Climate change is one of the greatest challenges facing humanity, and we, as machine
learning (ML) experts, may wonder how we can help. Here we describe how ML can be a …

In the context of simulation-based optimisation, this paper reviews recent work related to the
role of metaheuristics, matheuristics (combinations of exact optimisation methods with …

We postulate a continuous‐time heterogeneous agent model with a financial sector and
households to study the nonlinear linkages between aggregate and financial variables. In …

We introduce deep equilibrium nets (DEQNs)—a deep learning‐based method to compute
approximate functional rational expectations equilibria of economic models featuring a …

In recent years there has been a surge of interest in the potential of Artificial Intelligence (AI)
to address the global threat of climate change. Here, we consider climate change …

We develop a machine-learning solution algorithm to solve for optimal portfolio choice in a
lifecycle model that includes many features of reality modelled only separately in previous …

An efficient, reliable, and interpretable global solution method, the Deep learning-based
algorithm for Heterogeneous Agent Models (DeepHAM), is proposed for solving high …

We contrast machine learning (ML) and structural econometrics (SE), focusing on areas
where ML can advance the goals of SE. Our views have been informed and inspired by the …

This article clarifies the connections between certain algorithms to develop artificial
intelligence (AI) and the econometrics of dynamic structural models, with concrete examples …

This paper proposes a global algorithm to solve a large class of nonlinear continuous-time
models in finance and economics. Using tools from machine learning, I recast problem of …