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Towards a More Comprehensive Public Innovation With Data Science. Part II

The views expressed are those of the author and do not necessarily reflect the views of ASPA as an organization.

By Mauricio Covarrubias
September 9, 2022

The gap between the complex problems facing institutions and their ability to address them has led to a growing interest in systems thinking and other systems approaches. Changing the dynamics of an entrenched and complex system requires not only a new way of looking at problems, but also decision-making that fundamentally challenges public sector institutions. Traditionally, policymakers have addressed social problems through discrete interventions layered on top of one the other, building on a “cause and effect” relationship. However, these interventions may shift consequences from one part of the system to another, or simply address symptoms while ignoring causes.

Governments are at a crossroads: much of their success in dealing with complex public challenges will depend on how public policies and systems are formed. For this reason, it advocates more holistic policy approaches that consider the whole system rather than the separate parts; that value results over processes; and encompassing a variety of voices and inputs rather than self-interest: a systems approach has the potential to fundamentally transform the policy-making process, allowing policy makers to focus on areas where change can have the greatest impact.

In its report entitled “Systems Approaches to Public Sector Challenges: Working with Change”, OECD (2017) makes a strong call to explore the theory and practice behind the use of systems approaches to address public challenges. It postulates the need for systemic thinking in the public sector, its theoretical foundations and its (still infrequent) use. It is an open invitation to policymakers to reflect on the systemic nature of most public challenges and consider how systems approaches based on integrated interventions and stakeholder engagement can help achieve better outcomes for all.

Although the complexity of social problems is related to the process of social construction, without a doubt, it also resides in the very nature of the problems. Complexity theorists do not offer a univocal definition, but there is consensus that the complexity of phenomena or systems, whether social or natural, is the product of non-linear relationships as an emergent property.

For example, as Göktuğ Morçöl points out, global warming is complex, not only because there are many interpretations of it, which are related to individual and group perceptions of their own interests and the dominant value system in societies.  It is also complex, because the natural processes that generate global warming (atmospheric conditions, interactions between the levels of greenhouse gases in the atmosphere with temperatures, etc.) are complex. Therefore, a good understanding of the natural complexities of the problems should be part of the understanding of the complexities of the policies.

Therefore, although the definition of a problem is a political problem that has a lot to do with the process of social construction, it does not mean that the need to advance in a more objective definition of them should be neglected. According to André-Noël Roth Deubel, this task should be seriously assumed by the public administration, universities and research centers, with the aim of providing information that allows a better understanding of the problem before a decision is made. The ability to know the dimensions of a problem would allow carrying out a more reasoned discussion with the political actors who demand an intervention and, thus, legitimize more action of the State.

In this sense, the use of Big Data can benefit the definition of the problem, which consists, among other things, in determining the nature, causes, scope, temporality, dynamics or evolution, those directly and indirectly affected, as well as its present and future consequences. Big Data represents an opportunity to advance more comprehensive policymaking to address issues where incremental responses are not only ineffective, but also counterproductive.

In his book “The Model Thinker: What You Need to Know to Make Data Work for You“, Scott E. Page expressed his concern about the tendency of those who govern towards reductive simplification due, in large part, to schematic views that disdain complex interdependencies and limited measurement tools. Entrepreneurial bureaucrats today can use Big Data and human insight to understand a problem as ordinary people experience it, and to design collaboratively more-effective solutions tailored to achieving the public’s desired outcome (See The Innovative State by Beth Noveck).

As mentioned before, the main problems of our time cannot be understood in isolation; they must be approached systemically, that is, in terms of relationships, patterns and context. Kenneth Neil and Mayer Viktor point out that with Big Data, researchers can collect and analyze massive amounts of information about certain events and everything that is associated with them, looking for patterns that can help predict future occurrences. A worldview built on the importance of causality is being challenged by a preponderance of correlations. The possession of knowledge, which once meant an understanding of the past, is coming to mean the ability to predict the future. The challenges posed by Big Data will not be easy to solve. Rather, they are simply the next step in the eternal debate about how to better understand the world.

Author: Mauricio Covarrubias is Professor at the National Institute of Public Administration in Mexico.  He is co-founder of the International Academy of Political-Administrative Sciences (IAPAS).  He is the founder and Editor of the International Journal of Studies on Educational Systems (RIESED). Coordinator in Mexico of the TOGIVE Project: Transatlantic Open Government Virtual Education, of the ERASMUS + Program of the European Union. Member of the National System of Researchers of CONACYT.  He received his Ph.D. from the National Autonomous University of Mexico. He can be reached at [email protected] and followed on Twitter @OMCovarrubias

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