Modularity and Complex Adaptive Systems

Modularity is ubiquitous in complex adaptive systems. Modules are clusters of components that interact with their environment as a single unit. They provide the most widespread means of coping with complexity, in both natural and artificial systems. When modules occur at several different levels, they form a hierarchy. The effects of modules and hierarchies can be understood using network theory, which makes predictions about certain properties of systems such as the effects of critical phase changes in connectivity. Modular and hierarchic structures simplify complex systems by reducing long-range connections, thus constraining groups of components to act as a single component. In both plants and animals, the organisation of development includes modules, such as branches and organs. In artificial systems, modularity is used to simplify design, provide fault tolerance, and solve difficult problems by decomposition.

Emergence of Creativity

This chapter presents a theory of natural creativity and its relation to certain features of intelligence and cognitive faculties. To test the theory we employ simulated worlds of varying complexity, inhabited by creatures with a genetically-evolving mental model. Plan-making strategies are compared between creatures in each of these worlds. The results show that creative behaviors are governed by the world’s structural coherence and complexity. In light of the results we present a new definition of creativity, propose a theory for why creativity evolves in nature, and discuss creativity’s relation to perception, goals, logic, understanding, and imagination. Creativity has been a difficult concept to define and its exact relationship with intelligence remains to be explained. The theoretical framework presented is proposed as a foundation and tool for furthering understanding of natural creativity and to help develop creative artificially intelligent systems.

Emergent Specialization in Biologically Inspired Collective Behavior Systems

Specialization is observable in many complex adaptive systems and is thought by many to be a fundamental mechanism for achieving optimal efficiency within organizations operating within complex adaptive systems. This chapter presents a survey and critique of collective behavior systems designed using biologically inspired principles. Specifically, we are interested in collective behavior systems where specialization emerges as a result of system dynamics and where emergent specialization is used as a problem solver or means to increase task performance. The chapter presents an argument for developing design methodologies and principles that facilitate emergent specialization in collective behavior systems. Open problems of current research as well as future research directions are highlighted for the purpose of encouraging the development of such emergent specialization design methodologies.

Emergence in Agent-Based Computational Social Science

This chapter provides a critical survey of emergence definitions both from a conceptual and formal standpoint. The notions of downward/backward causation and weak/strong emergence are specially discussed for application to complex social system with cognitive agents. Particular attention is devoted to the formal definitions introduced by Müller (2004) and Bonabeau and Dessalles (1997), which are operative in multi-agent frameworks and make sense from both cognitive and social point of view. A diagrammatic 4-Quadrant approach allows us to understand complex phenomena along both interior/exterior and individual/collective dimensions.

Solving the Sensory Information Bottleneck to Central Processing in Adaptive Systems

Information bottlenecks are an inevitable consequence when a complex system adapts by increasing its information input. Input and output bottlenecks are due to geometrical limits that arise because the area available for connections from an external surface always exceeds the area available for connections to an internal surface. Processing of the additional information faces an internal bottleneck As more elements increase the size of a processor, its interface surface increases more slowly than its volume. These bottlenecks had to be overcome before more complex life forms could evolve. Based on mapping studies, it is generally agreed that sensory inputs to the brain are organized as convergent-divergent networks. However, no one has previously explained how such networks can conserve the location and magnitude of any particular stimulus. The solution to a convergent-divergent network that overcomes bottleneck problems turns out to be surprisingly simple and yet restricted.

Concept and Definition of Complexity

The term complexity is used informally both as a quality and as a quantity. As a quality, complexity has something to do with our ability to understand a system or object—we understand simple systems, but not complex ones. On another level, complexity is used as a quantity when we talk about something being more complicated than another. In this chapter, we explore the formalisation of both meanings of complexity, which happened during the latter half of the twentieth century.

The Allocation of Complexity in Economic Systems

This chapter isolates a classic allocation problem in the substitution relation between two primary carriers of complex rules—agents and institutions—as a function of the relative costs of embedding rules in these carriers, all subject to the constraint of maintaining overall system complexity. We call this generic model the allocation of complexity, which we propose as a bridge between neoclassical and complexity economics.

Method of Systems Potential as "Top-Bottom" Technique of the Complex Adaptive Systems Modelling

Emergent properties of complex adaptive systems (CAS) are explored by means of “agent-based modelling” (ABM), which are compared with results from modelling on the basis of the method of systems potential (MSP). MSP describes CAS as a holistic system whereas ABM-methodology considers CAS as set of interacting “agents.” It is argued that MSP is a “top-bottom” approach, which supplements ABM “bottom-up” modeling of CAS. Adaptive principles incorporated into CAS at the level of a holistic system exploit Lamarck’s ideas about evolution, while the adaptive rules incorporated in the inner structure of CAS reflect Darwin’s ideas. Both ABM and MSP exhibit the same macroscopic properties: (1) “punctuated equilibrium”; (2) sudden jumps in macro-indices; (3) cyclical dynamics; (4) superposition of deterministic and stochastic patterns in dynamics; (5) fractal properties of structure and dynamics; (6) SOC-phenomenon. ABM demonstrates these properties via simulations of the different models whereas MSP derives these phenomena analytically.

From Reductive to Robust

This chapter seeks to identify the core of complex adaptive systems (CAS) theory. To achieve this end, this chapter introduces innovative methods for measuring and advancing the validity of a theory by understanding the structure of theory. Two studies of CAS theory are presented that show how the outer belt of atomistic and loosely connected concepts support the evolution of a theory; while, in contrast, the robust core of theory, consisting of co-causal propositions, supports the validity and testability of a theory. Each may be seen as being derived from differing epistemologies. It is hoped that the tools presented in this chapter will be used to support the purposeful evolution of theory by improving the validity of intelligent complex adaptive systems (ICAS) theory.

Ontological Reflections on Peace and War

Responding to a provocative question by Hiroharu Seki about Hiroshima ontologies, this chapter reviews related thinking about the ontological primitives appropriate for event-data making, accessing high-performance knowledge bases, and modeling intelligent complex adaptive systems of use to researchers on war and peace. It cautions against “Cliocide,” defined as of the “silencing” or symbolic killing of collective historical-political or historical-disciplinary identities and identifying practices by historical or discipline deficient “scientific” coding practices. It proposes that more intelligent multi-agent models in the “complex, adaptive systems” tradition of the Santa Fe Institute should include the socially shared memories of nations and international societies, including their identity-redefining traumas and their relational/migrational/ecological histories of community-building success and failure. Historicity in an ontologically distinctive sense of the “time ordered self-understandings of a continuing human society” is still a challenge for the computationally oriented literature on war and peace.