Agent-based modelling to predict policy outcomes: A food waste recycling example. Issue 87 (September 2018) - Skeldon, A.C.; Schiller, F.; Yang, A.; Balke-Visser, T.; Penn, A.; Gilbert, N.
Highlights: Agent-based models (ABMs) are a useful scenario planning tool for setting policy. An ABM to explore the impact of financial incentives and taxes on food waste. Landfill tax initially promotes recycling but later does little to reduce waste. RO policy supports anaerobic digestors but undermines in-vessel composting. Policy outcome can depend on the history of implementation. Abstract: Optimising policy choices to steer social/economic systems efficiently towards desirable outcomes is challenging. The inter-dependent nature of many elements of society and the economy means that policies designed to promote one particular aspect often have secondary, unintended, effects. In order to make rational decisions, methodologies and tools to assist the development of intuition in this complex world are needed. One approach is the use of agent-based models. These have the ability to capture essential features and interactions and predict outcomes in a way that is not readily achievable through either equations or words alone. In this paper we illustrate how agent-based models can be used in a policy setting by using an example drawn from the biowaste industry. This example describes the growth of in-vessel composting and anaerobic digestion to reduce food waste going to landfill in response to policies in the form of taxes and financial incentives. The fundamentally dynamic nature of an agent-based modelling approach is used to demonstrate that policy outcomes depend not just on current policy levels but also on the historical path taken.