Edward Dongmyung LeeAssist
RESEARCH
Education
- · 2008-2012 B.A. in Physics, Minor in Biophysics, Princeton University
- · 2014-2019 Ph.D., Cornell University
Career
- · 2015-2018 National Science Foundation Graduate Research Fellow
- · 2020-2021 Santa Fe Institute Program Postdoctoral Fellow
- · 2021-2022 Complexity Science Hub Postdoctoral Fellow
- · 2022-2025 Austrian Science Fund ESPRIT Fellow
- · 2026 Joint Complexity Science Hub & BOKU Postdoctoral Fellow
Research Interests
Whether we look at politics or at biological aggregates, we are seeing the outcomes of collective decisions — decisions that emerge from dynamic, structured interactions among many complex components. The EPIC Lab, the laboratory for Emergent Phenomena from Interactions & Constraints, studies the physics of collective decisions.
There is a gap between the systems physics knows how to treat and the systems that actually make collective decisions. Canonical statistical mechanics describes particles with no goal-direction and no internal model of their environment. The components of a parliament, a firm, a primate group, or a forest are more sophisticated: they are goal-directed, they act on heuristic representations of the environment and of their own history, they are adaptive. Building models minimal enough to be solvable but rich enough to be faithful is a central theoretical problem.
Three questions organize the effort. How should we represent sophisticated decision-makers without discarding what makes them sophisticated? How do the costs of computing and of acquiring information constrain what collective decisions are reachable? And how does the set of possible decisions transform through innovation and obsolescence? These are foundational questions for the physics of complex systems, and they carry practical weight — for how we build and reason about artificial intelligence, for how organizations should be structured, for how ecosystems absorb environmental change, and for how innovation can be measured and encouraged.
There is a gap between the systems physics knows how to treat and the systems that actually make collective decisions. Canonical statistical mechanics describes particles with no goal-direction and no internal model of their environment. The components of a parliament, a firm, a primate group, or a forest are more sophisticated: they are goal-directed, they act on heuristic representations of the environment and of their own history, they are adaptive. Building models minimal enough to be solvable but rich enough to be faithful is a central theoretical problem.
Three questions organize the effort. How should we represent sophisticated decision-makers without discarding what makes them sophisticated? How do the costs of computing and of acquiring information constrain what collective decisions are reachable? And how does the set of possible decisions transform through innovation and obsolescence? These are foundational questions for the physics of complex systems, and they carry practical weight — for how we build and reason about artificial intelligence, for how organizations should be structured, for how ecosystems absorb environmental change, and for how innovation can be measured and encouraged.
Selected Publications in the last 5 years
- Lee, E. D. & Ortega-Díaz, E. Innovation-exnovation dynamics on trees and trusses. Phys. Rev. Research 7, 033102 (2025).
- Lee, E. D., Kwan, A. P., Hanel, R., Bhatt, A. & Neffke, F. Information consumption and firm size. R. Soc. Open Sci. 11, 240027 (2024).
- Lee, E. D., Kempes, C. P. & West, G. B. Idea engines: Unifying innovation & obsolescence from markets & genetic evolution to science. Proc. Natl. Acad. Sci. U.S.A. 121, e2312468120 (2024).
- Kushwaha, N. & Lee, E.D. Discovering the mesoscale for chains of conflict. PNAS Nexus 2, pgad228 (2023). https://doi.org/10.1093/pnasnexus/pgad228
- Lee, E.D., Kempes, C.P. & West, G.B. Growth, death, and resource competition in sessile organisms. PNAS 118, e2020424118 (2021). https://doi.org/10.1073/pnas.2020424118