Journal on Policy and Complex Systems
increasing or decreasing individual wealth and ultimately societal productivity ( Axelrod , 1997b ). Thus , we create the population adjusting the mean and standard deviation of fertility , income , and human capital at the society level . Each individual agent carries all three variables that are randomized from the society ’ s distribution . At the beginning of this process , agents are allowed to give birth to new agents based on their fertility variable . Here we use empirically validated parameter values from three-stage least-squares estimation as a good first approximation . This method has been widely used by many scholars recently ( Abdollahian et al ., 2013 ) to simulate the dynamic process at the individual level . In this module , feedback is used to model individual and social phenomena . The value of the system dynamic component is tied to the extent that constructs and parameters represent actual observed project states . As discussed in Madachy ( 2007 ), system dynamics models help facilitate human understanding and communication of the process , and are more accurate to model time-based relationships between factors and simulate a system continuously over time .
Similar to the micro-agent process , we also use the system dynamics technique in this macro-society process . Instead of taking each individual agent as a system , this module takes the entire society as the system , with political instability , political capacity , economic condition , human capital , and fertility rate as main attributes . This module is critical as it connects the micro-individual level and the macro-society level . A society ’ s economic condition is aggregated from individual wealth by taking the mean . Human capital is aggregated from the individual level of education , and the fertility rate is also aggregated from the individual level in the same way . The feedback loop is completed in the way that initial individual variables are randomized from the society distribution , get updated in the micro-agent and evolutionary game processes , and then get aggregated at the society level and interact with other society variables , while society variables also impact the evolutionary game process . We also use empirically validated parameter values from three-stage least-squares estimation in the simulation . The updated instability is brought into the evolutionary game process to affect the probability that agents interact with each other . This feedback loop is helpful when we focus on how individual behavior changes the macro environment , and how the environment in turn impacts individual behavior .
Evolutionary game theory provides insights into understanding individual , repeated societal transactions in heterogeneous populations ( Fudenberg & Maskin , 1986 ). Social co-evolutionary systems allow each individual to either influence or be influenced by all other individuals as well as macro society ( Snijders , Steglich , & Schweinberger , 2007 ; Zheleva , Sharara , & Getoor , 2009 ), perhaps eventually becoming coupled and quasi-path interdependent . Therefore , after the micro-agent process and the macro-society process , we choose to focus on the noncooperative game in the macro-political stability environment . Prisoner ’ s dilemma game is chosen because it allows agents to choose between maximizing
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