Recent work has shown that search query volume correlates well with a variety of phenomena, from influenza caseloads to economic indicators like real-estate prices, auto sales, and travel statistics. In this paper, we investigate the degree to which search behavior predicts the commercial success of cultural products, namely movies, video games, and songs. In contrast with previous work that has focused on realtime reporting of current trends, we emphasize that here our objective is to predict future activity, typically days to weeks in advance. Specifically, we use query volume to forecast opening weekend box-office revenue for feature films, first month sales of video games, and the rank of songs on the Billboard Hot 100 chart. In all cases that we consider, we find that search counts are indicative of future outcomes, but when compared with baseline models trained on publicly available data, the performance boost associated with search counts is generally modest—a pattern that, as we show, also applies to previous work on tracking flu trends. We conclude that in the absence of other data sources, or where small improvements in predictive performance are material, search queries may provide a useful guide to the near future.

We propose a general framework for the design of securities markets over combinatorial or infinite state or outcome spaces. The framework enables the design of computationally efficient markets tailored to an arbitrary, yet relatively small, space of securities with bounded payoff. We prove that any market satisfying a set of intuitive conditions must price securities via a convex cost function, which is constructed via conjugate duality. Rather than deal with an exponentially large or infinite outcome space directly, our framework only requires optimization over a convex hull. By reducing the problem of automated market making to convex optimization, where many efficient algorithms exist, we arrive at a range of new polynomial-time pricing mechanisms for various problems. We demonstrate the advantages of this framework with the design of some particular markets. We also show that by relaxing the convex hull we can gain computational tractability without compromising the market institution’s bounded budget.

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.

Prediction markets show considerable promise for developing flexible mechanisms for machine learning. Here, machine learning markets for multivariate systems are defined, and a utilitybased framework is established for their analysis. This differs from the usual approach of defining static betting functions. It is shown that such markets can implement model combination methods used in machine learning, such as product of expert and mixture of expert approaches as equilibrium pricing models, by varying agent utility functions. They can also implement models composed of local potentials, and message passing methods. Prediction markets also allow for more flexible combinations, by combining multiple different utility functions. Conversely, the market mechanisms implement inference in the relevant probabilistic models. This means that market mechanism can be utilized for implementing parallelized model building and inference for probabilistic modelling. 1

Machine Learning competitions such as the Netflix Prize have proven reasonably successful as a method of “crowdsourcing ” prediction tasks. But these competitions have a number of weaknesses, particularly in the incentive structure they create for the participants. We propose a new approach, called a Crowdsourced Learning Mechanism, in which participants collaboratively “learn ” a hypothesis for a given prediction task. The approach draws heavily from the concept of a prediction market, where traders bet on the likelihood of a future event. In our framework, the mechanism continues to publish the current hypothesis, and participants can modify this hypothesis by wagering on an update. The critical incentive property is that a participant will profit an amount that scales according to how much her update improves performance on a released test set. 1

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Abstract One of the key challenges of the information society is responsible handling of personal data. An often-cited reason why people fail to make rational decisions regarding their own informational privacy is the high uncertainty about future consequences of information disclosures today. This paper builds an analogy to financial options and draws on principles of option pricing to account for this uncertainty in the valuation of privacy. For this purpose, the development of a data subject’s personal attributes over time and the development of the attribute distribution in the population are modelled as two stochastic processes, which fit into the Binomial Option Pricing Model (BOPM). Possible applications of such valuation methods to guide decision support in future privacy-enhancing technologies (PET) are sketched. 1

Abstract. We present Turing Trade, a web-based game that is a hybrid of a Turing test and a prediction market. In this game, there is a mystery conversation partner, the “target, ” who is trying to appear human, but may in reality be either a human or a bot. There are multiple judges (or “bettors”), who interrogate the target in order to assess whether it is a human or a bot. Throughout the interrogation, each bettor bets on the nature of the target by buying or selling human (or bot) securities, which pay out if the target is a human (bot). The resulting market price represents the bettors ’ aggregate belief that the target is a human. This game offers multiple advantages over standard variants of the Turing test. Most significantly, our game gathers much more fine-grained data, since we obtain not only the judges ’ final assessment of the target’s humanity, but rather the entire progression of their aggregate belief over time. This gives us the precise moments in conversations where the target’s response caused a significant shift in the aggregate belief, indicating that the response was decidedly human or unhuman. An additional benefit is that (we believe) the game is more enjoyable to participants than a standard Turing test. This is important because otherwise, we will fail to collect significant amounts of data. In this paper, we describe in detail how Turing Trade works, exhibit some example logs, and analyze how well Turing Trade functions as a prediction market by studying the calibration and sharpness of its forecasts (from real user data). Key words: prediction markets, Turing tests, games with a purpose, deployed webbased applications, using points as an artificial currency 1

The rapid growth of human computation within research and industry has produced many novel ideas aimed at organizing web users to do great things. However, the growth is not adequately supported by an overarching framework with which to understand each new system in the context of the old. We give a human computation classification system that can help identify parallels between different systems and reveal “holes ” in the existing work as opportunities for new research. Since human computation is often confused with “crowdsourcing ” and other terms, we explore the precise position of human computation with respect to other related topics.

Recently, prediction markets have shown considerable promise for developing flexible mechanisms for machine learning. In this paper, agents with isoelastic utilities are considered. It is shown that the costs associated with homogeneous markets of agents with isoelastic utilities produce equilibrium prices corresponding to alpha-mixtures, with a particular form of mixing component relating to each agent’s wealth. We also demonstrate that wealth accumulation for logarithmic and other isoelastic agents (through payoffs on prediction of training targets) can implement both Bayesian model updates and mixture weight updates by imposing different market payoff structures. An iterative algorithm is given for market equilibrium computation. We demonstrate that inhomogeneous markets of agents with isoelastic utilities outperform state of the art aggregate classifiers such as random forests, as well as single classifiers (neural networks, decision trees) on a number of machine learning benchmarks, and show that isoelastic combination methods are generally better than their logarithmic counterparts. 1.