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Head: Big Data $7,000,000 $6,000,000 $5,000,000 $4,000,000 $3,000,000 $2,000,000 $1,000,000 $0 1 586 1171 1756 2341 2926 3511 4096 4681 5266 5851 6436 7021 7606 8191 8776 Power Law Number of Grants Dollar Amount Total Grants (NSF07) 80% 7,478 $938,548,595 9,347 (Count) 20% 1,859 $1,199,088,125 $2,137,636,716 (Amount) given their other investments and information technology usage. Big Data advancements have included better targeted Web ads by the likes of Google and Facebook, as well as better recommendation systems by Netflix and Amazon. The use of data on the part of these and similarly savvy data-driven companies has seemingly had a positive, unilateral impact on operations, offering supporting evidence for a data-driven approach. The social turn in Big Data, however, promises the marked improvement of policy and governance decisions affecting the lives of everyone. Big Social Data makes great effort to disspell a number of enduring malpositions of Big Data. The contributions on the part of several socially conscious data scientists are Long Tail: Intelligence Reporting, Science Data, Dark Data ultimately highlighting the differences between the demands on data and analysis on the part of private industry and the demands on data and analysis on the part of security, governance, and policy – foreign or domestic. Lieutenant General Michael Flynn (Director of the Defense Intelligence Agency, 2012) recently noted that focuses on the precursors of war are needed – what he calls “left of boom.” When speaking of national security, he notes that one thing needed in greater frequency is the engagement of populations before starting a counterinsurgency campaign. He notes that there have been 32 major insurgencies since 1960, and in 22 of these cases the insurgent forces were the victors. Irregular warfare is the template for future battlefields; U.S. interests are best served 1 2 by preventive measures that ensure these wars are never fought. Ethnic tension is frequently a part of the developing world, a world often in the greatest need of aid or humanitarian assistance, and the frontlines of current or perhaps future engagements. These areas have also seen some significant, if not dramatic, increases in data. Flynn cautiously accounts for potential social ills facing the industrialized world as well. Big Social Data is a potential that is largely untapped and will allow decision makers to track progress, better understand and improve social conditions of local populations, and understand where existing policies require adjustment. Big Data, Small Theory The typical state-centric analysis that seeks to determine how states can or do maintain stability must also develop a sensory capability to better detect the precursors to political change, a social radar of sorts with a level of granularity that enables policy leaders to make informed decisions that maximize national influence left of boom. (Flynn, 2012) The Failed States Index (Foreign Policy, 2012) claims that 108 of the 178 countries evaluated are in high warning of failing. Assuming the remaining unmeasured countries (18) are “stable,” then Foreign Policy estimates 55% of the world’s countries to be in serious danger of failing. Many of these countries are important pivots in foreign policy, currently receiving aid and/or simply in close proximity to the United States, (see Figure 3) either geographically or economically. The Failed States Index provides annual snapshots of a state at a national level. It turns out that states fail for a variety of reasons, shaped in innumerable ways, many of which are not violence. Violence, as it is, may result from the failure of the state, what Thomas Hobbes called the “state of nature.” Flynn’s idea of left of boom is an acknowledgement of the “state of nature” and a plea for better data analysis. State failures are not 36 IMAGING NOTES // WINTER 2013 // WWW.IMAGINGNOTES.COM
