Colorscape: a creative artificial ecosystem model of communication and collective creativity in global participatory science

With the increasing use of cyberinfrastructure and popularity of e-Science initiatives, science is becoming truly globalized, reducing barriers to entry and enabling formation of open and global networked innovation communities. Yet, relatively little is known about the mechanisms that govern such globalized communities. Meanwhile, creative artificial ecosystem metaphors and interaction processes among communities have potential to shed light on the effects of communication styles in the emergence of global knowledge communities. So, this study explores how networks of scientifc communities and epistemic cultures form and evolve, what network patterns emerge from different socio-technical communication theories, and the relationship between environmental constraints, community traits, and innovation performance and potential. Understanding scientific communities and their associated communication networks is key to understanding the dynamics of knowledge creation, as well as formation and growth of scientifc communities to facilitate informed science and innovation policy-making. A benefit of this research is to offer federal agencies a computer-aided decision-making tool so as to evaluate investment decision and policies. To this end, an agent-based simulation model combining boundary processes and theories of communication is developed. The model is verified and validated with respect to empirical network data. Simulation results suggest that communities with highly connected clusters are likely to thrive if resource availability is low. So far as the resource allocation strategy is concerned, key area investment with technology transferring results in the highest variety. Exploration of the impact of socio-technical communication theories suggest that under low communication frequency, openness and receptivity lead to higher variety. On the contrary, variety decreases with increasing receptivity under high communication frequency.

[1]  J. M. Buchanan.  Individual Interests and Collective Action: Studies in Rationality and Social Change.James S. Coleman , 1987 .

[2]  David Krackhardt,et al.  Cognitive social structures , 1987 .

[3]  S. Vincent.  Input Data Analysis , 2007 .

[4]  Caroline S. Wagner,et al.  THE DYNAMICS OF KNOWLEDGE CREATION: A BASELINE FOR THE ASSESSMENT OF THE ROLE AND CONTRIBUTION OF THE DEPARTMENT OF ENERGY'S NANOSCALE SCIENCE RESEARCH CENTERS , 2006 .

[5]  Detlef Schoder,et al.  Finding collaborative innovation networks through correlating performance with social network structure , 2008 .

[6]  C. S. Holling,et al.  Panarchy Understanding Transformations in Human and Natural Systems , 2002 .

[7]  M. West.  Innovation and creativity at work , 1997 .

[8]  K. Weick.  The social psychology of organizing , 1969 .

[9]  Jon McCormack.  Artificial ecosystems for creative discovery , 2007, GECCO '07.

[10]  R. Burt.  THE GENDER OF SOCIAL CAPITAL , 1998 .

[11]  Robin Cowan,et al.  Network Structure and the Diffusion of Knowledge , 2004 .

[12]  Levent Yilmaz,et al.  A robust evolutionary strategy for generative validation of agent-based models using adaptive simulation ensembles , 2011, Proceedings of the 2011 Winter Simulation Conference (WSC).

[13]  Liz Sonenberg,et al.  Design and Analysis of Organization Adaptation in Agent Systems , 2010 .

[14]  Alex Kacelnik,et al.  Timing and Foraging: Gibbon's Scalar Expectancy Theory and Optimal Patch Exploitation , 2002 .

[15]  Peter Pirolli,et al.  An elementary social information foraging model , 2009, CHI.

[16]  Sorin Solomon,et al.  Power laws in cities population, financial markets and internet sites (scaling in systems with a variable number of components) , 2000 .

[17]  Hans Friedrich Witschel,et al.  What is Organizational Knowledge Maturing and how can it be assessed , 2009 .

[18]  Alan L. Porter,et al.  Science overlay maps: A new tool for research policy and library management , 2009, J. Assoc. Inf. Sci. Technol..

[19]  Andreas Pyka,et al.  Learning in innovation networks: Some simulation experiments , 2007 .

[20]  F. Heider.  ATTITUDES AND COGNITIVE ORGANIZATION , 1977 .

[21]  Levent Yilmaz,et al.  On the Synergy of Conflict and Collective Creativity in Open Innovation Socio-technical Ecologies , 2009, 2009 International Conference on Computational Science and Engineering.

[22]  Bruce Edmonds.  When Simple Measures Fail: Characterising Social Networks Using Simulation , 2005 .

[23]  Daniel J. Brass.  A social network perspective on human resources management , 1992 .

[24]  Daniel J. Brass.  Being in the right place: A structural analysis of individual influence in an organization. , 1984 .

[25]  P. Samuelson.  The Pure Theory of Public Expanditure , 1954 .

[26]  Martyn Amos,et al.  Theoretical and Experimental DNA Computation , 1999, Bull. EATCS.

[27]  Mark E. J. Newman.  A measure of betweenness centrality based on random walks , 2005, Soc. Networks.

[28]  Arjun Bhutkar,et al.  Synthetic biology: navigating the challenges ahead. , 2005, The journal of biolaw & business.

[29]  John R. Koza,et al.  Hidden Order: How Adaptation Builds Complexity. , 1995, Artificial Life.

[30]  P. Ahmed,et al.  Relationships between Innovation Stimulus, Innovation Capacity, and Innovation Performance , 2006 .

