Time series: theory and methods
Time series: theory and methods
Algorithmic information theory
Algorithmic information theory
Evaluating Software Complexity Measures
IEEE Transactions on Software Engineering
Elements of information theory
Elements of information theory
An introduction to symbolic dynamics and coding
An introduction to symbolic dynamics and coding
Identifying controlling features of engineering design iteration
Management Science
An introduction to Kolmogorov complexity and its applications (2nd ed.)
An introduction to Kolmogorov complexity and its applications (2nd ed.)
Estimation of parameters and eigenmodes of multivariate autoregressive models
ACM Transactions on Mathematical Software (TOMS)
Stochastic Complexity in Statistical Inquiry Theory
Stochastic Complexity in Statistical Inquiry Theory
Design Rules: The Power of Modularity Volume 1
Design Rules: The Power of Modularity Volume 1
Problem-Solving Oscillations in Complex Engineering Projects
Management Science
Performance Variability and Project Dynamics
Computational & Mathematical Organization Theory
Predictability, Complexity, and Learning
Neural Computation
CSCW '06 Proceedings of the 2006 20th anniversary conference on Computer supported cooperative work
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
The Minimum Description Length Principle (Adaptive Computation and Machine Learning)
Information and Complexity in Statistical Modeling
Information and Complexity in Statistical Modeling
Proceedings of the Second ACM-IEEE international symposium on Empirical software engineering and measurement
Patterned Interactions in Complex Systems: Implications for Exploration
Management Science
New Introduction to Multiple Time Series Analysis
New Introduction to Multiple Time Series Analysis
Information Systems Research
Go (Con)figure: Subgroups, Imbalance, and Isolates in Geographically Dispersed Teams
Organization Science
Hierarchical Structure and Search in Complex Organizations
Management Science
Project Management: A Systems Approach to Planning, Scheduling, and Controlling
Project Management: A Systems Approach to Planning, Scheduling, and Controlling
The measurement of a design structural and functional complexity
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Fisher information and stochastic complexity
IEEE Transactions on Information Theory
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This paper presents a theoretical analysis of project dynamics and emergent complexity in new product development (NPD) projects subjected to the management concept of concurrent engineering. To provide a comprehensive study, the complexity frameworks, theories and measures that have been developed in organizational theory, systematic engineering design and basic scientific research are reviewed. For the evaluation of emergent complexity in NPD projects, an information-theory quantity--termed "effective measure complexity" (EMC)--is selected from a variety of measures, because it can be derived from first principles and therefore has high construct validity. Furthermore, it can be calculated efficiently from dynamic generative models or purely from historical data, without intervening models. The EMC measures the mutual information between the infinite past and future histories of a stochastic process. According to this principle, it is particularly interesting to evaluate the time-dependent complexity in NPD and to uncover the relevant interactions. To obtain analytical results, a model-driven approach is taken and a vector autoregression (VAR) model of cooperative work is formulated. The formulated VAR model provided the foundation for the calculation of a closed-form solution of the EMC in the original state space. This solution can be used to analyze and optimize complexity based on the model's independent parameters. Moreover, a transformation into the spectral basis is carried out to obtain more expressive solutions in matrix form. The matrix form allows identification of the surprisingly few essential parameters and calculation of two lower complexity bounds. The essential parameters include the eigenvalues of the work transformation matrix of the VAR model and the correlations between components of performance fluctuations.