The Validity and Verification of Complex Systems Models: Discussion

1973 ◽  
Vol 55 (2) ◽  
pp. 271-273 ◽  
Author(s):  
Christ Carl F.
Keyword(s):  
Complex Systems ◽  
Systems Models

Complex systems models for causal inference in social epidemiology

2020 ◽  
Vol 75 (7) ◽  
pp. 702-708
Author(s):  
Hiba N Kouser ◽  
Ruby Barnard-Mayers ◽  
Eleanor Murray
Keyword(s):  
Complex Systems ◽  
Causal Inference ◽  
Social Inequalities ◽  
Social Epidemiology ◽  
Causal Effects ◽  
Natural Choice ◽  
Systems Models ◽  
Multi Level

Systems models, which by design aim to capture multi-level complexity, are a natural choice of tool for bridging the divide between social epidemiology and causal inference. In this commentary, we discuss the potential uses of complex systems models for improving our understanding of quantitative causal effects in social epidemiology. To put systems models in context, we will describe how this approach could be used to optimise the distribution of COVID-19 response resources to minimise social inequalities during and after the pandemic.

scholarly journals Evolution, Complex Systems and the Dialectic

1999 ◽  
pp. 74-103 ◽  
Author(s):  
Peter Knapp
Keyword(s):  
Complex Systems ◽  
Systems Theory ◽  
Complex Adaptive Systems ◽  
Adaptive Systems ◽  
Large Scale ◽  
Dynamic Models ◽  
Dynamic Properties ◽  
World Systems ◽  
World Systems Theory ◽  
Systems Models

The status of large scale historical macro-theories is contested both in world-systems theory and in sociology as a whole. I distinguish three types of such dynamic models: evolutionary models, systems models and dialectical models. I define dialectical models as a family of complex systems models characterized by positive feedback (self-reinforcement or auto-catalysis). Such models lead to processes of accumulation and polarization, leading to system crisis. The games of Monopoly and Risk provide popular examples. This paper investigates the dynamic properties of three examples of such models: Myrdal's model of cumulative causation; Collins's models of Marxian transformations and geopolitics; and Chaso-Dunn and Hall's iterative model of world-systems transformations. A combination of evolutionary, complex systems and dialectical analyses has consideralble overlap with chaotic, far-from-equilibrium types of models and with analyses of complex adaptive systems. Such discontinuous, nonlinear dynamic models show great potential for solving problems of dynamic analysis both within world-systems theory and within sociology as a whole.

scholarly journals Population growth and environment as a self-organizing system

1999 ◽  
Vol 3 (2-3) ◽  
pp. 81-108 ◽  
Author(s):  
Peter M. Allen
Keyword(s):  
Energy Consumption ◽  
Complex Systems ◽  
21St Century ◽  
Human Activities ◽  
Multiple Scales ◽  
Successful Implementation ◽  
Continental Scale ◽  
Holistic Understanding ◽  
Systems Models

Over recent years a new understanding of complex systems, and their dynamics and evolution has emerged, and these have been shown to provide a new basis for models of the changing patterns of population and economic activities that shape the landscape. In this paper we make clear the necessarily partial description that any particular model must provide, and show the importance of a multidisciplinary, holistic understanding, linking any particular model to the co-evolution of its environment. In addition, we show how evolutionary processes link the microscopic level of molecules through successive scales of structure and organization ultimately to the biosphere itself, to issues of climatic change, of biomes at the continental scale and atmospheric and oceanic circulation patterns. Some very recent results will be shown which demonstrate that the world climate has already been modified considerably by human activities, particularly agriculture, underlining the vital need to understand better the on-going interaction between human activities and the biosphere.Models will be described which can link the co-evolution of these multiple scales of organization and change, and which can be used to help to explore the consequences of different possible policies, and in this way to provide information concerning the agendas, risks and issues to be addressed in the 21st Century, as well as pointing to possible policies that may be appropriate. Already models exist which can explore the dynamics of urban development, the patterns of land-use, and the possible environmental impacts of these in the context of a still fast growing population. Such models provide a framework within which questions such as those concerning energy consumption, transportation, social conditions can be explored and agendas and priorities set. Clearly, advances in information and telecommunications technologies present great opportunities for increasing accessibilities without necessarily increasing mobility or energy consumption, and models which can help in assessing their potential impact on development and in their successful implementation are of great value.Complex system models can also be of great use in exploring the long term implications of the present, increasing, reliance on market systems and economic signals in the allocation of resources and patterns of investment. In particular, complex systems models can explore the effects of the precise regulatory framework within which a market operates, and as a result may be able to suggest ways in which long term, sustainable development can be achieved despite the present short term horizons of the players in market dynamics. In addition, of course, they can illuminate and inform actors about the longer term, and perhaps actually lengthen the time horizon considered by market participants. In short, the insights arising from complex systems models could, hopefully, play a role in expanding the understanding, the conceptual framework and the ethical basis of decision making in the 21st Century.

Complex systems models and the management of error and uncertainty

2008 ◽  
Vol 3 (1) ◽  
pp. 11-25 ◽  
Author(s):  
J. P. Messina ◽  
T. P. Evans ◽  
S. M. Manson ◽  
A. M. Shortridge ◽  
P. J. Deadman ◽  
...  
Keyword(s):  
Complex Systems ◽  
Systems Models

A COMPLEX SYSTEMS APPROACH TO LEARNING IN ADAPTIVE NETWORKS

2001 ◽  
Vol 05 (02) ◽  
pp. 149-180 ◽  
Author(s):  
PETER M. ALLEN
Keyword(s):  
Complex Systems ◽  
Systems Approach ◽  
Knowledge Generation ◽  
Adaptive Networks ◽  
Term Survival ◽  
Complex Products ◽  
Industrial Networks ◽  
Complex Systems Thinking ◽  
Systems Models

In today's economy, the key ingredients in success and survival are adaptability and the capacity to learn and change. Recent progress in the theory of complex systems provides a new basis for our understanding of how this may actually occur, and the factors on which it depends. Complex systems thinking shows what assumptions underlie the reduction of some part of reality to a mechanical model. They demonstrate that the simplicity and "knowledge" derived from such representations can lead to an understanding that entirely misses the most important, strategic changes that may occur. Complex systems models reveal the key processes that underlie "learning", and recognise the limits to knowledge and the inherent reality of uncertainty. They demonstrate the fundamental importance of internal, microdiversity within systems, as the source of exploration that drives learning. These ideas are explained and presented in a simple model of emergent co-evolution, where the exploration of internal diversity leads to the formation of a complex, with synergetic attributes. The paper describes and models briefly the uncertainties inherent in the definition and development of a new product or service. A further model involving complex products is briefly described which shows the importance of "search" in "knowledge generation" for the success of adaptive industrial networks and clusters. All this leads to the statement of a "law of excess diversity" which states that the long-term survival of a system requires more internal diversity than appears requisite at any time.

scholarly journals Recommended Research Directions for Improving the Validation of Complex Systems Models.

2017 ◽  
Author(s):  
Eric D. Vugrin ◽  
Timothy G. Trucano ◽  
Laura Painton Swiler ◽  
Patrick D. Finley ◽  
Tatiana Paz Flanagan ◽  
...  
Keyword(s):  
Complex Systems ◽  
Research Directions ◽  
Systems Models
Sign in / Sign up

Export Citation Format

Share Document