New Article by Gregory Michener in Governance: “Prevailing thinking surrounding the politics of secrecy and transparency is biased by assumptions regarding single-party and small coalition governments. Here, the “politics of secrecy” dominates: Leaders delay or resist strong transparency and freedom of information (FOI) policies when they control parliament, and yield to strong laws because of imposition, symbolic ambition, or concessions when they do not. In effect, leaders weigh the benefits of secrecy against gains in monitorial capacity. Their support for strong transparency policies grows as the number of parties in their cabinet rises. So while the costs of surrendering secrecy trump the benefits of strong transparency reforms in single-party governments, in broad multiparty coalitions leaders trade secrecy for tools to monitor coalition “allies.” Drawing on vivid international examples, patterns of FOI reform in Latin America, and an in-depth study of FOI in Brazil, this article generates new theoretical insights into transparency and the “politics of monitoring.”
New study proves economic benefits of open data for Berlin
ePSI Platform: “The study “Digitales Gold: Nutzen und Wertschöpfung durch Open Data für Berlin” – or “Digital Gold: the open data benefits and its added value for Berlin” in english – released by TSB Technologiestiftung Berlin estimates that Open Data will bring around 32 million euros per year of economic benefit to the city of Berlin for the next few years. …
The estimations made for Berlin are inspired by previous reasoning included in two other studies: Pollock R. (2011), Welfare Gains from opening up public sector information in the UK; and Fuchs, S. et al. (2013), Open Government Data – Offene Daten für Österreich. Mit Community-Strategien von heute zum Potential von morgen.
Upon presenting the study data journalist Michael Hörz shows various examples of how to develop interesting new information and services with publicly available information. You can read more about it (in German) here.”
What is (and what is not) open government
“This Prezi by César Nicandro Cruz-Rubiois designed for educational purposes. It presents open government concept and uses some Youtube source videos in order to give some examples”
This algorithm can predict a revolution
Russell Brandom at the Verge: “For students of international conflict, 2013 provided plenty to examine. There was civil war in Syria, ethnic violence in China, and riots to the point of revolution in Ukraine. For those working at Duke University’s Ward Lab, all specialists in predicting conflict, the year looks like a betting sheet, full of predictions that worked and others that didn’t pan out.
Guerrilla campaigns intensified, proving out the prediction
When the lab put out their semiannual predictions in July, they gave Paraguay a 97 percent chance of insurgency, largely based on reports of Marxist rebels. The next month, guerrilla campaigns intensified, proving out the prediction. In the case of China’s armed clashes between Uighurs and Hans, the models showed a 33 percent chance of violence, even as the cause of each individual flare-up was concealed by the country’s state-run media. On the other hand, the unrest in Ukraine didn’t start raising alarms until the action had already started, so the country was left off the report entirely.
According to Ward Lab’s staff, the purpose of the project isn’t to make predictions but to test theories. If a certain theory of geopolitics can predict an uprising in Ukraine, then maybe that theory is onto something. And even if these specialists could predict every conflict, it would only be half the battle. “It’s a success only if it doesn’t come at the cost of predicting a lot of incidents that don’t occur,” says Michael D. Ward, the lab’s founder and chief investigator, who also runs the blog Predictive Heuristics. “But it suggests that we might be on the right track.”
If a certain theory of geopolitics can predict an uprising in Ukraine, maybe that theory is onto something
Forecasting the future of a country wasn’t always done this way. Traditionally, predicting revolution or war has been a secretive project, for the simple reason that any reliable prediction would be too valuable to share. But as predictions lean more on data, they’ve actually become harder to keep secret, ushering in a new generation of open-source prediction models that butt against the siloed status quo.
Will this country’s government face an acute existential threat in the next six months?
The story of automated conflict prediction starts at the Defense Advance Research Projects Agency, known as the Pentagon’s R&D wing. In the 1990s, DARPA wanted to try out software-based approaches to anticipating which governments might collapse in the near future. The CIA was already on the case, with section chiefs from every region filing regular forecasts, but DARPA wanted to see if a computerized approach could do better. They looked at a simple question: will this country’s government face an acute existential threat in the next six months? When CIA analysts were put to the test, they averaged roughly 60 percent accuracy, so DARPA’s new system set the bar at 80 percent, looking at 29 different countries in Asia with populations over half a million. It was dubbed ICEWS, the Integrated Conflict Early Warning System, and it succeeded almost immediately, clearing 80 percent with algorithms built on simple regression analysis….
