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

组织有效性的计算建模方法：绘制领导力、群体结构和环境冲击的影响。

• 批准号: 1533499
• 负责人: Steve Kozlowski
• 金额: $ 10.66万
• 依托单位: Michigan State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-08-15 至 2017-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1533499&HistoricalAwards=false
• 关键词: Computational; Modeling; Approach; Organizational; Effectiveness

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.

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，团队组成和环境冲击适应性方面的理论差距，特别强调多团队系统（MTS）。该项目通过将计算模型应用于mts的扩展网络，扩展了我们之前在团队中出现现象的工作。目标是设计一个高度灵活的计算代理体系结构，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，顺序，互惠，密集）和MTS上下文（例如，军事，医疗，商业）。利用计算模型进行虚拟实验，评估不同团队组成和领导配置对团队（阶段1）和MTS（阶段2）有效性的影响，以及不同配置在内外部冲击（阶段3）下的弹性和适应潜力。该建模研究具有许多实际应用价值。特别地，它被设计用来识别支撑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，并基于这些发现，为组成团队和任命团队领导提供说明性原则，以确保团队和团队系统的有效性得到优化。技术描述使用组织心理学和行为学（OPB）中使用的主流研究方法（实验和相关研究）来研究与团队和MTSs有效性相关的现象的出现和动态已被证明是困难的。如果OPB要推进对过程动力学的理解，就需要基于计算建模的“第三门学科”。基于马尔可夫决策过程（MDP）架构的综合计算模型的开发，将团队组成、领导结构和团队内部和团队之间的过程机制结合起来，将标志着OPB的重大进步。计算模拟的一个关键优势是能够系统和彻底地绘制理论空间。虚拟实验将使基本机制具体化，这些机制驱动领导、团队结构和成员组成之间的动态相互联系，以及他们对内部和外部冲击的敏感性、弹性和适应性。此外，我们对MDP“引擎”的使用将使最优性和偏离它的规范成为可能。研究结果将使后续的实证研究更有针对性，并确定具体的干预点。正式的计算模型将能够生成各种团队和MTS领导结构在不同团队内部和团队之间条件下的有效性的可推广的预测性预测。因此，本研究的建议旨在提高后续实证研究在效率（即，关注有希望的目标，避免不太可能产生成果的研究）和有效性（即，关注更有可能成功的干预措施）方面的成果。这是至关重要的，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，投资于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精确地确定研究资助的优先事项。此外，该研究具有广泛的潜在应用前景。同样的灵活性允许检查和量化不同系统配置对意外冲击的适应性和响应性。因此，决策者将拥有预测和规范的工具，从中做出关于关键人员、团队结构和组织设计决策的明智决策。