Complex adaptive system

About: Complex adaptive system is a research topic. Over the lifetime, 3190 publications have been published within this topic receiving 111947 citations. The topic is also known as: Complex adaptive system, CAS.

Adaptive Allocation Modeling for a Complex System of Regional Water and Land Resources Based on Information Entropy and its Application

Abstract: Currently, water and land resources are treated as separate resources in allocation optimization for complex systems of water and land resources, which may have negative impacts on these water-land resource systems. In our study, an adaptive allocation model was established for a complex system of regional water and land resources using complex adaptive systems theory. The users of water and land resources were treated as adaptive agents, and the competition and synergy among various agents toward water and land resources were used as the driving forces for the evolution of the model. The model was accurately solved using a nested genetic algorithm to achieve the optimal joint allocation of regional water and land resources. A case study was conducted in the city of Kiamusze in Heilongjiang Province, and the results indicated that the evolution of the model was consistent with the actual behaviors of adaptive agents. Moreover, after the implementation of the optimized allocation results, the economic benefits in the study area were expected to increase by 3.34 %, and the comprehensive user satisfaction index regarding water increased from 0.61 to 0.73; moreover, the ecological footprint of the ecological sector increased by 5.6 %. Our results provide important guidance for achieving the sustainable use of regional water and land resources.

Complex intervention modelling should capture the dynamics of adaptation.

Abstract: Complexity has been linked to health interventions in two ways: first as a property of the intervention, and secondly as a property of the system into which the intervention is implemented. The former recognizes that interventions may consist of multiple components that act both independently and interdependently, making it difficult to identify the components or combinations of components (and their contexts) that are important mechanisms of change. The latter recognizes that interventions are implemented in complex adaptive systems comprised of intelligent agents who modify their behaviour (including any actions required to implement the intervention) in an effort to improve outcomes relative to their own perspective and objectives. Although an intervention may be intended to take a particular form, its implementation and impact within the system may deviate from its original intentions as a result of adaptation. Complexity highlights the challenge in developing interventions as effective health solutions. The UK Medical Research Council provides guidelines on the development and evaluation of complex interventions. While mathematical modelling is included in the guidelines, there is potential for mathematical modeling to play a greater role. The dynamic non-linear nature of complex adaptive systems makes mathematical modelling crucial. However, the tendency is for models of interventions to limit focus on the ecology of the system - the ‘real-time’ operation of the system and impacts of the intervention. These models are deficient by not modelling the way the system reacts to the intervention via agent adaptation. Complex intervention modelling needs to capture the consequences of adaptation through the inclusion of an evolutionary dynamic to describe the long-term emergent outcomes that result as agents respond to the ecological changes introduced by intervention in an effort to produce better outcomes for themselves. Mathematical approaches such as those found in economics in evolutionary game theory and mechanism design can inform the design and evaluation of health interventions. As an illustration, the introduction of a central screening clinic is modeled as an example of a health services delivery intervention. Complexity necessitates a greater role for mathematical models, especially those that capture the dynamics of human actions and interactions.

Embedding system dynamics in agent based models for complex adaptive systems

Abstract: Complex adaptive systems (CAS) are composed of interacting agents, exhibit nonlinear properties such as positive and negative feedback, and tend to produce emergent behavior that cannot be wholly explained by deconstructing the system into its constituent parts. Both system dynamics (equation-based) approaches and agent-based approaches have been used to model such systems, and each has its benefits and drawbacks. In this paper, we introduce a class of agent-based models with an embedded system dynamics model, and detail the semantics of a simulation framework for these models. This model definition, along with the simulation framework, combines agent-based and system dynamics approaches in a way that retains the strengths of both paradigms. We show the applicability of our model by instantiating it for two example complex adaptive systems in the field of Computational Sustainability, drawn from ecology and epidemiology. We then present a more detailed application in epidemiology, in which we compare a previously unstudied intervention strategy to established ones. Our experimental results, unattainable using previous methods, yield insight into the effectiveness of these intervention strategies.

The Industrial Cluster as a Complex Adaptive System

Abstract: In the economic debate of recent years, the rediscovery of industrial clusters is concurrent with the rediscovery of complexity. And with good reason.