• DOI:10.1093/acrefore/9780190228637.013.913
• Corpus ID: 163156410

Agent-Based Modeling in Political Decision Making

@inproceedings{Qiu2020AgentBasedMI,
  title={Agent-Based Modeling in Political Decision Making},
  author={Lin Qiu and Riyang Phang},
  year={2020}
}

• Lin Qiu, Riyang Phang
• Published 28 February 2020
• Political Science

Political systems involve citizens, voters, politicians, parties, legislatures, and governments. These political actors interact with each other and dynamically alter their strategies according to the results of their interactions. A major challenge in political science is to extrapolate from the process of individual political decision-making to collective outcomes such as voter turnout and election results. Complexity science offers a means to comprehend and theorize the non-linear, recursive, and interactive political processes. It views political systems as complex, self-organizing, self-reproducing, and adaptive systems consisting of large numbers of heterogeneous agents that follow a set of rules governing their interactions (Pierson, 2000). These agents produce higher-level emergent phenomena which cannot be simply explained by mere aggregation of individual behavior (Bonabeau, 2002). For example, each voter tries to support politicians with policies that best match their personal policy preferences. Meanwhile, politicians take positions and offer packages that aim to win maximum support from voters (within certain constraints). Therefore, what voters choose at an earlier time of the election process affects the choices of politicians and voters at a later time. This dynamic and continual interaction between voters and politicians leads to complex political phenomena.