Agent-based modelling to predict policy outcomes: A food waste recycling example

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

Agent‐based modeling and simulation for the circular economy: Lessons learned and path forward

“…In the literature, similar results from an ABMS model dedicated to food waste recycling further illustrate the issues of path dependency and lock-in (Skeldon et al, 2018). In this study, the authors show that the historical state of the market may have a strong influence on recycling policy outcomes.…”

“…Besides calibration and model exploration, ML algorithms can set up the agents' behavioral rules (Rand, 2019). For instance, Sert et al (2020) used a reinforcement learning (RL) algorithm to define how agents behave in a reproduction of Schelling's segregation model. According to the authors, the ABMS model provides RL access to complexity (how the collective behavior emerges from individual ones), while RL equips the ABMS model with the information processing capabilities to explore and optimize strategies that satisfy agents' objectives.…”

A consequential approach to life cycle sustainability assessment with an agent‐based model to determine the potential contribution of chemical recycling to UN Sustainable Development Goals

“…Note that based on Skeldon et al. (2018), administrative areas or MBT plants close contracts with system external plants if they receive no offer (cf. Figure 1).…”

“…where Administrative areas or MBT plants decide for the best offer they receive according to the "Principle of Economical Budget Management" (Jänicke, 2020). Note that based on Skeldon et al (2018), administrative areas or MBT plants close contracts with system external plants if they receive no offer (cf. Figure 1).…”