On the data side, researchers at Georgetown University are cataloging every significant political event of the past century into a single database called GDELT, and leaving the whole thing open for public research. Already, projects have used it to map the Syrian civil war and diplomatic gestures between Japan and South Korea, looking at dynamics that had never been mapped before. And then, of course, there’s Ward Lab, releasing a new sheet of predictions every six months and tweaking its algorithms with every development. It’s a mirror of the same open-vs.-closed debate in software — only now, instead of fighting over source code and security audits, it’s a fight over who can see the future the best.”
Big Data, Big New Businesses
Nigel Shaboldt and Michael Chui: “Many people have long believed that if government and the private sector agreed to share their data more freely, and allow it to be processed using the right analytics, previously unimaginable solutions to countless social, economic, and commercial problems would emerge. They may have no idea how right they are.
Even the most vocal proponents of open data appear to have underestimated how many profitable ideas and businesses stand to be created. More than 40 governments worldwide have committed to opening up their electronic data – including weather records, crime statistics, transport information, and much more – to businesses, consumers, and the general public. The McKinsey Global Institute estimates that the annual value of open data in education, transportation, consumer products, electricity, oil and gas, health care, and consumer finance could reach $3 trillion.
These benefits come in the form of new and better goods and services, as well as efficiency savings for businesses, consumers, and citizens. The range is vast. For example, drawing on data from various government agencies, the Climate Corporation (recently bought for $1 billion) has taken 30 years of weather data, 60 years of data on crop yields, and 14 terabytes of information on soil types to create customized insurance products.
Similarly, real-time traffic and transit information can be accessed on smartphone apps to inform users when the next bus is coming or how to avoid traffic congestion. And, by analyzing online comments about their products, manufacturers can identify which features consumers are most willing to pay for, and develop their business and investment strategies accordingly.
Opportunities are everywhere. A raft of open-data start-ups are now being incubated at the London-based Open Data Institute (ODI), which focuses on improving our understanding of corporate ownership, health-care delivery, energy, finance, transport, and many other areas of public interest.
Consumers are the main beneficiaries, especially in the household-goods market. It is estimated that consumers making better-informed buying decisions across sectors could capture an estimated $1.1 trillion in value annually. Third-party data aggregators are already allowing customers to compare prices across online and brick-and-mortar shops. Many also permit customers to compare quality ratings, safety data (drawn, for example, from official injury reports), information about the provenance of food, and producers’ environmental and labor practices.
Consider the book industry. Bookstores once regarded their inventory as a trade secret. Customers, competitors, and even suppliers seldom knew what stock bookstores held. Nowadays, by contrast, bookstores not only report what stock they carry but also when customers’ orders will arrive. If they did not, they would be excluded from the product-aggregation sites that have come to determine so many buying decisions.
The health-care sector is a prime target for achieving new efficiencies. By sharing the treatment data of a large patient population, for example, care providers can better identify practices that could save $180 billion annually.
The Open Data Institute-backed start-up Mastodon C uses open data on doctors’ prescriptions to differentiate among expensive patent medicines and cheaper “off-patent” varieties; when applied to just one class of drug, that could save around $400 million in one year for the British National Health Service. Meanwhile, open data on acquired infections in British hospitals has led to the publication of hospital-performance tables, a major factor in the 85% drop in reported infections.
There are also opportunities to prevent lifestyle-related diseases and improve treatment by enabling patients to compare their own data with aggregated data on similar patients. This has been shown to motivate patients to improve their diet, exercise more often, and take their medicines regularly. Similarly, letting people compare their energy use with that of their peers could prompt them to save hundreds of billions of dollars in electricity costs each year, to say nothing of reducing carbon emissions.
Such benchmarking is even more valuable for businesses seeking to improve their operational efficiency. The oil and gas industry, for example, could save $450 billion annually by sharing anonymized and aggregated data on the management of upstream and downstream facilities.
Finally, the move toward open data serves a variety of socially desirable ends, ranging from the reuse of publicly funded research to support work on poverty, inclusion, or discrimination, to the disclosure by corporations such as Nike of their supply-chain data and environmental impact.
There are, of course, challenges arising from the proliferation and systematic use of open data. Companies fear for their intellectual property; ordinary citizens worry about how their private information might be used and abused. Last year, Telefónica, the world’s fifth-largest mobile-network provider, tried to allay such fears by launching a digital confidence program to reassure customers that innovations in transparency would be implemented responsibly and without compromising users’ personal information.
