Evolutionary Perspective on Collective Decision Making

集体决策在当今世界各地发挥着越来越重要的作用。的人类社会。现有的文献解决问题的集体决策与线性统计分析或相对简单的动态建模。这两种方法仍然是有限的，在捕捉真实的人类决策动态的复杂性，通常涉及高维非线性问题空间，非平凡的社会结构，在个人的认知和行为模式，和/或个体之间的多样性。在这个项目中，PI将开发一个新的概念/计算复杂的集体决策动态的多层次模型，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。集体决策将被重新定义为思想生态在社会网络栖息地上的进化，其中潜在解决方案的种群通过不断应用进化算子（如繁殖，重组，突变，选择和解决方案的迁移）而进化，每个都由参与的人类进行。将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论引入人类和社会复杂行为的建模中，对人类和社会动态产生新的视角，使理论从传统的、以个人为中心的心理或社会科学范式发展为更具动态性、多层次、进化性的范式。集体社会过程。将产生一些实际影响，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绩效的影响，这将广泛适用于当今许多人类组织面临的问题。该项目的成果将被纳入宾厄姆顿大学的本科和研究生教育。

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.