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 email@example.com.
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. 2022-06 - Heterogeneous Effects and Spillovers of Macroprudential Policy in an Agent-Based Model of the UK Housing Market
28 Apr 22
We develop an agent-based model of the UK housing market to study the impact of macroprudential poli...
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...
No. 2022-02 - Reconstructing production networks using machine learning
24 Jan 22
In this study, we formulate supply chain networks’ reconstruction as a link prediction problem and t...
How production networks amplify economic growth
04 Jan 22
Technological improvement is the most important cause of long-term economic growth. We study the eff...
Towards a taxonomy of learning dynamics in 2 × 2 games
21 Dec 21
Studying 2X2 games for tractability, we recover some well-known results in the limiting cases in whi...
The New Economics of Innovation and Transition: Evaluating Opportunities and Risks
17 Oct 21
A report by the Economics of Energy Innovation and System Transition (EEIST) consortium
Blog: Is the Too-Big-To-Fail Problem Resolved?
29 Sep 21
An evaluation of the stability implications of the bail-in design suggests the answer is no (unless ...
No. 2021-20 - Estimating initial conditions for dynamical systems with incomplete information
20 Sep 21
Studying several model systems, we infer the latent microstates that best reproduce an observed time...
No. 2021-01 - Empirically grounded technology forecasts and the energy transition
14 Sep 21
Rapidly decarbonising the global energy system is critical for addressing climate change, but concer...
Simultaneous supply and demand constraints in input–output networks: the case of Covid-19 in Germany, Italy, and Spain
01 Sep 21
Natural and anthropogenic disasters frequently affect both the supply and demand sides of an economy...
Stress Testing the Financial Macrocosm
01 Sep 21
We argue that next-generation stress test models must take a comprehensive a view of the financial m...
No. 2021-21 - Systemic implications of the bail-in design
30 Aug 21
Our analysis suggests that the current bail-in design may be in the region of instability. While pol...