Tom Slee: “A new wave of technology companies claims to be expanding the possibilities of sharing and collaboration, and is clashing with established industries such as hospitality and transit. These companies make up what is being called the “sharing economy”: they provide web sites and applications through which individual residents or drivers can offer to “share” their apartment or car with a guest, for a price.
The industries they threaten have long been subject to city-level consumer protection and zoning regulations, but sharing economy advocates claim that these rules are rendered obsolete by the Internet. Battle lines are being drawn between the new companies and city governments. Where’s a good leftist to stand in all of this?
To figure this out, we need to look at the nature of the sharing economy. Some would say it fits squarely into an ideology of unregulated free markets, as described recently by David Golumbia here in Jacobin. Others note that the people involved in American technology industries lean liberal. There’s also a clear Euro/American split in the sharing economy: while the Americans are entrepreneurial and commercial in the way they drive the initiative, the Europeans focus more on the civic, the collaborative, and the non-commercial.
The sharing economy invokes values familiar to many on the Left: decentralization, sustainability, community-level connectedness, and opposition to hierarchical and rigid regulatory regimes, seen mostly clearly in the movement’s bible What’s Mine is Yours: The Rise of Collaborative Consumption by Rachel Botsman and Roo Rogers. It’s the language of co-operatives and of civic groups.
There’s a definite green slant to the movement, too: ideas of “sharing rather than owning” make appeals to sustainability, and the language of sharing also appeals to anti-consumerist sentiments popular on the Left: property and consumption do not make us happy, and we should put aside the pursuit of possessions in favour of connections and experiences. All of which leads us to ideas of community: the sharing economy invokes images of neighbourhoods, villages, and “human-scale” interactions. Instead of buying from a mega-store, we get to share with neighbours.
These ideals have been around for centuries, but the Internet has given them a new slant. An influential line of thought emphasizes that the web lowers the “transaction costs” of group formation and collaboration. The key text is Yochai Benkler’s 2006 book The Wealth of Networks, which argues that the Internet brings with it an alternative style of economic production: networked rather than managed, self-organized rather than ordered. It’s a language associated strongly with both the Left (who see it as an alternative to monopoly capital), and the free-market libertarian right (who see it as an alternative to the state).
Clay Shirky’s 2008 book Here Comes Everybody popularized the ideas further, and in 2012 Steven Johnson announced the appearance of the “Peer Progressive” in his book Future Perfect. The idea of internet-enabled collaboration in the “real” world is a next step from online collaboration in the form of open source software, open government data, and Wikipedia, and the sharing economy is its manifestation.
As with all things technological, there’s an additional angle: the involvement of capital…”
Predictive Modeling With Big Data: Is Bigger Really Better?
New Paper by Junqué de Fortuny, Enric, Martens, David, and Provost, Foster in Big Data :“With the increasingly widespread collection and processing of “big data,” there is natural interest in using these data assets to improve decision making. One of the best understood ways to use data to improve decision making is via predictive analytics. An important, open question is: to what extent do larger data actually lead to better predictive models? In this article we empirically demonstrate that when predictive models are built from sparse, fine-grained data—such as data on low-level human behavior—we continue to see marginal increases in predictive performance even to very large scale. The empirical results are based on data drawn from nine different predictive modeling applications, from book reviews to banking transactions. This study provides a clear illustration that larger data indeed can be more valuable assets for predictive analytics. This implies that institutions with larger data assets—plus the skill to take advantage of them—potentially can obtain substantial competitive advantage over institutions without such access or skill. Moreover, the results suggest that it is worthwhile for companies with access to such fine-grained data, in the context of a key predictive task, to gather both more data instances and more possible data features. As an additional contribution, we introduce an implementation of the multivariate Bernoulli Naïve Bayes algorithm that can scale to massive, sparse data.”
How Government Can Make Open Data Work
Joel Gurin in Information Week: “At the GovLab at New York University, where I am senior adviser, we’re taking a different approach than McKinsey’s to understand the evolving value of government open data: We’re studying open data companies from the ground up. I’m now leading the GovLab’s Open Data 500 project, funded by the John S. and James L. Knight Foundation, to identify and examine 500 American companies that use government open data as a key business resource.
