ABSTRACT
Maize is a very exhausting crop; scarce any thing exhausts the land more. (Mitchell and Young 1775:52)
Introduction
The research framework associated with complex adaptive systems (CAS) differs from more traditional, reductionist approaches in a number of ways. CAS concepts of emergence, fitness landscapes, self-organization, non-equilibrium conditions, etc. change our research objects and their observational scope. Single, linear cause and effect relationships no longer have relevance when our goal is an understanding of emergence. Even earlier systems theory approaches, after describing societies as composites of interconnected components, tended to regress (no pun intended) to simple dependent-independent analyses. Understanding emergence requires a simultaneous evaluation of cumulative individual actions (agents) operating within a maze of boundary conditions (limitations/costs). This convergence of behaviours and situations is interpreted as a fitness landscape (Kauffman 1993:39) which simultaneously serves as the evolutionary specification of organisms and their environments. Alandscape becomes a metaphor for an organism's (or society's) evolutionary trajectory over time when it is quantified as a cost function visualized across a three dimensional surface of hills (high costs) and valleys (low costs). 'Fit' entities will seek out and take the path of minimal, local cost. Translating this search process into an algorithm facilitates the use of computer simulations - the principle operationalizing tool of CAS.
