App pays commuters to take routes that ease congestion

Curated on Posted on by Stefaan Verhulst

Springwise: “Congestion at peak hours is a major problem in the world’s busiest city centres. We’ve recently seen Gothenburg in Sweden offering free bicycles to ease the burden on public transport services, but now a new app is looking to take a different approach to the same problem. Urban Engines uses algorithms to help cities determine key congestion choke points and times, and can then reward commuters for avoiding them.
The Urban Engines system is based on commuters using the smart commuter cards already found in many major cities. The company tracks journeys made with those commuter cards, and uses that data to identify main areas of congestion, and at what times the congestion occurs. The system has already been employed in Washington, D.C, and Sao Paulo, Brazil, helping provide valuable data for work with city planners.
It’s in Singapore, however, where the most interesting work has been achieved so far. There, commuters who have signed up and registered their commuter cards can earn rewards when they travel. They will earn one point for every kilometre travelled during peak hours, or triple that when travelling off-peak. The points earned can then be converted into discounts on future journeys, or put towards an in-app raffle game, where they have the opportunity to win sums of money. Urban Engines claim there’s been a 7 to 13 percent reduction in journeys made during peak hours, with 200,000 commuters taking part.
The company is based on an original experiment carried out in Bangalore. The rewards program there, carried out among 20,000 employees of the Indian company Infosys, lead to 17 percent of traffic shifting to off-peak travel times in six months. A similarly successful experiment has also been carried out on the Stanford University campus, and the plan is to now expand to other major cities…”

Poetica

Curated on Posted on by Stefaan Verhulst

at TechnologyCrunch: “The ability to collaborate on the draft of a document is actually fiendishly tedious online. Many people might be used to Microsoft Word ‘Track Changes’ (ugh) despite the fact it looks awful and takes some getting used to. Nor does Google Docs really create a collaboration experience that mere mortals can get into. Step in Poetica, a brand new startup co-founded by Blaine Cook, formerly Twitter’s founding lead engineer.
Cook has now raised an angel round of funding for the London-based company which is hoping to change how teams create, share and edit work on the web, across any devices and mediums.
Poetica, which opens its doors to new signups today, is a browser-based editor and Chrome extension that portrays a more traditional view of text collaboration – in the same way you might see someone scribble on a piece of paper….
Cook says the goal is to “bring rich collaboration tools based on cutting-edge technology and design to everyone” who wants to communicate online. In other words, they are going for a fairly big play here. And he reckons he can do it from London, over the Valley, where he worked at Twitter: “London has an incredible community of brilliant software engineers and designers, and a growing and supportive investor base.”

