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The Complexity of Social Problems and Big Data. Part I.

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

By Mauricio Covarrubias
December 9, 2022

As part of the process of conceptualizing the complex nature of social problems, we can look to Göktuğ Morçöl who offers the following questions: Is it in the nature of things? Or is it a function of the way humans know about things in their worlds? In other words, is complexity ontological or epistemological or both? So, is an issue complex because it is perhaps beyond the comprehension of our cognitive abilities? Or, because there is a mismatch between the way our minds work, and the way things are?

In accordance with the above, we can distinguish from the outset that the complex nature of social problems is due to two things. The first consider the process of social construction which determines what issues are defined as “social problems”. This suggests that in order for a condition or behavior to be considered a social problem, it must first be perceived as something that needs to be addressed. The second refers to the “material causes” of the problem; such that it involves uncertain relationships between variables at the causal level. That is, circumstances in which a small change in one variable can produce much larger changes in other variables or conditions such as in the classic case of the chaos theory—the flapping of a butterfly’s wings potentially causing a hurricane.

Social construction of problems

Social problems involve multiple actors, interests, conflicts and veto points. As we have seen, the pandemic has given rise to conflicting and value-laden opinions regarding the definition and solution of a problem. COVID-19 has caused the simultaneous existence of multiple urgent and interdependent social objectives, creating the fundamental problem of prioritizing one aspect over another. Such goals can be identified in short-term reduction of COVID-19 morbidity and mortality, mitigation of long-term social impacts of containment policies and adverse financial consequences. Angeli, Camporesi and Dal Fabbro indicate that prioritization options generate conflicting views from stakeholders about what the problem is (for example, the number of catastrophic deaths versus possible economic collapse) and related solutions (for example, lockdown measures versus softer virus control mechanisms).

Material causes of problems

The complexity of social problems is part of their nature. The theory does not offer a univocal definition, but there is consensus that the complexity of phenomena and systems, social or natural, is the product of uncertain non-linear relationships as an emergent property.  For example, global warming is complex, and not only because there are many interpretations of it, which are related to perceptions, interests and the dominant value system in societies. As Morçöl mentions, it is also complex because the natural processes that warming generates (atmospheric conditions, interactions between the levels of greenhouse gases in the atmosphere with temperatures, etc.) are complex as well.

However, although the definition of a problem has a lot to do with the process of its social construction, this does not mean that the need to advance a more objective definition of it 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 for the carrying out a more reasoned discussion with the political actors who demand an intervention and, thus, legitimize more action of the State.

The promise of Big Data

Scott E. Page warns against policymakers’ tendency toward reductionist simplification due, in large part, to schematic views that dismiss complex interdependencies and limited measurement tools. This change is occurring as a result of advances in computational power and mathematical applications together with a rapid growth in the availability of data. We now have the computational heft to handle large, complex, high-dimensional data.

In this sense, two questions should be considered:

  1. a) Can the use of Big Data benefit the definition of the problem, which consists, among other things, in determining the nature, causes, scope, temporality, dynamics or evolution, of those directly and indirectly affected, as well as their present and future consequences?
  2. b) Does Big Data represent an opportunity to advance the formulation of more comprehensive policies which can be used to address problems where incremental responses are not only ineffective, but also counterproductive?

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 revealed 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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