A Computational Modeling Approach to Organizational Effectiveness: Mapping the Effects of Leadership, Group Structure, and Environmental Shocks.

非技术描述该项目是一个为期三年的基础研究计划，旨在解决团队领导，团队组成和适应环境冲击的理论差距，特别强调多团队系统（MTS）。这个项目扩展了我们以前的工作，通过将计算建模应用到扩展的MTS网络中，来研究团队中的涌现现象。我们的目标是设计一个高度灵活的计算代理架构，可以应用于广泛的团队类型（例如，行动、项目、决策团队），任务结构（例如，池化的、顺序的、相互的、密集的），以及MTS上下文（例如，军事、医疗、商业）。计算模型将用于进行虚拟实验，以评估不同的团队组成和领导配置对团队（第一阶段）和MTS（第二阶段）有效性的影响，以及不同配置在内部和外部冲击下的弹性和适应潜力（第三阶段）。这种建模研究具有许多实际应用。特别是，它旨在确定支持团队效率的基本机制。 然后，该模型可以用于预测特定团队和MTS配置的有效性，并根据这些发现，为组成团队和任命团队领导者提供规定性原则，以确保团队和团队系统的有效性得到优化。技术描述与团队和MTS有效性相关的现象的出现和动态已经证明很难使用组织心理学和行为学（OPB）中采用的主导研究方法（实验和相关研究）。如果OPB要提高对过程动态的理解，就需要基于计算建模的“第三学科”。一个全面的计算模型的发展，基于马尔可夫决策过程（MDP）架构，其中包括团队的组成，领导结构和过程机制内和之间的团队，将标志着OPB的重大进展。 计算模拟的一个关键优势是能够系统和彻底地映射理论空间。 虚拟实验将使规范的基本机制，驱动领导，团队结构和成员组成之间的动态互连及其敏感性，弹性和适应性的内部和外部冲击。此外，我们使用MDP“引擎”将能够指定最优性和偏离最优性的情况。研究结果将使随后的实证研究更准确地针对性，并确定具体的干预点。 正式的计算模型将使生成的各种团队和MTS的领导结构的有效性，在不同的内部和团队之间的条件下的可推广的预测预测。因此，本研究的建议旨在提高后续实证研究在效率（即，专注于有希望的目标，避免不太可能产生成果的研究）和有效性（即，重点放在更有可能成功的干预措施上）。这是非常重要的，因为对团队和MTS的研究是高度资源密集型的。通过基于虚拟实验的结果更精确地针对人类研究，投入人类研究的资源可能会获得更高的回报。因此，这项研究有可能帮助资助机构更准确地确定研究资金的优先事项。 此外，该研究具有广泛的潜在应用。 同样的灵活性允许检查和量化不同的系统配置将如何适应和响应意外冲击。 因此，决策者将拥有预测性和规范性的工具，从而对关键人员、团队结构和组织设计决策做出明智的决策。

Non-Technical DescriptionThe project is a three-year program of basic research designed to address gaps in theory on team leadership, team composition, and adaptability to environmental shocks, with a specific emphasis on multi-team systems (MTS). This project extends our prior work on emergent phenomena in teams by applying computational modeling to an extended network of MTSs. The goal is to design a highly flexible computational agent architecture that can be applied to a broad range of team types (e.g., action, project, decision-making teams), task structures (e.g., pooled, sequential, reciprocal, intensive), and MTS contexts (e.g., military, medical, business). The computational model will be used to conduct virtual experiments to evaluate the effects of different team composition and leadership configurations on team (Phase 1) and MTS (Phase 2) effectiveness, and the resilience and adaptability potential of different configurations given internal and external shocks (Phase 3). This modeling research has many practical applications. In particular, it is designed to identify the basic mechanisms that underpin team effectiveness. The model can then be used to predict the effectiveness of particular team and MTS configurations and, based on those findings, provide prescriptive principles for composing teams, and appointing team leaders, to ensure that teams and systems of teams are optimized for effectiveness. Technical DescriptionThe emergence and dynamics of phenomena relevant to the effectiveness of team and MTSs has proven difficult using the dominant research methods (experimental and correlational research) employed in organizational psychology and behavior (OPB). A "third discipline" based on computational modeling is needed if OPB is to advance understanding of process dynamics. The development of a comprehensive computational model, based on a Markov Decision Process (MDP) architecture, which incorporates team composition, leadership structure, and process mechanisms within- and between-teams, will mark a significant advance in OPB. A key advantage of computational simulation is the ability to systematically and thoroughly map a theoretical space. Virtual experimentation will enable specification of the fundamental mechanisms that drive dynamic interconnections among leadership, team structures, and member composition ad their sensitivity, resilience, and adaptability to internal and external shocks. Moreover, our use of the MDP "engine" will enable specification of optimality and deviations from it. Research findings will enable subsequent empirical research to be more precisely targeted, with specific points for intervention identified. The formal computational model will enable the generation of generalizable predictive forecasts of the effectiveness of various team and MTS leadership structures under different within- and between-team conditions. Thus, recommendations from this research are intended to enhance the gains of subsequent empirical research in terms of both efficiency (i.e., focusing on promising targets, avoiding research that is less likely to be productive) and effectiveness (i.e., focusing on interventions that are more likely to be successful). This is vitally important because research on teams and MTSs is highly resource intensive. By more precisely targeting at human research based on the findings of virtual experiments, the resources invested in human research are likely to have a much higher return. Thus, this research has the potential to aid funding agencies to more precisely target research funding priorities. Moreover, the research has a wide range of potential applications. This same flexibility permits examination and quantification of how adaptive and responsive different system configurations would be to unexpected shocks. As a result, decision-makers would have the predictive and prescriptive tools from which to make informed decisions about critical personnel, team structure, and organizational design decisions.