Background
The global financial system is a quintessential complex adaptive system, a dense network of interacting actors whose collective behaviour is more than the sum of its parts. This research arena sits at the thrilling intersection of finance, climate science, and complexity theory, and addresses a critical question: how can we understand and quantify the financial risks that emerge from the complex, cascading interactions triggered by climate change? Researchers use advanced computational techniques, including Agent-Based Modelling (ABM), network science, and machine learning, to simulate how climate stressors (e.g., droughts, floods, policy shifts) propagate through economic networks, causing potentially catastrophic systemic failures. Further research is vital because our current economic and financial models, which assume equilibrium and rational actors, are dangerously ill-equipped for the non-linear, interconnected, and profoundly uncertain financial system shaped by climate shocks.

PhD Opportunity
The key research question is: How can we move from traditional, static risk models and develop dynamic frameworks that capture uncertainty in cascading climate shocks?

You will start by reviewing the literature and existing open-source models (see e.g. https://www.comses.net/codebases/?query=finance) to identify what further developments are required to integrate data e.g. on supply chains (e.g. FAOSTAT trade matrix) and climate vulnerability (e.g. AgMIP data). You will then explore the uncertainty in a cascading climate shock related to synchronous droughts.
The primary outcome will be a simulation tool that provides a new, dynamic representation of climate-financial uncertainty under cascading shocks. This will enable decision-makers to move from assessing single risks to stress-testing against complex, cascading scenarios currently impossible to model, supporting the design of robust strategies in the face of climate uncertainty.

The project will be co-supervised by Dr. Roger Cremades (main supervisor, University of Leeds) and Dr. Celian Colon (co-supervisor, Advancing Systems Analysis Program, IIASA), providing a unique combination of expertise in complex systems science, financial modelling and cascading climate shocks. The successful candidate will be enrolled and based at Leeds and is expected to engage with IIASA online and through short in-person visits, e.g. via programs like the YSSP and the SSSM.

Applicant Profile
Students with pre-doctoral experience in the financial sector and a curiosity for complexity science.

Other Information
https://environment.leeds.ac.uk/see/staff/13034/roger-cremades
https://eprints.lse.ac.uk/86505/1/Surminski_Using%20adaptive%20cycle_2018.pdf