Big Data Influencing Microeconomics and Human Behaviors

Trang Le '17

Staff Writer

Like every other innovation in the history of human evolution, the rise of technology is a double-edged sword: it assists us in our never-ending strive to boost our productivity and creativity, but at the same time “dehumanizes” human behaviors into a quantified set of numbers prone to mathematical interpretations.

With the availability of data at an incredibly low cost, the world’s technological storage capacity per capita has doubled every 40 months since 1980. In 2012, 2.5 exabytes of data were created every day. Data can be easily gathered by sensing mobile devices, aerial, software logs, cameras, microphones and wireless sensor networks. This vast resource accompanied, by increasingly sophisticated analytical tools, has given an edge to the world of microeconomics, which focuses on the study of human behaviors through replicates of observations. Macroeconomics, which fuses classroom discussions with policies of the Federal Reserve and the European Central Bank that could shape the world’s economies, starts to fall behind with a decade of failed predictions. Microeconomics, however, promises exciting possibilities for businesses to grow, for individuals to make decisions and for the tech geeks to thrive.

In their book “Big Data: A Revolution That Will Transform How We Live, Work and Think,” Viktor Mayer-Schonberger and Kenneth Cukier examine the case of Amazon, which utilized data on what customers purchased, what books they looked at but did not buy and how long they looked at them. The first few purchase recommendations made based on these data were underdeveloped, as customers’ homepages were filled with similar products that did not spur new demand. Greg Linden, who took time off from his PhD research in artificial intelligence to join the growing book-selling startup, came up with an idea to find links between products, called “item-to-item” collaborative filtering. The sales by machine-generated recommendations outrun those recommended by human editors. Machines do not know why someone who bought Ernest Hemingway would also like F. Scott Fitzgerald, but all that matters is the tremendous profit that awaits firms and businesses if they know how to capture data and use them in an effective way.

This rising business intelligence, together with our increasing dependence on automaton, has brought us wealth and convenience, but it’s not without worries. Gerard Baker, in his 10-point guide to the day’s top news, provides us with a simultaneously concerning and soothing remark: “Computers do legal research, write stock reports and news stories, as well as translate conversations – all jobs once considered uniquely human. But there is no need for us to have an existential crisis just yet.” With the vandalism detection algorithm, cluebot-NG, one of the hundreds of “bots,” or autonomous computer programs, is assisting volunteer Wikipedia writers with editing articles, a job once done by human editors. On a similar note concerning the replacement of human efforts, it is widely observed that various jobs in publishing, the industry heavily reshaped by the rise of computers, are increasingly made redundant.

The use of big data in business analytics also has the tendency to ignore causality and focus on correlation. In the example above, the reason for a product sales to increase is not as much important as the fact that there is a sign of increase itself. This side of data, with its vast applications elsewhere, can bring about implications that charge us for our behaviors. With the technical side of data analytics comes the study of behavioral economics that optimizes the interpretation of data. In a section called “The Dark Side of Big Data,” Mayer-Schonberger and Cukier examined the loss of privacy, the denial of free will, the erosion of human dignity and the risk of extrapolation, which is to wrongly apply analysis techniques that do not fit and result in false predictions.

With the good and the bad collaged in our everyday observation of technological advancements, it cannot be denied that they are playing a more important role in our lives. Regarding our inability to resist the strong current of big data, the best way is to acknowledge its good purposes (such as reducing accidents by analyzing drivers’ behaviors or cars’ features) while relying on our philosophical stances to point out the dangers of excessive dependence on our available mathematical tools and number-crunching capacity.