Needed: A New Generation of Game Changers to Solve Public Problems

Curated on Posted on by Stefaan Verhulst

Beth Noveck: “In order to change the way we govern, it is important to train and nurture a new generation of problem solvers who possess the multidisciplinary skills to become effective agents of change. That’s why we at the GovLab have launched The GovLab Academy with the support of the Knight Foundation.
In an effort to help people in their own communities become more effective at developing and implementing creative solutions to compelling challenges, The Gov Lab Academy is offering two new training programs:
1) An online platform with an unbundled and evolving set of topics, modules and instructors on innovations in governance, including themes such as big and open data and crowdsourcing and forthcoming topics on behavioral economics, prizes and challenges, open contracting and performance management for governance;
2) Gov 3.0: A curated and sequenced, 14-week mentoring and training program.
While the online-platform is always freely available, Gov 3.0 begins on January 29, 2014 and we invite you to to participate. Please forward this email to your networks and help us spread the word about the opportunity to participate.
Please consider applying (individuals or teams may apply), if you are:

  • an expert in communications, public policy, law, computer science, engineering, business or design who wants to expand your ability to bring about social change;

  • a public servant who wants to bring innovation to your job;

  • someone with an important idea for positive change but who lacks key skills or resources to realize the vision;

  • interested in joining a network of like-minded, purpose-driven individuals across the country; or

  • someone who is passionate about using technology to solve public problems.

The program includes live instruction and conversation every Wednesday from 5:00– 6:30 PM EST for 14 weeks starting Jan 29, 2014. You will be able to participate remotely via Google Hangout.

Gov 3.0 will allow you to apply evolving technology to the design and implementation of effective solutions to public interest challenges. It will give you an overview of the most current approaches to smarter governance and help you improve your skills in collaboration, communication, and developing and presenting innovative ideas.

Over 14 weeks, you will develop a project and a plan for its implementation, including a long and short description, a presentation deck, a persuasive video and a project blog. Last term’s projects covered such diverse issues as post-Fukushima food safety, science literacy for high schoolers and prison reform for the elderly. In every case, the goal was to identify realistic strategies for making a difference quickly.  You can read the entire Gov 3.0 syllabus here.

The program will include national experts and instructors in technology and governance both as guests and as mentors to help you design your project. Last term’s mentors included current and former officials from the White House and various state, local and international governments, academics from a variety of fields, and prominent philanthropists.

People who complete the program will have the opportunity to apply for a special fellowship to pursue their projects further.

Previously taught only on campus, we are offering Gov 3.0 in beta as an online program. This is not a MOOC. It is a mentoring-intensive coaching experience. To maximize the quality of the experience, enrollment is limited.

Please submit your application by January 22, 2014. Accepted applicants (individuals and teams) will be notified on January 24, 2014. We hope to expand the program in the future so please use the same form to let us know if you would like to be kept informed about future opportunities.”

Rethinking Democratic Governance: Looking Back, Moving Forward

Curated on Posted on by Stefaan Verhulst

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.”

