The Origins of Order: Self-Organization and Selection in Evolution
Complex adaptive systems may operate optimally at the 'edge of chaos' - between order and disorder
Kauffman's work on Boolean networks and the 'edge of chaos' provides theoretical foundations for understanding when and how emergence produces adaptive, generative outcomes. His research suggests that complex adaptive systems - gene networks, neural networks, ecosystems, and potentially organizations - may perform optimally at critical points between rigid order and chaotic disorder.
For organizations, this work offers a framework for understanding why some structures are too rigid to innovate while others are too chaotic to function. The 'edge of chaos' concept helps explain how to balance structure (enabling reliable core functions) with flexibility (enabling adaptation and novelty).
Key Findings from Kauffman (1993)
- Complex adaptive systems may operate optimally at the 'edge of chaos' - between order and disorder
- Self-organization can produce order and function without central control
- Boolean network models demonstrate how simple local rules generate complex emergent behavior
- Evolution may work with self-organization, not just random mutation and selection