On Selfish Memes: culture as complex adaptive system

We present the formal definition of meme in the sense of the equivalence between memetics and the theory of cultural evolution. From the formal definition we find that culture can be seen analytically and persuade that memetic gives important role in the exploration of sociological theory, especially in the cultural studies. We show that we are not allowed to assume meme as smallest information unit in cultural evolution in general, but it is the smallest information we use on explaining cultural evolution. We construct a computational model and do simulation in advance presenting the selfish meme powerlaw distributed. The simulation result shows that the contagion of meme as well as cultural evolution is a complex adaptive system. Memetics is the system and art of importing genetics to social sciences

Governing science as a complex adaptive system

Research policy is a complex matter. Copying best practices in research policy, as identified by benchmarking studies, is popular amongst policy makers but fails because of ‘knowledge asymmetries’. Research fields exhibit distinct knowledge dynamics that respond differently to governance interventions. Extending the idea of search regimes, this paper aims at providing a policy model for different knowledge dynamics by elaborating the notion of knowledge production as a complex adaptive system. Complex regimes emerge from three interacting sources of variance. In our conceptualisation, researchers are the nodes that carry the science system. Research can be considered as geographically situated practices with site specific skills, equipments and tools. The emergent science level refers to the formal communication activities of the knowledge published in journals and books, and announced in conferences. The contextual dynamics refer to the ways in which knowledge production provides resources for social and economic development. This conceptualization allows us to disaggregate knowledge dynamics both in horizontal (field related) and vertical (level related) dimensions by articulating the three different dynamics and their path dependencies (in research, science and society) in co-evolution with each other to produce distinct search regimes in each field. The implication for research governance is that generic measures can sometimes be helpful but there is clear need for disaggregated measures targeting field specific search regimes. Governing knowledge production through disaggregated measures means targeting in a distinct way not only different fields, but also, and more importantly, the interactions between local research practices, emergent scientific landscapes, and the field’s relationship to its societal context. If all three “levels” are aligned, there is a stable regime.search regime, research and innovation governance, complex adaptive system

Mapping wisdom as a complex adaptive system

This is the second of two papers concerning wisdom as an ecosystem appearing in sequential editions of Management & Marketing journal. The notion of wisdom as an ecosystem, or "the wisdom ecology", builds on work by Hays (2007) who first identified wisdom as an organisational construct and proposed a dynamic model of it. The centrepiece of this and its former companion paper is a relationship map of the Wisdom Ecosystem (the Causal Loop Diagram at Figure 1). The first paper, "The Ecology of Wisdom", introduced readers to the topics of wisdom and complex adaptive systems, and presented a dynamic model of the Wisdom Ecosystem. This second paper discusses systems dynamics modelling (mapping systems) and covers the Wisdom Ecosystem model in detail. It describes the four domains, or subsystems, of the Wisdom Ecosystem, Dialogue, Communal Mind, Collective Intelligence, and Wisdom, and walks readers through the model, exploring each of its 25 elements in turn. It examines the relationships amongst system elements and illuminates important aspects of systems function, providing a rare tutorial on developing and using Causal Loop Diagrams.Causal Loop Diagramming, Complexity, Dialogue, Organisational Learning, Systems Dynamics, Wisdom.

Anatomy of extreme events in a complex adaptive system

We provide an analytic, microscopic analysis of extreme events in an adaptive population comprising competing agents (e.g. species, cells, traders, data-packets). Such large changes tend to dictate the long-term dynamical behaviour of many real-world systems in both the natural and social sciences. Our results reveal a taxonomy of extreme events, and provide a microscopic understanding as to their build-up and likely duration.Comment: 8 pages, 4 figures. Now with Postscript figure

On Selfish Memes: culture as complex adaptive system

We present the formal definition of meme in the sense of the equivalence between memetics and the theory of cultural evolution. From the formal definition we find that culture can be seen analytically and persuade that memetic gives important role in the exploration of sociological theory, especially in the cultural studies. We show that we are not allowed to assume meme as smallest information unit in cultural evolution in general, but it is the smallest information we use on explaining cultural evolution. We construct a computational model and do simulation in advance presenting the selfish meme powerlaw distributed. The simulation result shows that the contagion of meme as well as cultural evolution is a complex adaptive system. Memetics is the system and art of importing genetics to social sciences.meme, memetics, memeplex, cultural evolution, cultural unit, complex system.

Application of Method of System Potential in Analysis of Economic System Evolution (english version).

The mathematical formulation of new System Method based on ideas of “system potential” and “conditions of its realization” is given. Cyclical dynamics of “efficiency of work” of a complex adaptive system follows from this Method. These cycles have the properties like to the properties of typical business cycles. It is proposed to identify these evolution cycles of complex adaptive systems as applied to economic system with business cycles.business cycle, catastrophe jump, economic potential, complex adaptive system

Complex Adaptive System Modelling of River Murray Salinity Policy Options

This paper reports on complex adaptive system (CAS) simulation of the River Murray Basin in Australia to compare capacity of institutional options to maintain functioning of key river system within a "bandwidth" that limits irreversible system state changes and highly adverse consequences. The modelling framework characterise diverse irrigation agents who profit from water diversion and cause external salinity impacts, water and salt process that form the link between irrigator actions and agricultural profits and external costs, and a river manager who sets institutional rules. Emphasis is on the CAS nature of the system and on institutional rules to accommodate choosing actions differently based on con dition of the system has been referred to as state contingent management (Wills, 2003) or threshold based management (Roe and Van Eeten, 2001). Key findings are that policy focus on the source of salinity by reducing drainage are much more cost effective than strategies to mitigate salinity once it occurs and that state contingent dilution provision when it has high benefit and low opportunity cost is also a cost effective way to manage salinity.Resource /Energy Economics and Policy,

Adaptive Capacity through Complex Adaptive System

Problem: The corrugated board industry is highly affected by customer uncertainty, various demands and short delivery times. In combination with a complex multi-step production process managers have to be able to identify bottle-necks and gain knowledge and understanding of how different changes in process will affect the production output. Purpose: The purpose of this master thesis is twofold, (1) to examine applicability of complexity theory through agent-based modelling on a production process (2) to identify improvement areas in order to increase the production output at SKS production site in Eslöv, by modelling and simulating the production process through an agent-based model. Method: The chosen method of this study is a combination of a case study and a complex system approach. The empirical data was collected through interviews, observation and document studies which were analysed through an agent-based simulation model. Conclusions: Through the holistic complex system approach and by iteratively exploring the SKS production process’s components the authors were able to distinguished essential factors of the production process wherefrom the complexity emerged. The production process exhibited several internal complex properties whereby the authors consider that SKS production process can undoubtedly be consider as a Complex Adaptive Systems (CAS). By mapping and utilising agent-based modelling the complexity of the system could be transferred to an agent-based model. Through analysis of the agent-based model the authors identified and simulated four improvement areas which provide possibility of an increased capacity utilisation and production output. Based on the results the authors recommend a higher individual freedom for the machines and a greater interaction both within the company and with the customer