Reviewed by
Pamela Mischen
Binghamton University
I was excited to read Policy Practice and Digital Science because of its subtitle—Integrating Complex Systems, Social Simulation and Public Administration in Policy Research. I was hoping to find within its covers a real integration of the topics and for it to be chock full of examples of how complex systems, social simulation, and public administration had influenced the policy process. Alas, I was somewhat disappointed. My primary disappointment comes not from the book, but from the fact that this integration is not happening as much as many of us believe that it could and should, therefore, there simply aren’t enough examples to draw upon. That said, the book itself would have benefitted from more careful editing to bring the individual contributions together in a more thoughtful way.Because there are 19 chapters related to quite disparate topics, I will focus the remainder of this review on some of the chapters that I believe make the greatest contributions.
In Chapter 2, Koliba and Zia outline how MPA and MPP programs can incorporate complex systems and social simulation into current curricula to help educate the next generation of practitioners who might use these methods. They argue that future public managers “will likely not need to know how to explicitly build models or manipulate big data. Instead they will need to know what kinds of questions that policy informatics projects or programs can answer or not answer” (p.17). The chapter presents sample courses and a curriculum from the University of Vermont. This chapter would have been a good final chapter and summary of how we, in the field of public administration, can encourage the integration of complex systems and social simulation into policy research.
In Chapter 6, Majstorovic et al. provide a well-organised overview of the types of simulation models and modelling approaches. This chapter contains an excellent summary of various paradigms that exist within the literature on simulation modelling and focus on three approaches: systems dynamics (macro-simulation), ABM (modelling social behaviour of groups), and micro-simulation (modelling individual’s evolution). They provide a detailed example of each type, outline its advantages and disadvantages, and provide suggestions for extension.
Likewise, Kamateri et al. present a comparative analysis of ICT tools and technologies in Chapter 7. This chapter is a valuable resource for exploring the various visualisation tools, argumentation tools, eParticipation tools, opinion mining tools, simulation tools, serious games tools (specifically developed for policy makers), persuasive tools, social network analysis tools, big data analytics tools, and semantics and linked data tools. The authors identify to which stage(s) of the policy each tool/technology applies, and the stakeholder types who would likely engage with it. An extensive appendix provided links to over 60 examples discussed in the chapter.
In Chapter 10, Moody and Gerrits tackle an important question regarding the use of computational models in public decision-making processes—the role that values play in commissioning, designing, and using models. They state, “[t]he question we need to ask ourselves here is not only whether computational models are a value-free or neutral tool, but moreover who or what determines the values within these models” (p. 208). Reviewing six cases they find that values are present in data, the model, and the decision-making process. They conclude, “[t]he current research shows that models and data never speak for itself [sic]; … [Values] have been put there by the designers’ choice, often unintentionally; however, public policy makers are unaware of these choices” (p. 217).
At first glance, Chapter 11 by Tyreerd Andringa seems to be out of place in this volume. However, I felt was the most interesting and broadly applicable chapter in this text. Andringa considers “two attitudes toward a complex world”—the authoritarians and the libertarians—and argues that organisations reflect the attitudes of their leaders or the attitudes of the individuals that dominate that organisation. Authoritarians and therefore authoritarian organisations seek a world in which complexity is limited. When this attitude dominates an organisation, the organisation exhibits a high degree of bureaucratization in which workers are seen as sources of problems, and formal rules and regulations dominate. This structure, according to Andringa, imposes psychological costs on the workers themselves. However, in libertarian organisations, formal hierarchy is still important but its role is to manage autonomy of workers as opposed to enforcing compliance. This is because libertarians have a greater level of comfort with the level of complexity and ambiguity the world presents, and view the world as full of opportunities.
I presume that those of us who study complexity theory and complex adaptive systems fall into the libertarian category. We don’t model complex systems to attempt to control them, but to understand them better. Our natural partners will be libertarian organisations. Within the field of public administration, New Public Management, which Andringa argues is still guided by the coping mechanisms of authoritarianism, is giving way to New Public Governance (or Public Value Management) as a management paradigm. According to Stephen Osborne (2010, p. 7), New Public Governance “captures the reality of public policy implementation and public services delivery within the plural and pluralist complexities of the state in the twenty-first century”. Therefore, advances in systems modelling will be critical to the success of New Public Governance, just as New Public Governance will be critical to the successful use of systems modelling.
References
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