Open Access
2024 Asymptotics of generalized Pólya urns with non-linear feedback
Thomas Gottfried, Stefan Grosskinsky
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Electron. J. Probab. 29: 1-56 (2024). DOI: 10.1214/24-EJP1157

Abstract

Generalized Pólya urns with non-linear feedback are an established probabilistic model to describe the dynamics of growth processes with reinforcement, a generic example being competition of agents in evolving markets. It is well known which conditions on the feedback mechanism lead to monopoly where a single agent achieves full market share, and various further results for particular feedback mechanisms have been derived from different perspectives. In this paper we provide a comprehensive account of the possible asymptotic behaviour for a large general class of feedback, and describe in detail how monopolies emerge in a transition from sub-linear to super-linear feedback via hierarchical states close to linearity. We further distinguish super- and sub-exponential feedback, which show conceptually interesting differences to understand the monopoly case, and study robustness of the asymptotics with respect to initial conditions, heterogeneities and small changes of the feedback mechanisms. Finally, we derive a scaling limit for the full time evolution of market shares in the limit of diverging initial market size, including the description of typical fluctuations and extending previous results in the context of stochastic approximation.

Acknowledgments

The authors are grateful to Simon Gabriel and Dirk Blömker for useful discussions.

Citation

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Thomas Gottfried. Stefan Grosskinsky. "Asymptotics of generalized Pólya urns with non-linear feedback." Electron. J. Probab. 29 1 - 56, 2024. https://doi.org/10.1214/24-EJP1157

Information

Received: 26 November 2023; Accepted: 4 June 2024; Published: 2024
First available in Project Euclid: 20 June 2024

Digital Object Identifier: 10.1214/24-EJP1157

Subjects:
Primary: 60F17 , 60J05 , 62L20 , 91B62

Keywords: economic growth models , Functional limit theorem , Markov process , Pólya urn , random process with reinforcement , stochastic approximation

Vol.29 • 2024
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