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Intriguing effects of selection intensity on the evolution of prosocial behaviors
- Source :
- PLoS Computational Biology, Vol 17, Iss 11, p e1009611 (2021), PLoS Computational Biology
- Publication Year :
- 2021
- Publisher :
- Public Library of Science (PLoS), 2021.
-
Abstract
- In many models of evolving populations, genetic drift has an outsized role relative to natural selection, or vice versa. While there are many scenarios in which one of these two assumptions is reasonable, intermediate balances between these forces are also biologically relevant. In this study, we consider some natural axioms for modeling intermediate selection intensities, and we explore how to quantify the long-term evolutionary dynamics of such a process. To illustrate the sensitivity of evolutionary dynamics to drift and selection, we show that there can be a “sweet spot” for the balance of these two forces, with sufficient noise for rare mutants to become established and sufficient selection to spread. This balance allows prosocial traits to evolve in evolutionary models that were previously thought to be unconducive to the emergence and spread of altruistic behaviors. Furthermore, the effects of selection intensity on long-run evolutionary outcomes in these settings, such as when there is global competition for reproduction, can be highly non-monotonic. Although intermediate selection intensities (neither weak nor strong) are notoriously difficult to study analytically, they are often biologically relevant; and the results we report suggest that they can elicit novel and rich dynamics in the evolution of prosocial behaviors.<br />Author summary Theoretical models of populations have been useful for assessing when and how traits spread, in large part because they are simple. Rather than being used to reproduce empirical data, these idealized models involve relatively few parameters and are utilized to gain a qualitative understanding of what promotes or suppresses a trait. For prosocial traits, which entail a cost to self to help another, one thing that mathematical models often suggest is that competition to reproduce must be localized, meaning an individual must be fitter than just a small subset of the population in order to produce an offspring. We show here that this finding is not robust. Such traits can indeed proliferate when there is global competition for reproduction, which we demonstrate by increasing the degree to which payoffs from games affect birth rates. Since this kind of “stronger selection” has also been observed empirically, we discuss how it is incorporated into theoretical models more broadly.
- Subjects :
- 0106 biological sciences
Social Cognition
Computer science
Social Sciences
01 natural sciences
Infographics
0302 clinical medicine
Natural Selection
Econometrics
Psychology
Prisoner's Dilemma
Biology (General)
Data Management
media_common
0303 health sciences
Natural selection
Ecology
Applied Mathematics
Fecundity
Computational Theory and Mathematics
Prosocial behavior
Modeling and Simulation
Physical Sciences
Games
Graphs
Game theory
Research Article
Computer and Information Sciences
Evolutionary Processes
Social Psychology
QH301-705.5
media_common.quotation_subject
010603 evolutionary biology
Competition (biology)
Evolution, Molecular
03 medical and health sciences
Cellular and Molecular Neuroscience
Population Metrics
Game Theory
Genetic drift
Genetics
Selection, Genetic
Evolutionary dynamics
Molecular Biology
Axiom
Ecology, Evolution, Behavior and Systematics
Selection (genetic algorithm)
030304 developmental biology
Balance (metaphysics)
Evolutionary Biology
Behavior
Models, Genetic
Population Biology
Data Visualization
Cognitive Psychology
Biology and Life Sciences
Prisoner's dilemma
Altruistic Behavior
Prosocial Behavior
Video Games
Cognitive Science
Recreation
Mathematics
030217 neurology & neurosurgery
Neuroscience
Subjects
Details
- Language :
- English
- ISSN :
- 15537358
- Volume :
- 17
- Issue :
- 11
- Database :
- OpenAIRE
- Journal :
- PLoS Computational Biology
- Accession number :
- edsair.doi.dedup.....52d5204192865e131bee90584a8c83b5