The sensitive handling of these issues will be essential if we are to reap the potential $3 trillion in value that usage of open data could deliver each year. Consumers, policymakers, and companies must work together, not just to agree on common standards of analysis, but also to set the ground rules for the protection of privacy and property.”
Visualising Information for Advocacy
New book: “Visualising Information for Advocacy is a book about how advocates and activists use visual elements in their campaigns. This 170-page guide features over 60 case studies from around the world to provide an introduction to understanding visual information and a framework for using images for influence.
At Tactical Tech we have been analysing how different kinds of visual techniques serve the work of advocacy, and have been testing out our ideas. We have developed three ways to classify how the visual works in advocacy campaigns:
- Get the idea is about making simple, eye-catching products that convey one concise point, provoking and inviting audiences to find out more about the issue.
- Get the picture is about creating a visual summary of an argument by crafting a narrative with visuals and data.
- Get the detail is about presenting data through interactive digital formats in a way that allows the audience to dig deeper and explore the issue for themselves.
Flick through Visualising Information for Advocacy to get inspiration for your project, try out some of the visual techniques showcased, or find advice on how we produce visuals for advocates.”
The Power of Knowledge,' by Jeremy Black
Book review by Roger Kimball: “It’s surprising to me that the English historian Jeremy Black isn’t better known on these shores. He has written dozens of books on subjects as various as the Battle of Waterloo, the history of the slave trade, both world wars and the rise of European power in the 18th century. His bibliography includes books about maps and naval power, and even “The Politics of James Bond.” What is more, he commands an engaging prose style and possesses an outlook appealingly grounded in the principles of a high but undoctrinaire Toryism. This combination of fluency and wide-ranging erudition are conspicuously on display in “The Power of Knowledge,” an ambitious, synoptic, “big idea” book that is likely to extend the frontiers of Mr. Black’s audience in this country.
The thesis of the book is neatly encapsulated in the title of Chapter 13: “Information Is All.” The idea that there is an intimate link between knowledge and what we might call administrative power is perennial. It is inscribed in the nature of things that the more you know, the more you control.
The technological side of that syllogism, Mr. Black notes, didn’t really come to the fore until the 17th century. “Knowledge is Power,” wrote Francis Bacon, enunciating a principle that seems obvious only because we have been the beneficiaries of the processes he helped to define. Writing a few years later, René Descartes promised that his method of inquiry would make man “the master and possessor of nature.” Descartes looked forward to all manner of material benefits, not least in the realm of medicine. He was prescient.
Mr. Black touches on Bacon and Descartes in his chapter on the Scientific Revolution, but his focus is much more encompassing. He begins his story with the formation of the Mongol Empire in the 13th century and describes how the Great Khan’s superior deployment of information, prominently including his command of trade along the Silk Road, stood behind the creation of the largest contiguous empire in history.
The technological side of that syllogism, Mr. Black notes, didn’t really come to the fore until the 17th century. “Knowledge is Power,” wrote Francis Bacon, enunciating a principle that seems obvious only because we have been the beneficiaries of the processes he helped to define. Writing a few years later, René Descartes promised that his method of inquiry would make man “the master and possessor of nature.” Descartes looked forward to all manner of material benefits, not least in the realm of medicine. He was prescient.
Mr. Black touches on Bacon and Descartes in his chapter on the Scientific Revolution, but his focus is much more encompassing. He begins his story with the formation of the Mongol Empire in the 13th century and describes how the Great Khan’s superior deployment of information, prominently including his command of trade along the Silk Road, stood behind the creation of the largest contiguous empire in history.
The guiding theme of this book is the complex linkage (what Mr. Black calls “synergies”) between information and power—political power, above all, but also military, economic and technological power. The increasingly sophisticated acquisition and manipulation of information, Mr. Black argues, is “a defining characteristic of modernity,” and it fueled the rise and shaped the distinctiveness of the West from the 18th century through the early 21st.
Part of what makes modernity modern—and a large part of what has made the West its crucible—is the interpenetration of information and technique under the guidance of an increasingly secularized idea of human flourishing. Other cultures made local contributions to this drama, but Mr. Black is right: The breathtaking spectacle of modern technological achievement has been overwhelmingly a Western achievement. This fact has been a source of great sadness for politically correct exponents of multiculturalism. Mr. Black knows this, but he early on hedges his account to avoid “triumphalism.”
As Mr. Black concedes, many of the master terms of his narrative are “porous.” What, after all, is information? It is data, yes, but also statistics, rumor, propaganda and practical know-how. All enter into the story he tells, but their aggregation makes for an argument that is more kaleidoscopic than discursive.
