Complexity: Entailment Mesh

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Complexity as Social Entailment

Keywords
chaos, near decomposability, probabilistic, hierarchical, attractor, span, productions, uncertainty, self organization, entailment, agencies, adaptive

Description
Complex systems involve phenomena characterized by interactions of individual agencies (sub-systems or elements), that self-organize at a higher systems level, and exhibit emergent and adaptive characteristics. The complex behavior and hierarchical structures evidenced in most social organizations suggest that the relationships of sub-organizational elements are such that they perform specific autonomous functions are nearly decomposable.

RCSP Phase State Entailment Mesh Equilibrium.

Information theory explains organized complexity in terms of the reduction of entropy (disorder) that is achieved when systems absorb energy from external sources and convey it to pattern or structure. Complex systems take in data from their environments, find regularities in the data, and compress these perceived regularities into internal models that are used to describe and predict its future. Adaptation results from the selection pressures of specific environmental conditions which are often composed of many degrees of uncertainty. Conditions under which complexity emerges can best be described as intermediate between chaos and order.

In that social organizations can be understood as non-linear and dynamic artificial systems, theories of complexity can be used to describe properties which can be algorithmically and semantically described.

Methodology
This experiment serves to demonstrate several properties of complexity theory employed as design considerations in generating surveillance data from a social entailment involving multiple probes. Potential applications include: military intelligence, strategic operations, data mining and environmental analysis.

Five Radio Controlled Surveillance Probes and their assigned operators function in a self-organizing and adaptive social entailment resulting from hierarchical inter relationships (span of control) which emerge patterns of complex behavior. Locally interacting semi-autonomous behavior protocols are connected only by a system of relations and abilities to react (predict) to the dynamics of cooperation using probability.

Adaptive Interaction/Reaction Based on Attractor/Repellor State Assignments.

Of particular concern is the enabling of adaptive reaction/interaction with the many uncertainties present in a public architectural spaces. Therefore, the complexity of the social entailment is necessarily derivative of its ability to inference about the uncertain relationships and evolving circumstances present in the selected test environment.

Bayes' theorem provides a means of describing parameters for addressing inference and uncertain relationships.

p(H|E,I) = p(H|I)*p(E|H,I)/p(E|I) [Bayes Rule]

The geometry of behavior associated with the dynamics of chaos provides a framework from which specific RCSP maneuvers are developed. Attractor/repellor states and procedures assigned to each RCSP influence and are influenced by the hierarchical entailment structure, leading towards a non-predictive equilibrium.

Non-Predictive Equilibrium.

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