Tech Policy Is Not A Religion

Curated on Posted on by Stefaan Verhulst

Opinion Piece by Robert Atkinson: “”Digital libertarians” and “digital technocrats” want us to believe their way is the truth and the light. It’s not that black and white. Manichaeism, an ancient religion, took a dualistic view of the world. It described the struggle between a good, spiritual world of light, and an evil, material world of darkness. Listening to tech policy debates, especially in America, one would presume that Manichaeism is alive and well.
On one side (light or dark, depending on your view) are the folks who embrace free markets, bottom-up processes, multi-stakeholderism, open-source systems, and crowdsourced innovations. On the other are those who embrace government intervention, top-down processes, additional regulation, proprietary systems, and expert-based innovations.
For the first group, whom I’ll call the digital libertarians, government is the problem, not the solution. Tech enables freedom, and statist actions can only limit it.
According to this camp, tech is moving so fast that government can’t hope to keep up — the only workable governance system is a nimble one based on multi-stakeholder processes, such as ICANN and W3C. With Web 2.0, everyone can be a contributor, and it is through the proliferation of multiple and disparate voices that we discover the truth. And because of the ability of communities of coders to add their contributions, the only viable tech systems are based on open-source models.
For the second group, the digital technocrats, the problem is the anarchic, lawless, corporate-dominated nature of the digital world. Tech is so disruptive, including to long-established norms and laws, it needs to be limited and shaped, and only the strong hand of the state can do that. Because of the influence of tech on all aspects of society, any legitimate governance process must stem from democratic institutions — not from a select group of insiders — and that can only happen with government oversight such as through the UN’s International Telecommunication Union.
According to this camp, because there are so many uninformed voices on the Internet spreading urban myths like wildfire, we need carefully vetted experts, whether in media or other organizations, to sort through the mass of information and provide expert, unbiased analysis. And because IT systems are so critical to the safety and well-functioning of  society, we need companies to build and profit from them through a closed-source model.
Of course, just as religious Manichaeism leads to distorted practices of faith, tech Manichaeism leads to distorted policy practices and views. Take Internet governance. The process of ensuring Internet governance and evolution is complex and rapidly changing. A strong case can be made for the multi-stakeholder process as the driving force.
But this situation doesn’t mean, as digital libertarians would assert, that governments should stay out of the Internet altogether. Governments are not, as digital libertarian John Perry Barlow arrogantly asserts, “weary giants of flesh and steel.” Governments can and do play legitimate roles in many Internet policy issues, from establishing cybersecurity guidelines to setting online sales tax policy to combatting spam and digital piracy to setting rules governing unfair and deceptive online marketing practices.
This assertion doesn’t mean governments always get things right. They don’t. But as the Information Technology and Innovation Foundation writes in its recent response to Barlow’s manifesto, to deny people the right to regulate Internet activity through their government officials ignores the significant contribution the government can play in promoting the continued development of the Internet and digital economy.
At the same time, the digital technocrats must understand that the digital world is different from the analog one, and that old rules, regulations, and governing structures simply don’t apply. When ITU Secretary General Hamadoun Toure argues that “at the behest of all the world’s nations, the UN must lead this effort” to manage the global Internet, and that “for big commercial interests, it’s about maximizing the bottom line,” he’s ignoring the critical role that tech companies and other non-government stakeholders play in the Internet ecosystem.
Because digital technology is such a vastly complex system, digital libertarians claim that their “light” approach is superior to the “dark,” controlling, technocratic approach. In fact, this very complexity requires that we base Internet policy on pragmatism, not religion.
Conversely, because technology is so important to opportunity and the functioning of societies, digital technocrats assert that only governments can maximize these benefits. In fact, its importance requires us to respect its complexity and the role of private sector innovators in driving digital progress.
In short, the belief that one or the other of these approaches is sufficient in itself to maximize tech innovation is misleading at best and damaging at worst.”

Garbage In, Garbage Out… Or, How to Lie with Bad Data

Curated on Posted on by Stefaan Verhulst
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.

The Failure and the Promise of Public Participation

Curated on Posted on by Stefaan Verhulst

Dr. Mark Funkhouser in Governing: “In a recent study entitled Making Public Participation Legal, Matt Leighninger cites a Knight Foundation report that found that attending a public meeting was more likely to reduce a person’s sense of efficacy and attachment to the community than to increase it. That sad fact is no surprise to the government officials who have to run — and endure — public meetings.
Every public official who has served for any length of time has horror stories about these forums. The usual suspects show up — the self-appointed activists (who sometimes seem to be just a little nuts) and the lobbyists. Regular folks have made the calculation that only in extreme circumstance, when they are really scared or angry, is attending a public hearing worth their time. And who can blame them when it seems clear that the game is rigged, the decisions already have been made, and they’ll probably have to sit through hours of blather before they get their three minutes at the microphone?
So much transparency and yet so little trust. Despite the fact that governments are pumping out more and more information to citizens, trust in government has edged lower and lower, pushed in part no doubt by the lingering economic hardships and government cutbacks resulting from the recession. Most public officials I talk to now take it as an article of faith that the public generally disrespects them and the governments they work for.
Clearly the relationship between citizens and their governments needs to be reframed. Fortunately, over the last couple of decades lots of techniques have been developed by advocates of deliberative democracy and citizen participation that provide both more meaningful engagement and better community outcomes. There are decision-making forums, “visioning” forums and facilitated group meetings, most of which feature some combination of large-group, small-group and online interactions.
But here’s the rub: Our legal framework doesn’t support these new methods of public participation. This fact is made clear in Making Public Participation Legal, which was compiled by a working group that included people from the National Civic League, the American Bar Association, the International City/County Management Association and a number of leading practitioners of public participation.
The requirements for public meetings in local governments are generally built into state statutes such as sunshine or open-meetings laws or other laws governing administrative procedures. These laws may require public hearings in certain circumstances and mandate that advance notice, along with an agenda, be posted for any meeting of an “official body” — from the state legislature to a subcommittee of the city council or an advisory board of some kind. And a “meeting” is one in which a quorum attends. So if three of a city council’s nine members sit on the finance committee and two of the committee members happen to show up at a public meeting, they may risk having violated the open-meetings law…”

