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)