Stackelberg evolutionary game theory: how to manage evolving systems.

Darwinian dynamics cancer evolution evolutionary game theory evolutionary rescue fisheries management optimization

Journal

Philosophical transactions of the Royal Society of London. Series B, Biological sciences
ISSN: 1471-2970
Titre abrégé: Philos Trans R Soc Lond B Biol Sci
Pays: England
ID NLM: 7503623

Informations de publication

Date de publication:
08 05 2023
Historique:
entrez: 19 3 2023
pubmed: 20 3 2023
medline: 22 3 2023
Statut: ppublish

Résumé

Stackelberg evolutionary game (SEG) theory combines classical and evolutionary game theory to frame interactions between a rational leader and evolving followers. In some of these interactions, the leader wants to preserve the evolving system (e.g. fisheries management), while in others, they try to drive the system to extinction (e.g. pest control). Often the worst strategy for the leader is to adopt a constant aggressive strategy (e.g. overfishing in fisheries management or maximum tolerable dose in cancer treatment). Taking into account the ecological dynamics typically leads to better outcomes for the leader and corresponds to the Nash equilibria in game-theoretic terms. However, the leader's most profitable strategy is to anticipate and steer the eco-evolutionary dynamics, leading to the Stackelberg equilibrium of the game. We show how our results have the potential to help in fields where humans try to bring an evolutionary system into the desired outcome, such as, among others, fisheries management, pest management and cancer treatment. Finally, we discuss limitations and opportunities for applying SEGs to improve the management of evolving biological systems. This article is part of the theme issue 'Half a century of evolutionary games: a synthesis of theory, application and future directions'.

Identifiants

pubmed: 36934755
doi: 10.1098/rstb.2021.0495
pmc: PMC10024980
doi:

Types de publication

Journal Article Review Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

20210495

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Auteurs

Alexander Stein (A)

Centre for Cancer Genomics and Computational Biology, Barts Cancer Institute, Queen Mary University London, London EC1M 5PZ, UK.

Monica Salvioli (M)

Institute for Health Systems Science, Faculty of Technology, Policy and Management, Delft University of Technology, 2628 BX Delft, The Netherlands.

Hasti Garjani (H)

Delft Institute of Applied Mathematics, Delft University of Technology, 2628 CD Delft, The Netherlands.

Johan Dubbeldam (J)

Delft Institute of Applied Mathematics, Delft University of Technology, 2628 CD Delft, The Netherlands.

Yannick Viossat (Y)

CEREMADE, CNRS, Université Paris-Dauphine, Université PSL, 75016 Paris, France.

Joel S Brown (JS)

Department of Integrated Mathematical Oncology, Moffitt Cancer Center, Tampa, FL 33612, USA.

Kateřina Staňková (K)

Institute for Health Systems Science, Faculty of Technology, Policy and Management, Delft University of Technology, 2628 BX Delft, The Netherlands.

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