Setting the Stage: Complex Systems, Emergence and Evolution
3
TABLE 1.1: Examples of research carried out in complex
adaptive systems. Adapted from Schuster [171].
Research Area
Researchers
Year
Darwinian evolution
Smith and Szathmary [189] 1995
Chemical networks
Kauffman [103]
1993
Ecological networks
Sigmund [177]
1993
Insect colonies
Bonabeau and Dorigo [25]
1999
Immune system
Segel and Cohen
2000
Nervous system
Kandel [101]
2000
Economic networks
Lane and Durlauf [11]
1997
Social networks
Frank [67]
1998
Communication networks Barabasi [4]
2000
Transportation networks Narguney [137]
2000
Evolutionary games
Hofbauer and Sigmund [85] 1998
1.1 Complex and Adaptive Systems
Multiple disciplines use complex systems to explain unusual phenomena
and systems characteristics by artificially creating large simulated systems
modeling real systems, aiding understanding on how these systems behave.
Table 1.1 discusses some of the early examples in various disciplines and complex system modeling. Some of the common features of these systems are
summarized in Figure 1.2.
Individual elements exist on multiple levels within the system, allowing
hierarchies, and even hierarchies, to develop where two systems are mutually
exclusive and continuously interacting. These elements can act as representatives of either a single performing individual or as a collection of individuals
such as groups of multiple individuals. Each element evaluates its behavior
based on a reward system and adapts to perform better in the current system conditions. The reward system is determined by a performance measure,
where individuals use receptors to read signals and functions to assess these
performances.
Adaptiveness of elements is a unique feature that complex systems possess. Researchers have studied how systems predict and readjust efficiently to
changing conditions. For example, Hopfield [91] showed that system adaptiveness is highly influenced by presence of noise and attractors in the system.
Attractors are environmental points that cause elements to deviate from their
ideal paths of behavior, possibly when systems start to show chaotic behaviors.
3
TABLE 1.1: Examples of research carried out in complex
adaptive systems. Adapted from Schuster [171].
Research Area
Researchers
Year
Darwinian evolution
Smith and Szathmary [189] 1995
Chemical networks
Kauffman [103]
1993
Ecological networks
Sigmund [177]
1993
Insect colonies
Bonabeau and Dorigo [25]
1999
Immune system
Segel and Cohen
2000
Nervous system
Kandel [101]
2000
Economic networks
Lane and Durlauf [11]
1997
Social networks
Frank [67]
1998
Communication networks Barabasi [4]
2000
Transportation networks Narguney [137]
2000
Evolutionary games
Hofbauer and Sigmund [85] 1998
1.1 Complex and Adaptive Systems
Multiple disciplines use complex systems to explain unusual phenomena
and systems characteristics by artificially creating large simulated systems
modeling real systems, aiding understanding on how these systems behave.
Table 1.1 discusses some of the early examples in various disciplines and complex system modeling. Some of the common features of these systems are
summarized in Figure 1.2.
Individual elements exist on multiple levels within the system, allowing
hierarchies, and even hierarchies, to develop where two systems are mutually
exclusive and continuously interacting. These elements can act as representatives of either a single performing individual or as a collection of individuals
such as groups of multiple individuals. Each element evaluates its behavior
based on a reward system and adapts to perform better in the current system conditions. The reward system is determined by a performance measure,
where individuals use receptors to read signals and functions to assess these
performances.
Adaptiveness of elements is a unique feature that complex systems possess. Researchers have studied how systems predict and readjust efficiently to
changing conditions. For example, Hopfield [91] showed that system adaptiveness is highly influenced by presence of noise and attractors in the system.
Attractors are environmental points that cause elements to deviate from their
ideal paths of behavior, possibly when systems start to show chaotic behaviors.