An assignment index for Professor Frazier's DATA 100 class
“Revolutions in science have often been preceded by revolutions in measurement.” - Sinan Aral (cited in Kitchen, 2014) “And his (Nicholas Georgescu-Roegen) insight, which is, really, at the heart of all this stuff, is that economic systems are not like evolutionary systems, they are evolutionary systems.” - Owen Barder (Kapuscinski lecture titled Development and Complexity, 2012) “Measurement has played a central role in the development of our understanding of the entire universe around us. Data provide the basis for constructing, testing, and refining our theories and models whether they seek to explain the origins of the universe, the nature of evolutionary processes, or the growth of the economy.” - Geoff West in Scale, 2018
Using these ideas from Aral, Georgescu-Roegen, and Geoff West as well as other sources from this semester (including your own personal insights and reflections), how is the advent of data science serving to advance our understanding of complex adaptive, economic and social systems?
Over the past ten years, data science has been alternatively described as a tool for shaping the future, as a lens through which to observe and correct the mistakes of the past, and as a type of scaffold to help objectively frame and quantitatively understand the present. With so many different interpretations of such a deceptively simple term, it may seem frightening to fully embrace a field whose leaders cannot even agree on a solid definition as the solution to humanity’s problems. However, it is this very aspect of data science and its staggered advent in countries across the world that makes it so intriguing in the context of human systems. Data science is a multi-faceted discipline that can intersect with everything from medical research to the applied humanities; as a tool for both measurement and analysis as well as prediction, this malleability has helped advance society’s understanding of complex and adaptive systems across realms of work and study. More importantly, it has helped advance society’s understanding of the factors that contribute to the harmonious interaction between economic and social systems by acting as a descriptor, a method of measurement, and a model of human development. As evidenced by the writings of Owen Barder, Rob Kitchin, and Geoff West, the use of data science as an extension of distinctively human and empathetic thinking has the potential to radically change the world for the better, but only if we keep in mind the people who live in it.
In Rob Kitchin’s article, “Big Data, New Epistemologies and Paradigm Shifts,” he references MIT professor Sinan Aral’s idea of new forms of measurement as a preemptive indication of a new paradigm. The same concept holds true for data science, which has routinely modified existing technologies to serve as benchmarks, counters, and signals of change on a broad scale. Take, for example, the difference between a cell phone from the early 2000s and a “smart” phone manufactured in 2021. The data collected from the modes of communication we use on a daily basis today is incredible in its breadth and variety, and can reveal much about a population of interest depending on their average usage, the number of people who actually own a device, and the applications utilized most by a random person in the corpus. Even without considering the usefulness of the massive amounts of data available from this type of technology, the sheer commonality of phones across the world have created an entirely new system of measurement that, from a contemporary viewpoint, seems irreplaceable as a reference point for quantitative analysis. According to an article from Nature by Joshua Blumenstock, “95 percent of the global population has mobile-phone coverage,” a statistic comprised of individuals that range wildly in identity across racial, ethnic, socioeconomic, and gender. The ability of big data to take advantage of these new technologies and turn them into tools of measurement should be viewed as a positive harbinger of hope for the future, signaling the arrival of a new paradigm of science that can measure and predict human behaviors in never-before-seen ways. Populations who have previously fallen through the cracks created by census measures, community surveys, and sweeping government policies may finally find themselves identified by the reach of data science’s intrinsically pervasive tools of measurement.
This type of evolution of technology – a kind that is forced to suit or measure the needs of a rapidly-changing society – is not a new concept. Owen Barder explains this type of technological evolution as the inevitable result of the demands of a complex adaptive system, with people, institutions, inspiration, and the free market acting as interdependent catalysts for change. The speed of progress has become faster and faster, following the superlinearly scaled rate of required growth and production for companies, institutions, and products outlined by Geoff West in his book Scale. It is safe to anticipate that the adaptation of existing technologies will continue in the future, as it is undeniably easier to alter the purpose of a tool than it is to create an entirely new one. In this sense, there is hope for data science to continually expand its sources of measure, utilizing more and more types of broadly-available technology to generate a more detailed, “bottom-up” (as opposed to “top-down”) picture of society. This type of description is especially important in low- and middle-income countries, where blind applications of pre-formatted “solutions” for development routinely fail due to the lack of consideration attributed to unique, but overlooked, factors of economic decline, social unrest, political instability, or crumbling infrastructure. In order to avoid this kind of developmental failure, global leaders and society as a whole should take Barder’s advice and resist engineering as a policy implication, instead allowing communities of all sizes – from neighborhoods to countries – to adapt organically in the face of emerging phenomena.
Data science holds almost infinite potential as a tool for furthering the development of humanity as a whole. With so many applications available to such a vast variety of industries, it seems there are no barriers to what regulated big data and its associated analyses can achieve. From predicting population caps and food demands to isolating incidents of political violence, data-based algorithms that describe, predict, and motivate progress have only become more successful at what they do in recent years, and will only continue to learn from human behavior in the future. However, it is important to keep in mind the focus of data science in all of its forms. Regardless of the specific field of application that a particular project lies in, its leaders must make an active effort to consider the impacts of any results on real people. This conclusion is shared by Kitchin, Barder, and West alike, and even Chris Andersen, who parts form most of his peers and argues in “The End of Theory” for the titular disruption of man-borne theorizing and an increase in data from data-made algorithms, acknowledges that data science should prioritize humans, not data, above all else. Danger definitely can arise from the increased application of data science to more and more aspects of everyday life, especially when those who hold the key to analysis and data collection are among the world’s richest few elites. Making data and the process of its collection available on a broad scale will make data science a more equitable endeavor. Still, data science should ultimately be viewed as a source of hope for the future, acting as a great equalizer in terms of information accessibility, development design, and the proliferation of freedoms through knowledge.