A ground cover-based incentive approach to enhancing resilience in rangelands viewed as complex adaptive systems

2010 ◽  
Vol 32 (3) ◽  
pp. 283 ◽  
Author(s):  
Ronald B. Hacker ◽  
Peter J. Jessop ◽  
Warren J. Smith ◽  
Gavin J. Melville
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
New South ◽  
Ground Cover ◽  
Policy Mix ◽  
South Wales ◽  
Practical Operation ◽  
Complex Adaptive ◽  
Grazing Lands ◽  
Theoretical Issues

Inconsistencies can commonly be expected between the financial goals of rangeland grazing enterprises and public conservation goals such as maintenance of ground cover to reduce erosion. Where the State wishes to promote conservation outcomes, incentive schemes which reward these outcomes on privately managed grazing lands are an option. We describe one such scheme intended to achieve conservation outcomes and support the development of resilience in the complex adaptive (human–environmental) rangeland system through payments related to measured ground cover. A pilot program in western New South Wales has shown that the practical operation of such a program is uncomplicated and that while several theoretical issues could be further refined there is a rationale for extension of the program based on parameters and processes that are agreed by the participants. We suggest that development of such a scheme should be considered as part of the policy mix related to natural resource management and drought assistance.

scholarly journals Radical Embodied Computation: Reproduction of Similarity by Analogy as an Order Generating Mechanism in Complex Systems

2020 ◽  
Author(s):  
Fred Hasselman
Keyword(s):  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
The Body ◽  
Affine Scaling ◽  
Theoretical Frameworks ◽  
Scientific Disciplines ◽  
Complex Adaptive ◽  
The Mind ◽  
Theoretical Issues ◽  
Behavior And Cognition

About two decades ago, scholars from the scientific disciplines that study human behavior and cognition, suggested an era of post-cognitivism was imminent, in which the computer metaphor, computationalism and representationalism would be discarded as viable theoretical frameworks for explaining phenomena of the body and the mind. In the present paper I argue that explanations of complex adaptive behavior require a theory of meaning mechanics that explains how complex adaptive systems can use semantic information to coordinate their behavior. This calls for a unification of sorts between the insights obtained in ecological psychology and embodied embedded cognition with principles of natural computation (cf. Decastro, 2007) in the context of explaining the behavior and properties of complex adaptive systems and networks (see e.g., Freeman et al., 2001; Chialvo, 2010; Flack, 2017a; Scheffer et al., 2018). I will refer to this framework as Radical Embodied Computation (REC++) and discuss some of the philosophical and theoretical issues that have to be resolved. I conclude by suggesting a mechanism for the emergence of meaning that is based the conception of self-affine scaling as the reproduction of similarity by analogy.

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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