Our preliminary results show that government open data is fueling companies both large and small, across the country, and in many sectors of the economy, including health, finance, education, energy, and more. But it’s not always easy to use this resource. Companies that use government open data tell us it is often incomplete, inaccurate, or trapped in hard-to-use systems and formats.
It will take a thorough and extended effort to make government data truly useful. Based on what we are hearing and the research I did for my book, here are some of the most important steps the federal government can take, starting now, to make it easier for companies to add economic value to the government’s data.
1. Improve data quality
The Open Data Policy not only directs federal agencies to release more open data; it also requires them to release information about data quality. Agencies will have to begin improving the quality of their data simply to avoid public embarrassment. We can hope and expect that they will do some data cleanup themselves, demand better data from the businesses they regulate, or use creative solutions like turning to crowdsourcing for help, as USAID did to improve geospatial data on its grantees.
2. Keep improving open data resources
The government has steadily made Data.gov, the central repository of federal open data, more accessible and useful, including a significant relaunch last week. To the agency’s credit, the GSA, which administers Data.gov, plans to keep working to make this key website still better. As part of implementing the Open Data Policy, the administration has also set up Project Open Data on GitHub, the world’s largest community for open-source software. These resources will be helpful for anyone working with open data either inside or outside of government. They need to be maintained and continually improved.
3. Pass DATA
The Digital Accountability and Transparency Act would bring transparency to federal government spending at an unprecedented level of detail. The Act has strong bipartisan support. It passed the House with only one dissenting vote and was unanimously approved by a Senate committee, but still needs full Senate approval and the President’s signature to become law. DATA is also supported by technology companies who see it as a source of new open data they can use in their businesses. Congress should move forward and pass DATA as the logical next step in the work that the Obama administration’s Open Data Policy has begun.
4. Reform the Freedom of Information Act
Since it was passed in 1966, the federal Freedom of Information Act has gone through two major revisions, both of which strengthened citizens’ ability to access many kinds of government data. It’s time for another step forward. Current legislative proposals would establish a centralized web portal for all federal FOIA requests, strengthen the FOIA ombudsman’s office, and require agencies to post more high-interest information online before they receive formal requests for it. These changes could make more information from FOIA requests available as open data.
5. Engage stakeholders in a genuine way
Up to now, the government’s release of open data has largely been a one-way affair: Agencies publish datasets that they hope will be useful without consulting the organizations and companies that want to use it. Other countries, including the UK, France, and Mexico, are building in feedback loops from data users to government data providers, and the US should, too. The Open Data Policy calls for agencies to establish points of contact for public feedback. At the GovLab, we hope that the Open Data 500 will help move that process forward. Our research will provide a basis for new, productive dialogue between government agencies and the businesses that rely on them.
6. Keep using federal challenges to encourage innovation
The federal Challenge.gov website applies the best principles of crowdsourcing and collective intelligence. Agencies should use this approach extensively, and should pose challenges using the government’s open data resources to solve business, social, or scientific problems. Other approaches to citizen engagement, including federally sponsored hackathons and the White House Champions of Change program, can play a similar role.
Through the Open Data Policy and other initiatives, the Obama administration has set the right goals. Now it’s time to implement and move toward what US CTO Todd Park calls “data liberation.” Thousands of companies, organizations, and individuals will benefit.”
Introduction to Computational Social Science: Principles and Applications
New book by Claudio Cioffi-Revilla: “This reader-friendly textbook is the first work of its kind to provide a unified Introduction to Computational Social Science (CSS). Four distinct methodological approaches are examined in detail, namely automated social information extraction, social network analysis, social complexity theory and social simulation modeling. The coverage of these approaches is supported by a discussion of the historical context, as well as by a list of texts for further reading. Features: highlights the main theories of the CSS paradigm as causal explanatory frameworks that shed new light on the nature of human and social dynamics; explains how to distinguish and analyze the different levels of analysis of social complexity using computational approaches; discusses a number of methodological tools; presents the main classes of entities, objects and relations common to the computational analysis of social complexity; examines the interdisciplinary integration of knowledge in the context of social phenomena.”
