Abstract: Decision markets are social media for decision making where the options to choose from are traded for (with real or play money) by the decision makers. The market equilibrium resulting from the competition between the options offered by sellers and sought for by buyers is interpreted as a collective consent and the relative market prices are interpreted as a ranking of the options. However, on decision markets like on financial markets market equilibrium prices may also arise out of mimicry resulting from either indecision or pure greed. The more the trading behavior is driven by indecision or greed, the less the equilibrium prices reflect genuine preferences. This article proposes a novel approach to decision making. It further describes to rely on artificial perturbations of a market’s equilibrium for uncovering indecision or greed on decision markets. Based on the hypothesis that profit seeking is affected by psychological norms that can be activated by context cues and social interaction, an experimental evaluation is proposed that shifts a market’s framing between a competitive individualistic and a collaborative communal setting. Social norms in the collaborative communal setting are expected to lessen greed and thus give ways to true preferences: The equilibria of markets with a collaborative communal setting are therefore expected to be less vulnerable to artificial pertubations than those with a competitive individualistic setting. This article describes in a principled manner first the market perturbations, second the experimental evaluation framework. 1

Democracies often fail to aggregate information, while speculative markets excel at this task. We consider a new form of governance, wherein voters would say what we want, but speculators would say how to get it. Elected representatives would oversee the after-the-fact measurement of national welfare, while market speculators would say which policies they expect to raise national welfare. Those who recommend policies that regressions suggest will raise GDP should be willing to endorse similar market advice. Using a qualitative engineering-style approach, we present three scenarios, consider thirty-three design issues, and finally a more specific design responding to those concerns.

This paper was prepared as the entry on “Prediction Markets ” for the New Palgrave Dictionary of

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.

There is significant experimental evidence that prediction markets are efficient mechanisms for aggregating information and are more accurate in forecasting events than traditional forecasting methods, such as polls. Interpretation of prediction market prices as probabilities has been extensively studied in the literature. However there is little research on the volatility of prediction market prices. Given that volatility is fundamental in estimating significance of price movements, it is important to have a better understanding of the volatility of the contract prices. This paper presents a model of a prediction market with binary payoff on a competitive event involving two parties. In our model, each party has a latent underlying “ability” process that describes its ability to win and evolves as an Ito diffusion. We show that, if the prediction market for this event is efficient and unbiased, the price of the corresponding contract also follows a diffusion and its instantaneous volatility is a function of the current contract price and its time to expiration. In the experimental section, we validate our model on a set of InTrade prediction markets and show that our model is consistent with the observed volatility of contract returns. Our model also outperforms existing volatility models in predicting future contract volatility from historical price data. To demonstrate the practical value of our model, we apply it to pricing options on prediction market contracts, such as those recently introduced by InTrade. Other potential applications of this model include detection of significant market moves and improving forecast standard errors.

with aggregate uncertainty

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.

Abstract We study the equilibrium behavior of informed traders interacting with market scoring rule (MSR) market makers. One attractive feature of MSR is that it is myopically incentive compatible: it is optimal for traders to report their true beliefs about the likelihood of an event outcome provided that they ignore the impact of their reports on the profit they might garner from future trades. In this paper, we analyze non-myopic strategies and examine what information structures lead to truthful betting by traders. Specifically, we analyze the behavior of risk-neutral traders with incomplete information playing in a dynamic game. We consider finite-stage and infinite-stage game models. For each model, we study the logarithmic market scoring rule (LMSR) with two different information structures: conditionally independent signals and (unconditionally) independent signals. In the finite-stage model, when signals of traders are independent conditional on the state of the world, truthful betting is a Perfect Bayesian Equilibrium (PBE). Moreover, it is the unique Weak Perfect Bayesian Equilibrium (WPBE) of the game. In contrast, when signals of traders are unconditionally independent, truthful betting

We develop a new goodness-of-fit test for event forecasting, which builds on two components. The first component tests the level of the estimated probabilities. The second component validates the shape, measuring the differentiation between high and low probability events. We construct test statistics for both level and shape together with a global goodness-of-fit statistic, which is asymptotically χ2-distributed. Since we rely on a minimal set of assumptions, our test statistics are applicable in very general settings, including situations in which forecasted events exhibit correlation and data is sparse. In a simulation exercise, we explore the reliability, power, and robustness of our approach. We illustrate the usefulness of our out-of-sample test with an empirical application, for which we focus on validating the forecasting system for credit defaults. This application