Applying complex adaptive systems to agent-based models for social programme evaluation (original) (raw)

Handbook of Research Methods in Complexity Science

Abstract

Human services planners and evaluators require an increasing high level of flexibility and adaptability to remain effective in measuring the effectiveness of social interventions. Understanding the logic and assessing the impact behind the intervention can be difficult because commonly-used evaluative tools are based primarily on linear methods that assume that a set amount of input, throughput, and output will result in a set outcome. This chapter takes a complexity science approach and facilitates the use of agent-based modelling (ABM). It provides the requisite background for evaluators and researchers to frame their efforts as complex adaptive systems. These systems have several components that include agents having options, boundaries, self-organising behaviour, different options from which to choose, feedback to adapt, and an emergent behaviour. Complexity is viewed as a mathematical field where the relations between inputs and are better understood through simulations. Both qualitative and quantitative aspects of complexity are addressed through two applications of ABM that consider related social policy issues.

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