To explore the influence of primary and caucus results during the 2008 nomination process we leverage a previously unused methodology—the analysis of prediction market contracts. The unique structure of prediction markets allows us to address two questions. First, we analyze whether primary and caucus results affect candidates ’ chances in the general election, as candidates who take extreme positions during the nomination contest may be unable to easily appeal to centrist voters in the general election. We also assess whether states with early primaries, such as Iowa and New Hampshire, have a disproportionate effect on the nominating process. We show that the length of the primary process has a minimal impact of the electability of candidates in the general election, and that some states have a disproportionate impact on the nominating process. However, the states that have the largest impact are not necessarily New Hampshire and Iowa, the two that have often been assumed to be the most influential because of their early position on the primary calendar. The 2008 primary process was extraordinary for its length, the movement of contests earlier on the calendar, and the high level of voter participation (Tolbert and Squire 2009). Yet, scholars and commentators were concerned in 2008, as in previous elections, with how the

Many psychology experiments show that individually judged probabilities of the same event can vary depending on the partition of the state space (a framing effect called "partitiondependence"). We show that these biases transfer to competitive prediction markets in which multiple informed traders are provided economic incentives to bet on their beliefs about events. We report results of a short controlled lab study, a longer field experiment (betting on the NBA playoffs and the FIFA World Cup), and naturally-occurring trading in macroeconomic derivatives. The combined evidence suggests that partition-dependence can exist and persist in lab and field prediction markets.

Existing studies of market efficiency in sports betting markets concentrate on professional sports. We test for betting market efficiency in an amateur sport, American college football, using data from over 11,000 games from 1985 to 2003. Sizable betting markets exist for amateur sports, but little is known about their efficiency. We find that the market is inefficient. In particular, we find that favorites are systematically overpriced. We show that the magnitude of this bias is large enough to generate both economic and statistical inefficiency in this betting market. Furthermore, we provide suggestive evidence for the cause of this inefficiency — betting houses deliberately inflate the betting lines for favorites in order to counteract bettor’s “hot hand ” beliefs. Tempering the “hot hand ” bias results in consistently profitable betting strategies.

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Summary I am interested in (1) the design of intelligent agents and systems, primarily guided by machine learning; (2) modeling and understanding collective dynamics that result from intelligent individual behavior; and (3) using this understanding to inform the design of venues where people and automated agents come together to interact. A central focus of my research is on understanding how information flows through systems, how it can be best used by intelligent agents, and how its presence, absence, or the form in which it is available impacts decisions at the individual and systemic levels. My work can be categorized into four broad themes. 1: Collective intelligence I am interested in both modeling and understanding the dynamics of collective intelligence, and in designing algorithms that allow us to use the power of collective wisdom to make better decisions. I have been working on the foundations of a rigorous theory of how information grows in novel social media like Wikipedia and the blogosphere, and on information aggregation and dissemination in prediction markets. In recent work, we have documented some remarkable regularities in the life cycles of average Wikipedia pages and blog posts [26, 27]. They exhibit a concave rise to an editing / commenting peak, followed by decay at a 1/t rate over time. We have proposed a simple model of information creation that matches the data

We analyze a binary prediction market in which traders have heterogeneous prior beliefs and private information. Realistically, we assume that traders are allowed to invest a limited amount of money (or have decreasing absolute risk aversion). We show that the rational expectations equilibrium price underreacts to information. When favorable information to an event is available and is revealed by the market, the price increases and this forces optimists to reduce the number of assets they can (or want to) buy. For the market to equilibrate, the price must increase less than a posterior belief of an outside observer.

This article describes a novel approach to linguistic field research consisting in exploiting the self-regulation of a market for collecting data on language use. The market is conceived as an output-agreement game with a purpose called Borsa Parole. The agreement can be traded with by the players what makes it adjustable. Borsa Parole has been conceived and is deployed for a linguistic study on the divergence of Italian dialects and vernaculars.

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