Prior theory and empirical work emphasize the enormous free-riding problem facing peer-to-peer (P2P) sharing net-works. Nonetheless, many P2P networks thrive. We ex-plore two possible explanations that do not rely on altru-ism or explicit mechanisms imposed on the network: di-rect and indirect private incentives for the provision of public goods. The direct incentive is a traffic redistri-bution 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 suffi-cient conditions for sharing and free-riding to co-exist due to indirect incentives we call generalized reciprocity. 1

Humans are "smart components" in a system, but cannot be directly programmed to perform; rather, their autonomy must be respected as a design constraint and incentives provided to induce desired behavior. Sometimes these incentives are properly aligned, and the humans don't represent a vulnerability. But often, a misalignment of incentives causes a weakness in the system that can be exploited by clever attackers. Incentive-centered design tools help us understand these problems, and provide design principles to alleviate them. We describe incentive-centered design and some tools it provides. We provide a number of examples of security problems for which Incentive Centered Design might be helpful. We elaborate with a general screening model that offers strong design principles for a class of security problems.

No abstract available.

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.

As online markets for the exchange of goods and services become more common, the study of markets composed, at least in part, of autonomous agents has taken on increasing importance. In contrast to traditional complete–information economic scenarios, agents that are operating in an electronic marketplace often do so under considerable uncertainty. In order to reduce their uncertainty, these agents must learn about the world around them. When an agent producer is engaged in a learning task in which data collection is costly, such as learning the preferences of a consumer population, it is faced with a classic decision problem: when to explore and when to exploit. If the agent 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. By explicitly considering both the learnability and the profit extracted by different price schedules, a producer can extract more profit as it learns than if it naively chose models that are accurate once learned.

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.

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 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.

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.

We describe the basic economic theory of pricing a congestible resource such as an FTP server, a router, a Web site, etc. In particular, we examine the implications of "congestion pricing" as a way to encourage efficient use of network resources. We explore the implications of flat pricing and congestion pricing for capacity expansion in centrally planned, competitive, and monopolistic environments. The most common form of Internet pricing is pricing by access, with no usage-sensitive prices. With a fixed set of users, we expect to see greater capacity when usage is not priced, but also greater congestion. However, with greater congestion, congestion-sensitive users might not use the resource.>