Why the Nate Silvers of the World Don’t Know Everything

Curated on Posted on by Stefaan Verhulst

Felix Salmon in Wired: “This shift in US intelligence mirrors a definite pattern of the past 30 years, one that we can see across fields and institutions. It’s the rise of the quants—that is, the ascent to power of people whose native tongue is numbers and algorithms and systems rather than personal relationships or human intuition. Michael Lewis’ Moneyball vividly recounts how the quants took over baseball, as statistical analy­sis trumped traditional scouting and propelled the underfunded Oakland A’s to a division-winning 2002 season. More recently we’ve seen the rise of the quants in politics. Commentators who “trusted their gut” about Mitt Romney’s chances had their gut kicked by Nate Silver, the stats whiz who called the election days before­hand as a lock for Obama, down to the very last electoral vote in the very last state.
The reason the quants win is that they’re almost always right—at least at first. They find numerical patterns or invent ingenious algorithms that increase profits or solve problems in ways that no amount of subjective experience can match. But what happens after the quants win is not always the data-driven paradise that they and their boosters expected. The more a field is run by a system, the more that system creates incentives for everyone (employees, customers, competitors) to change their behavior in perverse ways—providing more of whatever the system is designed to measure and produce, whether that actually creates any value or not. It’s a problem that can’t be solved until the quants learn a little bit from the old-fashioned ways of thinking they’ve displaced.
No matter the discipline or industry, the rise of the quants tends to happen in four stages. Stage one is what you might call pre-disruption, and it’s generally best visible in hindsight. Think about quaint dating agencies in the days before the arrival of Match .com and all the other algorithm-powered online replacements. Or think about retail in the era before floor-space management analytics helped quantify exactly which goods ought to go where. For a live example, consider Hollywood, which, for all the money it spends on market research, is still run by a small group of lavishly compensated studio executives, all of whom are well aware that the first rule of Hollywood, as memorably summed up by screenwriter William Goldman, is “Nobody knows anything.” On its face, Hollywood is ripe for quantifi­cation—there’s a huge amount of data to be mined, considering that every movie and TV show can be classified along hundreds of different axes, from stars to genre to running time, and they can all be correlated to box office receipts and other measures of profitability.
Next comes stage two, disruption. In most industries, the rise of the quants is a recent phenomenon, but in the world of finance it began back in the 1980s. The unmistakable sign of this change was hard to miss: the point at which you started getting targeted and personalized offers for credit cards and other financial services based not on the relationship you had with your local bank manager but on what the bank’s algorithms deduced about your finances and creditworthiness. Pretty soon, when you went into a branch to inquire about a loan, all they could do was punch numbers into a computer and then give you the computer’s answer.
For a present-day example of disruption, think about politics. In the 2012 election, Obama’s old-fashioned campaign operatives didn’t disappear. But they gave money and freedom to a core group of technologists in Chicago—including Harper Reed, former CTO of the Chicago-based online retailer Threadless—and allowed them to make huge decisions about fund-raising and voter targeting. Whereas earlier campaigns had tried to target segments of the population defined by geography or demographic profile, Obama’s team made the campaign granular right down to the individual level. So if a mom in Cedar Rapids was on the fence about who to vote for, or whether to vote at all, then instead of buying yet another TV ad, the Obama campaign would message one of her Facebook friends and try the much more effective personal approach…
After disruption, though, there comes at least some version of stage three: over­shoot. The most common problem is that all these new systems—metrics, algo­rithms, automated decisionmaking processes—result in humans gaming the system in rational but often unpredictable ways. Sociologist Donald T. Campbell noted this dynamic back in the ’70s, when he articulated what’s come to be known as Campbell’s law: “The more any quantitative social indicator is used for social decision-making,” he wrote, “the more subject it will be to corruption pressures and the more apt it will be to distort and corrupt the social processes it is intended to monitor.”…
Policing is a good example, as explained by Harvard sociologist Peter Moskos in his book Cop in the Hood: My Year Policing Baltimore’s Eastern District. Most cops have a pretty good idea of what they should be doing, if their goal is public safety: reducing crime, locking up kingpins, confiscating drugs. It involves foot patrols, deep investigations, and building good relations with the community. But under statistically driven regimes, individual officers have almost no incentive to actually do that stuff. Instead, they’re all too often judged on results—specifically, arrests. (Not even convictions, just arrests: If a suspect throws away his drugs while fleeing police, the police will chase and arrest him just to get the arrest, even when they know there’s no chance of a conviction.)…
It’s increasingly clear that for smart organizations, living by numbers alone simply won’t work. That’s why they arrive at stage four: synthesis—the practice of marrying quantitative insights with old-fashioned subjective experience. Nate Silver himself has written thoughtfully about examples of this in his book, The Signal and the Noise. He cites baseball, which in the post-Moneyball era adopted a “fusion approach” that leans on both statistics and scouting. Silver credits it with delivering the Boston Red Sox’s first World Series title in 86 years. Or consider weather forecasting: The National Weather Service employs meteorologists who, understanding the dynamics of weather systems, can improve forecasts by as much as 25 percent compared with computers alone. A similar synthesis holds in eco­nomic forecasting: Adding human judgment to statistical methods makes results roughly 15 percent more accurate. And it’s even true in chess: While the best computers can now easily beat the best humans, they can in turn be beaten by humans aided by computers….
That’s what a good synthesis of big data and human intuition tends to look like. As long as the humans are in control, and understand what it is they’re controlling, we’re fine. It’s when they become slaves to the numbers that trouble breaks out. So let’s celebrate the value of disruption by data—but let’s not forget that data isn’t everything.

