Sunbelt XXXI International Network for Social Network ... - INSNA

Games And The Power Of Capturing Player Data: Using Human Computation To Investigate Belief Creation In NetworksLandwehr, Peter M.; Spraragen, Marc; Carley, Kathleen M.Simulation and Agent Based ModelsAgent Based Models, Game, Human ComputationFRI.PM1Recent human‐computer interaction research has successfully leveraged humans’ instincts towards play by developing games that, when played, performuseful tasks for a variety of domains. Such games use humans as black box heuristics for solving problems such as folding proteins and improving internetimage searches. Most of these games are united by constraining the solutions that people develop to be evaluable as empirically correct; it is possible toquantitatively compare two protein folds and determine which is better. In this paper, we describe a project to leverage human computation throughgameplay to investigate how to optimally propagate beliefs in a network simulation, a task that is relatively complex and the success of which can be hard toevaluate. In the Sudan Game, players take on the role of a super‐analyst, able to both look at a variety of metrics about Sudan and to engage with local opinionleaders to carry out belief interventions. If players can cause average belief homophily between two different tribes to pass a particular threshold, we considerthe tribal relationship to be stable. Because of the number of possible actions available at each time step, the number of simulation replications that would berequired to parse all versions of this model is computationally intensive. By collating and vetting the successful stabilizations across thousands of plays, weintend to develop a sequence of actions that could be taken to increase stability in Sudan. We describe the current state of this project, how different networkmodels can be used to alter the scenario in which the game takes place, and discuss the broader applicability of human computation to network analysis.Generating Large‐scale Networks From Egocentric DataLee, Ju‐Sung; Carley, Kathleen M.Empirical Large‐N NetworksLarge‐scale Networks, Distribution, Dyadic Analysis, Egonet, assortativeSUN.AM2We propose a data‐driven method for generating distributions of large‐scale close‐tie networks. In particular, we turn to the egocentric networks in the SocialNetwork Module of the 1985 General Social Survey. Using maximum likelihood estimation of key properties, we generate networks whose dyadic and egonetworkcharacteristics coincide with those of the GSS networks. We demonstrate our approach on city subsamples of the U.S. census. Since the generatednetworks are directly valid only on the ego‐network level, we examine the range of topologies and network properties induced by both the local structures anddegree of assortative mixing (or homophily) found in the data. We also explore the benefits of constraining network level properties during the generationprocess. Finally, we will discuss the implications and contributions of our method to the study of diffusion‐related dynamics.