The very breadth of Mr. Black’s subject makes reading “The Power of Knowledge” partly thrilling, partly vertiginous. It is a long journey that Mr. Black engages here, but all of the stops are express stops. There is no lingering. In a section on advances in maritime charting, for example, he mentions that an equivalent was the development during the Renaissance of one-point perspective using mathematical rules. True enough, but that rich topic is confined to a few sentences. Still, the book bristles with interesting tidbits. I took some smuggish satisfaction in knowing that the term “bureaucracy” was coined by a Frenchman (de Gournay) but that we owe “scientist” to the British geologist William Whewell.
Although deeply grounded in history from the Middle Ages on down, this book also conjures with contemporary issues. Some are technical, like the vistas of information unraveled in the human genome project. Some are political. Until fairly recently, Mr. Black notes, central governments lacked the mechanisms to intervene consistently in everyday life. That, as anyone who can pronounce the acronym “NSA” knows, has changed dramatically. Mr. Black devotes an entire chapter to what he calls “the scrutinized society.” The effort to control public opinion and the flow of information has been most flagrant in totalitarian regimes, but he shows that in democracies, too, information is “filtered and deployed as part of the battle for public opinion.”…
Disinformation Visualization: How to lie with datavis
Mushon Zer-Aviv at School of Data: “Seeing is believing. When working with raw data we’re often encouraged to present it differently, to give it a form, to map it or visualize it. But all maps lie. In fact, maps have to lie, otherwise they wouldn’t be useful. Some are transparent and obvious lies, such as a tree icon on a map often represents more than one tree. Others are white lies – rounding numbers and prioritising details to create a more legible representation. And then there’s the third type of lie, those lies that convey a bias, be it deliberately or subconsciously. A bias that misrepresents the data and skews it towards a certain reading.
It all sounds very sinister, and indeed sometimes it is. It’s hard to see through a lie unless you stare it right in the face, and what better way to do that than to get our minds dirty and look at some examples of creative and mischievous visual manipulation.
Over the past year I’ve had a few opportunities to run Disinformation Visualization workshops, encouraging activists, designers, statisticians, analysts, researchers, technologists and artists to visualize lies. During these sessions I have used the DIKW pyramid (Data > Information > Knowledge > Wisdom), a framework for thinking about how data gains context and meaning and becomes information. This information needs to be consumed and understood to become knowledge. And finally when knowledge influences our insights and our decision making about the future it becomes wisdom. Data visualization is one of the ways to push data up the pyramid towards wisdom in order to affect our actions and decisions. It would be wise then to look at visualizations suspiciously.
Centuries before big data, computer graphics and social media collided and gave us the datavis explosion, visualization was mostly a scientific tool for inquiry and documentation. This history gave the artform its authority as an integral part of the scientific process. Being a product of human brains and hands, a certain degree of bias was always there, no matter how scientific the process was. The effect of these early off-white lies are still felt today, as even our most celebrated interactive maps still echo the biases of the Mercator map projection, grounding Europe and North America on the top of the world, over emphasizing their size and perceived importance over the Global South. Our contemporary practices of programmatically data driven visualization hide both the human eyes and hands that produce them behind data sets, algorithms and computer graphics, but the same biases are still there, only they’re harder to decipher…”
The Problem With Serious Games–Solved
Emerging Technology From the arXiv:” Serious games are becoming increasingly popular but the inability to generate realistic new content has hampered their progress. Until now.
Here’s an imaginary scenario: you’re a law enforcement officer confronted with John, a 21-year-old male suspect who is accused of breaking into a private house on Sunday evening and stealing a laptop, jewellery and some cash. Your job is to find out whether John has an alibi and if so whether it is coherent and believable.
That’s exactly the kind of scenario that police officers the world over face on a regular basis. But how do you train for such a situation? How do you learn the skills necessary to gather the right kind of information?
An increasingly common way of doing this is with serious games, those designed primarily for purposes other than entertainment. In the last 10 years or so, medical, military and commercial organisations all over the world began to experiment with game-based scenarios that are designed to teach people how to perform their jobs and tasks in realistic situations.
But there is a problem with serious games which require realistic interaction is with another person. It’s relatively straightforward to design one or two scenarios that are coherent, lifelike and believable but it’s much harder to generate them continually on an ongoing basis.
