Evolutionary Perspective on Collective Decision Making

今天，集体决策在任何地方都扮演着越来越重要的角色。人类社会。现有文献用线性统计分析或相对简单的动态建模来解决集体决策中的问题。这两种方法在捕捉真实人类决策动态的复杂性方面仍然受到限制，这些动态通常涉及高维非线性问题空间、重要的社会结构、个体内部认知和行为模式以及/或个体之间的多样性。在这个项目中，pi将通过将观点从参与者的动态转移到正在讨论的想法的动态，开发一个新的概念/计算多层次的复杂集体决策动态模型。集体决策将被重新定义为在社会网络栖息地中思想生态的进化，其中潜在解决方案的种群通过不断应用进化操作来进化，例如繁殖、重组、突变、选择和解决方案的迁移，每个操作都由参与的人类进行。通过计算机模拟研究各种模型假设对集体决策的影响，并通过与人类受试者进行复杂协作任务的团队决策实验来评估其结果。该项目将通过将进化原理和方法引入人类复杂行为的建模，为人类和社会动态提供一个新的视角，使理论从传统的、以个体为中心的心理学或社会科学范式向更动态的、多层次的、集体社会过程的进化范式迈进。将产生许多实际影响，例如，团队内部共享信息和组织结构的一致性对其探索性和适应性表现的影响，这将广泛适用于许多人类组织今天面临的当前问题。该项目的成果将被整合到宾厄姆顿大学的本科和研究生教育中。

Collective decision making plays an increasingly important role everywhere in today?s human society. Existing literature addresses issues in collective decision making with linear statistical analysis or relatively simple dynamical modeling. Either approach is still limited in capturing the complexity of real human decision making dynamics that typically involve high-dimensional nonlinear problem space, nontrivial societal structure, within-individual cognitive and behavioral patterns, and/or between-individual diversity. In this project the PIs will develop a novel conceptual/computational multi-level model of the dynamics of complex collective decision making by shifting the viewpoint from the dynamics of participants to the dynamics of ideas being discussed. Collective decision making will be redefined as evolution of ecologies of ideas over a social network habitat, where populations of potential solutions evolve via continual applications of evolutionary operators such as reproduction, recombination, mutation, selection, and migration of solutions, each conducted by participating humans. The effects of various model as-sumptions on collective decision making will be studied through computer simulations, and their results will be evaluated through experiments of team decision making on complex collaborative tasks with human subjects. This project will generate a novel perspective on human and social dynamics by introducing evolutionary principles and methodologies into the modeling of their complex behaviors, making a theoretical advancement from a traditional, individually-focused psychological or social science paradigm to a more dynamic, multilevel, evolutionary paradigm for collective social processes. A number of practical implications will be produced, e.g., the effects of coherence of shared information and organizational structure within teams upon their exploratory and adaptive performances, which will be widely applicable to current issues that many human organizations are facing today. The outcomes of this project will be integrated into undergraduate and graduate education at Binghamton University.