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Complexity management in engineering design: a primer
Maurer M., Springer Vieweg, New York, NY, 2017. 153 pp. Type: Book (978-3-662534-47-2)
Date Reviewed: Apr 4 2018

The purpose of this well-written habilitation thesis by Maik Maurer is to present a “big picture of complexity management” to guide students “through the necessities, ideas, concepts and implementations” and to give engineers “a framework at hand for identifying needs and possibilities in dealing with complexity as it appears in their day-to-day projects.” The author’s emphasis is on engineering products, but he properly observes that it is often useful to transfer knowledge and methods between domains and integrate them in a new context. He also notes the importance and role of organizational, and not only technical, structures.

The book may be considered as a breadth-first succinct survey of the field including quite a few excellent observations by the author. For example, already on page 1 we read that “compared to the epoch of industrialization and the product lifetime of technical systems, highly complex natural systems seem to outperform anything we design.” For another example, Maurer properly emphasizes the structure of a system, that is, its internal and external relations that are “decisive for the system’s reaction to changes from the system’s environment” (p. 13) and observes that “in most modeling approaches, system elements receive close examination while the origin of system dependencies often gets neglected.” He also proposes that a thorough system definition “must precede any determination of strategy and method application,” a desideratum for buzzword-compliant software system design methods. “Interdependencies and patterns out of these interdependencies across domains” are noted as the basis of Maurer’s analysis in the foreword by Udo Lindemann.

Maurer surveys a great lot of references (hundreds in total), although a few important ones are missing (see below). He includes a rather detailed interesting chapter on the history of complexity management, starting with Aristotle, who introduced early systems thinking and whose system-based worldview “has largely influenced later scientific development until the Renaissance when it was challenged for example by the mechanical philosophy” (p. 46). The fallacy of reductionism is mentioned by Maurer in this and other contexts. He further suggests that “none of the single scientific disciplines (mathematics, physics, biology, etc.) by itself is able to model and explain the phenomenon of complexity” (p. 63), and here I disagree with respect to mathematics: category theory has been rather successful in doing just that (see, for example, [1]). While Maurer does distinguish between qualitative and quantitative approaches to modeling, he does not emphasize the role of precise specifications that need not be quantitative [1,2], although he refers to Goethe’s observation that the existence of things “is not directly bundled to the possibility of quantification” (p. 47). More generally, Maurer properly stresses that “systems thinking and complexity management generally require holistic approaches” (p. 45), but does not mention F. A. Hayek’s theory of complex phenomena ([3] and elsewhere) that demonstrates the need for and importance of explanation in principle (that is, of patterns) as opposed to dealing with details in understanding and specifying any complex system. Note that Hayek’s explanation in principle is specifically emphasized by Ludwig von Bartalanffy, whose general systems theory is described by Maurer in detail. For a final example, Maurer properly refers to the questionable assumption of “homo oeconomicus,” a[n allegedly] fully rational human being (p. 80), but mentions only Herbert Simon in this context, while Ludwig von Mises discussed this subject much earlier [4].

After describing the history of complexity management, Maurer presents “a map of complexity management approaches” identifying, presenting on a Venn diagram, and discussing in some detail seven engineering disciplines (and their intersections) that have direct relevance to the issues of complexity. He acknowledges that this classification “must be incomplete,” that other viewpoints are possible, and invites the reader to “challenge, adapt, and extend” his picture. Finally, he discusses what to do when “confronted with a specific complexity challenge” and properly suggests starting with the basics of the business domain: with a clear definition of the system, its boundaries, its elements, and interdependencies between them. Regretfully, the need to specify relationship semantics is not stressed (compare, for example, with [5] and references there), although the fallacy of relying on “meaningful names,” rather than on precise definitions, when specifying systems is mentioned, for example, when Maurer observes that different vocabulary terms “can make it difficult to see similarities, correlations, but also transferability between approaches” (p. 108). This is especially important in dealing with inevitable multiple viewpoints and multiple levels of abstraction in complex system specifications, but regretfully this has not been emphasized enough in the book. In this context, I want to commend Maurer’s emphasis on social and technical problems related to information acquisition (including such important barriers as stakeholders’ lack of motivation, social pressure, and intentional concealment). Such problems are (or ought to be) well known to good business modelers, but regretfully often are not even mentioned.

Summing up, I liked this book and agree with Maurer’s observation that it should serve as a starting point for future discussions (p. 146). Future editions, in my opinion, ought to stress, to a greater extent, the need to compress a huge amount of complex information in order to explicitly distinguish between terse patterns (“laws”) and their possibly enormously large particular manifestations (“data”) [3,6].

Reviewer:  H. I. Kilov Review #: CR145949 (1806-0304)
1) Ehresmann, A.; Vanbremeersch, J. P. Memory evolutive systems: hierarchy, emergence, cognition. Elsevier, Amsterdam, the Netherlands, 2007.
2) Bunge, M. Treatise on basic philosophy (vol.5): exploring the world. D. Reidel, Dordrecht, the Netherlands, 1983.
3) Hayek, F. A. The theory of complex phenomena. In: The critical approach to science and philosophy (in honor of Karl R. Popper). 22-42, The Free Press of Glencoe, London, UK, 1964.
4) von Mises, L. Human action: a treatise on economics. Yale University Press, New Haven, CT, 1949.
5) UML Profile for Relationships. Object Management Group, http://www.omg.org/cgi-bin/doc?formal/2004-02-07 (02/19/2018).
6) Manin, Y. Cognition and complexity. In: Information and complexity. World Scientific Series in Information Studies 6. 338-352, World Scientific, Hackensack, NJ, 2017.
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