E-government research in the United States

Curated on Posted on by Stefaan Verhulst

Paper by JT Snead, E Wright in Government Information Quarterly: “The purpose of this exploratory study is to review scholarly publications and assess egovernment research efforts as a field of study specific to the United States e-government environment. Study results reveal that researchers who focus on the U.S. e-government environment assess specific e-government topics at the federal, state, and local levels; however, there are gaps in the research efforts by topic areas and across different levels of government, which indicate opportunities for future areas of research. Results also find that a multitude of methodology approaches are used to assess e-government. Issues, however, exist that include lack of or weak presentations of methodologies in publications, few studies include multi-method evaluation approaches for data collection and analysis efforts, and few studies take a theory-based approach to understanding the U.S. e-government environment.”

Can a Better Taxonomy Help Behavioral Energy Efficiency?

Curated on Posted on by Stefaan Verhulst

Article at GreenTechEfficiency: “Hundreds of behavioral energy efficiency programs have sprung up across the U.S. in the past five years, but the effectiveness of the programs — both in terms of cost savings and reduced energy use — can be difficult to gauge.
Of nearly 300 programs, a new report from the American Council for an Energy-Efficient Economy was able to accurately calculate the cost of saved energy from only ten programs….
To help utilities and regulators better define and measure behavioral programs, ACEEE offers a new taxonomy of utility-run behavior programs that breaks them into three major categories:
Cognition: Programs that focus on delivering information to consumers.  (This includes general communication efforts, enhanced billing and bill inserts, social media and classroom-based education.)
Calculus: Programs that rely on consumers making economically rational decisions. (This includes real-time and asynchronous feedback, dynamic pricing, games, incentives and rebates and home energy audits.)
Social interaction: Programs whose key drivers are social interaction and belonging. (This includes community-based social marketing, peer champions, online forums and incentive-based gifts.)
….
While the report was mostly preliminary, it also offered four steps forward for utilities that want to make the most of behavioral programs.
Stack. The types of programs might fit into three broad categories, but judiciously blending cues based on emotion, reason and social interaction into programs is key, according to ACEEE. Even though the report recommends stacked programs that have a multi-modal approach, the authors acknowledge, “This hypothesis will remain untested until we see more stacked programs in the marketplace.”
Track. Just like other areas of grid modernization, utilities need to rethink how they collect, analyze and report the data coming out of behavioral programs. This should include metrics that go beyond just energy savings.
Share. As with other utility programs, behavior-based energy efficiency programs can be improved upon if utilities share results and if reporting is standardized across the country instead of varying by state.
Coordinate. Sharing is only the first step. Programs that merge water, gas and electricity efficiency can often gain better results than siloed programs. That approach, however, requires a coordinated effort by regional utilities and a change to how programs are funded and evaluated by regulators.”