Social Media: A Critical Introduction
New book: “Now more than ever, we need to understand social media – the good as well as the bad. We need critical knowledge that helps us to navigate the controversies and contradictions of this complex digital media landscape. Only then can we make informed judgements about what’s
happening in our media world, and why.
Showing the reader how to ask the right kinds of questions about social media, Christian Fuchs takes us on a journey across social media,
delving deep into case studies on Google, Facebook, Twitter, WikiLeaks and Wikipedia. The result lays bare the structures and power relations
at the heart of our media landscape.
This book is the essential, critical guide for understanding social media and for all students of media studies and sociology. Readers will
never look at social media the same way again.
Sample chapter:
Twitter and Democracy: A New Public Sphere?
Introduction: What is a Critical Introduction to Social Media?“
How should we analyse our lives?
Gillian Tett in the Financial Times on the challenge of using the new form of data science: “A few years ago, Alex “Sandy” Pentland, a professor of computational social sciences at MIT Media Lab, conducted a curious experiment at a Bank of America call centre in Rhode Island. He fitted 80 employees with biometric devices to track all their movements, physical conversations and email interactions for six weeks, and then used a computer to analyse “some 10 gigabytes of behaviour data”, as he recalls.
The results showed that the workers were isolated from each other, partly because at this call centre, like others of its ilk, the staff took their breaks in rotation so that the phones were constantly manned. In response, Bank of America decided to change its system to enable staff to hang out together over coffee and swap ideas in an unstructured way. Almost immediately there was a dramatic improvement in performance. “The average call-handle time decreased sharply, which means that the employees were much more productive,” Pentland writes in his forthcoming book Social Physics. “[So] the call centre management staff converted the break structure of all their call centres to this new system and forecast a $15m per year productivity increase.”
When I first heard Pentland relate this tale, I was tempted to give a loud cheer on behalf of all long-suffering call centre staff and corporate drones. Pentland’s data essentially give credibility to a point that many people know instinctively: that it is horribly dispiriting – and unproductive – to have to toil in a tiny isolated cubicle by yourself all day. Bank of America deserves credit both for letting Pentland’s team engage in this people-watching – and for changing its coffee-break schedule in response.
But there is a bigger issue at stake here too: namely how academics such as Pentland analyse our lives. We have known for centuries that cultural and social dynamics influence how we behave but until now academics could usually only measure this by looking at micro-level data, which were often subjective. Anthropology (a discipline I know well) is a case in point: anthropologists typically study cultures by painstakingly observing small groups of people and then extrapolating this in a subjective manner.
Pentland and others like him are now convinced that the great academic divide between “hard” and “soft” sciences is set to disappear, since researchers these days can gather massive volumes of data about human behaviour with precision. Sometimes this information is volunteered by individuals, on sites such as Facebook; sometimes it can be gathered from the electronic traces – the “digital breadcrumbs” – that we all deposit (when we use a mobile phone, say) or deliberately collected with biometric devices like the ones used at Bank of America. Either way, it can enable academics to monitor and forecast social interaction in a manner we could never have dreamed of before. “Social physics helps us understand how ideas flow from person to person . . . and ends up shaping the norms, productivity and creative output of our companies, cities and societies,” writes Pentland. “Just as the goal of traditional physics is to understand how the flow of energy translates into change in motion, social physics seems to understand how the flow of ideas and information translates into changes in behaviour….
But perhaps the most important point is this: whether you love or hate this new form of data science, the genie cannot be put back in the bottle. The experiments that Pentland and many others are conducting at call centres, offices and other institutions across America are simply the leading edge of a trend.
The only question now is whether these powerful new tools will be mostly used for good (to predict traffic queues or flu epidemics) or for more malevolent ends (to enable companies to flog needless goods, say, or for government control). Sadly, “social physics” and data crunching don’t offer any prediction on this issue, even though it is one of the dominant questions of our age.”
