Citing recent successes in forecasting elections, movies, products, and other outcomes, prediction market advocates call for widespread use of market-based methods for government and corporate decision making. Though theoretical and empirical evidence suggests that markets do often outperform alternative mechanisms, less attention has been paid to the magnitude of improvement. Here we compare the performance of prediction markets to conventional methods of prediction, namely polls and statistical models. Examining thousands of sporting and movie events, we find that the relative advantage of prediction markets is surprisingly small, as measured by squared error, calibration, and discrimination. Moreover, these domains also exhibit remarkably steep diminishing returns to information, with nearly all the predictive power captured by only two or three parameters. As policy makers consider adoption of prediction markets, costs should be weighed against potentially modest benefits.

Current automated market makers over binary events suffer from two problems that make them impractical. First, they are unable to adapt to liquidity, so trades cause prices to move the same amount in both thick and thin markets. Second, under normal circumstances, the market maker runs at a deficit. In this paper, we construct a market maker that is both sensitive to liquidity and can run at a profit. Our market maker has bounded loss for any initial level of liquidity and, as the initial level of liquidity approaches zero, worst-case loss approaches zero. For any level of initial liquidity we can establish a boundary in market state space such that, if the market terminates within that boundary, the market maker books a profit regardless of the realized outcome. Furthermore, we provide guidance as to how our market maker can be implemented over very large event spaces through a novel cost-function-based sampling method

Decision makers can benefit from the subjective judgment of experts. For example, estimates of disease prevalence are quite valuable, yet can be difficult to measure objectively. Useful features of mechanisms for aggregating expert opinions include the ability to: (1) incentivize participants to be truthful; (2) adjust for the fact that some experts are better informed than others; and (3) circumvent the need for objective, "ground truth" observations. Subsets of these properties are attainable by previous elicitation methods, including proper scoring rules, prediction markets, and the Bayesian truth serum. Our mechanism of collective revelation, however, is the first to simultaneously achieve all three. Furthermore, we introduce a general technique for constructing budget-balanced mechanisms-where no net payments are made to participants--that applies both to collective revelation and to past peer-prediction methods.

We examine a class of wagering mechanisms designed to elicit truthful predictions from a group of people without requiring any outside subsidy. We propose a number of desirable properties for wagering mechanisms, identifying one mechanism - weighted-score wagering - that satisfies all of the properties. Moreover, we show that a single-parameter generalization of weighted-score wagering is the only mechanism that satisfies these properties. We explore some variants of the core mechanism based on practical considerations.

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.

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.

The 2002 Trading Agent Competition (TAC) presents 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. We survey agent approaches, finding an interesting diversity of techniques, taking into account differing sources of evidence bearing on prices. Based on agents' actual predictions in the TAC-02 finals and semifinals, we analyze the relative efficacy of these approaches. Employing a new measure of prediction quality, we relate absolute accuracy to bottom-line performance in the game.

Our approach for automating the negotiation of business contracts proceeds in three broad steps. First, determine the structure of the negotiation process by applying general knowledge about auctions and domain–specific knowledge about the contract subject along with preferences from potential buyers and sellers. Second, translate the determined negotiation structure into an operational specification for an auction platform. Third, after the negotiation has completed, map the negotiation results to a final contract. We have implemented a prototype which supports these steps by employing a declarative specification (in courteous logic programs) of (1) high–level knowledge about alternative negotiation structures, (2) general–case rules about auction parameters, (3) rules to map the auction parameters to a specific auction platform, and (4) special–case rules for subject domains. We demonstrate the flexibility of this approach by automatically generating several alternative negotiation structures for the domain of travel shopping in a trading agent competition.