About the project
This PhD explores how AI tools and research techniques, such as algorithmic game theory, AI-supported mechanism design, and agent-based simulation, can help evaluate industrial collaboration opportunities and policy incentives in the circular economy. Working across disciplines, you’ll develop decision-support tools to assess B2B synergies, design smart contracts, and simulate policy outcomes for more sustainable economic transitions.
The circular economy (CE) aims to minimise waste and maximise resource reuse in supply chains and manufacturing through systems of sustainable production and consumption. But identifying viable collaboration opportunities, designing fair contracts, and evaluating policy incentives in this space remains a significant challenge. This project investigates how AI can support opportunity evaluation and decision-making in the CE. You'll develop methods to assess business-to-business (B2B) synergies, simulate policy interventions, and design smart contract mechanisms.
Building on techniques from game theory, algorithmic mechanism design, and agent-based simulation, this project contributes novel methods for answering the following questions:
- How can decision support methods such as cooperative game theory help evaluate potential B2B industrial synergies for circular material and energy exchange? If a firm has multiple opportunities, which ones should it filter out, and how can advanced machine learning techniques support this, given that firms have limited observability over the involved costs and benefits?
- How can mechanism design enable fair, stable, and dynamic contracts (especially for cost and benefit sharing) in circular bilateral relations and industrial parks or networks? Once we have decided on whom to collaborate with, what forms of smart contracts work to ensure that our collaboration remains stable against the seasonality of waste streams and volatilities in the market, consumer behaviour, and supply and demand?
- How can local authorities use AI-supported simulation techniques (e.g., agent-based modelling) to evaluate the long-term effects of different policy instruments (e.g., taxes, subsidies) on the adoption of CE practices? If an authority has a budget to provide support, whom should they support, to what extent, and how should they prioritise?
The project requires contributions to fundamental research as well as contextualisation in real-world industrial settings.
To have an idea about the background and earlier work, see:
- Yazdanpanah et al., Normative Industrial Symbiotic Networks, EUMAS 2016
- Fraccascia et al., Energy-based Industrial Symbiosis: A Literature Review, Environment, Development and Sustainability, 2021
- Yazdanpanah et al., Industrial Symbiotic Networks as Coordinated Games, AAMAS 2018
