Agent-Based and Machine Learning Models for understanding the Economic Impacts of AI and/or the Green Transition

Research Focus:

Economies worldwide are undergoing transitions driven by new technologies and the move toward a green economy. These transitions, if not done carefully managed, can increase inequality and political polarisation, but under the right policies they can contribute to a more equitable and prosperous future. To guide these transitions, we need data-driven models that can predict economic responses to various policy scenarios.

During your PhD you will leverage cutting-edge techniques such as agent-based modelling, reinforcement learning, statistical inference, network analysis, and/or large language models, to simulate human behaviour and economic environments and assess the impact of different policies. The exact model will depend on the candidate’s skills and interests. Example projects are included below:

·        Career pathways during the Green Transition and new AI technologies. Use networks and graph neural networks to map workers’ careers and adaptation pathways in response to the green economy and AI technologies. Develop a labour market model to evaluate the effectiveness of policies like retraining, universal basic income, and early retirement.

·        Leverage LLM-ABMs to explore how Socio-Economic Factors and Work Identity shape political preferences. Develop Agent-Based Models that leverage Large Language Models to model behaviour and preferences. Explore how socio-economic factors and work identity influence public preferences for green policies. Analyse the effects of economic policy on inequality, political polarization, and green discontent during the net-zero and AI transitions.

·        Reinforcement Learning, LLMs, and Behavioural Economics. Most economic models assume economic agents as rational utility maximizing entities. However, behavioural economics has shown that behaviour is more complex and rationality is bounded. We will use reinforcement learning agents to more realistically model adaptation, capturing human rationality by exploring the spectrum between random choices and perfectly rational decision.

What We Are Looking For:

We are looking for an excellent scientist with a master’s degree (or equivalent) with a quantitative focus (e.g. computer science, physics, economics, engineering, finance, computational social science, biology, operations research, etc.) and passionate about solving economic and social problems. The ideal candidate will have strong programming skills, excellent English communication abilities, and a passion for addressing economic and social challenges. Experience in agent-based modelling, machine learning, networks, large language models, reinforcement learning, computational social science, and/or complex systems is a plus. We encourage applications from candidates of all backgrounds, particularly those from underrepresented groups in academia.

Faculty Expertise:

You will work under the supervision of Dr. Maria del Rio Chanona, an expert in Complexity Economics. Dr. Del Rio Chanona’s research focuses on understanding the impact of the Covid-19 pandemic, AI and the green transition on labour markets. As a member of her newly established group, you will have the opportunity to work closely with your supervisors and contribute innovative work that has already informed policy institutions.

What We Offer:

– Full Scholarship: Covers all tuition fees (national or international) and provides a yearly stipend for a minimum of three years.

– Unlike many PhD positions, this opportunity is not initially tied to a specific predetermined project. We are looking for an outstanding candidate who can leverage newly available computational tools to address important economic and societal issues, making the scope of this research position broad.

– Location: Study at UCL’s historic Bloomsbury campus in central London, with access to excellent research, sports, and social facilities.

– Interdisciplinary Environment: Engage with researchers from various disciplines and types of institutions, including university, research centres, industry and policy organizations

– Career Prospects: A PhD in computer science with a specialisation in Financial Computing and Analytics offers excellent career outcomes in academia, industry, and policy or financial institutions.