Systems thinking and regulatory governance (4): Evidence and findings

We now have a good understanding of the breadth and depth of systems thinking and its possible role in regulatory governance and practice. We have also seen some examples of how systems thinking has inspired scholars in studying regulatory governance and practice. It is now time to gain some understanding of the evidence and findings that have resulted from systems thinking in regulatory scholarship.

A relevant first question to ask is: how much is systems thinking applied in regulatory scholarship? The answer: very little.

How much is systems thinking applied in regulatory scholarship?

Between July and December 2019, I have carried out a systematic review of the use of systems thinking in peer-reviewed articles in the 15 top journals for regulatory scholarship.[1] To do so, I have first explored how often articles use terms that indicate the use of systems thinking (such as ‘complex system’, ‘adaptive system’, ‘system(s) theory’ or ‘system(s) thinking’) and use terms that indicate a regulatory focus.

Across these 15 top journals, I found 636 articles that indicate both the use of systems thinking and indicate having a regulatory focus. In the next step, I have read all the abstracts of these 636 articles to trace those articles that address a regulatory topic (such as regulatory policy, regulatory design, regulatory delivery, and various aspects of regulatory regimes). This resulted in 59 articles that have a central focus on a regulatory topic and use systems thinking terminology. I have read all these 59 articles to understand how they engage with the systems thinking literature discussed in the previous blog.

Somewhat to my surprise, I only found nine articles that explicitly engage with the ideas and concepts from the systems thinking literature. This indicates that systems terminology is used very much by regulatory scholars, but often in passing or as an umbrella term.[2] That is comparable to how the terminology is used in regulatory policy and practice, as we have seen in the first blog post in this series. We can conclude that systems thinking and systems science has seen very little application in regulatory scholarship.[3]

With this limited application in regulatory scholarship, there is considerably less material to work with than in the earlier reviews on the use of behavioural insights in regulatory practice and risk governance and risk-based regulation. Still, some notable findings stand out.

Findings on regulation in a functionally differentiated society

The work of Niklas Luhmann—the idea of society consisting of functionally differentiated systems that all have their own logic and language (coding)—is embraced most in the articles I reviewed. Seven of the nine articles take inspiration from this trajectory of systems thinking. These articles address three specific themes that can broadly be summarised as: “To regulate is to perform a social act of intentional communication”.[4]

First, scholars illustrate how miscommunication between systems may result in noncompliance and regulatory failure. The binary code of law often does not allow for capturing the nuanced and often fuzzy reality that regulators face. Noncompliance may result when the rational-legal language and concepts used in law (and often also in regulation) do not reflect or resonate with those used in the economic, environmental or societal areas it seeks to address. Such noncompliance is systemic, so argue these scholars, rather than an unwillingness to comply on the side of regulatees. A typical example is ‘creative compliance’ when regulatees comply with the letter but not the intention or spirit of the law.

Second, scholars use this trajectory of systems thinking to illustrate the limits of external, state-led regulatory governance to regulate various areas of society. All these areas (function systems such as the economy, science, and religion, as well as their subsystems such as market areas and firms) come with their internal logic, objectives, structure, culture, rewards, punishments, and criteria to establish expertise. Law and regulation will never be able to determine and directly steer their activities because they cannot specify the form and interpretation of all these relevant aspects. Therefore, so argue these scholars, at best regulatory governance can negotiate and influence the self-regulatory tendencies and processes in the various areas of society.

Third, scholars stress the role of regulation, regulatory agencies, and regulatory practitioners in bridging the various functional systems and subsystems of society. Regulation allows for embracing the fuzziness of reality and help to find the optional paths towards compliance and legality—in the drafting of regulation and day-to-day regulatory practice. Also, regulation can help to create jargon, concepts and tools that bridge those of different (sub)systems with those of the legal system. For example, licensing of a firm’s internal self-regulatory regime bridges the logic of the economic system and the logic of the legal system. For the regulator, the question is then not about distinguishing between profitable/nonprofitable (as the firm does) or legal/illegal (as the law does), but acceptance/rejection of the firm’s self-regulatory regime.

Findings on regulation as a (cybernetic) system of control

The other trajectories of systems thinking discussed in an earlier blog post have seen very limited application on the pages of the 15 top journals for regulatory scholarship. One of the remaining two articles, one is written by Karen Yeung and discusses the notion of algorithmic regulation—discussed in the previous blog post in this series.

In her article, Yeung does not provide findings or evidence of how algorithmic regulation has improved regulatory governance and practice, but how it may help to do so. Yeung presents a taxonomy of eight different forms of algorithmic systems “based on how these systems are configured in relation to each of the three components of a cybernetic system: that is, at the level of standard‐setting (whether behavioral standards are “simple”/fixed or “complex”/adaptive), information gathering and monitoring (reactive or pre‐emptive), and behavior modification (automated or recommender systems).”[5]

The article is a valuable read for those interested in the use of big data and information technology in regulatory governance. Yeung maps a range of debates in this area and discusses concerns about the legitimacy of algorithmic regulation, concerns about its accountability, redistributive powers, ideologies, authority, and the capacity algorithmic regulation to affect the lives of individual citizens. An early version of it is available in open access format. Strictly speaking, the article does not provide evidence on how systems thinking has helped regulatory practice in a real-world setting (or failed to do so).

