Prof J. Doyne Farmer
Director of Complexity Economics
Baillie Gifford Professor of Complex Systems Science, Smith School for Enterprise and the Environment, 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 of Complex Systems Science at the Smith School for Enterprise and the Environment, 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.
Black-box Bayesian inference for agent-based models
15 Feb 24
We present a number of benchmarking experiments in which we demonstrate that neural network-based bl...
Economic modelling fit for the demands of energy decision makers
02 Feb 24
Decision makers need sector-specific, policy-focused, dynamic economic models with rich representati...
Population synthesis as scenario generation for simulation-based planning under uncertainty
08 Jan 24
We propose and compare two generic approaches to generating synthetic populations that produce targe...
Discounting the distant future: What do historical bond prices imply about the long term discount rate?
27 Dec 23
We present a thorough empirical study on real interest rates by also including risk aversion through...
No. 2023-28 - Employment dynamics in a rapid decarbonization of the power sector
30 Nov 23
e analyze the employ- ment dynamics of a fast transition scenario for the US electricity sector that...
Gradient-assisted calibration for financial agent-based models
28 Nov 23
In this paper, we discuss and present experiments that demonstrate how differentiable programming ma...
The unequal effects of the health–economy trade-off during the COVID-19 pandemic
16 Nov 23
In counterfactual experiments, we show that a similar trade-off between epidemic and economic outcom...
Building an alliance to map global supply networks
20 Oct 23
The global economy consists of more than 300 million firms, connected through an estimated 13bn supp...
No. 2023-21 - Modelling labour market transitions: the case of productivity shifts in Brazil
19 Oct 23
How would occupation-level unemployment be affected by growth paths with different drivers and emiss...
A fast clean energy transition would save trillions
11 Oct 23
A rapid transition to a fully decarbonised energy system by 2050 would save at least $12 trillion in...
Black-box inference for differentiable simulators
04 Oct 23
BlackBIRDS is a Python package consisting of generically applicable, black-box inference methods for...
No. 2023-16 - Liquidity Spirals
08 Sep 23
We introduce a novel method for studying liquidity spirals and use this method to identify spirals b...
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...