{"id":7277,"date":"2016-09-03T12:25:18","date_gmt":"2016-09-03T18:25:18","guid":{"rendered":"https:\/\/www.roughtype.com\/?p=7277"},"modified":"2016-09-03T15:18:15","modified_gmt":"2016-09-03T21:18:15","slug":"big-data-and-the-limits-of-social-engineering","status":"publish","type":"post","link":"https:\/\/www.roughtype.com\/?p=7277","title":{"rendered":"Big data and the limits of social engineering"},"content":{"rendered":"

\"simcity\"<\/a><\/p>\n

The following review of Alex Pentland’s book\u00a0<\/em>Social Physics appeared<\/a> originally, in a slightly different form, in <\/em>MIT Technology Review.<\/em><\/p>\n

In 1969, Playboy<\/em> published a long, freewheeling interview with Marshall McLuhan in which the media theorist and sixties icon sketched a portrait of the future that was at once seductive and repellent. Noting the ability of digital computers to analyze data and communicate messages, McLuhan\u00a0predicted that the machines eventually would be deployed to fine-tune society\u2019s workings. \u201cThe computer can be used to direct a network of global thermostats to pattern life in ways that will optimize human awareness,\u201d he said. \u201cAlready, it\u2019s technologically feasible to employ the computer to program societies in beneficial ways.\u201d He acknowledged that such centralized control raised the specter of \u201cbrainwashing, or far worse,\u201d but he stressed that \u201cthe programming of societies could actually be conducted quite constructively and humanistically.\u201d<\/p>\n

The interview appeared when computers were used mainly for arcane scientific and industrial number-crunching. To most readers at the time, McLuhan\u2019s words must have sounded far-fetched, if not nutty. Now, they seem prophetic. With smartphones ubiquitous, Facebook inescapable, and wearable computers proliferating, society is gaining a digital sensing system. People\u2019s location and behavior are being tracked as they go through their days, and the resulting information is being transmitted instantaneously to vast server farms. Once we write the algorithms needed to parse all that \u201cbig data,\u201d many sociologists and statisticians believe, we\u2019ll be rewarded with a much deeper understanding of what makes society tick.<\/p>\n

One of big data\u2019s keenest advocates is Alex \u201cSandy\u201d Pentland, a data scientist who, as the director of MIT\u2019s Human Dynamics Laboratory, has long used computers to probe the dynamics of businesses and other organizations. In his brief but ambitious book, Social Physics,<\/em> Pentland argues that our greatly expanded ability to gather behavioral data will allow scientists to develop \u201ca causal theory of social structure\u201d and ultimately establish \u201ca mathematical explanation for why society reacts as it does\u201d in all manner of circumstances. As the book\u2019s title makes clear, Pentland thinks that the social world, no less than the material world, operates according to rules. There are \u201cstatistical regularities within human movement and communication,\u201d he writes, and once we fully understand those regularities, we\u2019ll discover \u201cthe basic mechanisms of social interactions.\u201d<\/p>\n

What\u00a0has\u00a0prevented us from deciphering society\u2019s mathematical underpinnings up to now, Pentland believes, is a lack of empirical rigor in the social sciences. Unlike physicists, who can measure the movements of objects with great precision, sociologists have had to make do with fuzzy observations. They\u2019ve had to work with rough and incomplete data sets drawn from small samples of the population, and they\u2019ve had to rely on people\u2019s notoriously flawed recollections of what they did, when they did it, and whom they did it with. Computer networks promise to remedy those shortcomings. Tapping into the streams of data that flow through gadgets, search engines, social media, and credit-card payment systems, scientists will be able to collect precise, real-time information on the behavior of millions, if not billions, of individuals. And because computers neither forget nor fib, the information will be reliable.<\/p>\n

To illustrate what lies in store, Pentland describes a series of experiments that he and his associates have been conducting in the private sector. They go into a business and give each employee an electronic ID card, called a \u201csociometric badge,\u201d that hangs from the neck and communicates with the badges worn by colleagues. Incorporating microphones, location sensors, and accelerometers, the badges monitor where people go and whom they talk with, taking note of their tone of voice and even their body language. The devices are able to measure not only the chains of communication and influence within an organization but also \u201cpersonal energy levels\u201d and traits such as \u201cextraversion and empathy.\u201d In one such study of a bank\u2019s call center, the researchers discovered that productivity could be increased simply by tweaking the coffee-break schedule.<\/p>\n

Pentland dubs this data-processing technique \u201creality mining,\u201d and he suggests that similar kinds of information can be collected on a much broader scale by smartphones outfitted with specialized sensors and apps. Fed into statistical modeling programs, the data could reveal \u201chow things such as ideas, decisions, mood, or the seasonal flu are spread in the community.\u201d<\/p>\n

The mathematical modeling of society is made possible, according to Pentland, by the innate tractability of human beings. We may think of ourselves as rational actors, in conscious control of our choices, but in reality most of what we do is reflexive. Our behavior is determined by our subliminal reactions to the influence of other people, particularly those in the various peer groups we belong to. \u201cThe power of social physics,\u201d he writes, \u201ccomes from the fact that almost all of our day-to-day actions are habitual, based mostly on what we have learned from observing the behavior of others.\u201d Once you map and measure all of a person\u2019s social influences, you can develop a statistical model that predicts that person\u2019s behavior, just as you can model the path a billiard ball will take after it strikes other balls.<\/p>\n

