- Title
- Seeding leading cooperators and institutions in networked climate dilemmas
- Creator
- Chica, Manuel; Santos, Francisco C.
- Relation
- Chaos, Solitons & Fractals: an interdisciplinary journal of nonlinear Vol. 167, Issue February 2023, no. 112987
- Publisher Link
- http://dx.doi.org/10.1016/j.chaos.2022.112987
- Publisher
- Elsevier
- Resource Type
- journal article
- Date
- 2023
- Description
- Global cooperation poses problems shaped by the effects of stakeholders’ decisions on future losses, and how collective risks are perceived. Institutional sanctioning of free-riders may prevent widespread defection in this context. Yet, the prevalence of such institutions represents another second-order dilemma, often as tricky as the tragedy of the commons we aim to avert. Here, we combine evolutionary game theory with techniques widely used in marketing to find, target, and seed leaders and institutions’ promoters to boost cooperation in climate dilemmas. By doing so, we identify the increase in cooperation when introducing a heterogeneous networked population and the conditions under which seeding policies can ensure the self-organization of cooperation and stable institutions. Counter-intuitively, we show that seeding a small fraction of institutional supporters at random network positions practically is as good policy as seeding highly central players in climate dilemmas. We show that cooperation and institutions prevalence are mainly determined by the interaction network and, to a less extent, by imitation ties, and that seeding cooperators only offers marginal benefits when compared with directly seeding sanctioning institutions. Our study also presents a way of incorporating costs when deciding the best policy to apply. Finally, this work suggests that the potential benefits of seeding and targeting techniques are not exclusive to collective dilemmas and can be applied to other dilemmas having structured populations.
- Subject
- evolutionary game theory; collective risk dilemmas; seeding; leadership
- Identifier
- http://hdl.handle.net/1959.13/1489829
- Identifier
- uon:52784
- Identifier
- ISSN:0960-0779
- Language
- eng
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