Crowdsourcing moving beyond the fringe

Curated on Posted on by Stefaan Verhulst

Bob Brown in Networked World: ” Depending up on how you look at it, crowdsourcing is all the rage these days — think Wikipedia, X Prize and Kickstarter — or at the other extreme, greatly underused.
To the team behind the new “insight network” Yegii, crowdsourcing has not nearly reached its potential despite having its roots as far back as the early 1700s and a famous case of the British Government seeking a solution to “The Longitude Problem” in order to make sailing less life threatening. (I get the impression that mention of this example is obligatory at any crowdsourcing event.)
This angel-funded startup, headed by an MIT Sloan School of Management senior lecturer and operating from a Boston suburb, is looking to exploit crowdsourcing’s potential through a service that connects financial, healthcare, technology and other organizations seeking knowledge with experts who can provide it – and fairly fast. To CEO Trond Undheim, crowdsourcing is “no longer for fringe freelance work,” and the goal is to get more organizations and smart individuals involved.
“Yegii is essentially a network of networks, connecting people, organizations, and knowledge in new ways,” says Undheim, who explains that the name Yegii is Korean for “talk” or “discussion”. “Our focus is laser sharp: we only rank and rate knowledge that says something essential about what I see as the four forces of industry disruption: technology, policy, user dynamics and business models.  We tackle challenging business issues across domains, from life sciences to energy to finance.  The point is that today’s industry classification is falling apart. We need more specific insight than in-house strategizing or generalist consulting advice.”
Undheim attempted to drum up interest in the new business last week at an event at Babson College during which a handful of crowdsourcing experts spoke. Harvard Business School adjunct professor Alan MacCormack discussed the X Prize, Netflix Prize and other examples of spurring competition through crowdsourcing. MIT’s Peter Gloor extolled the virtue of collaborative and smart swarms of people vs. stupid crowds (such as football hooligans). A couple of advertising/marketing execs shared stories of how clients and other brands are increasingly tapping into their customer base and the general public for new ideas from slogans to products, figuring that potential new customers are more likely to trust their peers than corporate ads. Another speaker dove into more details about how to run a crowdsourcing challenge, which includes identifying motivation that goes beyond money.
All of this was to frame Yegii’s crowdsourcing plan, which is at the beta stage with about a dozen clients (including Akamai and Santander bank) and is slated for mass production later this year. Yegii’s team consists of five part-timers, plus a few interns, who are building a web-based platform that consists of “knowledge assets,” that is market research, news reports and datasets from free and paid sources. That content – on topics that range from Bitcoin’s impact on banks to telecom bandwidth costs — is reviewed and ranked through a combination of machine learning and human peers. Information seekers would pay Yegii up to hundreds of dollars per month or up to tens of thousands of dollars per project, and then multidisciplinary teams would accept the challenge of answering their questions via customized reports within staged deadlines.
“We are focused on building partnerships with other expert networks and associations that have access to smart people with spare capacity, wherever they are,” Undheim says.
One reason organizations can benefit from crowdsourcing, Undheim says, is because of the “ephemeral nature of expertise in today’s society.” In other words, people within your organization might think of themselves as experts in this or that, but when they really think about it, they might realize their level of expertise has faded. Yegii will strive to narrow down the best sources of information for those looking to come up to speed on a subject over a weekend, whereas hunting for that information across a vast search engine would not be nearly as efficient….”

Putting Open Data to Work for Communities

Curated on Posted on by Stefaan Verhulst
Report by  Kathryn L.S. Pettit, Leah Hendey, Brianna Losoya, G. Thomas Kingsley  at the Urban Institute: “The National Neighborhood Indicators Partnership (NNIP) is a network of local organizations that collect, organize, and use neighborhood data to tackle issues in their communities. As the movement for government transparency has spread at the local level, more NNIP partners are participating in the call for governments to release data and are using open data to provide information for decisionmaking and community engagement. Local NNIP partners and open data advocates have complementary strengths and should work together to more effectively advance open government data that benefits all residents.”

In Defense of Transit Apps

Curated on Posted on by Stefaan Verhulst

Mark Headd at Civic Innovations: “The civic technology community has a love-hate relationship with transit apps.
We love to, and often do, use the example of open transit data and the cottage industry of civic app development it has helped spawn as justification for governments releasing open data. Some of the earliest, most enduring and most successful civic applications have been built on transit data and there literally hundreds of different apps available.
The General Transit Feed Specification (GTFS), which has helped to encourage the release of transit data from dozens and dozens of transportation authorities across the country, is used as the model for the development of other open data standards. I once described work being done to develop a data standard for locations dispensing vaccinations as “GTFS for flu shots.”
bracken-tweet
But some in the civic technology community chafe at the overuse of transit apps as the example cited for the release of open data and engagement with outside civic hackers. Surely there are other examples we can point to that get at deeper, more fundamental problems with civic engagement and the operation of government. Is the best articulation of the benefits of open data and civic hacking a simple bus stop application?
Last week at Transparency Camp in DC, during a session I ran on open data, I was asked what data governments should focus on releasing as open data. I stated my belief that – at a minimum – governments should concentrate on The 3 B’s: Buses (transit data), Bullets (crime data) and Bucks (budget & expenditure data).
To be clear – transit data and the apps it helps generate are critical to the open data and civic technology movements. I think it is vital to exploring the role that transit apps have played in the development of the civic technology ecosystem and their impact on open data.