6 New Year’s Strategies for Open Data Entrepreneurs

Curated on Posted on by Stefaan Verhulst

The GovLab’s Senior Advisor Joel Gurin: “Open Data has fueled a wide range of startups, including consumer-focused websites, business-to-business services, data-management tech firms, and more. Many of the companies in the Open Data 500 study are new ones like these. New Year’s is a classic time to start new ventures, and with 2014 looking like a hot year for Open Data, we can expect more startups using this abundant, free resource. For my new book, Open Data Now, I interviewed dozens of entrepreneurs and distilled six of the basic strategies that they’ve used.
1. Learn how to add value to free Open Data. We’re seeing an inversion of the value proposition for data. It used to be that whoever owned the data—particularly Big Data—had greater opportunities than those who didn’t. While this is still true in many areas, it’s also clear that successful businesses can be built on free Open Data that anyone can use. The value isn’t in the data itself but rather in the analytical tools, expertise, and interpretation that’s brought to bear. One oft-cited example: The Climate Corporation, which built a billion-dollar business out of government weather and satellite data that’s freely available for use.
2. Focus on big opportunities: health, finance, energy, education. A business can be built on just about any kind of Open Data. But the greatest number of startup opportunities will likely be in the four big areas where the federal government is focused on Open Data release. Last June’s Health Datapalooza showcased the opportunities in health. Companies like Opower in energy, GreatSchools in education, and Calcbench, SigFig, and Capital Cube in finance are examples in these other major sectors.
3. Explore choice engines and Smart Disclosure apps. Smart Disclosure – releasing data that consumers can use to make marketplace choices – is a powerful tool that can be the basis for a new sector of online startups. No one, it seems, has quite figured out how to make this form of Open Data work best, although sites like CompareTheMarket in the UK may be possible models. Business opportunities await anyone who can find ways to provide these much-needed consumer services. One example: Kayak, which competed in the crowded travel field by providing a great consumer interface, and which was sold to Priceline for $1.8 billion last year.
4. Help consumers tap the value of personal data. In a privacy-conscious society, more people will be interested in controlling their personal data and sharing it selectively for their own benefit. The value of personal data is just being recognized, and opportunities remain to be developed. There are business opportunities in setting up and providing “personal data vaults” and more opportunity in applying the many ways they can be used. Personal and Reputation.com are two leaders in this field.
5. Provide new data solutions to governments at all levels. Government datasets at the federal, state, and local level can be notoriously difficult to use. The good news is that these governments are now realizing that they need help. Data management for government is a growing industry, as Socrata, OpenGov, 3RoundStones, and others are finding, while companies like Enigma.io are turning government data into a more usable resource.
6. Look for unusual Open Data opportunities. Building a successful business by gathering data on restaurant menus and recipes is not an obvious route to success. But it’s working for Food Genius, whose founders showed a kind of genius in tapping an opportunity others had missed. While the big areas for Open Data are becoming clear, there are countless opportunities to build more niche businesses that can still be highly successful. If you have expertise in an area and see a customer need, there’s an increasingly good chance that the Open Data to help meet that need is somewhere to be found.”

How could technology improve policy-making?

Curated on Posted on by Stefaan Verhulst

Beccy Allen from the Hansard Society (UK): “How can civil servants be sure they have the most relevant, current and reliable data? How can open data be incorporated into the policy making process now and what is the potential for the future use of this vast array of information? How can parliamentary clerks ensure they are aware of the broadest range of expert opinion to inform committee scrutiny? And how can citizens’ views help policy makers to design better policy at all stages of the process?
These are the kind of questions that Sense4us will be exploring over the next three years. The aim is to build a digital tool for policy-makers that can:

  1. locate a broad range of relevant and current information, specific to a particular policy, incorporating open data sets and citizens’ views particularly from social media; and
  2. simulate the consequences and impact of potential policies, allowing policy-makers to change variables and thereby better understand the likely outcomes of a range of policy options before deciding which to adopt.

It is early days for open data and open policy making. The word ‘digital’ peppers the Civil Service Reform Plan but the focus is often on providing information and transactional services digitally. Less attention is paid to how digital tools could improve the nature of policy-making itself.
The Sense4us tool aims to help bridge the gap. It will be developed in consultation with policy-makers at different levels of government across Europe to ensure its potential use by a wide range of stakeholders. At the local level, our partners GESIS (the Leibniz-Institute for the Social Sciences) will be responsible for engaging with users at the city level in Berlin and in the North Rhine-Westphalia state legislature At the multi-national level Government to You (Gov2u) will engage with users in the European Parliament and Commission. Meanwhile the Society will be responsible for national level consultation with civil servants, parliamentarians and parliamentary officials in Whitehall and Westminster exploring how the tool can be used to support the UK policy process. Our academic partners leading on technical development of the tool are the IT Innovation Centre at Southampton University, eGovlab at Stockholm University, the University of Koblenz-Landau and the Knowledge Media Institute at the Open University.”