Project Dynamics and Emergent Complexity
Christopher M. Schlick, Bruno Demissie

TL;DR
This paper develops mathematical models to analyze project dynamics and emergent complexity in new product development, validating them with field data and demonstrating their use in optimizing project organization.
Contribution
It introduces a model-driven approach using VAR and HM processes to measure emergent complexity via effective measure complexity (EMC) in NPD projects.
Findings
EMC can be explicitly calculated from model parameters.
The models effectively evaluate project complexity.
Optimizing team design reduces emergent complexity.
Abstract
The paper presents theoretical and empirical analyses of project dynamics and emergent complexity in new product development (NPD) projects. A model-driven approach is taken and mathematical models of cooperative work are formulated based on the theory of vector-autoregressive (VAR)and hidden Marvov (HM)processes. To validate the models with field data, a case study was carried out in an industrial company. Furthermore, concepts and measures of complex systems science are analyzed. To evaluate emergent complexity in NPD projects, an information-theory measure-termed "effective measure complexity" (EMC)- is chosen, because it can be derived from first principles and can be calculated efficiently. EMC measures the mutual information between the infinite past and future histories of a stochastic process. According to this principle, EMC is of particular interest for evaluating…
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Taxonomy
TopicsComplex Systems and Decision Making · Product Development and Customization · Systems Engineering Methodologies and Applications
