Simulating Society Transitions: Standstill, Collapse and Growth in an Evolving Network Model

PLOS ONE, Dec 2019

We developed a model society composed of various occupations that interact with each other and the environment, with the capability of simulating three widely recognized societal transition patterns: standstill, collapse and growth, which are important compositions of society evolving dynamics. Each occupation is equipped with a number of inhabitants that may randomly flow to other occupations, during which process new occupations may be created and then interact with existing ones. Total population of society is associated with productivity, which is determined by the structure and volume of the society. We ran the model under scenarios such as parasitism, environment fluctuation and invasion, which correspond to different driving forces of societal transition, and obtained reasonable simulation results. This work adds to our understanding of societal evolving dynamics as well as provides theoretical clues to sustainable development.

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Simulating Society Transitions: Standstill, Collapse and Growth in an Evolving Network Model

Citation: Xu G, Yang J, Li G ( Simulating Society Transitions: Standstill, Collapse and Growth in an Evolving Network Model Guanghua Xu 0 Junjie Yang 0 Guoqing Li 0 Rodrigo Huerta-Quintanilla, Cinvestav-Merida, Mexico 0 1 State Key Laboratory of Vegetation and Environmental Change, Institute of Botany, Chinese Academy of Sciences , Beijing , China , 2 University of Chinese Academy of Sciences , Beijing , China , 3 State Key Laboratory of Soil Erosion and Dryland Farming, Institute of Soil and Water Conservation, Northwest A&F University , Yangling, Shaanxi , China We developed a model society composed of various occupations that interact with each other and the environment, with the capability of simulating three widely recognized societal transition patterns: standstill, collapse and growth, which are important compositions of society evolving dynamics. Each occupation is equipped with a number of inhabitants that may randomly flow to other occupations, during which process new occupations may be created and then interact with existing ones. Total population of society is associated with productivity, which is determined by the structure and volume of the society. We ran the model under scenarios such as parasitism, environment fluctuation and invasion, which correspond to different driving forces of societal transition, and obtained reasonable simulation results. This work adds to our understanding of societal evolving dynamics as well as provides theoretical clues to sustainable development. - The evolving dynamics of society is well worthy of our concern for it signifies whether we would have a sustainable future or a doomed crash. Many of us nowadays may suppose an ever ascending trend of civilization, because modern society is much more complex and has a much larger population compared to ancient ones. However, this could be an illusion. Real societies could take various evolving tracks. For example, there were countless declined or collapsed societies in history. In Rossiia i Europa [1], the Russian philosopher Nikolai Danilewski wrote that each civilization has a life cycle, like a perennial plant that has a continued growing period, but would finally decay. Similar theories were proposed by Spengler and Toynbee. More recently, Turchin and his colleagues coined the term Secular Cycles to imply the oscillations between population growth and instability in historical societies [2,3]. Aside from ascending and cyclic patterns, there are also societies that persisted for a long time with only tiny changes in its structure and population. Still, merging of adjacent societies into a bigger one is possible when communication and transportation technologies are sufficiently advanced. All these possibilities made the evolving dynamics of human civilization a great mist. Various theories that explain the mechanisms under societal dynamics have been developed. Tainter [4] proposed that the complication process that improves a societys reaction to challenges has a diminishing marginal return, which finally leads to collapse. Diamond [5] proposed that societies have an internal tendency to overshoot ecological capacity. Environmental impact [6] and enemy invasion are also widely known exogenous factors that have great impact on the course of a society. Recently, complex system theories have also been applied to describe societal dynamics [7], among which are the Self-Organized Criticality theory (SOC) [8,9], the Dual Phase Evolution theory (DPE) [10,11], the Adaptive Cycle and Panarchy theory [1216]. Such theories have greatly advanced our understanding of societal dynamics. However, they are generally narrative and thus vague. Simulation models can help make ideas more explicit, and different modeling approaches are available. Abundant models are on dynastic cycles, which boast of dynamic details, e.g., interaction between social classes such as farmers, bandits and rulers [1719]; between sowing area, population and number of peasants and handicraftsmen [20]; or between population density, warfare intensity and state resource reserve [21]. These models have successfully demonstrated the sociodemographic cycles for complex agrarian systems. They are, however, too specific, failing to modeling other societal dynamic patterns. A more abstract modeling approach derives from other disciplines such as artificial chemistry, utilizing an evolving network method [22]. Jain and Krishna [23,24] developed a model system where species populations co-evolve with their network of interaction, crashes and recoveries that arise dynamically can be observed. Models with similar mechanisms have been developed to explain evolution process and Schumpeterian economic dynamics [2527]. The advantage of this type of models is their capability of illustrating the structure changing process, which is the evolving nature of society, thus could be applied to more areas of societal dynamics. Most of these theories and models capture o (...truncated)


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Guanghua Xu, Junjie Yang, Guoqing Li. Simulating Society Transitions: Standstill, Collapse and Growth in an Evolving Network Model, PLOS ONE, 2013, Volume 8, Issue 9, DOI: 10.1371/journal.pone.0075433