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

Michigan State University, East Lansing MI

Investigators

Abstract

Non-Technical Description The 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 Description The 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.