Prof J. Doyne Farmer
Director of Complexity Economics
Baillie Gifford Professor of Mathematics, Mathematical Institute, University of Oxford
J. Doyne Farmer is Director of the Complexity Economics programme at the Institute for New Economic Thinking at the Oxford Martin School, Baillie Gifford Professor in the Mathematical Institute at the University of Oxford, and an External Professor at the Santa Fe Institute.
His current research is in economics, including agent-based modeling, financial instability and technological progress. He was a founder of Prediction Company, a quantitative automated trading firm that was sold to the United Bank of Switzerland in 2006. His past research includes complex systems, dynamical systems theory, time series analysis and theoretical biology.
During the 1980s he was an Oppenheimer Fellow and the founder of the Complex Systems Group at Los Alamos National Laboratory. While a graduate student in the 1970s he built the first wearable digital computer, which was successfully used to predict the game of roulette.
Prof Farmer's PA, Dorothy Nicholas, can be reached on 01865 610403 or firstname.lastname@example.org.
New economic models of energy innovation and transition
06 Apr 23
This new report represents a major effort to demonstrate the value of new economic modelling to poli...
Reconstructing production networks using machine learning
27 Feb 23
The vulnerability of supply chains and their role in the propagation of shocks has been highlighted ...
No. 2022-02 - Reconstructing production networks using machine learning
09 Jan 23
In this study, we formulate supply chain networks’ reconstruction as a link prediction problem and t...
The unequal effects of the health-economy tradeoff during the COVID-19 pandemic
13 Dec 22
The potential tradeoff between health outcomes and economic impact has been a major challenge to pol...
Forecasting the propagation of pandemic shocks with a dynamic input-output model
28 Sep 22
We introduce a dynamic disequilibrium input-output model that was used to forecast the economics of ...
Empirically grounded technology forecasts and the energy transition
13 Sep 22
Future energy system costs are estimated for three different scenarios. A rapid green energy transit...
Heterogeneous effects and spillovers of macroprudential policy in an agent-based model of the UK housing market
21 Jul 22
We develop an agent-based model of the UK housing market to study the impact of macroprudential poli...
Can Stimulating Demand Drive Costs Down? World War II as a Natural Experiment
11 Jul 22
U.S. military production during World War II increased at an impressive rate and led to large declin...
No. 2022-30 - Calibrating agent-based models to microdata with graph neural networks
29 Jun 22
Calibrating agent-based models (ABMs) to data is among the most fundamental requirements to ensure t...
No. 2022-10 - Agent-Based Modeling in Economics and Finance: Past, Present, and Future
21 Jun 22
We review ABM in economics and finance and highlight how it can be used to relax conventional assump...
Valuing the Future and Discounting in Random Environments: A Review
16 Jun 22
We address the process of discounting in random environments, which allows valuation of the future i...
Tipping elements discussion series - Tipping towards positive social change
20 May 22
This is the eighth webinar in the Discussion Series on 'Tipping Elements, Irreversibility, and Abrup...
No. 2019-14 - Measuring productivity dispersion: a parametric approach using the Lévy alpha-stable distribution
28 Apr 22
We examine in detail the distribution of labor productivity levels and growth, and observe that they...
Estimating initial conditions for dynamical systems with incomplete information
20 Apr 22
In this paper, we study the problem of inferring the latent initial conditions of a dynamical system...
No. 2022-05 - Black-box Bayesian inference for economic agent-based models
01 Feb 22
In this paper, we investigate the efficacy of two classes of simulation-efficient black-box approxim...