Story telling with transit data

Transit data supports more than just “next bus” apps. In fact, characterizing all transit apps this way does a disservice to the talented and creative people working to build things with transit data. Transit data supports a wide range of different visualizations that can tell an intimate, granular story about how a transit system works and how it’s operation impacts a city.
One inspiring example of this kind of app was developed recently by Mike Barry and Brian Card, and looked at the operation of MBTA in Boston. Their motive was simple:

We attempt to present this information to help people in Boston better understand the trains, how people use the trains, and how the people and trains interact with each other.

We’re able to tell nuanced stories about transit systems because the quality of data being released continues to expand and improve in quality. This happens because developers building apps in cities across the country have provided feedback to transit officials on what they want to see and the quality of what is provided.
Developers building the powerful visualizations we see today are standing on the shoulders of the people that built the “next bus” apps a few years ago. Without these humble apps, we don’t get to tell these powerful stories today.

Holding government accountable

Transit apps are about more than just getting to the train on time.
Support for transit system operations can run into the billions of dollars and affect the lives of millions of people in an urban area. With this much investment, it’s important that transit riders and taxpayers are able to hold officials accountable for the efficient operation of transit systems. To help us do this, we now have a new generation of transit apps that can examine things like the scheduled arrival and departure times of trains with their actual arrival and departure time.
Not only does this give citizens transparency into how well their transit system is being run, it offers a pathway for engagement – by knowing which routes are not performing close to scheduled times, transit riders and others can offer suggestions for changes and improvements.

A gateway to more open data

One of the most important things that transit apps can do is provide a pathway for more open data.
In Philadelphia, the city’s formal open data policy and the creation of an open data portal all followed after the efforts of a small group of developers working to obtain transit schedule data from the Southeastern Pennsylvania Transportation Authority (SEPTA). This group eventually built the region’s first transit app.
This small group pushed SEPTA to make their data open, and the Authority eventually embraced open data. This, in turn, raised the profile of open data with other city leaders and directly contributed to the adoption of an open data policy by the City of Philadelphia several years later. Without this simple transit app and the push for more open transit data, I don’t think this would have happened. Certainly not as soon as it did.
And it isn’t just big cities like Philadelphia. In Syracuse, NY – a small city with no tradition of civic hacking and no formal open data program – a group at a local hackathon decided that they wanted to build a platform for government open data.
The first data source they selected to focus on? Transit data. The first app they built? A transit app…”

The Art and Science of Data-driven Journalism

Curated on Posted on by Stefaan Verhulst

Alex Howard for the Tow Center for digital journalism: “Journalists have been using data in their stories for as long as the profession has existed. A revolution in computing in the 20th century created opportunities for data integration into investigations, as journalists began to bring technology into their work. In the 21st century, a revolution in connectivity is leading the media toward new horizons. The Internet, cloud computing, agile development, mobile devices, and open source software have transformed the practice of journalism, leading to the emergence of a new term: data journalism. Although journalists have been using data in their stories for as long as they have been engaged in reporting, data journalism is more than traditional journalism with more data. Decades after early pioneers successfully applied computer-assisted reporting and social science to investigative journalism, journalists are creating news apps and interactive features that help people understand data, explore it, and act upon the insights derived from it. New business models are emerging in which data is a raw material for profit, impact, and insight, co-created with an audience that was formerly reduced to passive consumption. Journalists around the world are grappling with the excitement and the challenge of telling compelling stories by harnessing the vast quantity of data that our increasingly networked lives, devices, businesses, and governments produce every day. While the potential of data journalism is immense, the pitfalls and challenges to its adoption throughout the media are similarly significant, from digital literacy to competition for scarce resources in newsrooms. Global threats to press freedom, digital security, and limited access to data create difficult working conditions for journalists in many countries. A combination of peer-to-peer learning, mentorship, online training, open data initiatives, and new programs at journalism schools rising to the challenge, however, offer reasons to be optimistic about more journalists learning to treat data as a source. (Download the report)”

