Prediction markets are often used as mechanisms to aggregate information about a future event, for example, whether a candidate will win an election. The event is typically assumed to be exogenous. In reality, participants may influence the outcome,
and therefore (1) running the prediction market could change the incentives of participants in the process that creates the outcome (for example, agents may want to change their vote in an election), and (2) simple results such as the myopic incentive compatibility of proper scoring rules no longer hold in the prediction market itself. We introduce a model of games of this kind, where agents first trade in a prediction market and then take an action that influences the market outcome. Our two-stage two-player model, despite its simplicity, captures two aspects of real-world prediction markets: (1) agents may directly influence the outcome, (2) some of the agents instrumental in deciding the outcome may not take part in the prediction market. We show that this game has two different types of perfect Bayesian equilibria, which we term LPP and HPP, depending on the values of the belief parameters: in the LPP domain, equilibrium prices reveal expected market outcomes conditional on the participants private information, whereas HPP equilibria are collusive -- participants effectively coordinate in an uninformative and untruthful way.
Collective motion of cells is critical to some of the most vital tasks including wound healing, development, and immune response [Friedl and Gilmour 2009; Tokarski et al. 2012; Lee et al. 2012; Beltman et al. 2009], and is common to many pathological
processes including cancer cell invasion and teratogenesis [Khalil and Friedl 2010]. The extensive understanding of movement by single cells [R{o}rth 2011; Insall and Machesky 2011; Houk et al. 2012] is insufficient to predict the behavior of cellular groups [Theveneau et al. 2013; Trepat, X. and Fredberg 2011], and identifying underlying rules of coordination in collective cell migration is still evasive. Few of the supposed benefits of collective motion have ever been tested at the cellular scale. As an example, though collective sensing allows for larger groups to exhibit greater accuracy in navigation [Simons 2004; Berdahl et al. 2013] and group taxis is possible through the leadership of only a few individuals [Couzin et al. 2005], such effects have never been investigated in collective cell migration. We will investigate collective motion and decision-making in a primitive multicellular animal, Trichoplax adhaerens to understand how intercellular coordination affects animal behavior and how migration accuracy scales with cellular group size.
Human groups can perform extraordinary accurate estimations compared to individuals by simply using the mean, median or geometric mean of the individual estimations [Galton 1907, Surowiecki 2005, Page 2008]. However, this is true only for some tasks
and in general these collective estimations show strong biases. The method fails also when allowing for social interactions, which makes the collective estimation worse as individuals tend to converge to the biased result [Lorenz et al. 2011]. Here we show that there is a bright side of this apparently negative impact of social interactions into collective intelligence. We found that some individuals resist the social influence and, when using the median of this subgroup, we can eliminate the bias of the wisdom of the full crowd. To find this subgroup of individuals more confident in their private estimations than in the social influence, we model individuals as estimators that combine private and social information with different relative weights [Perez-Escudero & de Polavieja 2011, Arganda et al. 2012]. We then computed the geometric mean for increasingly smaller groups by eliminating those using in their estimations higher values of the social influence weight. The trend obtained in this procedure gives unbiased results, in contrast to the simpler method of computing the median of the complete group. Our results show that, while a simple operation like the mean, median or geometric mean of a group may not allow groups to make good estimations, a more complex operation taking into account individuality in the social dynamics can lead to a better collective intelligence.
Social learning -by observing and copying others- is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human cap
acity for reflective, analytical reasoning. A hallmark of the human mind is our ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of lab-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions, and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an unreflective copying bias, which limits their social learning to the output, rather than the process, of their peers reasoning -even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behavior through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.
Numerous studies and anecdotes demonstrate the wisdom of the crowd, the surprising accuracy of a groups aggregated judgments. Less is known, however, about the generality of crowd wisdom. For example, are crowds wise even if their members have system
atic judgmental biases, or can influence each other before members render their judgments? If so, are there situations in which we can expect a crowd to be less accurate than skilled individuals? We provide a precise but general definition of crowd wisdom: A crowd is wise if a linear aggregate, for example a mean, of its members judgments is closer to the target value than a randomly, but not necessarily uniformly, sampled member of the crowd. Building on this definition, we develop a theoretical framework for examining, a priori, when and to what degree a crowd will be wise. We systematically investigate the boundary conditions for crowd wisdom within this framework and determine conditions under which the accuracy advantage for crowds is maximized. Our results demonstrate that crowd wisdom is highly robust: Even if judgments are biased and correlated, one would need to nearly deterministically select only a highly skilled judge before an individuals judgment could be expected to be more accurate than a simple averaging of the crowd. Our results also provide an accuracy rationale behind the need for diversity of judgments among group members. Contrary to folk explanations of crowd wisdom which hold that judgments should ideally be independent so that errors cancel out, we find that crowd wisdom is maximized when judgments systematically differ as much as possible. We re-analyze data from two published studies that confirm our theoretical results.
The emergence and ongoing development of Web 2.0 technologies have enabled new and advanced forms of collective intelligence at unprecedented scales, allowing large numbers of individuals to act collectively and create high quality intellectual artif
acts. However, little is known about how and when they indeed promote collective intelligence. In this manuscript, we provide a survey of the automated tools developed to analyze discourse-centric collective intelligence. By conducting a thematic analysis of the current research direction, a set of gaps and limitations are identified.
The recent emergence of online citizen science is illustrative of an efficient and effective means to harness the crowd in order to achieve a range of scientific discoveries. Fundamentally, citizen science projects draw upon crowds of non-expert volu
nteers to complete short Tasks, which can vary in domain and complexity. However, unlike most human-computational systems, participants in these systems, the `citizen scientists are volunteers, whereby no incentives, financial or otherwise, are offered. Furthermore, encouraged by citizen science platforms such as Zooniverse, online communities have emerged, providing them with an environment to discuss, share ideas, and solve problems. In fact, it is the result of these forums that has enabled a number of scientific discoveries to be made. In this paper we explore the phenomenon of collective intelligence via the relationship between the activities of online citizen science communities and the discovery of scientific knowledge. We perform a cross-project analysis of ten Zooniverse citizen science projects and analyse the behaviour of users with regards to their Task completion activity and participation in discussion and discover collective behaviour amongst highly active users. Whilst our findings have implications for future citizen science design, we also consider the wider implications for understanding collective intelligence research in general.
The increasing relevance of Internet-based markets requires a sustained investigation into the relationship between design and user behavior. This research begins within the sociology of quantification and markets to investigate the impacts of basic
design decisions on user behavior and individual success on a widely used crowdfunding website. This study looks at one common design feature, publishing recipients sex, on the probability of receiving funding. Following research in the sociology of gender, these effects are defined along individual, behavioral, and structural dimensions. The results reveal that before teachers sex was published, gender discrimination was weak and inconsistent. However, afterward gender discrimination increases by an order of magnitude and becomes systematized. Contrary to expectation, donors did not discriminate by sex category, but by teachers structural position and the kinds of language they used. Implications for research on gender discrimination, priming, and online behavior are discussed.
