Informing Agent-Based Models of Social Innovation Uptake

Abstract

This paper discusses how data and theory were used to inform ten agent-based models in an EU Horizon 2020 project SMARTEES. The project investigates cases of social innovations implemented in different European cities, which promote low-carbon energy sources, ranging from communities insulating houses to cycling for urban transportation. The aim is to support local governments of cities in transitioning to energy efficiency and sustainability through simulating plausible effects of implementing similar social innovations in new contexts. We describe the concept for using theory together with quantitative and qualitative data to inform model assumption, calibration and validation, and the consequences of that concept for the research design in the ten case studies conducted in the project. We outline the role of (1) primary data collection of individual in-depth interviews, questionnaires and stakeholder workshops, and (2) secondary desk research including socio-demographic data and media analysis in developing agent-based models. We emphasize challenges encountered in how to use data from different sources to calibrate and validate agent-based models. The article is a compendium of lessons learned from the project, which can be useful for future collaborations in multi-case study, multi-research teams, and mixed-methods projects where one of the methods used is agent-based modelling.