Policy and Complex Systems
son , we identified areas where ABM can be enhanced :
– Enabling participatory model development to enhance problem definition and identification of evaluation criteria . – Enabling the detection , exploration , and control of social emergence to empower the selection of policy alternatives by gaining more insights into possible outcomes . – Combining bottom-up and top-down modeling , so that agents can make decisions and act in a more realistic environment where social and physical structures are present and influence their behavior . This would support the selection process of policy alternatives by also providing explicit representation of policies as social structures . – Enabling conceptual as well as computational evaluation of agent-based model to increase the reliability of such models . – Increasing the accessibility of agent-related research for social scientists and policy analysts who are less experienced in computational sciences .
In conclusion , we believe that because of the importance of individual-based study of policy problems and to make use of the computation power of simulations , ABM is one of the most instrumental tools for policy analysis . To enhance ABM in the identified areas , combining this approach with SG and SD can be effective . In addition , uncertainty analysis , case loading , and data calibration are some of the methods that need to be focused on when choosing alternative policies using ABM ( Bankes 2002 ). Also , complex agent-based models result in enormous amount of data which require powerful data analysis tools .
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