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Products continuously change over time to satisfy new customer needs and technologies. This successive emergence of new products within a product family can be considered as product family evolution, which is derived from inherent evolving properties and adaptation to dynamic product environments. Although product family design has been widely discussed, product family evolution has not been sufficiently explored in an analytical manner. To tackle this issue, this research aims to demonstrate the underlying properties of product family evolution and to model the process of product family evolution. The methodology of this research is based on a network science approach and consists of two phases: 1) characterization of product family evolution and 2) modeling of product family evolution. In the first phase, a network representation for product family architectures is devised to effectively describe relationships between components. Then, topological properties and significant network motifs in the product family network at each time period are investigated through a case study using the smartphone models of a major company. The results show that both individual product and product family networks follow the properties observed in other real networks. The network properties and significant motifs of the individual smartphone networks suggest that the individual product structure grows as a scale-free network with common building blocks. On the other hand, the smartphone product family network evolves with both scale-free and small-world properties due to commonality in a product family. Also, the product family network changes with common and specific motifs during each specific time duration. In the second phase, the complex adaptive process of product family evolution is simplified and modeled to estimate configurational changes in product family architecture design. In this model, evolution modes embedding specific variant generation mechanisms are realized through a mixed-strategy game theory and an evolving network model - fitness model. For the proposed product family evolution model, illustrative examples are introduced to describe the dynamic configuration process of a product family structure. Finally, evolutionary opportunities for product and product family design focusing on the universality of network properties are addressed from the perspectives of topological robustness and complexity. From theoretical and empirical evidences, it is identified that a design structure with scale-free network topology and commonality has topological robustness since it has less complexity in structural connectivity and variety. Given the increasing awareness of dynamic product environment, this research serves as a foundation for the analysis of product family evolution.

Evolution by natural selection explains the tree of life and the complex adaptations found throughout nature. The power and versatility of evolutionary explanations have proved tempting to scientists outside of biology, but adapting evolutionary concepts to new domains has been challenging. Even within biology, there are many difficult questions and problem cases that face evolutionary theory. Modelling Evolution offers a new, general account of evolution by natural selection that identifies the essential features of evolutionary models that transcend any particular discipline. Evolution by natural selection in its broad sense is the systemic advantage of a type, in contrast to the narrow definition using heritable variation in fitness. This account is explained, contextualised and applied to a variety of questions in both biology and the social sciences. Offering an accessible and comprehensive account of evolution that is applicable both to biology and the broader social sciences, Modelling Evolution will appeal to students and researchers interested in fields such as biology, economics, sociology, history, and psychology.

Quantitative approaches to evolutionary biology traditionally consider evolutionary change in isolation from an important pressure in natural selection: the demography of coevolving populations. In Analysis of Evolutionary Processes, Fabio Dercole and Sergio Rinaldi have written the first comprehensive book on Adaptive Dynamics (AD), a quantitative modeling approach that explicitly links evolutionary changes to demographic ones. The book shows how the so-called AD canonical equation can answer questions of paramount interest in biology, engineering, and the social sciences, especially economics. After introducing the basics of evolutionary processes and classifying available modeling approaches, Dercole and Rinaldi give a detailed presentation of the derivation of the AD canonical equation, an ordinary differential equation that focuses on evolutionary processes driven by rare and small innovations. The authors then look at important features of evolutionary dynamics as viewed through the lens of AD. They present their discovery of the first chaotic evolutionary attractor, which calls into question the common view that coevolution produces exquisitely harmonious adaptations between species. And, opening up potential new lines of research by providing the first application of AD to economics, they show how AD can explain the emergence of technological variety. Analysis of Evolutionary Processes will interest anyone looking for a self-contained treatment of AD for self-study or teaching, including graduate students and researchers in mathematical and theoretical biology, applied mathematics, and theoretical economics.

Open source product development refers to a paradigm where large numbers of participants collaborate in the form of communities to develop new products and services. The fundamental differences between traditional product development and open source processes are that the former is based on top-down decomposition while the latter is based on evolution and self-organization. The paradigm of open source processes has resulted in highly successful products such as Linux and Apache. Despite the success of various projects using open source processes, it is not well understood how the product structures and community structures evolve over time.

(R)Evolution studies the adaptation of industrial organisations to the dynamics of the environment by drawing an analogy with evolutionary biology, by extensively studying literature in management science, and by case studies. These investigations have lead to the insight that companies might evolve slower than generally expected; they doubt the effect of reorganizations, as commonly practiced in industry. Additionally, this work proposes the model for the Innovation Impact Point, the model for the Dynamic Adaptation Capability, the model for Collaboration.