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Future Enterprise- Social Computing 2.0
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| Written by David Hunter Tow |
Social computing has traditionally applied computational and analytic techniques to the study of social and cultural behaviour and the modelling of social interactions and communication.
In a more recent trend, social computing applications now frequently focus on web-supported online communities such as social networks, mobile media, wikis, blogs and virtual worlds, providing feedback on interactive social comment, entertainment, scientific and medical advances and business services. Social Computing 2.0 also supports techniques for utilising the combined power of groups and communities- collective forecasting and decision-making- to solve difficult problems such as those associated with major disasters and conflict resolution.
Such techniques often involve simulation and modelling as well as more traditional data mining and pattern discovery. In recent years there has been increasing use of autonomous agents to assist in simulating complex human interactions. Researchers now use this approach to study a wide range of social and economic issues, including social belief systems, resource allocation, traffic patterns, social cooperation, market trends, retail dynamics and organisational decision-making.
IN addition, business strategies and competitive markets have been increasingly characterised by turbulence, uncertainty and complexity. Consequently there is a need to model such markets and strategies as dynamic, evolutionary processes or as complex adaptive systems.
Competitive strategies, like most evolutionary processes, involve non-linear processes and are more effectively implemented over longer time periods. Therefore traditional techniques, such as data mining and linear analytics might not reveal the true dynamic patterns underlying the impact of a competitive global environment.
There is as yet no effective widely accepted methods for modelling complex systems, especially those involving human behaviour and social organisations, but collaborative agent-based artificial life is currently the most promising approach. Agent-based modelling of an enterprise can construct a virtual competitive market that allows business strategists a way of investigating a range of realistic scenarios.
Current financial and economic theory includes rigorous theoretical techniques for equilibrium market asset pricing, but among the assumptions on which this paradigm is based is the homogeneity and rationality of investors. Over the past few years however it has been increasingly apparent that price dynamics often fail to obey such standard theory, so that economists and financial engineers have had to adopt alternate evolutionary approaches, testable by agent models. For example agent-based modeling can more realistically simulate the interactions between irrational and rational investors in financial markets.
Future Trends
Business is at last realising that a deeper understanding of the drivers and outcomes of social interactions are vital both from a human resource and customer perspective.
Already many organisations are beginning to embrace social networks as a way of more effectively marketing services and tracking customer behaviour.
In the future Social Computing 2.0 will be an integral component of the strategic and operational management of the future enterprise and of its IT infrastructure.
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