� We survey the literature on prediction mechanisms, including prediction markets and peer prediction systems. We pay particular attention to the design process, highlighting the objectives and properties that are important in the design of good prediction mechanisms. Mechanism design has been described as “inverse game theory. ” Whereas game theorists ask what outcome results from a game, mechanism designers ask what game produces a desired outcome. In this sense, game theorists act like scientists and mechanism designers like engineers. In this article, we survey a number of mechanisms created to elicit predictions, many newly proposed within the last decade. We focus on the engineering questions: How do they work and why? What factors and goals are most important in their

Abstract. A fundamental task in reputation systems is to aggregate multiple feedback ratings into a single value that can be used to compare the reputation of different entities. Feedback is most commonly aggregated using the arithmetic mean. However, the mean is quite susceptible to outliers and biases, and thus may not be the most informative aggregate of the reports. We consider three criteria to assess the quality of an aggregator: the informativeness, the robustness and the strategyproofness, and analyze how different aggregators, in particular the mean, weighted mean, median and mode, perform with respect to these criteria. The results show that the arithmetic mean may not always be the best choice. 1

Any entity operating in cyberspace is susceptible to debilitating attacks. With cyber attacks intended to gather intelligence and disrupt communications rapidly replacing the threat of conventional and nuclear attacks, a new age of warfare is at hand. In 2003, the United States acknowledged that the speed and anonymity of cyber attacks makes distinguishing among the actions of terrorists, criminals, and nation states difficult. Even President Obama’s Cybersecurity Chief-elect recognizes the challenge of increasingly sophisticated cyber attacks. Now through April 2009, the White House is reviewing federal cyber initiatives to protect US citizen privacy rights. Indeed, the rising quantity and ubiquity of new surveillance technologies in cyberspace enables instant, undetectable, and unsolicited information collection about entities. Hence, anonymity and privacy are becoming increasingly important issues. Anonymization enables

We study the emergence of cooperation in dynamic, anonymous social networks, such as in online communities. We examine prisoner’s dilemma played under a social matching protocol, where individuals form random links to partners with whom they can interact. Cooperation results in mutual benefits, whereas defection results in a high short-term gain. Moreover, an agent that defects can escape reciprocity by virtue of anonymity: it is always possible for an agent to abandon his history and re-enter the network as a new user. We find that cooperation is sustainable at equilibrium in such a model. Indeed, cooperation allows an individual to interact with an increasing number of other cooperators, resulting in the formation of a type of social capital. This process arises endogenously, without the need for potentially harmful social enforcement rules. Additionally, for a rich class of parameter settings, our model predicts a stable coexistence of cooperating and defecting agents at equilibrium.

become increasingly popular honest feedback is NOT easy to get – cost of reporting => only altruists report – or external incentives reward reporting – honesty becomes rational

An increasing number of applications of artificial intelligence extract knowledge from large groups of agents, also termed the wisdom of the crowds. One example are online feedback forums (also known as reputation mechanisms) for obtaining information about the products or services. The testimonies of previous buyers disclose hidden product attributes such as quality, reliability, ease of use, etc., that can only be observed after the purchase. This previously unavailable information allows the buyers to make more efficient decisions, and eliminates some of the problems that would otherwise lead to the collapse of online markets [Akerlof 1970]. Recent studies, however, raise important questions regarding the ability of existing reputation mechanisms to reflect the real quality of a product. First, the absence of clear incentives drives only some of the users to voice their opinions. For example, most Amazon ratings for a book or CD are either very good, or very bad, while controlled experiments on the same items reveal normally distributed opinions [Hu et al. 2006]. Second, some users intentionally lie to distort the public reputation in their favor. Fake reviews can be seen on Amazon [Harmon 2004],