Rethinking Democratic Governance: Looking Back, Moving Forward
Chapter by M. Shamsul Haque in Challenges to Democratic Governance in Developing Countries Public Administration, Governance and Globalization: “The recent three decades witnessed massive reforms in the mode of public governance worldwide. This period of restructuring public policy and public administration has been unprecedented in terms of the speed and intensity of such reforms encapsulated often as Reinventing Government or New Public Management or NPM. There also has emerged a series of post-NPM reform proposals—which largely represent the revision rather than rejection of NPM—under catchy expressions like Shared Governance, Collaborative Governance, Joined-Up Governance, Networked Governance, Good Governance, Digital Era Governance, and Good Enough Governance (Lodge and Gill 2011; Ferlie and Steane 2002). These trends of reforms are characterized, first, by their neoliberal ideological assumptions that free market competition is better than state intervention for optimizing customer satisfaction (utility) and cost-effectiveness or efficiency, and thus, the role of the state should be minimal so that a greater role can be played by market forces. Reflecting these ideological underlying predispositions of contemporary reforms in governance are the market-led redirections in state policies, government institutions, and civil service. More specifically, while state policies are reoriented towards privatization, deregulation, liberalization, downsizing, and outsourcing, most public organizations and their management are restructured in favor of organizational disaggregation or agencification, managerial autonomy, performance-driven indicators, result-based finance and budget, and customer-led priorities. It should be mentioned here that while both NPM and post-NPM prescribe pro-market policies and organizational and managerial reforms in order to roll back the state and to transfer much of the state sector role in service delivery to non-state actors, there is a distinction. The basic distinction is that while the NPM model prescribes this transfer of the public sector’s role mainly to the private sector, the post-NPM alternatives recommend such transfer to other additional stakeholders like Nongovernment Organizations (NGO) and grassroots groups.”
Garbage In, Garbage Out… Or, How to Lie with Bad Data
Medium: For everyone who slept through Stats 101, Charles Wheelan’s Naked Statistics is a lifesaver. From batting averages and political polls to Schlitz ads and medical research, Wheelan “illustrates exactly why even the most reluctant mathophobe is well advised to achieve a personal understanding of the statistical underpinnings of life” (New York Times). What follows is adapted from the book, out now in paperback.
Behind every important study there are good data that made the analysis possible. And behind every bad study . . . well, read on. People often speak about “lying with statistics.” I would argue that some of the most egregious statistical mistakes involve lying with data; the statistical analysis is fine, but the data on which the calculations are performed are bogus or inappropriate. Here are some common examples of “garbage in, garbage out.”
Selection Bias
….Selection bias can be introduced in many other ways. A survey of consumers in an airport is going to be biased by the fact that people who fly are likely to be wealthier than the general public; a survey at a rest stop on Interstate 90 may have the opposite problem. Both surveys are likely to be biased by the fact that people who are willing to answer a survey in a public place are different from people who would prefer not to be bothered. If you ask 100 people in a public place to complete a short survey, and 60 are willing to answer your questions, those 60 are likely to be different in significant ways from the 40 who walked by without making eye contact.
Publication Bias
Positive findings are more likely to be published than negative findings, which can skew the results that we see. Suppose you have just conducted a rigorous, longitudinal study in which you find conclusively that playing video games does not prevent colon cancer. You’ve followed a representative sample of 100,000 Americans for twenty years; those participants who spend hours playing video games have roughly the same incidence of colon cancer as the participants who do not play video games at all. We’ll assume your methodology is impeccable. Which prestigious medical journal is going to publish your results?
Most things don’t prevent cancer.
None, for two reasons. First, there is no strong scientific reason to believe that playing video games has any impact on colon cancer, so it is not obvious why you were doing this study. Second, and more relevant here, the fact that something does not prevent cancer is not a particularly interesting finding. After all, most things don’t prevent cancer. Negative findings are not especially sexy, in medicine or elsewhere.
The net effect is to distort the research that we see, or do not see. Suppose that one of your graduate school classmates has conducted a different longitudinal study. She finds that people who spend a lot of time playing video games do have a lower incidence of colon cancer. Now that is interesting! That is exactly the kind of finding that would catch the attention of a medical journal, the popular press, bloggers, and video game makers (who would slap labels on their products extolling the health benefits of their products). It wouldn’t be long before Tiger Moms all over the country were “protecting” their children from cancer by snatching books out of their hands and forcing them to play video games instead.
