Abstract
To date, many communities of practice (COP) in the social sciences have been struggling with how to deal with rapidly growing bodies of information. Many CoPs across broad disciplines have turned to community frameworks for complexity modeling (CFCMs) but this strategy has been slow to be discussed let alone adopted by the social sciences communities of practice (SS-CoPs). In this paper we urge the SS-CoPs that it is timely to develop & establish a CBCF for the social sciences for two major reasons: the rapid acquisition of data & the emergence of critical cybertools which can facilitate agent-based, spatially-explicit models. The goal of this paper is not to prescribe how a CFCM might be set up but to suggest of what components it might consist & what its advantages would be. Agent based models serve the establishment of a CFCM because they allow robust & diverse inputs & are amenable to output-driven modifications. In other words, as phenomena are resolved by a SS-CoP it is possible to adjust & refine ABMs (& their predictive ability) as a recursive & collective process. Existing & emerging cybertools such as computer networks, digital data collections & advances in programming languages mean the SS-CoP must now carefully consider committing the human organization to enabling a cyberinfrastructure tool. The combination of technologies with human interfaces can allow scenarios to be incorporated through 'if' 'then' rules & provide a powerful basis for addressing the dynamics of coupled & complex social ecological systems (cSESs). The need for social scientists to be more engaged participants in the growing challenges of characterizing chaotic, self-organizing social systems & predicting emergent patterns makes the application of ABMs timely. The enabling of a SS-CoP CFCM human-cyberinfrastructure represents an unprecedented opportunity to synthesize, compare & evaluate diverse sociological phenomena as a cohesive & recursive community-driven process. Tables, References. Adapted from the source document.