Modeling complex adaptive systems

Abstract

The paper describes ways to model systems that dynamically change as their environment changes. The generic term complex adaptive systems (Kovacs, 2005) is increasingly used to describe systems in such environments. Complex adaptive systems are generally defined (Holland, 1995) as made up of many agents (which may represent cells, individuals, firms, projects) acting in parallel, constantly acting and reacting to what the other agents are doing. The control of complex adaptive systems tends to be highly dispersed and decentralized. The overall behaviour of the system is the result of a huge number of decisions made every moment by many individual agents. Processes in such systems need to be equally adaptive and we refer to them as complex adaptive processes. Currently there are no widely accepted methodologies to model and design complex adaptive processes. Most methodologies for information systems design focus on prescribed processes. The paper describes ways to model such systems. The models will differ from existing modeling techniques as they combine business functions with social structures in ways that facilitate social connectivity and interactivity needed to adapt to changing situations within the business context. At the same time the social networks will be used to define the knowledge requirements that capture the outcome of work exchanges to support process continuity. It develops the idea of collaboration graphs to integrate social network into business models. It develops a blueprint based on three parts, business models, collaboration and knowledge and develops models based on integrating these three components. It then demonstrates the methods in outsourcing environments and principles of implementation of the models using contemporary technologies. © 2009 Springer Berlin Heidelberg.