Deciphering people\u2019s behavior is only the first step. What really excites Pentland is the prospect of using digital media and related tools to change people\u2019s behavior, to motivate groups and individuals to act in more productive and responsible ways. If people react predictably to social influences, then governments and businesses can use computers to develop and deliver carefully tailored incentives, such as messages of praise or small cash payments, to \u201ctune\u201d the flows of influence in a group and thereby modify the habits of its members. Beyond improving the efficiency of transit and health-care systems, Pentland suggests that group-based incentive programs can enhance the harmony and creativity of communities. \u201cOur main insight,\u201d he reports, \u201cis that by targeting [an] individual\u2019s peers, peer pressure can amplify the desired effect of a reward on the target individual.\u201d Computers become, as McLuhan foresaw, civic thermostats. They not only register society\u2019s state but bring it into line with some prescribed ideal. Both the tracking and the maintenance of the social order are automated.<\/p>\n

Ultimately, Pentland argues, looking at people\u2019s interactions through a mathematical lens will free us of time-worn notions about class and class struggle. Political and economic classes, he contends, are \u201coversimplified stereotypes of a fluid and overlapping matrix of peer groups.\u201d Peer groups, unlike classes, are defined by \u201cshared norms\u201d rather than just \u201cstandard features such as income\u201d or \u201ctheir relationship to the means of production.\u201d Armed with exhaustive information about individuals\u2019 habits and associations, civic planners will be able to trace the full flow of influences that shape personal behavior. Abandoning general categories like \u201crich\u201d and \u201cpoor\u201d or \u201chaves\u201d and \u201chave-nots,\u201d we\u2019ll be able to understand people as individuals\u2014even if those individuals are no more than the sums of all the peer pressures and other social influences that affect them.<\/p>\n

Replacing politics with programming might sound appealing, particularly given Washington\u2019s paralysis. But there are good reasons to be nervous about this sort of social engineering. Most obvious are the privacy concerns raised by the collection of ever more intimate personal information. Pentland anticipates such criticism by arguing that public fears about privacy can be ameliorated through a \u201cNew Deal on Data\u201d that gives people more control over the information collected about them. It\u2019s hard, though, to imagine Internet companies agreeing to give up ownership of the behavioral information they hoard. The data are, after all, crucial to their competitive advantage and their profit.<\/p>\n

Even if we assume that the privacy issues can be resolved, the idea of what Pentland calls \u201ca data-driven society\u201d remains problematical. Social physics is a variation on the theory of behavioralism that found favor in McLuhan\u2019s day, and it suffers from the same limitations that doomed its predecessor. Defining social relations as a pattern of stimulus and response makes the math easier, but it ignores the deep, structural sources of social ills. Pentland may be right that people\u2019s behavior is determined in large part by the social norms and influences exerted upon them by their peers, but what he fails to see is that those norms and influences are themselves shaped by history, politics, and economics, not to mention the ever-present forces of power and prejudice. People don\u2019t have complete freedom in choosing their peer groups. Their choices are constrained by where they live, where they come from, how much money they have, and what they look like. A statistical model of society that ignores issues of class, that takes patterns of influence as givens rather than as historical contingencies, will tend to perpetuate existing social structures and dynamics. It will encourage us to optimize the status quo rather than challenge it.<\/p>\n

Politics is messy because society is messy, not the other way around. Pentland does a commendable job in describing how better data can enhance social planning. But like other would-be social engineers he overreaches. Letting his enthusiasm get the better of him, he begins to take the metaphor of \u201csocial physics\u201d literally, even as he acknowledges that influence-based mathematical models will always be reductive. \u201cBecause it does not try to capture internal cognitive processes,\u201d he writes at one point, \u201csocial physics is inherently probabilistic, with an irreducible kernel of uncertainty caused by avoiding the generative nature of conscious human thought.\u201d What big data can\u2019t account for is what\u2019s most unpredictable, and most interesting, about us.<\/p>\n","protected":false},"excerpt":{"rendered":"

The following review of Alex Pentland’s book\u00a0Social Physics appeared originally, in a slightly different form, in MIT Technology Review. In 1969, Playboy published a long, freewheeling interview with Marshall McLuhan in which the media theorist and sixties icon sketched a portrait of the future that was at once seductive and repellent. Noting the ability of […]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"jetpack_post_was_ever_published":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2}},"categories":[1],"tags":[],"class_list":["post-7277","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"jetpack_publicize_connections":[],"jetpack_featured_media_url":"","jetpack_sharing_enabled":true,"_links":{"self":[{"href":"https:\/\/www.roughtype.com\/index.php?rest_route=\/wp\/v2\/posts\/7277","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.roughtype.com\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.roughtype.com\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.roughtype.com\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.roughtype.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=7277"}],"version-history":[{"count":6,"href":"https:\/\/www.roughtype.com\/index.php?rest_route=\/wp\/v2\/posts\/7277\/revisions"}],"predecessor-version":[{"id":7284,"href":"https:\/\/www.roughtype.com\/index.php?rest_route=\/wp\/v2\/posts\/7277\/revisions\/7284"}],"wp:attachment":[{"href":"https:\/\/www.roughtype.com\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=7277"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.roughtype.com\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=7277"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.roughtype.com\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=7277"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}