Draft:Macrocosm Group
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| Type | Private |
| Founded | 2023 |
| Key People |
J. Doyne Farmer (Founder & Chief Scientist) Dan Eichelsdoerfer (CEO) Eric Beinhocker (Board Member) David Young (Board Member) |
| Headquarters | New York, New York |
| Website | macrocosm.group |
Macrocosm is an American and UK company founded as a spinout from Oxford Universityin 2023 by J. Doyne Farmer. The company uses techniques from complexity economics and agent-based modeling to analyze macroeconomic shocks, investment strategies, and policy decisions.
Macrocosm describes its longer-term aim as building an Economic World Model, a simulator intended to serve as a "physics engine" for the economy. The company positions this work within an emerging category it calls "complexity intelligence." [1][2]
History
[edit]Origins at INET Oxford
Macrocosm’s conception is closely tied to the work of its Chief Scientist and founder, J. Doyne Farmer, who applies physics-based computational methods to economic problems. Farmer conducted research in complexity economics at the Santa Fe Institute for 13 years before moving to Oxford, where he is the Baillie Gifford Professor of Complex Systems Science and the Director of the Complexity Economics program at the Institute for New Economic Thinking (INET).[3]
During the COVID-19 pandemic, Farmer and his colleagues used an agent-based simulation of production networks to model the economic implications of lockdown and reopening scenarios in the United Kingdom. The team predicted the economic impact of COVID-19 lockdowns at 21.5% GDP loss; an actual 22.1% GDP loss was later recorded.[4][5][6]
In a 2022 paper published in Joule, the Oxford team applied their technology cost forecasting approach to a probabilistic assessment of the global energy transition.[7] Their research found that a rapid green energy transition could yield net savings relative to a fossil-fuel-dependent baseline by 2050. The paper also argued that traditional energy models have consistently overestimated the future costs of renewable technologies.[8]
Technical approach
[edit]Complexity economics
Complexity economics is the theoretical framework underpinning Macrocosm’s approach. Originating through the Santa Fe Institute in the late 1980s, complexity economics understands the economy as a complex adaptive system rather than a system in equilibrium.[9] Complexity economics helps address concerns with traditional economic models, including the assumptions of perfectly rational agents and equilibrium. [10] The New York Times describes Farmer’s approach as applying "insights from chaos theory and complexity economics" to difficult macroeconomic problems.[11]
Agent-based modeling
Macrocosm’s simulations are primarily built on agent-based modeling (ABM), a bottom-up computational approach rooted in complexity economics. In ABM, individual economic entities are represented as autonomous agents that operate according to behavioral constraints. This process allows agents to be heterogeneous and adaptive, generating macro-level patterns as emergent properties of individual interactions. [11]
Applications
[edit]Supply chain resilience
One of Macrocosm's primary focuses is supply chain shocks and resilience. In a 2025 paper presented at the EurIPS Workshop on Differentiable Systems and Scientific Machine Learning, Macrocosm’s research team introduced a differentiable supply-chain agent-based model implemented in JAX. Running simulations on GPUs, the team argued that large production-network ABMs could be calibrated faster than previously possible.[12]
In a 2026 paper, the team explored the conditions under which supply chain networks shift from resilient to fragile states. They found that competitive pressure on firms to minimize inventories pushes production networks to a critical point at which small, localized shocks can lead to large aggregate disruptions.[13] Macrocosm also published a paper examining the macroeconomic consequences of a potential closure of the Strait of Hormuz. The paper applies the company’s supply-chain modeling framework to a real geopolitical risk scenario.[14]
References
[edit]- ↑ "Solutions". Macrocosm. Retrieved 18 June 2026.
- ↑ "Home". Macrocosm. Retrieved 18 June 2026.
- ↑ "Team". Macrocosm. Retrieved 18 June 2026.
- ↑ Del Rio-Chanona, R.M.; Mealy, P.; Pichler, A.; Lafond, F.; Farmer, J.D. (2020). "Supply and demand shocks in the COVID-19 pandemic: An industry and occupation perspective." Oxford Review of Economic Policy. 36: S94–S137. doi:10.1093/oxrep/graa033.
- ↑ Pichler, A.; Pangallo, M.; del Rio-Chanona, R.M.; Lafond, F.; Farmer, J.D. (2020). "Production networks and epidemic spreading: How to restart the UK economy?" INET Oxford Working Paper No. 2020-12. arXiv:2005.10585.
- ↑ University of Oxford (17 November 2023). "Epidemic-economic model provides answers to key pandemic policy questions." ox.ac.uk/news.
- ↑ Way, R.; Ives, M.C.; Mealy, P.; Farmer, J.D. (2022). "Empirically grounded technology forecasts and the energy transition." Joule. 6(9): 2057–2082. doi:10.1016/j.joule.2022.08.009.
- ↑ Srivastav, S. (5 September 2024). "How cheap solar power could have arrived decades ago." Science Business.
- ↑ Anderson, P. W., K. Arrow, and D. Pines, eds. 1988. The Economy as an Evolving Complex System. Boston, MA: Addison-Wesley.
- ↑ Arthur, W. Brian (1999). ”Complexity and the Economy.” Science. 284(5411): 107–109. doi:10.1126/science.284.5411.107.
- 1 2 Coy, Peter (12 August 2024). "Is Chaos the Key to Better Economics?” The New York Times (Opinion).
- ↑ Hamid, S.; Moran, J.; Mungo, L.; Quera-Bofarull, A.; Towers, S. (2025). "A differentiable model of supply-chain shocks." 1st Workshop on Differentiable Systems and Scientific Machine Learning @ EurIPS 2025. openreview.net/pdf?id=Ie00BqBvdm.
- ↑ Martin, D.; Moran, J.; Panja, D.; Bouchaud, J.P. (2026). "Resilient-to-Fragile Transition and Excess Volatility in Supply Chain Networks." https://arxiv.org/abs/2601.20450.
- ↑ Macrocosm. (2026). "Hormuz Closure: Analysis Report." macrocosm.group/gates/hormuz-paper.
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