Using Agent Based Modelling to Integrate Data on Attitude Change

Abstract

This article has two goals. Firstly, it shows how a relatively novel technique (Agent Based Modelling, hereafter ABM) can integrate different data types that are often used only in separate strands of research (interviews, experiments and surveys). It does this by comparing a well-known ABM of attitude dynamics with an alternative model using data from surveys and experiments. Secondly, the article explains ABM methodology and why it is important to the distinctiveness of ABM as a research method. In particular, the ramifications of differing approaches to ABM calibration and validation are discussed using the two different ABM as examples. The article concludes by showing how ABM might provide a progressive research strategy for integrating different data types and thus different disciplines in attitude research.