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: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=707941
• 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，形状各异。在这里，我们将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，我们研究了共存、专业化或社会组织等方面的多样性互动与多样性的产生和维持之间的关系。 1.多样性、合作与社会分层 自然环境以多重并发交互的形式对个人提出了各种挑战。例如，微生物和人类都通过细胞外产物或不断减少的全球资源参与各种公共产品的相互作用。我们将传统的EGT扩展到多个相互作用，以确定多态性与相互作用的数量（和类型）的缩放，以及在特定任务的专业化产生更大的稳定性的条件下划定。然而，对于可变大小的有限种群中的随机动态，性状多样性总是丢失的，除非通过额外的变异源（如突变或迁移）来维持。这就需要发展随机生态进化博弈理论和标准，以表征进化的成功和多样性的基础上瞬态配置，以调查社会多样化的根源。 2.非对称交互和结构化群体 在自然界中，可供个人使用的资源通常会因环境差异、表型变异或过去积累的盈余等因素而异。在这里，我们扩展EGT来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，社会行为的演变抵消了资源不平等，以实现公平和平等的结果，或者反过来说，把平等的配置变成不平等的资源分配，从而导致不对称的互动。这揭示了另一种社会多样化和分层办法的条件。 社交网络是动态的人口结构，捕捉短暂的互动模式，定义社会。遵循形式服从功能的原则，我们采用了一个新的框架，基于行动，包括合作，奖励和惩罚，探索社会组织的进化起源和复杂社会的形成。 3.行为实验与实证分析 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地协调他们的行为，并专注于不同的任务，以实现更有效的结果-劳动分工和社会分层的一个重要方面。在大规模的互联网实验和使用经验数据，我们调查社会学习和动态的社交网络。