{"title":"Evolutionary stability of social interaction rules in collective decision-making.","authors":"Anna Sigalou, Richard P Mann","doi":"10.1088/1478-3975/acd297","DOIUrl":null,"url":null,"abstract":"<p><p>Social animals can use the choices made by other members of their groups as cues in decision making. Individuals must balance the private information they receive from their own sensory cues with the social information provided by observing what others have chosen. These two cues can be integrated using decision making rules, which specify the probability to select one or other options based on the quality and quantity of social and non-social information. Previous empirical work has investigated which decision making rules can replicate the observable features of collective decision making, while other theoretical research has derived forms for decision making rules based on normative assumptions about how rational agents should respond to the available information. Here we explore the performance of one commonly used decision making rule in terms of the expected decision accuracy of individuals employing it. We show that parameters of this model which have typically been treated as independent variables in empirical model-fitting studies obey necessary relationships under the assumption that animals are evolutionarily optimised to their environment. We further investigate whether this decision making model is appropriate to all animal groups by testing its evolutionary stability to invasion by alternative strategies that use social information differently, and show that the likely evolutionary equilibrium of these strategies depends sensitively on the precise nature of group identity among the wider population of animals it is embedded within.</p>","PeriodicalId":20207,"journal":{"name":"Physical biology","volume":null,"pages":null},"PeriodicalIF":2.0000,"publicationDate":"2023-05-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Physical biology","FirstCategoryId":"99","ListUrlMain":"https://doi.org/10.1088/1478-3975/acd297","RegionNum":4,"RegionCategory":"生物学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"BIOCHEMISTRY & MOLECULAR BIOLOGY","Score":null,"Total":0}
引用次数: 0
Abstract
Social animals can use the choices made by other members of their groups as cues in decision making. Individuals must balance the private information they receive from their own sensory cues with the social information provided by observing what others have chosen. These two cues can be integrated using decision making rules, which specify the probability to select one or other options based on the quality and quantity of social and non-social information. Previous empirical work has investigated which decision making rules can replicate the observable features of collective decision making, while other theoretical research has derived forms for decision making rules based on normative assumptions about how rational agents should respond to the available information. Here we explore the performance of one commonly used decision making rule in terms of the expected decision accuracy of individuals employing it. We show that parameters of this model which have typically been treated as independent variables in empirical model-fitting studies obey necessary relationships under the assumption that animals are evolutionarily optimised to their environment. We further investigate whether this decision making model is appropriate to all animal groups by testing its evolutionary stability to invasion by alternative strategies that use social information differently, and show that the likely evolutionary equilibrium of these strategies depends sensitively on the precise nature of group identity among the wider population of animals it is embedded within.
期刊介绍:
Physical Biology publishes articles in the broad interdisciplinary field bridging biology with the physical sciences and engineering. This journal focuses on research in which quantitative approaches – experimental, theoretical and modeling – lead to new insights into biological systems at all scales of space and time, and all levels of organizational complexity.
Physical Biology accepts contributions from a wide range of biological sub-fields, including topics such as:
molecular biophysics, including single molecule studies, protein-protein and protein-DNA interactions
subcellular structures, organelle dynamics, membranes, protein assemblies, chromosome structure
intracellular processes, e.g. cytoskeleton dynamics, cellular transport, cell division
systems biology, e.g. signaling, gene regulation and metabolic networks
cells and their microenvironment, e.g. cell mechanics and motility, chemotaxis, extracellular matrix, biofilms
cell-material interactions, e.g. biointerfaces, electrical stimulation and sensing, endocytosis
cell-cell interactions, cell aggregates, organoids, tissues and organs
developmental dynamics, including pattern formation and morphogenesis
physical and evolutionary aspects of disease, e.g. cancer progression, amyloid formation
neuronal systems, including information processing by networks, memory and learning
population dynamics, ecology, and evolution
collective action and emergence of collective phenomena.