Social Insect Colonies as Complex Adaptive Systems

Ecosystems ◽  
1998 ◽  
Vol 1 (5) ◽  
pp. 437-443 ◽  
Author(s):  
Eric Bonabeau
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Social Insect ◽  
Insect Colonies ◽  
Complex Adaptive

Two Meanings of Complex Adaptive Systems

2016 ◽  
Author(s):  
David S. Wilson
Keyword(s):  
Complex System ◽  
Immune System ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Social Insect ◽  
Functional Organization ◽  
General Rule ◽  
Insect Colonies ◽  
Complex Adaptive ◽  
Selection For

In complex systems theory, two meanings of a complex adaptive system (CAS) need to be distinguished. The first, CAS1, refers to a complex system that is adaptive as a system; the second, CAS2, refers to a complex system of agents which follow adaptive strategies. Examples of CAS1 include the brain, the immune system, and social insect colonies. Examples of CAS2 include multispecies ecosystems and the biosphere. This chapter uses multilevel selection theory to clarify the relationships between CAS1 and CAS2. The general rule is that for a complex system to qualify as CAS1, selection must occur at the level of the complex system (e.g., individual-level selection for brains and the immune system, colony-level selection for social insect colonies). Selection below the level of the system tends to undermine system-level functional organization. This general rule applies to human social systems as well as biological systems and has profound consequences for economics and public policy.

Theories of Communication Networks

2003 ◽  
Author(s):  
Peter R. Monge ◽  
Noshir Contractor
Keyword(s):  
Communication Networks ◽  
Computer Simulations ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Cross Sectional ◽  
Network Decomposition ◽  
Network Computer ◽  
Social Theories ◽  
Complex Adaptive ◽  
Almost All

To date, most network research contains one or more of five major problems. First, it tends to be atheoretical, ignoring the various social theories that contain network implications. Second, it explores single levels of analysis rather than the multiple levels out of which most networks are comprised. Third, network analysis has employed very little the insights from contemporary complex systems analysis and computer simulations. Foruth, it typically uses descriptive rather than inferential statistics, thus robbing it of the ability to make claims about the larger universe of networks. Finally, almost all the research is static and cross-sectional rather than dynamic. Theories of Communication Networks presents solutions to all five problems. The authors develop a multitheoretical model that relates different social science theories with different network properties. This model is multilevel, providing a network decomposition that applies the various social theories to all network levels: individuals, dyads, triples, groups, and the entire network. The book then establishes a model from the perspective of complex adaptive systems and demonstrates how to use Blanche, an agent-based network computer simulation environment, to generate and test network theories and hypotheses. It presents recent developments in network statistical analysis, the p* family, which provides a basis for valid multilevel statistical inferences regarding networks. Finally, it shows how to relate communication networks to other networks, thus providing the basis in conjunction with computer simulations to study the emergence of dynamic organizational networks.

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