Introduction and description of election futures markets The Iowa Electronic Markets are small-scale, real-money futures markets conducted by the University of Iowa College of Business. In this review we focus on the best known of these markets, The Iowa Political Markets. Contracts in these markets are designed so that prices should predict election outcomes. The data set contains the results of 49 markets covering 41 elections in 13 countries. The Iowa Markets operate 24-hours a day, using a continuous, double-auction trading mechanism. Traders invest their own funds, make their own trades, and conduct their own information search. The markets occupy a niche between the stylized, tightly controlled markets conducted in the laboratory and the information-rich environments of naturally occurring markets. By virtue of this design, the Iowa Markets provide data to researchers that is not otherwise available. Investments are typically limited to a $500 maximum per trader and general

Information Aggregation Mechanisms are economics mechanisms designed explicitly for the purpose of collecting and aggregating information. The modern theory of rational expectations, together with the techniques and results of experimental economics, suggest that a set of properly designed markets can be a good information aggregation mechanism. The paper reports on the deployment of such an Information Aggregation Mechanism inside Hewlett-Packard Corporation for the purpose of making sales forecasts. Results show that IAMs performed better than traditional methods employed inside Hewlett-Packard. The structure of the mechanism, the methodology and the results are reported.

I develop a new mechanism for risk allocation and information speculation called a dynamic pari-mutuel market (DPM). A DPM acts as hybrid between a pari-mutuel market and a continuous double auction (CDA), inheriting some of the advantages of both. Like a pari-mutuel market, a DPM offers infinite buy-in liquidity and zero risk for the market institution; like a CDA, a DPM can continuously react to new information, dynamically incorporate information into prices, and allow traders to lock in gains or limit losses by selling prior to event resolution. The trader interface can be designed to mimic the familiar double auction format with bid-ask queues, though with an addition variable called the payoff per share. The DPM price function can be viewed as an automated market maker always offering to sell at some price, and moving the price appropriately according to demand. Since the mechanism is pari-mutuel (i.e., redistributive), it is guaranteed to pay out exactly the amount of money taken in. I explore a number of variations on the basic DPM, analyzing the properties of each, and solving in closed form for their respective price functions.

Why do economists conduct experiments? To answer that question, it is first necessary briefly to specify the ingredients of an experiment. Every laboratory experiment is defined by an environment, specifying the initial endowments, preferences and costs that motivate exchange. This environment is controlled using monetary rewards to induce the desired specific value/cost configuration (Smith, 1991, 6). 1 An experiment also uses an institution defining the language (messages) of market communication (bids, offers, acceptances), the rules that govern the exchange of information, and the rules under which messages become binding contracts. This institution is defined by the experimental instructions which describe the messages and procedures of the market, which are most often computer controlled. Finally, there is the observed behavior of the participants in the experiments as a function of the environment and institution that constitute the controlled variables. Using this framework of environment, institution, and behavior, I can think of at least seven prominent reasons in the literature as to why economists conduct experiments. Undoubtedly, there are more (Davis and Holt, 1992, chapter 1 and passim).

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Prediction markets are increasingly being considered as methods for gathering, summarizing and aggregating diffuse information by governments and businesses alike. Critics worry that these markets are susceptible to price manipulation by agents who wish to distort decision making. Westudytheeffect of manipulators on an experimental market, and find that manipulators are unable to distort price accuracy. Subjects without manipulation incentives compensate for the bias in offers from manipulators by setting a different threshold at which they are willing to accept trades. ∗ The authors thank Manuela Abbate for research assistance and an anonymous referee for helpful comments. We

We develop a framework for trading in compound securities: financial instruments that pay off contingent on the outcomes of arbitrary statements in propositional logic. Buying or selling securities -- which can be thought of as betting on or against a particular future outcome -- allows agents both to hedge risk and to profit (in expectation) on subjective predictions. A compound securities market allows agents to place bets on arbitrary boolean combinations of events, enabling them to more closely achieve their optimal risk exposure, and enabling the market as a whole to more closely achieve the social optimum. The tradeoff for allowing such expressivity is in the complexity of the agents' and auctioneer's optimization problems.

According to economic theory, supported by empirical and laboratory evidence, the equilibrium price of a financial security reflects all of the information regarding the security's value. We investigate the dynamics of the computational process on the path toward equilibrium, where information distributed among traders is revealed stepby -step over time and incorporated into the market price. We develop a simplified model of an information market, along with trading strategies, in order to formalize the computational properties of the process. We show that securities whose payoffs cannot be expressed as a weighted threshold function of distributed input bits are not guaranteed to converge to the proper equilibrium predicted by economic theory. On the other hand, securities whose payoffs are threshold functions are guaranteed to converge, for all prior probability distributions. Moreover, these threshold securities converge in at most n rounds, where n is the number of bits of distributed information. We also prove a lower bound, showing a type of threshold security that requires at least n/2 rounds to converge in the worst case.

We analyze the computational complexity of market maker pricing algorithms for combinatorial prediction markets. We focus on Hanson’s popular logarithmic market scoring rule market maker (LMSR). Our goal is to implicitly maintain correct LMSR prices across an exponentially large outcome space. We examine both permutation combinatorics, where outcomes are permutations of objects, and Boolean combinatorics, where outcomes are combinations of binary events. We look at three restrictive languages that limit what traders can bet on. Even with severely limited languages, we find that LMSR pricing is #P-hard, even when the same language admits polynomial-time matching without the market maker. We then propose an approximation technique for pricing permutation markets based on a recent algorithm for online permutation learning. The connections we draw between LMSR pricing and the vast literature on online learning with expert advice may be of independent interest.

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