A Geometric Embedding Approach to Multiple Games and Multiple Populations
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Abstract (arXiv)
This paper studies a meta-simplex concept and geometric embedding framework for multi-population replicator dynamics. Central results are two embedding theorems which constitute a formal reduction of multi-population replicator dynamics to single-population ones. In conjunction with a robust mathematical formalism, this provides a toolset for analyzing complex multi-population models. Our framework provides a unifying perspective on different population dynamics in the literature which in particular enables to establish a formal link between multi-population and multi-game dynamics.
Key facts. Cited by 6 papers in the map (data through 14 Oct 2026). First posted on arXiv 11 Jan 2024.
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Citing papers per year: 2024: 5; 2025: 1.
6 papers.
- 2024Generative Modeling of Discrete Joint Distributions by E-Geodesic Flow Matching on Assignment Manifoldscited by 9
- 2024Generative Assignment Flows for Representing and Learning Joint Distributions of Discrete Datacited by 6
- 2024Fisher-Rao Gradient Flows of Linear Programs and State-Action Natural Policy Gradientscited by 5
- 2024Optimal Rates of Convergence for Entropy Regularization in Discounted Markov Decision Processescited by 1
- 2024Sigma Flows for Image and Data Labeling and Learning Structured Predictioncited by 1
- 2025Riemannian Patch Assignment Gradient Flowscited by 0
- 2025Riemannian Patch Assignment Gradient Flows17 Apr 2025
- 2024Sigma Flows for Image and Data Labeling and Learning Structured Prediction28 Aug 2024
- 2024Generative Assignment Flows for Representing and Learning Joint Distributions of Discrete Data6 Jun 2024
- 2024Optimal Rates of Convergence for Entropy Regularization in Discounted Markov Decision Processes6 Jun 2024
- 2024Fisher-Rao Gradient Flows of Linear Programs and State-Action Natural Policy Gradients28 Mar 2024
- 2024Generative Modeling of Discrete Joint Distributions by E-Geodesic Flow Matching on Assignment Manifolds12 Feb 2024