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Icimsware: Community Informatics for Collaborative Competitiveness

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Icimsware: Community Informatics for Collaborative Competitiveness iCIMSware: Community Informatics for Collaborative Competitiveness By Jacqueline Caesar and Thomas MacCalla, National University and Santiago Nunez, Costa Rica Institute of Technology Communities are complex structures with interdependent elements not readily understood and collaborative competitiveness is an advantageous strategy in the marketplacei. The purpose of this paper is to introduce i–CIMSware or Integrated Community Informatics Management Systems as an Information Communication Technology application that enables researchers and community practitioners to optimize the dynamics of interrelationships in structured human environments and foster a community intelligence that provides insight on how people behave, think and affect that environmentii. Tools that profile such interactions are needed to develop social models of behavioriii and economicsiv without loosing touch with available evidence. To produce such a tool is very challenging because of the high intra- subject variability within a cultural backgroundv and the broad range of interactions and scales among individuals, groups and their environmentvi. Community-profiling tool also should identify and preserve the overall trends that represent all of the actors involved and prevent research from becoming cluttered with individual factsvii. Two main approaches for studying communities depart from seemingly disconnected paradigmsviii: The state of the community is completely understood by the individuals and their environment; they can be decomposed in multiple levels of decreasing complexity until some atomic social unit is reachedix. The state of the community is not decomposable in individual units or atomic interactions; it must be understood as a wholex. It is common for community profiling tools to focus on ex-post analyses, rather than being predictivexi. Some major advances have been produced in the last decade towards improving data resolution in both temporal and spatial dimensions by using technology (e.g. social networks and mediaxii) but translation towards predictive analysis techniques is still under developmentxiii. In that sense KEEPERRHHATT -an extension of KEEPRHAxiv- is a community asset mapping based on eleven factors that characterize how individuals interrelate among themselves and what their common vision about the community is. It describes how well connected people are not only in social network terms, but in terms of their emotional responses to challenges and problems in daily life taking into account political structures, geographic elements and social processes. These factors are: Kinship, Education, Economics, Politics, Environment, Religion, Recreation, Health, Human Services, Associations, Transportation Infrastructure, Technology and Team Science. KEEPERHHATT is therefore a collection of information requirements upon which social researchers design data collection and analysis. There are some key challenges for obtaining a coherent image of the interdependence and interrelation amongst all community factors such as linking multiple existing data sources and survey methods, ensuring data and study comparability and providing a single image of the community without exposing unnecessary detail. KEEPERRHHATT organizes collected data in the interdependence matrix. It summarizes the results from observations into a single unit of information from where further analysis can depart. A key observation is that, at each level of granularity available for the community, all elements are intrinsically present and linked in a complete multi-graph. Given any two assets social researchers apply analysis techniques that produce some form of numerical output. Analysis methods allow a quantitative description of social phenomena in terms of simple numerical indicators, classification and clustering results, discrete and continuous probability distributions or data mining analyses, for instance. Any reliable method should provide a way to quantify numerically the magnitude of the value in the relationship being studied within a meaningful range. It should also provide a way to quantify how much uncertainty the data analysis results have. Thus, any method provides at least a magnitude and its uncertainty. We show in this work a way in which, by integrating data sources from government bodies, community organizations though a formal framework for comparability, the resulting multi- graph provides a good approximation of the status of a community and a causal explanation involving related factors is possible by solving the Longest Hamiltonian Path problem (LHP) after normalizing and summarizing data though a procedure that is locally rough and globally smooth. Each path constitutes an explanation for the state of the community in our social reference framework. We also provide an algebra for individual paths that, given a substractive approach (e.g. not including a given data set or removing a factor completely), allows evaluation of partial scenarios. From a multi-graph with k factors, the computational complexity of finding explanations is in the order of 푂(푂 ∙ 푂!). By changing individual data in the original data sets, our approach allows the definition of research hypothesis in the form of simulation scenarios, including equation sets that describe known dynamics for social prediction. It should be pointed out that iCIMSware was developed in cooperation with business, industry and community organization leaders as a software implementation of KEEPERRHHATT-ω, an extensible model capable of including more community factors. The prototype tool will be used as a framework for an asset mapping study with the Southwest Innovation Cluster (SWIC) for collaborative competitiveness initiative in 2015. SWIC is a non-profit, business- industry-academic collaborative whose primary mission is to support regional economic growth through innovation and to foster collaboration among entrepreneurs, businesses, government agencies, community organizations, and academia for the development and advancement of advanced autonomous and unmanned systems technologies to solve the priority needs of the nation. References i Zhang, D., Guo, B., & Yu, Z. (2011). The emergence of social and community intelligence. Computer, (7), 21-28. ii Wang, F. Y., Carley, K. M., Zeng, D., & Mao, W. (2007). Social computing: From social informatics to social intelligence. Intelligent Systems, IEEE, 22(2), 79-83. iii Epstein, J. M. (2006). Generative social science: Studies in agent-based computational modeling. Princeton University Press. iv Tesfatsion, L. (2006). Agent-based computational economics: A constructive approach to economic theory. Handbook of computational economics, 2, 831-880. v Triandis, H. C. (1989). The self and social behavior in differing cultural contexts. Psychological review, 96(3), 506. vi Bergstrom, T. C. (2002). Evolution of social behavior: individual and group selection. Journal of Economic Perspectives, 67-88. vii Kaniki, A. M. (2001). Community profiling and needs assessment. 2001). Knowledge, Information and Development: an African Perspective. Scottsville, South Africa: School of Human and Social Studies, University of Natal (Pietermaritzburg), 187-199. viii Bailey, K. (2008). Methods of social research. Simon and Schuster. ix Young, H. P. (2001). Individual strategy and social structure: An evolutionary theory of institutions. Princeton University Press. x Turner, F. (2001). Transcending biological and social reductionism. SubStance, 30(1), 220-235. xi Hawtin, M., & Percy-Smith, J. (2007). Community Profiling: A Practical Guide: Auditing social needs. McGraw-Hill International. xii Zhou, W., Jin, H., & Liu, Y. (2012, August). Community discovery and profiling with social messages. In Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining (pp. 388-396). ACM. xiii Ali, G., Shaikh, N. A., & Shaikh, A. W. (2010). A research survey of software agents and implementation issues in vulnerability assessment and social profiling models. Australian Journal of Basic and Applied Sciences, 4(3), 442-449. xiv MacCalla, T. (1982) Psychosocial Aspects of Cancer. Raven Press, New York, 1982, In: Sociocultural Dimensions Relevant to Preventing Cancer,pp. 103-110. .
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