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

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.

Abstract. R&D portfolio management is a critical task with which the majority of the large companies are confronted. Despite its wide implementation in companies there are no widely accepted and used methods to perform this task. Each company uses its own mix of various qualitative and quantitative methods to achieve its goal. The objective of this thesis is to explore the adequacy and the design issues to use a prediction market for supporting the R&D portfolio management process. We chose prediction markets to perform this task since their aggregation mechanisms and information discovery process seems to solve most of the current issues of the R&D portfolio management process.

The main objective of this research is to use prediction markets as negotiation agents, for supporting R&D portfolio management. To support this research, we iteratively designed, developed, operated and evaluated several prototypes. We start by presenting the weaknesses of the current techniques for managing R&D portfolio. Then, we intend to demonstrate that prediction markets correct these weaknesses in R&D portfolio management. Furthermore, following a design science paradigm, we illustrate the design of our artifacts using build-andevaluate loops supported with a field study, which consisted in operating the prediction markets in different settings. 1.

We propose a model of prediction markets where participants are biased according to their social relationships. We relax the standard assumption of complete rationality and adopt an arguably more realistic model where agents are disproportionally influenced by their neighbors in a social network. We conduct extensive agent-based simulations of our model. We find that prices in prediction markets remain accurate even when participants are biased and irrational. Moreover, accuracy is robust to changes in many factors, including how individuals are motivated to participate in the market, the way that individuals use public information, individual utility functions, the topology of the social network, and the strength of social influences. Our model can explain the high volume of trade often observed in speculative markets that is hard or impossible to explain under standard market rationality assumptions. Our model can also explain the documented ability of prediction markets to succeed even in the face of biased and irrational participants.

In prediction markets, assets are created whose final cash value is tied to a particular event (e.g., Will a new factory be opened by the end of the quarter?) or a parameter (e.g., How many units of a product will be sold during the next quarter?). Prediction markets are based on the idea that the equilibrium price should reflect the information possessed by the market participants. Essentially, prediction markets are particularly simple financial markets that are created with the purpose of collecting information, but serve no liquidity purposes. Despite the hype in the press, there is a limited amount of theoretical analysis in this area. In this paper, we present a modeling framework that can guide practitioners and researchers to understand the role and improve the design of corporate prediction markets. As Wolfers and Zitzewitz (2006) stress, prediction markets must overcome a number of challenges in order to be used as effective prediction tools. In a corporate setting, market designers are often concerned about the possibility of outcome manipulation. Also being members of the organization, market participants are often in a position to take actions that directly influence

Documented results indicate prediction markets effectively aggregate information and form accurate predictions. This has led to a proliferation of markets predicting everything from the results of elections to a company’s sales to movie box office receipts. Recent research suggests prediction markets are robust to manipulation attacks and resulting market outcomes improve forecast accuracy. However, we present evidence from the lab indicating that well funded, single minded manipulators can in fact destroy a prediction market’s ability to aggregate information. Our results clearly indicate that the usefulness of prediction markets as inputs to decision making may be limited.

Abstract. We propose the development of a prediction market to provide a form of collective intelligence for forecasting prices for “toxic assets ” to be transferred from Irish banks to the National Asset Management Agency. Such a market allows participants to assume a stake in a security whose value is tied to a future event. We propose that securities are created whose value hinges on the transfer amount paid for loans from the agency to a bank. In essence, bets are accepted on whether the price is higher or lower than a quoted figure. The prices of securities indicate expected transfer costs for toxic assets. Prediction markets offer a proven means of aggregating distributed knowledge pertaining to estimates of uncertain quantities and are robust to strategic manipulation. We propose that a prediction market runs in parallel to a pricing procedure for individual assets conducted by the government agency. We advocate an approach whereby prices are chosen as a convex combination of the agency’s internal estimate and that of the prediction market. We argue that this will substantially reduce the cognitive burden for the government agency and improve the accuracy, speed and scalability of pricing. This approach also offers a means of empowering both property experts and non-experts in a cost-effective and transparent manner. 1

We study experimental markets where privately informed traders exchange simple assets, and where uninformed third parties are asked to forecast the values of these assets, guided only by market prices. Although prices only partially aggregate information, they significantly improve the forecasts of third parties. In a second treatment, a portion of traders are given preferences over the forecasts made by observers. Although we find evidence that these traders attempt to manipulate prices in order to influence the beliefs of observers, we find no evidence that observers make less accurate forecasts as a result. 1

We propose the development of a prediction market for forecasting prices for “toxic assets ” to be transferred from Irish banks to the National Asset Management Agency (NAMA). Such a market allows market participants to assume a stake in a security whose value is tied to a future event. We propose that securities are created whose value hinges on the transfer amount paid for loans from NAMA to a bank. In essence, bets are accepted on whether the price is higher or lower than a certain quoted figure. The prices of the securities represent transfer prices for toxic assets increases or decreases in line with market opinion. Prediction markets offer a proven means of aggregating distributed knowledge pertaining to fair market values in a scalable and transparent manner. They are incentive compatible (i.e. induce truthful reporting) and robust to strategic manipulation. We propose that a prediction market is run in parallel with the pricing procedure recommended by the European Commission. This procedure need not necessarily take heed of the prediction markets view in all cases but it may offer guidance and a means of anomaly detection. An online prediction market would offer everybody an opportunity to “have their say ” in an open and transparent manner. 1