Of course, one important recurring idea in statistics is that unusual things happen every once in a while, just as a matter of chance. If you conduct 100 studies, one of them is likely to turn up results that are pure nonsense—like a statistical association between playing video games and a lower incidence of colon cancer. Here is the problem: The 99 studies that find no link between video games and colon cancer will not get published, because they are not very interesting. The one study that does find a statistical link will make it into print and get loads of follow-on attention. The source of the bias stems not from the studies themselves but from the skewed information that actually reaches the public. Someone reading the scientific literature on video games and cancer would find only a single study, and that single study will suggest that playing video games can prevent cancer. In fact, 99 studies out of 100 would have found no such link.
Recall Bias
Memory is a fascinating thing—though not always a great source of good data. We have a natural human impulse to understand the present as a logical consequence of things that happened in the past—cause and effect. The problem is that our memories turn out to be “systematically fragile” when we are trying to explain some particularly good or bad outcome in the present. Consider a study looking at the relationship between diet and cancer. In 1993, a Harvard researcher compiled a data set comprising a group of women with breast cancer and an age-matched group of women who had not been diagnosed with cancer. Women in both groups were asked about their dietary habits earlier in life. The study produced clear results: The women with breast cancer were significantly more likely to have had diets that were high in fat when they were younger.
Ah, but this wasn’t actually a study of how diet affects the likelihood of getting cancer. This was a study of how getting cancer affects a woman’s memory of her diet earlier in life. All of the women in the study had completed a dietary survey years earlier, before any of them had been diagnosed with cancer. The striking finding was that women with breast cancer recalled a diet that was much higher in fat than what they actually consumed; the women with no cancer did not.
Women with breast cancer recalled a diet that was much higher in fat than what they actually consumed; the women with no cancer did not.
The New York Times Magazine described the insidious nature of this recall bias:
The diagnosis of breast cancer had not just changed a woman’s present and the future; it had altered her past. Women with breast cancer had (unconsciously) decided that a higher-fat diet was a likely predisposition for their disease and (unconsciously) recalled a high-fat diet. It was a pattern poignantly familiar to anyone who knows the history of this stigmatized illness: these women, like thousands of women before them, had searched their own memories for a cause and then summoned that cause into memory.
Recall bias is one reason that longitudinal studies are often preferred to cross-sectional studies. In a longitudinal study the data are collected contemporaneously. At age five, a participant can be asked about his attitudes toward school. Then, thirteen years later, we can revisit that same participant and determine whether he has dropped out of high school. In a cross-sectional study, in which all the data are collected at one point in time, we must ask an eighteen-year-old high school dropout how he or she felt about school at age five, which is inherently less reliable.
Survivorship Bias
Suppose a high school principal reports that test scores for a particular cohort of students has risen steadily for four years. The sophomore scores for this class were better than their freshman scores. The scores from junior year were better still, and the senior year scores were best of all. We’ll stipulate that there is no cheating going on, and not even any creative use of descriptive statistics. Every year this cohort of students has done better than it did the preceding year, by every possible measure: mean, median, percentage of students at grade level, and so on. Would you (a) nominate this school leader for “principal of the year” or (b) demand more data?
If you have a room of people with varying heights, forcing the short people to leave will raise the average height in the room, but it doesn’t make anyone taller.
I say “b.” I smell survivorship bias, which occurs when some or many of the observations are falling out of the sample, changing the composition of the observations that are left and therefore affecting the results of any analysis. Let’s suppose that our principal is truly awful. The students in his school are learning nothing; each year half of them drop out. Well, that could do very nice things for the school’s test scores—without any individual student testing better. If we make the reasonable assumption that the worst students (with the lowest test scores) are the most likely to drop out, then the average test scores of those students left behind will go up steadily as more and more students drop out. (If you have a room of people with varying heights, forcing the short people to leave will raise the average height in the room, but it doesn’t make anyone taller.)
Healthy User Bias
People who take vitamins regularly are likely to be healthy—because they are the kind of people who take vitamins regularly! Whether the vitamins have any impact is a separate issue. Consider the following thought experiment. Suppose public health officials promulgate a theory that all new parents should put their children to bed only in purple pajamas, because that helps stimulate brain development. Twenty years later, longitudinal research confirms that having worn purple pajamas as a child does have an overwhelmingly large positive association with success in life. We find, for example, that 98 percent of entering Harvard freshmen wore purple pajamas as children (and many still do) compared with only 3 percent of inmates in the Massachusetts state prison system.
