More generally however social science theory building

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More generally, however, social science theory building is likely to get a big boost from big data and machine learning. Never before have we been able to observe human behavior at a degree of granularity we are seeing now with increasing amounts of human interaction and economic activity being mediated by the Internet. While there are clearly limitations to the inductive method, the sheer volume of data being generated not only makes it feasible, but practically speaking, little us with little as an alternative. We do not mean to imply that the traditional scientific method is “dead” as claimed by Anderson. To the contrary, it continues to serve us well. However, we now have a new and powerful method at our disposal for theory development that was not previously practical due to the paucity of data. That era is largely over. 4. Concluding Remarks There is no free lunch. While large amounts of observational data provide us with unprecedented opportunity to develop predictive models, they are limited when it comes to explanation 16 . Since it is impossible to run controlled experiments, except by design, we cannot know the consequences of things that did not transpire. A limitation of this is that we are limited in our ability to impact the future through intervention that is possible when the causal mechanisms are well understood. The second limitation of predictive modeling with causation is that multiple models that appear different on the surface might represent the same underlying causal structure, but there is no way to know this. For example, in the diabetes example, there could be multiple uncorrelated robust patterns
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that predict complications. The good thing, however, is that If they are predictive, they are still useful in that they could suggest multiple observable conditions that lead to complications that should therefore be carefully monitored. Despite the limitations of observational data, however, the sheer size of the data allows the machine to slice and dice the data in many ways without losing sample size, a limitation that has traditionally hindered our ability to examine conditional relationships in data even if they were real. The machine appears poised to establish its place in the creative aspects to inquiry, not just in analysis driven by human-generated hypotheses which has been its primary historical role. The ability to interpret unstructured data and integrate it with numbers further increases our ability to extract useful knowledge in real- time and act on it. Incredibly, HAL isn’t just a fantasy now but an imminent reality. 1. Anderson, Chris. “The End of Theory: The Data Deluge Makes the Scientific Method Obsolete.” Wired Magazine , June 23, 2008. 2. Aral, Sinan and Dylan Walker. “Identifying Influential and Susceptible Members of Social Networks.” Science , June 21, 2012.
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