text: Many Web platforms rely on user collaboration to generate high-quality content: Wiki, Q&A communities, etc. Understanding and modeling the different collaborative behaviors is therefore critical. However, collaboration patterns are difficult to capture when the relationships between users are not directly observable, since they need to be inferred from the user actions. In this work, we propose a solution to this problem by adopting a systemic view of collaboration. Rather than modeling the users as independent actors in the system, we capture their coordinated actions with embedding methods which can, in turn, identify shared objectives and predict future user actions. To validate our approach, we perform a study on a dataset comprising more than 16M user actions, recorded on the online collaborative sandbox Reddit r place. Participants had access to a drawing canvas where they could change the color of one pixel at every fixed time interval. Users were not grouped in teams nor were given any specific goals, yet they organized themselves into a cohesive social fabric and collaborated to the creation of a multitude of artworks. Our contribution in this paper is multi-fold: i) we perform an in-depth analysis of the Reddit r place collaborative sandbox, extracting insights about its evolution over time; ii) we propose a predictive method that captures the latent structure of the emergent collaborative efforts; and iii) we show that our method provides an interpretable representation of the social structure.
  @cite_4: Emergence is a concept widely used in the sciences, the arts, and engineering. Some effort has been made to formalize it, but it is used in various contexts with different meanings, and a unified theory of emergence is still distant. The ultimate goal of a theory of emergence should include using emergence to model, design, or predict the behavior of multiagent systems. The author proposes a formal definition of a basic type of emergence using a language-theoretic and grammar systems approach. It is shown which types of phenomena can be modeled in this sense and what the consequences are for other more complex phenomena.
  @cite_12: 
  @cite_20: Concepts used in the scientific study of complex systems have become so widespread that their use and abuse has led to ambiguity and confusion in their meaning. In this article, we use information theory to provide abstract and concise measures of complexity, emergence, self-organization, and homeostasis. The purpose is to clarify the meaning of these concepts with the aid of the proposed formal measures. In a simplified version of the measures (focusing on the information produced by a system), emergence becomes the opposite of self-organization, while complexity represents their balance. Homeostasis can be seen as a measure of the stability of the system. We use computational experiments on random Boolean networks and elementary cellular automata to illustrate our measures at multiple scales. © 2012 Wiley Periodicals, Inc. Complexity, 2012 © 2012 Wiley Periodicals, Inc.
summary: The phenomenon of emergence has been studied in different domains, notably in the field of Complexity Science and in the context of agent based modeling. The term emergence has various definition across fields @cite_4 , alike complexity @cite_20 from which emergence has been suggested to arise from. Emergence generally refers to system-wide behaviors that cannot be explained by the sum of individual behaviors. Moreover, means of modeling emergence are still subject to debate. Counting interaction between agents is, however, a widely used method to infer complex behaviors in a system @cite_12 .