We introduce a generic method to infer collaboration pat-terns in environments where only user interactions are ob-servable. We show, through experiments, that the local prox-imity of users’ actions represents a sufficiently expressivesignal for the study of collaboration. Indeed, we report it tobe more predictive than the modeling of the interactions be-tween users and their environment. This finding reinforcesprevious results in the domain, that suggest the study ofemergent phenomenons requiring the modeling of interre-lationships between the parts of a system, rather than mod-eling their individual behaviors.Being able to capture rich social signals, such as collab-oration patterns, represents a unique opportunity to studycomplex social phenomenons.

We therefore conclude that, from the considered models, theparametrization of user interrelationships is the most predic-tive method of user actions in a sandbox environment.

the locality of useractions being a critical aspect in the design of a method topredict collaborations.

We therefore represent every user in the sys-tem by a latent representation: a real-valued vectorpuiofsizeKwhereKis the chosen dimensionality of the latentspace. We define a notion of distance between any pair ofusers in the considered population, where the distance met-ric represents the strength of collaboration between users. Iftwo users are actively collaborating, the response producedby the combination of their respective vectors (typically byusing dot product) should be high.

We opt for an embedding method, since wehypothesize less independent behaviors than individuals inthe system. Embedding methods are especially adapted toproducepersonalizedpredictions (e.g. collaborative filteringapplications), by making the assumption that the behaviorfrom an individual can be predicted by collecting data frommany users

In this section, we introduce a predictive method that modelscollaboration between users in order to predict future useractions. In this regard, we train a model to evaluate the like-lihood of a useruito perform a particular action at a givenmoment in time.

We first observe, in figure 2 (left), the activity distribu-tion of the users. This distribution highlight the presence offew power-users and a vast majority of users performing amoderate number of clicks. In figure 2 (middle), we observethe same type of distribution for the number of updates per-formed on every pixel. As few pixels have been highly dis-puted, the large majority of them have only been updated afew times.

In April 2017, the discussion platformRedditlaunchedPlace, an online canvas of 1000-by-1000 pixels, designedas a social experiment. Reddit users were allowed to changethe color of one pixel at every fixed time interval (the in-terval varied from 5 to 20 minutes during the events). Theevent lasted 72 hours and received a massive engagementfrom more than 1.2M unique users. Users collaborated tocreate various artworks by either directly interacting withthe canvas or by coordinating their actions from the discus-sion platform.

The most relevant line of researchis probably the task of detectingoverlappingcommunities,whose members can be part of multiple groups. Those linesof research have made use of Matrix Factorization methodsin order to relax the assumption of communities being dis-joint (Zhang and Yeung 2012) (Yang and Leskovec 2013).

High-level social behaviors, such as the bystander effect, havebeen observed inside a simple video-game based virtual en-vironment (Kozlov and Johansen 2010). Social interactionsin Massively Multilayer Online games have been studiedby Cole et al. (Cole and Griffiths 2007).

The termemergence has various definition across fields (Kub 2003),alike complexity (Gershenson and Fern ́andez 2012) fromwhich emergence has been suggested to arise from. Emer-gence generally refers to system-wide behaviors that can-not be explained by the sum of individual behaviors.

The exploration-exploitation trade-off in a collaborative problem solving task has been dis-cussed by Mason and Watts (Mason and Watts 2012). Kit-tur and Kraut (Kittur and Kraut 2008) studied various typesof collaboration taking place between Wikipedia editors andmeasured the impact on quality of the resulting articles.

In order to establish a predictive model of user behavior,we consider the sandbox as a complex social system, i.e., asystem inherently difficult to model due to the large amountof interdependencies between its parts. Previous research inthe field of Complexity Science (Bar-Yam 2002) hypothe-sized that the nature of such systems is favorable to the emer-gence of global behaviors, arising from the local interactionsof the actors. Following this evidence, we propose a modelthat assesses the likelihood of a user interaction by observ-ing its social context. In other terms, we propose a predictivemodel that captures inter-user relationships instead of mod-eling independent behaviors.

Users werenot grouped in teams nor were given any specific goals, yetthey organized themselves into a cohesive social fabric andcollaborated to the creation of a multitude of artworks.

Rather thanmodeling the users as independent actors in the system, wecapture their coordinated actions with embedding methodswhich can, in turn, identify shared objectives and predict fu-ture user actions.

Latent Structure in Collaboration: the Case of Reddit r/place

Peer interaction may be able to improve the isolation of online learning, as well as improving the learning.!•Potential to automatically group people based on what misperceptions they currently have.!

Reputation  Systems  in  MOOC  Forum

All  hypotheses  confirmed  •Engaging  in  discussion  leads  to  more  correct  answers.  •  The  bonus  incentive  leads  to  more  correct  changed  answers.  •The  participants  have  substantive  discussio

MOOC  Collaboration  Today  •Forums  •Really  Q&A  Tools  •Low  participation  •Participants  do  well:  correlation  or  causation?  •Informally  Organized  Groups  •Google  Hangous,  Facebook  groups,  in-­‐person  meetings  •Formal  Project  Groups  •NovoEd  •Peer  Assessment  (anonymous,  asynchronous)  •Kulkarni,  Klemmer  et  al.  TOCHI  2