Why Statistically Significant Studies Aren’t Necessarily Significant

Curated on Posted on by Stefaan Verhulst

Michael White in PSMagazine on how modern statistics have made it easier than ever for us to fool ourselves: “Scientific results often defy common sense. Sometimes this is because science deals with phenomena that occur on scales we don’t experience directly, like evolution over billions of years or molecules that span billionths of meters. Even when it comes to things that happen on scales we’re familiar with, scientists often draw counter-intuitive conclusions from subtle patterns in the data. Because these patterns are not obvious, researchers rely on statistics to distinguish the signal from the noise. Without the aid of statistics, it would be difficult to convincingly show that smoking causes cancer, that drugged bees can still find their way home, that hurricanes with female names are deadlier than ones with male names, or that some people have a precognitive sense for porn.
OK, very few scientists accept the existence of precognition. But Cornell psychologist Daryl Bem’s widely reported porn precognition study illustrates the thorny relationship between science, statistics, and common sense. While many criticisms were leveled against Bem’s study, in the end it became clear that the study did not suffer from an obvious killer flaw. If it hadn’t dealt with the paranormal, it’s unlikely that Bem’s work would have drawn much criticism. As one psychologist put it after explaining how the study went wrong, “I think Bem’s actually been relatively careful. The thing to remember is that this type of fudging isn’t unusual; to the contrary, it’s rampant–everyone does it. And that’s because it’s very difficult, and often outright impossible, to avoid.”…
That you can lie with statistics is well known; what is less commonly noted is how much scientists still struggle to define proper statistical procedures for handling the noisy data we collect in the real world. In an exchange published last month in the Proceedings of the National Academy of Sciences, statisticians argued over how to address the problem of false positive results, statistically significant findings that on further investigation don’t hold up. Non-reproducible results in science are a growing concern; so do researchers need to change their approach to statistics?
Valen Johnson, at Texas A&M University, argued that the commonly used threshold for statistical significance isn’t as stringent as scientists think it is, and therefore researchers should adopt a tighter threshold to better filter out spurious results. In reply, statisticians Andrew Gelman and Christian Robert argued that tighter thresholds won’t solve the problem; they simply “dodge the essential nature of any such rule, which is that it expresses a tradeoff between the risks of publishing misleading results and of important results being left unpublished.” The acceptable level of statistical significance should vary with the nature of the study. Another team of statisticians raised a similar point, arguing that a more stringent significance threshold would exacerbate the worrying publishing bias against negative results. Ultimately, good statistical decision making “depends on the magnitude of effects, the plausibility of scientific explanations of the mechanism, and the reproducibility of the findings by others.”
However, arguments over statistics usually occur because it is not always obvious how to make good statistical decisions. Some bad decisions are clear. As xkcd’s Randall Munroe illustrated in his comic on the spurious link between green jelly beans and acne, most people understand that if you keep testing slightly different versions of a hypothesis on the same set of data, sooner or later you’re likely to get a statistically significant result just by chance. This kind of statistical malpractice is called fishing or p-hacking, and most scientists know how to avoid it.
But there are more subtle forms of the problem that pervade the scientific literature. In an unpublished paper (PDF), statisticians Andrew Gelman, at Columbia University, and Eric Loken, at Penn State, argue that researchers who deliberately avoid p-hacking still unknowingly engage in a similar practice. The problem is that one scientific hypothesis can be translated into many different statistical hypotheses, with many chances for a spuriously significant result. After looking at their data, researchers decide which statistical hypothesis to test, but that decision is skewed by the data itself.
To see how this might happen, imagine a study designed to test the idea that green jellybeans cause acne. There are many ways the results could come out statistically significant in favor of the researchers’ hypothesis. Green jellybeans could cause acne in men, but not in women, or in women but not men. The results may be statistically significant if the jellybeans you call “green” include Lemon Lime, Kiwi, and Margarita but not Sour Apple. Gelman and Loken write that “researchers can perform a reasonable analysis given their assumptions and their data, but had the data turned out differently, they could have done other analyses that were just as reasonable in those circumstances.” In the end, the researchers may explicitly test only one or a few statistical hypotheses, but their decision-making process has already biased them toward the hypotheses most likely to be supported by their data. The result is “a sort of machine for producing and publicizing random patterns.”
Gelman and Loken are not alone in their concern. Last year Daniele Fanelli, at the University of Edingburgh, and John Ioannidis, at Stanford University, reported that many U.S. studies, particularly in the social sciences, may overestimate the effect sizes of their results. “All scientists have to make choices throughout a research project, from formulating the question to submitting results for publication.” These choices can be swayed “consciously or unconsciously, by scientists’ own beliefs, expectations, and wishes, and the most basic scientific desire is that of producing an important research finding.”
What is the solution? Part of the answer is to not let measures of statistical significance override our common sense—not our naïve common sense, but our scientifically-informed common sense…”