without disharmonies of local communities or without heterogeneous castings of ethnicities, economies, and other human characteristics of the landscape. Geographers already know stationarity is a tenuous claim due to heteroscedasticity in the response and spatial heterogeneity in the control. Analytical pursuits seeking nomothetic laws quickly sacrifice internal validity. At times, the reaction on the part of human geographers is to treat every observation as a special case. This is extreme heterogeneity with multiple equilibrium – one process for every observation over space. Analyses of this nature prove to be overly descriptive and quite idiographic, ultimately lacking any external validity, which is the hope of making generalizations to other places or times of interest. By clutching complexity, the computational social sciences can blend these two contrasting outlooks. Peter Norvig, Director of Research at Google Inc., (2009) writes in “The Unreasonable Effectiveness of Data” about seizing complexity. He draws direct comparison to Eugene Wigner’s article, “The Unreasonable Effectiveness of Mathematics in the Natural Sciences” (1960) and acknowledges that sciences that involve humans rather than elementary particles have proven resistant to elegant mathematics. Norvig continues, “(We) should stop acting as if our goal is to author extremely elegant theories, and instead embrace complexity and make use of the best ally we have: the unreasonable effectiveness of data.” Italian scientist Galileo’s experiment of dropping two balls of different masses from the Leaning Tower of Pisa demonstrated that their time of decent was independent of their mass. Wigner explains how this result is true everywhere, was always true, and will always be true. It is valid no matter whether it rains or not, whether the experiment is carried out in the Far East, Near East, or Northeast D.C., no matter whether the person is a man or a woman, rich or poor, Muslim or Catholic. This invariance property of physics is well recognized, and without invariance principles, physics would not be possible. Social phenomena are not invariant however. As Gelfand and others (Velupillai, Poli, Norvig) noted, there is only one thing more unreasonable than the unreasonable effectiveness of mathematics in physics, and that is the unreasonable ineffectiveness of mathematics in other sciences, including the humanities. While serial and spatial autocorrelation exist, so do temporal and spatial heterogeneity and, ultimately, uncontrolled variance. Exploiting the complexity of data in the head of the distribution holds certain promise, rather than creating models to decisively model all of human behavior. Big Social Data’s goal is to learn about social systems at a speed commensurate with decision making and at a spatial support commensurate with policy development and assessment. Small theory is one framework to operate within. These small theories are not small in significance but locally calibrated to the populations they measure. They are based on empirical observation and are expected to be generally true and sufficient to be useful norms. The social turn in big data mimics similar social turns in the Department of Defense and the U.S. Intelligence Community. One ought to wonder what U.S. Government engagements would look like if Big Social Data could improve decision making or intelligence analysis by a mere five to six percent, as industry has done for output and production. Big Social Data has untapped potential, as Anyon (1982) envisioned, for social sciences to study socially critical problems. WW FIGURE 1. The Long Tail of data WW FIGURE 2. Power Law Distribution example, similar to The Long Tail SS FIGURE 3. Failed States Index, Foreign Policy magazine, 2012 CITATIONS > > Anderson, C. (2004). Wired 12.10: The Long Tail. wired.com. Retrieved December 30, 2012, from http://www.wired.com/wired/ archive/12.10/tail.html > > Anyon, Jean. (1982). “Adequate Social Science, Curriculum Investigation, and Theory.” Theory Into Practice. Vol. 21, Number 1, 1982: 34-38. > > Brynjolfsson, E., Hitt, L. M., & Kim, H. H. (2011). Strength in Numbers: How Does Data-Driven Decision Making Affect Firm Performance SSRN Electronic Journal. doi:10.2139/ ssrn.1819486 > > Chesbrough, H. W. (2006). Open Innovation: The New Imperative for Creating And Profiting from Technology. Harvard Business School Press. > > Failed States Index. (n.d.). Retrieved from http://www.foreignpolicy.com/ failed_states_index_2012_interactive > > Halevy, A., Norvig, P., & Pereira, F. (2009). The Unreasonable Effectiveness of Data. Intelligent Systems, IEEE, 24(2), 8-12. > > NSI, A., OSD, M. Flynn., H., Casebeer, B., Chapman, M., & Rice, C. et al (2012). National Security Challenges: Insights from Social, Neurobiological, and Complexity Sciences. DTIC Document, (July). > > Long Tail. (n.d) In Wikipedia. Retrieved December 30, 2012, from http://en.wikipedia.org/wiki/Long_tail IMAGING NOTES // WINTER 2013 // WWW.IMAGINGNOTES.COM 37
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