Journal on Policy and Complex Systems
These topologies are fundamentally relational and accommodative of the embedded deep structure of social space . It is not clear what techniques would support the translation of such relational dynamics to mathematically defined environments , how these topologies affect fitness landscape potentials , and what that means for the movement of agent across them . An extremely useful contribution to the field would be a research program aimed at understanding whether , and if so how these three techniques might be combined .
In evolutionary models , the important system characteristics are not averages . They are adjacencies . The coding of state space is binary , and under normal ( non-chaotic ) conditions , evolutionary movement is between adjacent states ( Kauffman , 1993 ). In evolutionary modeling of social systems , the art will be in the specification of state space and in learning to appreciate the definition , characteristics , and dynamics of social systems as emergent . Regression analysis of social phenomena is problematic because of our lack of understanding of the topology of social space . In state space analyses , the topology is moot because the system evolves that topology and evolves in that topology all the time in real time and over time . The topology of the space is created by the evolutionary rules .
Evolutionary models have proven extraordinarily useful . Stuart Kauffman ’ s fitness landscapes are an important example of this tool . According to Kauffman ( 1993 ), the origin of order is evolutionary movement in state space .
If at the level of the organism , its state is represented by the bit string
0011010100100011
that evolutionary change is incremental and random would be captured by a single change ( called a mutation ) in any single bit to something like
0011010101100011
where the two are called “ nearest neighbors ” in state space , and the “ neighborhood ” consists of all states that can be represented by a one bit change from the original . If you add to this the understanding that the bits represent genes or characteristics of the organism , and that the bit string represents a chromosome , it becomes apparent that a change in a bit carries with it a change or an evolution in the physical characteristics of the organism .
Our bodies are governed by genes which are embedded in chromosomes which are embedded in cells which are embedded in tissue which are embedded in organs .... Thinking about the many different types of tissue that make up our bodies — liver tissue , brain tissue , heart tissue , nerve , skin , muscle , ... — and all of the interactions , processes , chemicals , and bodily fluids that combine to support life , makes it hard to believe that it is all orchestrated by random fluctuations at the level of the gene . Turns out that this unfathomable process repeats itself in the emergence , development , and functioning of all complex adaptive systems .
Starting with the bit string process described above , Kauffman has
6