Selected Readings on Crowdsourcing Tasks and Peer Production

Curated on Posted on by Andrew Young

The Living Library’s Selected Readings series seeks to build a knowledge base on innovative approaches for improving the effectiveness and legitimacy of governance. This curated and annotated collection of recommended works on the topic of crowdsourcing was originally published in 2014.

Technological advances are creating a new paradigm by which institutions and organizations are increasingly outsourcing tasks to an open community, allocating specific needs to a flexible, willing and dispersed workforce. “Microtasking” platforms like Amazon’s Mechanical Turk are a burgeoning source of income for individuals who contribute their time, skills and knowledge on a per-task basis. In parallel, citizen science projects – task-based initiatives in which citizens of any background can help contribute to scientific research – like Galaxy Zoo are demonstrating the ability of lay and expert citizens alike to make small, useful contributions to aid large, complex undertakings. As governing institutions seek to do more with less, looking to the success of citizen science and microtasking initiatives could provide a blueprint for engaging citizens to help accomplish difficult, time-consuming objectives at little cost. Moreover, the incredible success of peer-production projects – best exemplified by Wikipedia – instills optimism regarding the public’s willingness and ability to complete relatively small tasks that feed into a greater whole and benefit the public good. You can learn more about this new wave of “collective intelligence” by following the MIT Center for Collective Intelligence and their annual Collective Intelligence Conference.

Selected Reading List (in alphabetical order)

Annotated Selected Reading List (in alphabetical order)

Benkler, Yochai. The Wealth of Networks: How Social Production Transforms Markets and Freedom. Yale University Press, 2006. http://bit.ly/1aaU7Yb.

  • In this book, Benkler “describes how patterns of information, knowledge, and cultural production are changing – and shows that the way information and knowledge are made available can either limit or enlarge the ways people can create and express themselves.”
  • In his discussion on Wikipedia – one of many paradigmatic examples of people collaborating without financial reward – he calls attention to the notable ongoing cooperation taking place among a diversity of individuals. He argues that, “The important point is that Wikipedia requires not only mechanical cooperation among people, but a commitment to a particular style of writing and describing concepts that is far from intuitive or natural to people. It requires self-discipline. It enforces the behavior it requires primarily through appeal to the common enterprise that the participants are engaged in…”

Brabham, Daren C. Using Crowdsourcing in Government. Collaborating Across Boundaries Series. IBM Center for The Business of Government, 2013. http://bit.ly/17gzBTA.

