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Social Networks And Sustainability In Business: A Complexity PerspectivePorter, TerryNetwork DynamicsComplexity, Social Network Analysis, Sustainability, Business SystemsTHURS.PM2Sustainability in business is growing and will continue to grow. It is not a new topic but in an accelerating phase of expansion, affecting individuals, businessesand industries that may not have been engaged previously. Never have the stakes been higher for building proactive business models and practices that cancontribute to sustainability, defined as meeting present needs without compromising future generations’ ability to meet their own needs. Considerableknowledge exists on social network factors related to organizational performance, but little or no work has been done in the area of sustainability performancein organizations. This paper starts from the premise that reductionist systems analysis and linear optimization techniques are inadequate to capture thecomplexity, unpredictability, and dynamism that are inherent when considering businesses in their ‘ecosystems’—the greater worlds of market and nonmarketstakeholders that define the context of sustainability. Complexity theory and complex adaptive systems are assumed to be more appropriate ways toframe inquiry into how firms can improve their ‘sustainability footprints’. The paper also assumes a social relations approach, including quantitative andqualitative epistemologies. Reality is contingent upon the socially constructed interpretations of actors, thereby implying the importance of social networksand motivational drivers in decision making towards sustainability goals. The purpose of this paper is to develop a theoretical framework and testablepropositions relating to the role of social networks in enhancing sustainability in organizations, as framed from a complex adaptive systems perspective. Thenext step in this stream of research will be empirical testing.Social Networks Based On Semantic Network Similarity: Assessment Of Latent Dirichlet Approximation (lda)topic Modeling To Index Persons’ SimilarityDanowski, James A.Words and Networks ‐ Roles, Health, MethodsCommunication Networks, Bayesian Methods, Communities Of Practice, Semantic Networks, Computational Linguistics, Word Cooccurrence NetworksFRI.PM1Constructing social networks based on actors’ semantic similarity provides a definition of communities or cliques based on persons’ message productionprofiles. Semantic similarity is a novel kind of homophily that enables identification of potential future communication events between individuals as well asevents that have transpired. Recent research has used the entire semantic network produced by individuals as the basis for measuring semantic similarity.Such a fine‐grained approach, while conceptually appealing, requires long computation times. The goal of research reported here is to assess a type ofdimensionality reduction in the semantic networks, the Bayesian Latent Dirichlet Approximation (LDA) of topic modeling based on collapsed Gibbs Samplingthat identifies regions of semantic similarity, considered “topics.” The main appeal is several orders of magnitude faster computation. A problem is required apriori specification of the number of topics for a solution. Research has yet to systematically identify the effects of different numbers of topics on the numberof highly similar pairs of online discussion forum authors that result. This research systematically increments the number of topics in a solution and examinesas a criterion the number of highly similar pairs of authors. Three data sets are used: 1) Pakistani discussion forum on diverse topics, 2) Ten Indian discussionforums, and 3) Twenty‐five American politics discussion forums.