Persistent homology and the shape of evolutionary games
(2021) In Journal of Theoretical Biology 531.- Abstract
- For nearly three decades, spatial games have produced a wealth of insights to the study of behavior and its relation to population structure. However, as different rules and factors are added or altered, the dynamics of spatial models often become increasingly complicated to interpret. To tackle this problem,we introduce persistent homology as a rigorous framework that can be used to both define and compute higher-order features of data in a manner which is invariant to parameter choices, robust to noise, and independent of human observation. Our work demonstrates its relevance for spatial games by showing how topological features of simulation data that persist over different spatial scales reflect the stability of strategies in 2D... (More)
- For nearly three decades, spatial games have produced a wealth of insights to the study of behavior and its relation to population structure. However, as different rules and factors are added or altered, the dynamics of spatial models often become increasingly complicated to interpret. To tackle this problem,we introduce persistent homology as a rigorous framework that can be used to both define and compute higher-order features of data in a manner which is invariant to parameter choices, robust to noise, and independent of human observation. Our work demonstrates its relevance for spatial games by showing how topological features of simulation data that persist over different spatial scales reflect the stability of strategies in 2D lattice games. To do so, we analyze the persistent homology of scenarios from two games: a Prisoner’s Dilemma and a SIRS epidemic model. The experimental results show how the method accurately detects features that correspond to real aspects of the game dynamics. Unlike other tools that study dynamics of spatial systems, persistent homology can tell us something meaningful about population structure while remaining neutral about the underlying structure itself. Regardless of game complexity, since strategies either succeed or fail to conform to shapes of a certain topology there is much potential for the method to provide novel insights for a wide variety of spatially extended systems in biology, social science, and physics. (Less)
- Abstract (Swedish)
- For nearly three decades, spatial games have produced a wealth of insights to the study of behavior and its relation to population structure. However, as different rules and factors are added or altered, the dynamics of spatial models often become increasingly complicated to interpret. To tackle this problem, we introduce persistent homology as a rigorous framework that can be used to both define and compute higher-order features of data in a manner which is invariant to parameter choices, robust to noise, and independent of human observation. Our work demonstrates its relevance for spatial games by showing how topological features of simulation data that persist over different spatial scales reflect the stability of strategies in 2D... (More)
- For nearly three decades, spatial games have produced a wealth of insights to the study of behavior and its relation to population structure. However, as different rules and factors are added or altered, the dynamics of spatial models often become increasingly complicated to interpret. To tackle this problem, we introduce persistent homology as a rigorous framework that can be used to both define and compute higher-order features of data in a manner which is invariant to parameter choices, robust to noise, and independent of human observation. Our work demonstrates its relevance for spatial games by showing how topological features of simulation data that persist over different spatial scales reflect the stability of strategies in 2D lattice games. To do so, we analyze the persistent homology of scenarios from two games: a Prisoner's Dilemma and a SIRS epidemic model. The experimental results show how the method accurately detects features that correspond to real aspects of the game dynamics. Unlike other tools that study dynamics of spatial systems, persistent homology can tell us something meaningful about population structure while remaining neutral about the underlying structure itself. Regardless of game complexity, since strategies either succeed or fail to conform to shapes of a certain topology there is much potential for the method to provide novel insights for a wide variety of spatially extended systems in biology, social science, and physics. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/42f28c41-a60b-411a-8c14-c9d4a81bead4
- author
- Stenseke, Jakob LU
- organization
- publishing date
- 2021-12-21
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- Persistent homology, Evolutionary games, Game theory, ESS, evolutionary stability, Topological data analysis, Evolutionary game theory, Persistent homology, Evolutionary games, Game theory, ESS, Topological data analysis
- in
- Journal of Theoretical Biology
- volume
- 531
- article number
- 110903
- pages
- 13 pages
- publisher
- Academic Press
- external identifiers
-
- pmid:34534569
- scopus:85116059319
- ISSN
- 0022-5193
- DOI
- 10.1016/j.jtbi.2021.110903
- project
- Ethics for autonomous systems/AI
- How to build nice robots: ethics from theory to machine implementations
- language
- English
- LU publication?
- yes
- id
- 42f28c41-a60b-411a-8c14-c9d4a81bead4
- date added to LUP
- 2021-09-29 11:42:36
- date last changed
- 2022-06-14 17:33:45
@article{42f28c41-a60b-411a-8c14-c9d4a81bead4,
abstract = {{For nearly three decades, spatial games have produced a wealth of insights to the study of behavior and its relation to population structure. However, as different rules and factors are added or altered, the dynamics of spatial models often become increasingly complicated to interpret. To tackle this problem,we introduce persistent homology as a rigorous framework that can be used to both define and compute higher-order features of data in a manner which is invariant to parameter choices, robust to noise, and independent of human observation. Our work demonstrates its relevance for spatial games by showing how topological features of simulation data that persist over different spatial scales reflect the stability of strategies in 2D lattice games. To do so, we analyze the persistent homology of scenarios from two games: a Prisoner’s Dilemma and a SIRS epidemic model. The experimental results show how the method accurately detects features that correspond to real aspects of the game dynamics. Unlike other tools that study dynamics of spatial systems, persistent homology can tell us something meaningful about population structure while remaining neutral about the underlying structure itself. Regardless of game complexity, since strategies either succeed or fail to conform to shapes of a certain topology there is much potential for the method to provide novel insights for a wide variety of spatially extended systems in biology, social science, and physics.}},
author = {{Stenseke, Jakob}},
issn = {{0022-5193}},
keywords = {{Persistent homology; Evolutionary games; Game theory; ESS; evolutionary stability; Topological data analysis; Evolutionary game theory; Persistent homology; Evolutionary games; Game theory; ESS; Topological data analysis}},
language = {{eng}},
month = {{12}},
publisher = {{Academic Press}},
series = {{Journal of Theoretical Biology}},
title = {{Persistent homology and the shape of evolutionary games}},
url = {{http://dx.doi.org/10.1016/j.jtbi.2021.110903}},
doi = {{10.1016/j.jtbi.2021.110903}},
volume = {{531}},
year = {{2021}},
}