Perspective: Complexity Theory and Organization Science

Abstract

Complex organizations exhibit surprising, nonlinear behavior. Although organization scientists have studied complex organizations for many years, a developing set of conceptual and computational tools makes possible new approaches to modeling nonlinear interactions within and between organizations. Complex adaptive system models represent a genuinely new way of simplifying the complex. They are characterized by four key elements: agents with schemata, self-organizing networks sustained by importing energy, coevolution to the edge of chaos, and system evolution based on recombination. New types of models that incorporate these elements will push organization science forward by merging empirical observation with computational agent-based simulation. Applying complex adaptive systems models to strategic management leads to an emphasis on building systems that can rapidly evolve effective adaptive solutions. Strategic direction of complex organizations consists of establishing and modifying environments within which effective, improvised, self-organized solutions can evolve. Managers influence strategic behavior by altering the fitness landscape for local agents and reconfiguring the organizational architecture within which agents adapt.

The role of leadership in emergent, self-organization

“…Organizational theorists have begun to conceptualize organizations as complex adaptive systems (Anderson, 1999;McKelvey, 2001;Stacey, 1992;Wheatley, 1999) and increasingly reject the more traditional, mechanistic view of organizations because the simple models of classic science it is based on are unworkable (Marion & Uhl-Bien, 2001). Rather, organizations exist in conditions of instability, and as organizations move further away from equilibrium towards instability, they are capable of highly complex behavior.…”

“…System-level order emerges because of interactions among entities with individual schemas at a lower level in the system (Anderson, 1999), that is, nested systems (Ashmos & Huber, 1987). In self-organizing systems, order comes from the actions of interdependent agents who exchange information, take actions, and continuously adapt to feedback about others' actions rather than from the imposition of an overall plan by a central authority (Chiles et al, 2004).…”

“…made its way into organizational science, challenging this linear, mechanistic view of organizations with analogies from the physical sciences of systems whose future states are unpredictable and full of surprise (Anderson, 1999;Richardson and Cilliers, 2001). A central principal of complexity theory is emergent self-organization, whereby systems achieve order because multiple local agents interact and those interactions produce unintended outcomes without the intervention of a central controller (Chiles, Meyer, & Hench, 2004).…”

Agent-based modeling in managerial science: an illustrative survey and study

“…For example, it might be appropriate to regard a department or a family as a single agent. Grouping individual agents to ''aggregate'' agents is particularly interesting in managerial science since, for example, it allows hierarchical structures to be mapped (Chang and Harrington 2006;Anderson 1999). However, agents do not necessarily have to be human or, at least, solely composed of humans; rather they can be biological entities (e.g.…”

“…In particular, agents are assumed to decide on basis of bounded information such that they do not have global information about the entire search space and have limited computational power (Epstein 2006a;Safarzyńska and van den Bergh 2010;Anderson 1999). Hence, although agents are usually modeled as pursuing certain goals, they are not global optimizers.…”