Simina Branzei
Purdue University, IN·Computer Science·Early-career
When many players with their own goals interact, as in markets, auctions or traffic, economists often describe the outcome as an equilibrium. A Nash equilibrium is a state where no player can do better by changing strategy alone.
- Fields
Computer Science
Mathematics & Statistics
Social Science
- Topics
game theory
Nash equilibrium
competitive equilibrium
- Grants
- 1 known grant, through Mar 2028
What they work on
- Analyze market dynamics in which computationally limited players with only local information interact over time, testing whether globally efficient allocations emerge and whether markets grow or shrink.
- Study games where a few learning players have different incentives, and determine which learning algorithms players should use when their goals conflict.
- Characterize how the geometry of a graph relates to the complexity of local search, using models such as best-response dynamics in congestion games.
- Connect equilibrium concepts such as Nash and competitive equilibria to dynamic models of adapting players, combining tools from algorithms, dynamical systems and probability.
More about the research
A competitive equilibrium is one where prices balance supply and demand. These are static snapshots, though, while real players keep adapting to the world around them. This project asks what happens during that process of adaptation. When do these dynamics reach good outcomes, how fast do they get there, and what makes the search hard? The work is inspired by applications such as designing efficient and fair markets on online platforms and creating ways to allocate waste.
Why it matters
This is mainly theoretical work that strengthens the mathematical foundations of games, learning and dynamics. It could eventually inform the design of efficient and fair markets on online platforms, ways to allocate waste, and algorithms for settings where several learning agents have competing incentives.
Known grants
From NIH and NSF research grants. Labs often have other funding we can't see.
Closest labs
Igor Cialenco
Illinois Institute of Technology, IL·Mathematics & Statistics
Develop a tractable top-down stochastic model of groundwater levels and use it to study how prices of tradeable groundwater rights form as a Nash equilibrium among competing water users.
Grant Schoenebeck
University of Michigan, MI·Computer Science
Develop models of informed group decision making that combine agents' subjective and objective preferences, information and answers to questions, drawing on social choice theory and information elicitation.
Christian Kroer
Columbia University, NY·Computer Science·Early-career
Develop algorithms for online fair allocation and dynamic markets using online learning and first-order optimization methods, aiming for robustness across different input models rather than a single assumed arrival process.
The school
Purdue University: 43 labs on PI Pages, $932M a year on research, 833 research doctorates a year.
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