restriction on preferences is that they must be quasilinear.

Some economics of market-based distributed scheduling

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 auction protocols for implementing solutions, and analyze their computational and economic properties.

Price discrimination and patent policy

Patent and antitrust policy are often presumed to be in conflict. As an important example, there is ongoing controversy about whether price discrimination by a patent holder is an illegal or socially undesirable exploitation of monopoly power. In this article we show that no conflict exists in many price discrimination cases. Even ignoring the (dynamic) effects on incentives for innovation, third-degree price discrimination by patent holders can raise (static) social welfare. In fact, Pareto improvements may well occur. Welfare gains occur because price discrimination allows patent holders to open new markets and to achieve economies of scale or learning. Further, even in cases where discrimination incurs static welfare losses, it may be efficient relative to other mechanisms, such as length of patent life, for rewarding innovators with profits.

Some nonlinear tax effects on asset values and investment decisions under uncertainty

Abstract Tax system nonlinearities are often ignored but with uncertainty may have important implications for the magnitude and direction of tax effects. This paper studies a particular nonlinear tax rule. The policy subsidizes asset values, but may discourage some investments and encourage earlier shutdown of some projects. The marginal effective tax rate varies with project risk. Furthermore, a corporate cash-flow tax can encourage investment due to interactions with the nonlinear policy. The paper also presents a general statement of the stochastic equilibrium valuation method which can be used to analyze other nonlinear taxes. Numerical examples demonstrate computational feasibility.

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.

Automated strategy searches in an electronic goods market: learning and complex price schedules

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 producers 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 methods 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.

Some FAQs about usage pricing

This is a list of Frequently Asked Questions about usage-based pricing of the Internet. We argue that usage-based pricing is likely to come sooner or later and that some serious thought should be devoted to devising a sensible system of usage-based pricing.

Why share in peer-to-peer networks?

Prior theory and empirical work emphasize the enormous free-riding problem facing peer-to-peer (P2P) sharing networks. Nonetheless, many P2P networks thrive. We explore two possible explanations that do not rely on altruism or explicit mechanisms imposed on the network: direct and indirect private incentives for the provision of public goods. The direct incentive is a traffic redistribution effect that advantages the sharing peer. We find this incentive is likely insufficient to motivate equilibrium content sharing in large networks. We then approach P2P networks as a graph-theoretic problem and present sufficient conditions for sharing and free-riding to co-exist due to indirect incentives we call generalized reciprocity.