Imagine in the example above, that John is a computer-generated character. What kind of activities could he describe that would serve as a believable, coherent alibi for Sunday evening? And how could he do it a thousand times, each describing a different realistic alibi. Therein lies the problem.
Today, Sigal Sina at Bar-Ilan University in Israel, and a couple pals, say they’ve solved this probelm. These guys have come up with a novel way of generating ordinary, realistic scenarios that can be cut and pasted into a serious game to serve exactly this purpose. The secret sauce in their new approach is to crowdsource the new scenarios from real people using Amazon’s Mechanical Turk service.
The approach is straightforward. Sina and co simply ask Turkers to answer a set of questions asking what they did during each one-hour period throughout various days, offering bonuses to those who provide the most varied detail.
They then analyse the answers, categorising activities by factors such as the times they are performed, the age and sex of the person doing it, the number of people involved and so on.
This then allows a computer game to cut and paste activities into the action at appropriate times. So for example, the computer can select an appropriate alibi for John on a Sunday evening by choosing an activity described by a male Turker for the same time while avoiding activitiesthat a woman might describe for a Friday morning, which might otherwise seem unbelievable. The computer also changes certain details in the narrative, such as names, locations and so on to make the narrative coherent with John’s profile….
That solves a significant problem with serious games. Until now, developers have had to spend an awful lot of time producing realistic content, a process known as procedural content generation. That’s always been straightforward for things like textures, models and terrain in game settings. Now, thanks to this new crowdsourcing technique, it can be just as easy for human interactions in serious games too.
Ref: arxiv.org/abs/1402.5034 : Using the Crowd to Generate Content for Scenario-Based Serious-Games”
The Power to Give
Press Release: “HTC, a global leader in mobile innovation and design, today unveiled HTC Power To Give™, an initiative that aims to create the a supercomputer by harnessing the collective processing power of Android smartphones.
Currently in beta, HTC Power To Give aims to galvanize smartphone owners to unlock their unused processing power in order to help answer some of society’s biggest questions. Currently, the fight against cancer, AIDS and Alzheimer’s; the drive to ensure every child has clean water to drink and even the search for extra-terrestrial life are all being tackled by volunteer computing platforms.
Empowering people to use their Android smartphones to offer tangible support for vital fields of research, including medicine, science and ecology, HTC Power To Give has been developed in partnership with Dr. David Anderson of the University of California, Berkeley. The project will support the world’s largest volunteer computing initiative and tap into the powerful processing capabilities of a global network of smartphones.
Strength in numbers
One million HTC One smartphones, working towards a project via HTC Power To Give, could provide similar processing power to that of one of the world’s 30 supercomputers (one PetaFLOP). This could drastically shorten the research cycles for organizations that would otherwise have to spend years analyzing the same volume of data, potentially bringing forward important discoveries in vital subjects by weeks, months, years or even decades. For example, one of the programs available at launch is IBM’s World Community Grid, which gives anyone an opportunity to advance science by donating their computer, smartphone or tablet’s unused computing power to humanitarian research. To date, the World Community Grid volunteers have contributed almost 900,000 years’ worth of processing time to cutting-edge research.
Limitless future potential
Cher Wang, Chairwoman, HTC commented, “We’ve often used innovation to bring about change in the mobile industry, but this programme takes our vision one step further. With HTC Power To Give, we want to make it possible for anyone to dedicate their unused smartphone processing power to contribute to projects that have the potential to change the world.”
“HTC Power To Give will support the world’s largest volunteer computing initiative, and the impact that this project will have on the world over the years to come is huge. This changes everything,” noted Dr. David Anderson, Inventor of the Shared Computing Initiative BOINC, University of California, Berkeley.
Cher Wang added, “We’ve been discussing the impact that just one million HTC Power To Give-enabled smartphones could make, however analysts estimate that over 780 million Android phones were shipped in 2013i alone. Imagine the difference we could make to our children’s future if just a fraction of these Android users were able to divert some of their unused processing power to help find answers to the questions that concern us all.”
Opt-in with ease
After downloading the HTC Power To Give app from the Google Play™ store, smartphone owners can select the research programme to which they will divert a proportion of their phone’s processing power. HTC Power To Give will then run while the phone is chargingii and connected to a WiFi network, enabling people to change the world whilst sitting at their desk or relaxing at home.
The beta version of HTC Power To Give will be available to download from the Google Play store and will initially be compatible with the HTC One family, HTC Butterfly and HTC Butterfly s. HTC plans to make the app more widely available to other Android smartphone owners in the coming six months as the beta trial progresses.”