Diversity, Specialization & Cooperation: Evolutionary Foundations from Ecosystems to Societies

多元化、专业化

• 批准号: RGPIN-2015-05795
• 负责人: Hauert, Christoph
• 金额: $ 2.33万
• 依托单位: University of British Columbia
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2018
• 资助国家: 加拿大
• 起止时间: 2018-01-01 至 2019-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=653224
• 关键词: Diversity; Specialization; Cooperation; Evolutionary; Foundations

Diversity is ubiquitous in nature and comes in all shapes and sizes. Here we explore how diversity begets diversity in evolutionary processes. Based on evolutionary game theory (EGT) we study the relation between diverse interactions and the emergence and maintenance of diversity in terms of co-existence, specialization or social organization.***1. Diversity, Cooperation & Social Stratification****Natural environments pose various challenges to individuals in the form of multiple, concurrent interactions. For example, microbes and humans alike engage in a variety of public goods interactions either through extra-cellular products or dwindling global resources. We extend traditional EGT to multiple interactions in order to determine the scaling of polymorphisms with the number (and types) of interactions as well as to delineate conditions under which specialization in particular tasks yields greater stability. However, for stochastic dynamics in finite populations of variable size, trait diversity is invariably lost, unless maintained by additional sources of variation, such as mutation or migration. This requires developing stochastic eco-evolutionary game theory and criteria to characterize evolutionary success and diversity based on transient configurations, in order to investigate the roots of social diversification.***2. Asymmetric Interactions & Structured Populations****In nature, resources available to individuals typically vary based on environmental differences, phenotypic variation, or surpluses accumulated in the past, etc. Here we extend EGT to study effects of asymmetric interactions on the evolutionary success and diversity of behavioural traits. Of particular interest is evolved social behaviour that offsets resource inequalities to achieve fair and egalitarian outcomes, or, conversely, turns egalitarian configurations into unequal resource distributions, and hence causing asymmetric interactions. This reveals conditions for another approach to social diversification and stratification.***Social networks are dynamical population structures that capture fleeting interaction patterns, which define societies. Following the principle form follows function we employ a novel framework based on actions, including cooperation, reward and punishment, to explore the evolutionary origins of social organization and formation of complex societies.***3. Behavioural Experiments & Empirical Analyses****As an inspiring complement, we study concurrent interactions in human behavioural experiments and test whether participants succeed in coordinating their behaviour and specialize in different tasks to achieve more efficient outcomes - an essential aspect of division of labour as well as social stratification. In large-scale Internet experiments and using empirical data, we investigate social learning and the dynamics of social networks.**

多样性在自然界中无处不在，并且有各种形状和大小。在这里，我们探讨多样性如何在进化过程中产生多样性。基于进化博弈论（EGT），我们研究多样性相互作用与共存、专业化或社会组织方面多样性的出现和维持之间的关系。***1。多样性、合作和社会分层****自然环境以多种并发互动的形式向个体提出各种挑战。例如，微生物和人类都通过细胞外产品或日益减少的全球​​资源参与各种公共产品互动。我们将传统的 EGT 扩展到多重相互作用，以确定多态性与相互作用数量（和类型）的比例，并描述特定任务的专业化产生更大稳定性的条件。然而，对于可变大小的有限种群中的随机动力学，性状多样性总是会丢失，除非通过其他变异来源（例如突变或迁移）来维持。这需要发展随机生态进化博弈论和标准来描述基于瞬态配置的进化成功和多样性，以研究社会多样化的根源。***2。不对称相互作用和结构化群体****在自然界中，个体可用的资源通常会根据环境差异、表型变异或过去积累的剩余等而变化。在这里，我们将 EGT 扩展到研究不对称相互作用对进化成功和行为特征多样性的影响。特别令人感兴趣的是进化的社会行为，它抵消资源不平等以实现公平和平等的结果，或者相反，将平等主义配置转变为不平等的资源分配，从而导致不对称的互动。这揭示了社会多样化和分层的另一种方法的条件。***社交网络是动态的人口结构，捕捉短暂的互动模式，从而定义社会。遵循“形式服从功能”的原则，我们采用了一种基于行动（包括合作、奖励和惩罚）的新颖框架，来探索社会组织的进化起源和复杂社会的形成。***3。行为实验和实证分析****作为一个鼓舞人心的补充，我们研究人类行为实验中的并发交互，并测试参与者是否成功协调他们的行为并专门从事不同的任务以实现更有效的结果 - 这是劳动分工和社会分层的重要方面。在大规模互联网实验中并使用经验数据，我们研究了社交学习和社交网络的动态。**