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Conflict and complexity : countering terrorism, insurgency, ethnic and regional violence
Fellman P., Bar-Yam Y., Minai A., Springer International Publishing, New York, NY, 2015. 292 pp. Type: Book (978-1-493917-04-4)
Date Reviewed: Aug 15 2017

The 32 authors who have contributed to this book’s 16 chapters include military experts, political scientists, psychologists, economists, sociologists, and complexity scientists. Their objective is to show how concepts from the sciences of complex adaptive systems can be applied to modern geopolitical problems such as terrorism, insurgency, and ethnic and regional violence--in terms of the title, to build a bridge between “conflict and complexity.” There is no lack of modern literature analyzing conflict with geopolitical, psychological, and sociological tools, and the study of complex adaptive systems has enjoyed deep attention for three decades. The volume seeks to show that the latter set of tools can provide a powerful new perspective on the former set of problems. As a member of the complex adaptive systems research community, I am inclined to believe this claim, and would welcome an integrated and persuasive statement of the case. Unfortunately, this book is an uneven response to this need.

The chapters are grouped in three parts.

The first part promises “theoretical background.” It provides a great deal of background on conflict, but almost nothing concrete about complexity theory, and relates the two only in the most abstract, and sometimes inaccurate, ways. For example, chapter 2 purports to discuss “complex system studies and terrorism,” but its view of complexity is highly subjective, defining complexity in terms of the relation between the human observer and the system under study. This “I know it when I see it” approach may appeal to some social scientists, but it leaves the reader with no clear guidance as to what tools should be used or why. The chapter is by far the longest in the book, and it replaces precision in defining complexity with extensive name-dropping of complexity researchers. The title of chapter 4, “A Framework for Agent-Based Social Simulations of Social Identity Dynamics,” promises more specificity. It describes some basic concepts of agent-based social science, but shows no knowledge of how to do agent-based modeling, what it can be expected to show, or how to validate it, and it neglects the very active field of agent-based models of opinion dynamics [1], which is highly relevant to its topic. Chapter 5 reveals a serious lack of understanding. The author claims that “the same agents in an ABM can represent soldiers with a mission in one model and a set of mines in the ocean in another model” and asserts that “most of the [agent-based] model is contained in the data and the human interpretation that is attached to the implementation,” neither of which is true of agent-based modeling (ABM) as it is practiced today (for example, [2]).

The second part offers five “applications and case studies,” and is much more satisfying than the first. For example, chapter 7, “A Fractal Concept of War,” summarizes fascinating efforts to adapt the venerable Lanchester force-on-force model of conflict (a differential equation model relating force strength to attrition rate) to modern conflict. The fundamental insight is to bring to bear formal models of self-similarity, based on theoretical results by Barenblatt and Helmbold. Unfortunately, the author does not include either an explanation of their results or references to enable the interested reader to probe deeper. A web search shows that Barenblatt’s work is discussed in [3] and Helmbold’s in [4], but one really expects the authors to provide references to the work on which they build. But the section is uneven. Chapter 10, exploring numerical approaches to studying piracy, appears to be a rough draft. The maps are watermarked previews downloaded from the web, with the authors’ content overlaid on them in a way that is often illegible. The chapter promises an earlier study by the last author as an appendix, but in fact that study is just spliced onto the end of the chapter, so that one section entitled “Conclusion” and ending with a paragraph that begins “in closing” is followed immediately by another section on “Historical Background,” apparently the beginning of the promised study. Chapter 11, “Identities, Anonymity and Information Warfare,” argues that anonymity on the Internet enables troublemakers to do bad things, so we ought to enforce stronger identity protocols. The first statement is obvious, civil libertarians would contest the second, and neither has much to do with complexity science.

Part 3 is titled “Broader Horizons,” but really is another case study, this time spread across five chapters with heavy overlap among their authors. All of the chapters examine the related issues of ethnic violence and food shortages. These five chapters look like a monograph trying to happen. They present an intriguing complexity of ideas, but would be greatly improved if they followed the growing consensus in the social simulation community that models cited in published articles should be accompanied with publicly available model specifications in sufficient detail to allow replication of results (for example, the ODD protocol [5] documenting overview, design, and details of the model), and preferably with the models themselves shared via a site such as www.openabm.org.

Modern conflict is an important problem, and complexity science offers a powerful set of tools. Some of the studies in this collection show the potential for a synergy of research in these two areas, but most of them illustrate the difficulties that still must be overcome to achieve a coherent body of interdisciplinary work in this area.

Reviewer:  H. Van Dyke Parunak Review #: CR145487 (1710-0655)
1) Xia, H.; Wang, H.; Xuan, Z. Opinion dynamics: a multidisciplinary review and perspective on future research. Int. J. Knowl. Syst. Sci. 2, 4(2011), 72–91.
2) Wilensky, U.; Rand, W. An introduction to agent-based modeling: modeling natural, social, and engineered complex systems with NetLogo. MIT Press, Cambridge, MA, 2015.
3) Barenblatt, G. Scaling, self-similarity, and intermediate asymptotics. Cambridge University Press, Cambridge, UK, 1996.
4) Helmbold, R. A modification of Lanchester's equations. Operations Research 13, 5(1965), 857–859.
5) Grimm, V.; Berger, U.; DeAngelis, D. L.; Polhill, J. G.; Giske, J.; Railsback, S. F. The ODD protocol: a review and first update. Ecological Modelling 221, 23(2010), 2760–2768.
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