[31]  M. Batty.  Generative social science: Studies in agent-based computational modeling , 2008 .

[32]  Steven R. Corman,et al.  Perceived Networks, Activity Foci, and Observable Communication in Social Collectives , 1994 .

[33]  A. Stirling.  Diversity and ignorance in electricity supply investment: Addressing the solution rather than the problem☆ , 1994 .

[34]  John H. Reed,et al.  Impact evaluation framework for technology deployment programs , 2007 .

[35]  Rikard Stankiewicz,et al.  Technology as an Autonomous Socio-Cognitive System , 1992 .

[36]  Susan Cozzens,et al.  Final Report - A DEEPER LOOK AT THE VISUALIZATION OF SCIENTIFIC DISCOVERY IN THE FEDERAL CONTEXT , 2008 .

[37]  Ismael Rafols,et al.  Is science becoming more interdisciplinary? Measuring and mapping six research fields over time , 2009, Scientometrics.

[38]  C. S. Holling,et al.  Resilience, Adaptability and Transformability in Social–ecological Systems , 2004 .

[39]  Andreas Pyka,et al.  Agent-Based Modelling of Innovation Networks – The Fairytale of Spillover , 2009 .

[40]  G. Homans,et al.  Social Behavior: Its Elementary Forms. , 1961 .

[41]  N. Gilbert.  A Simulation of the Structure of Academic Science , 1997 .

[42]  M. Newman,et al.  The structure of scientific collaboration networks. , 2000, Proceedings of the National Academy of Sciences of the United States of America.

[43]  Robin Cowan,et al.  The dynamics of collective invention , 2003 .

[44]  Scott Shane,et al.  Encouraging university entrepreneurship? The effect of the Bayh-Dole Act on university patenting in the United States , 2004 .

[45]  C. Wagner.  The New Invisible College: Science for Development , 2008 .

[46]  E. Wenger.  Communities of Practice and Social Learning Systems , 2000 .

[47]  P. Holland,et al.  The Statistical Analysis of Local Structure in Social Networks , 1974 .

[48]  Teresa M. Amabile,et al.  Affect and Creativity at Work , 2005 .

[49]  T. Kuhn,et al.  The Structure of Scientific Revolutions. , 1964 .

[50]  P. Langley,et al.  Computational Models of Scientific Discovery and Theory Formation , 1990 .

[51]  Steven B. Andrews,et al.  Structural Holes: The Social Structure of Competition , 1995, The SAGE Encyclopedia of Research Design.

[52]  Ismael Rafols,et al.  Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience , 2009, Scientometrics.

[53]  M. Macy,et al.  FROM FACTORS TO ACTORS: Computational Sociology and Agent-Based Modeling , 2002 .

[54]  John H. Miller,et al.  Complex adaptive systems - an introduction to computational models of social life , 2009, Princeton studies in complexity.

[55]  M. Hohn.  The Relationship between Species Diversity and Population Density in Diatom Populations from Silver Springs, Florida , 1961 .

[56]  Charles E. Taylor,et al.  Evolutionary Computation: An Overview , 1999 .

[57]  L. Leydesdorff,et al.  The Triple Helix of university-industry-government relations , 2003, Scientometrics.

[58]  Andreas Pyka,et al.  A new model for university-industry links in knowledge-based economies , 2011 .

[59]  M. Ashburner,et al.  The OBO Foundry: coordinated evolution of ontologies to support biomedical data integration , 2007, Nature Biotechnology.

[60]  Edward J. Rykiel,et al.  Testing ecological models: the meaning of validation , 1996 .

[61]  Levent Yilmaz,et al.  An Agent Simulation Study on Conflict, Community Climate and Innovation in Open Source Communities , 2009, Int. J. Open Source Softw. Process..

[62]  Etienne Wenger,et al.  Communities of Practice: Learning, Meaning, and Identity , 1998 .

[63]  C. Shalley,et al.  The Social Side of Creativity: A Static and Dynamic Social Network Perspective , 2003 .

[64]  Csr Young,et al.  How to Do Things With Words , 2009 .

[65]  P. Oliver.  Formal Models of Collective Action , 1993 .

[66]  Jerry Banks,et al.  Handbook of simulation - principles, methodology, advances, applications, and practice , 1998, A Wiley-Interscience publication.

[67]  Arvind Parkhe,et al.  Orchestrating Innovation Networks , 2006 .

[68]  Daniel J. Brass.  Technology and the structuring of jobs: Employee satisfaction, performance, and influence , 1985 .

[69]  R. Burt.  Social Contagion and Innovation: Cohesion versus Structural Equivalence , 1987, American Journal of Sociology.

[70]  Eric Bonabeau,et al.  Agent-based modeling: Methods and techniques for simulating human systems , 2002, Proceedings of the National Academy of Sciences of the United States of America.

[71]  R. Swedberg.  Entrepreneurship : the social science view , 2000 .

[72]  J. Coleman.  Foundations of Social Theory , 1990 .

[73]  Laurie J. Heyer,et al.  Exploring expression data: identification and analysis of coexpressed genes. , 1999, Genome research.

[74]  Levent Yilmaz,et al.  Dynamics of knowledge creation in global participatory science communities: open innovation communities from a network perspective , 2011, Comput. Math. Organ. Theory.