Doboli Alex, Doboli Simona
Department of Electrical and Computer Engineering, Stony Brook University, Stony Brook, 117954-2350 NY USA.
Department of Computer Science, Hofstra University, Hempstead, 11549 NY USA.
Appl Intell (Dordr). 2021;51(4):2094-2127. doi: 10.1007/s10489-020-01919-6. Epub 2020 Oct 27.
Understanding the process of producing creative responses to open-ended problems solved in small groups is important for many modern domains, like health care, manufacturing, education, banking, and investment. Some of the main theoretical challenges include characterizing and measuring the dynamics of responses, relating social and individual aspects in group problem solving, incorporating soft skills (e.g., experience, social aspects, and emotions) to the theory of decision making in groups, and understanding the evolution of processes guided by soft utilities (hard-to-quantify utilities), e.g., social interactions and emotional rewards. This paper presents a novel theoretical model (TM) that describes the process of solving open-ended problems in small groups. It mathematically presents the connection between group member characteristics, interactions in a group, group knowledge evolution, and overall novelty of the responses created by a group as a whole. Each member is modeled as an agent with local knowledge, a way of interpreting the knowledge, resources, social skills, and emotional levels associated to problem goals and concepts. Five solving strategies can be employed by an agent to generate new knowledge. Group responses form a solution space, in which responses are grouped into categories based on their similarity and organized in abstraction levels. The solution space includes concrete features and samples, as well as the causal sequences that logically connect concepts with each other. The model was used to explain how member characteristics, e.g., the degree to which their knowledge is similar, relate to the solution novelty of the group. Model validation compared model simulations against results obtained through behavioral experiments with teams of human subjects, and suggests that TMs are a useful tool in improving the effectiveness of small teams.
理解在小组中解决开放式问题时产生创造性回应的过程，对于许多现代领域都很重要，如医疗保健、制造业、教育、银行业和投资领域。一些主要的理论挑战包括刻画和衡量回应的动态变化、在小组问题解决中关联社会和个体层面、将软技能（如经验、社会层面和情感）纳入小组决策理论，以及理解由软效用（难以量化的效用）引导的过程的演变，例如社会互动和情感奖励。本文提出了一种新颖的理论模型（TM），该模型描述了在小组中解决开放式问题的过程。它从数学角度展示了小组成员特征、小组内互动、小组知识演变以及整个小组所产生回应的整体新颖性之间的联系。每个成员都被建模为一个具有局部知识、一种解释与问题目标和概念相关的知识、资源（此处原文有误，应改为资源）、社交技能和情感水平的方式的智能体。智能体可以采用五种解决策略来生成新知识。小组回应形成一个解决方案空间，在这个空间中，回应根据其相似性被分组，并按照抽象层次进行组织。解决方案空间包括具体特征和样本，以及将概念相互逻辑连接的因果序列。该模型被用于解释成员特征（例如他们知识的相似程度）如何与小组的解决方案新颖性相关。模型验证将模型模拟结果与通过对人类受试者团队进行行为实验获得的结果进行了比较，并表明理论模型是提高小团队有效性的有用工具。
