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Cited by 406

The DOI matches its Crossref record; 23 of the 23 references it cites were found in a registry or an index.

Basins of Attraction, Long-Run Stochastic Stability, and the Speed of Step-by-Step Evolution

Open access
Ellison, Glenn·2000·Review of Economic Studies

Abstract

The paper examines the behaviour of “evolutionary” models with ɛ-noise like those which have been used recently to discuss the evolution of social conventions. The paper is built around two main observations: that the “long run stochastic stability” of a convention is related to the speed with which evolution toward and away from the convention occurs, and that evolution is more rapid (and hence more powerful) when it may proceed via a series of small steps between intermediate steady states. The formal analysis uses two new measures, the radius and modified coradius, to characterize the long run stochastically stable set of an evolutionary model and to bound the speed with which evolutionary change occurs. Though not universally powerful, the result can be used to make many previous analyses more transparent and extends them by providing results on waiting times. A number of applications are also discussed. The selection of the risk dominant equilibrium in 2 × 2 games is generalized to the selection of ½-dominant equilibria in arbitrary games. Other applications involve two-dimensional local interaction and cycles as long run stochastically stable sets.

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Metadata

Authors
Ellison, G.
Venue
Review of Economic Studies
Published
2000-01
Volume
67
Issue
1
Pages
17-45
ISSN
  • 0034-6527 (ISSN-L)
  • 1467-937X
OpenAlex
W2095803264
Cited by
406
References
23
FWCI
29.86
Type
journal article
Language
en
Open access
green

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CoChat. (2026). Citation check: Basins of Attraction, Long-Run Stochastic Stability, and the Speed of Step-by-Step Evolution. https://app.cochat.ai/papers/10.1111/1467-937x.00119
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Ellison, G. (2000). Basins of Attraction, Long-Run Stochastic Stability, and the Speed of Step-by-Step Evolution. Review of Economic Studies. https://doi.org/10.1111/1467-937x.00119

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.