Journal on Policy & Complex Systems Vol. 2, Issue 2, Fall 2015 | Page 16

The Freedom of Constraint : A Multilevel Simulation Model of Politics , Fertility and Economic Development
However , when using the number of cooperative strategies in the society as the new independent variable instead , the model fit dramatically increases to 54.3 %, with the new variable being positive , significant , and with the strongest impact compared to other variables . Finally , when the number of defective strategies is also added , the adjusted R 2 increases to 55.3 %. The last new variable is also significant and has strong negative impact on the level of income .
The sensitivity test first confirms the original POFED theory that the negative value of instability does significantly speed the pace of economic development , while technology has a positive impact , in increasing individual agents ’ ability to reach other like-minded agents spurs cooperation dramatically based on first-order local interactions . More importantly , individual decisions matter in society ’ s development trajectory , as micro-level variables explain more variance than macrolevel variables only . In the process of individuals communicating and making deals with each other , more products and services become available while the cost of which goes down . This logic at the societal level is well discussed and empirically tested in the globalization literature : in the process of increased interconnections among countries , benefits are derived from the specialization of products and services , which outweighs the economic and social costs by achieving higher efficiency . Cooperation pays higher dividends , while defective strategies reduce social wealth . In other words , this model captures the micro-level behavior that can better explain macrolevel phenomena .
The sensitivity test results suggest that the agent-based model more effectively captures the relationship between economic , political , and demographic factors than traditional econometric models . Now , we explore the growth path for a few selected countries to understand each specific situation . During this process , we control initial populations ’ mean and standard deviation ; density and social connectivity via talkspan to simulate any socio-economic conditions for a given society as well as political and economic patterns to simulate agent and system response to emergent behavior .
We chose to simulate the development path of China from 1960 to 1980 . Figure 2 shows the comparison between real data and simulated data for four main variables : income , human capital , fertility , and political capacity . The blue line plots real-world data taken at the macro level , while the orange line shows simulated data that gets aggregated from micro-agent interactions .
The graph on the top-left corner shows the dynamic of economic growth . China had a weak economic foundation at the beginning of the 1960s , when industrial development was severely hampered by the Civil War coupled with the inflow of cheap substitutable foreign goods ( Abdollahian et al ., 2013 ; Yang & Abdollahian , 2014 ). Both actual and simulated lines on the graph show a decrease in economic growth in the early 1960s , indicating the drop in living standards . Although there is a slight difference between the simulated line and the actual line at levels , the trend in both lines is very consistent , showing a low growth rate
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