Findings on regulation as systems of stocks and flows, nonlinearity, dynamics and feedback

The final article traced in the review assesses the use of systems dynamic computer modelling in analysing (potential) outcomes of regulation. Systems dynamic computer modelling builds on central ideas from systems thinking such as stocks and flows, nonlinearity, dynamics, and feedback. This article discusses the impact of an HIV testing law and regulation in New York. While this was the only systems modelling article I could find in the 15 top journals for regulatory scholarship, more specialised modelling journals use regulatory governance and practice as examples also.[6]

A few findings are worth mentioning here. In systems dynamic computer modelling for regulatory governance, it is essential that “modelers work extensively with key stakeholders and experts to develop the system structure and incorporate data”.[7] Such collaboration in the development of the model, the collection and incorporation of data, and the testing of various models increase the buy-in of decisionmakers and frontline workers in the modelling and later implementation.

Another relevant insight from the article is that testing various implementation scenario may help to prepare for different ‘what-if’ outcomes. For example, this study found that under different scenarios, the initial surge of newly diagnosed HIV cases would differ, in the long run, all scenarios did result in roughly similar patterns. Such knowledge eases planning and budgeting ahead and helps to understand when and how much trained staff is required in different stages of implementation.

Finally, by comparing the different scenarios, it became clear that specific indicators were better predictors for and representations of the performance of the HIV testing law and regulation than others. This insight is relevant because the model helps decisionmakers and frontline workers understand what condition(s) to monitor closely. Keep in mind, because of nonlinearity and dynamics, a small change in a condition may have a big impact over time. It is thus better to know what conditions may trigger such non-linear behaviour.

Take-home lessons for regulatory policymakers and practitioners

The findings summarised here are insightful, but they fall short in addressing pressing questions of what forms of systems thinking help improving regulatory governance and practice, where and why. Scholars are highly interested in systems thinking and systems science as an approach to studying regulatory delivery, but to date, little empirical research is available. Despite all the calls for ‘thinking in systems’ by regulatory agencies and those assessing regulatory agencies, we have little evidence that thinking in systems will improve regulatory performance.

Carrying out a systems analysis or a systemic study of regulation asks for substantial time and resource investments. Deciding on what approach to follow to systems thinking will be equally time and resource-intensive. However, without taking these steps seriously, there seems to be little point in applying systems thinking to a regulatory problem. Superficial use of systems thinking language then too easily becomes a cover-up of the inability to deal with complexities faced by regulators, their targets, and their superiors.

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[1] The journals are: Regulation and Governance, the Journal of Public Administration, Research and Theory (JPART), Law and Policy, Public Administration, Governance, the Journal of Policy Analysis and Management, Public Administration Review, Public Management Review, the Policy Studies Journal, Policy Sciences, Politics and Society, Administrative Science Quarterly, Social and Legal Studies, the Journal of Law and Society, and the International Journal of Law in Context.

[2] This limited engagement with the systems literature in regulatory scholarship that I observed made me question my method. Did I perhaps miss a large number of articles due to my selection process? As a reliability test, I have also carried out a random check of the original 636 articles traced. I randomly read one in four of them (giving me a confidence level of 95% with a margin of error of 7%). Of a total of 156 articles randomly read, I only traced three that engage with the systems thinking literature and have a central focus on a regulatory topic. This confirms my finding that scholars of regulation engage very little with the systems thinking literature.

[3] Likewise, others have concluded that systems thinking and systems science has seen very little application in academic studies of public policy and administration. See for example: Dekkers, R. (2015). Applied systems theory. Cham: Springer; Eppel, E., & Rhodes, M. L. (2018). Complexity theory and public management: A ‘becoming’ field. Public Management Review, 20(7), 949-959; Klijn, E.-H. (2008). Complexity theory and public administration: What’s new? Public Management Review, 10(3), 299-317.

[4] Born, A., & Goldschmidt, L. (1997). Legal regulation and communicative couplings. Law & Policy, 19(1), 23-49.

[5] Yeung, K. (2018). Algorithmic regulation: A critical interrogation. Regulation & Governance, 12(4), 505-523.

[6] For example: Arango, S. (2007). Simulation of alternative regulations in the Colombian electricity market. Socio-Economic Planning Sciences, 41(4), 305-319; and Carden, T., Goode, N., Read, G., & Salmon, P. (2019). Sociotechnical systems as a framework for regulatory system design and evaluation: Using work domain analysis to examine a new regulatory system. Applied Ergonomics, 80(October), 272-280.

[7] Martin, E. G., Macdonald, R. H., Smith, L. C., Gordon, D. E., Tesoriero, J. M., Laufer, F. N., . . . O’ Connell, D. A. (2015). Policy modelling to support administrative decisionmaking on the new york state hiv testing law. Journal of Policy Analysis and Management, 34(2), 403-423.