The purple pajamas do not matter.
Of course, the purple pajamas do not matter; but having the kind of parents who put their children in purple pajamas does matter. Even when we try to control for factors like parental education, we are still going to be left with unobservable differences between those parents who obsess about putting their children in purple pajamas and those who don’t. As New York Times health writer Gary Taubes explains, “At its simplest, the problem is that people who faithfully engage in activities that are good for them—taking a drug as prescribed, for instance, or eating what they believe is a healthy diet—are fundamentally different from those who don’t.” This effect can potentially confound any study trying to evaluate the real effect of activities perceived to be healthful, such as exercising regularly or eating kale. We think we are comparing the health effects of two diets: kale versus no kale. In fact, if the treatment and control groups are not randomly assigned, we are comparing two diets that are being eaten by two different kinds of people. We have a treatment group that is different from the control group in two respects, rather than just one.
If statistics is detective work, then the data are the clues. My wife spent a year teaching high school students in rural New Hampshire. One of her students was arrested for breaking into a hardware store and stealing some tools. The police were able to crack the case because (1) it had just snowed and there were tracks in the snow leading from the hardware store to the student’s home; and (2) the stolen tools were found inside. Good clues help.
Like good data. But first you have to get good data, and that is a lot harder than it seems.
Like good data. But first you have to get good data, and that is a lot harder than it seems.
Open Development (Networked Innovations in International Development)
New book edited by Matthew L. Smith and Katherine M. A. Reilly (Foreword by Yochai Benkler) : “The emergence of open networked models made possible by digital technology has the potential to transform international development. Open network structures allow people to come together to share information, organize, and collaborate. Open development harnesses this power, to create new organizational forms and improve people’s lives; it is not only an agenda for research and practice but also a statement about how to approach international development. In this volume, experts explore a variety of applications of openness, addressing challenges as well as opportunities.
Open development requires new theoretical tools that focus on real world problems, consider a variety of solutions, and recognize the complexity of local contexts. After exploring the new theoretical terrain, the book describes a range of cases in which open models address such specific development issues as biotechnology research, improving education, and access to scholarly publications. Contributors then examine tensions between open models and existing structures, including struggles over privacy, intellectual property, and implementation. Finally, contributors offer broader conceptual perspectives, considering processes of social construction, knowledge management, and the role of individual intent in the development and outcomes of social models.”
New Book: Open Data Now
New book by Joel Gurin (The GovLab): “Open Data is the world’s greatest free resource–unprecedented access to thousands of databases–and it is one of the most revolutionary developments since the Information Age began. Combining two major trends–the exponential growth of digital data and the emerging culture of disclosure and transparency–Open Data gives you and your business full access to information that has never been available to the average person until now. Unlike most Big Data, Open Data is transparent, accessible, and reusable in ways that give it the power to transform business, government, and society.
Open Data Now is an essential guide to understanding all kinds of open databases–business, government, science, technology, retail, social media, and more–and using those resources to your best advantage. You’ll learn how to tap crowds for fast innovation, conduct research through open collaboration, and manage and market your business in a transparent marketplace.
Open Data is open for business–and the opportunities are as big and boundless as the Internet itself. This powerful, practical book shows you how to harness the power of Open Data in a variety of applications:
- HOT STARTUPS: turn government data into profitable ventures
- SAVVY MARKETING: understand how reputational data drives your brand
- DATA-DRIVEN INVESTING: apply new tools for business analysis
- CONSUMER IN FORMATION: connect with your customers using smart disclosure
- GREEN BUSINESS: use data to bet on sustainable companies
- FAST R&D: turn the online world into your research lab
- NEW OPPORTUNITIES: explore open fields for new businesses
Whether you’re a marketing professional who wants to stay on top of what’s trending, a budding entrepreneur with a billion-dollar idea and limited resources, or a struggling business owner trying to stay competitive in a changing global market–or if you just want to understand the cutting edge of information technology–Open Data Now offers a wealth of big ideas, strategies, and techniques that wouldn’t have been possible before Open Data leveled the playing field.
The revolution is here and it’s now. It’s Open Data Now.”