  • In this report, Brabham categorizes government crowdsourcing cases into a “four-part, problem-based typology, encouraging government leaders and public administrators to consider these open problem-solving techniques as a way to engage the public and tackle difficult policy and administrative tasks more effectively and efficiently using online communities.”
  • The proposed four-part typology describes the following types of crowdsourcing in government:
    • Knowledge Discovery and Management
    • Distributed Human Intelligence Tasking
    • Broadcast Search
    • Peer-Vetted Creative Production
  • In his discussion on Distributed Human Intelligence Tasking, Brabham argues that Amazon’s Mechanical Turk and other microtasking platforms could be useful in a number of governance scenarios, including:
    • Governments and scholars transcribing historical document scans
    • Public health departments translating health campaign materials into foreign languages to benefit constituents who do not speak the native language
    • Governments translating tax documents, school enrollment and immunization brochures, and other important materials into minority languages
    • Helping governments predict citizens’ behavior, “such as for predicting their use of public transit or other services or for predicting behaviors that could inform public health practitioners and environmental policy makers”

Boudreau, Kevin J., Patrick Gaule, Karim Lakhani, Christoph Reidl, Anita Williams Woolley. “From Crowds to Collaborators: Initiating Effort & Catalyzing Interactions Among Online Creative Workers.” Harvard Business School Technology & Operations Mgt. Unit Working Paper No. 14-060. January 23, 2014. https://bit.ly/2QVmGUu.

  • In this working paper, the authors explore the “conditions necessary for eliciting effort from those affecting the quality of interdependent teamwork” and “consider the the role of incentives versus social processes in catalyzing collaboration.”
  • The paper’s findings are based on an experiment involving 260 individuals randomly assigned to 52 teams working toward solutions to a complex problem.
  • The authors determined the level of effort in such collaborative undertakings are sensitive to cash incentives. However, collaboration among teams was driven more by the active participation of teammates, rather than any monetary reward.

Franzoni, Chiara, and Henry Sauermann. “Crowd Science: The Organization of Scientific Research in Open Collaborative Projects.” Research Policy (August 14, 2013). http://bit.ly/HihFyj.

  • In this paper, the authors explore the concept of crowd science, which they define based on two important features: “participation in a project is open to a wide base of potential contributors, and intermediate inputs such as data or problem solving algorithms are made openly available.” The rationale for their study and conceptual framework is the “growing attention from the scientific community, but also policy makers, funding agencies and managers who seek to evaluate its potential benefits and challenges. Based on the experiences of early crowd science projects, the opportunities are considerable.”
  • Based on the study of a number of crowd science projects – including governance-related initiatives like Patients Like Me – the authors identify a number of potential benefits in the following categories:
    • Knowledge-related benefits
    • Benefits from open participation
    • Benefits from the open disclosure of intermediate inputs
    • Motivational benefits
  • The authors also identify a number of challenges:
    • Organizational challenges
    • Matching projects and people
    • Division of labor and integration of contributions
    • Project leadership
    • Motivational challenges
    • Sustaining contributor involvement
    • Supporting a broader set of motivations
    • Reconciling conflicting motivations

Kittur, Aniket, Ed H. Chi, and Bongwon Suh. “Crowdsourcing User Studies with Mechanical Turk.” In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, 453–456. CHI ’08. New York, NY, USA: ACM, 2008. http://bit.ly/1a3Op48.

  • In this paper, the authors examine “[m]icro-task markets, such as Amazon’s Mechanical Turk, [which] offer a potential paradigm for engaging a large number of users for low time and monetary costs. [They] investigate the utility of a micro-task market for collecting user measurements, and discuss design considerations for developing remote micro user evaluation tasks.”
  • The authors conclude that in addition to providing a means for crowdsourcing small, clearly defined, often non-skill-intensive tasks, “Micro-task markets such as Amazon’s Mechanical Turk are promising platforms for conducting a variety of user study tasks, ranging from surveys to rapid prototyping to quantitative measures. Hundreds of users can be recruited for highly interactive tasks for marginal costs within a timeframe of days or even minutes. However, special care must be taken in the design of the task, especially for user measurements that are subjective or qualitative.”

Kittur, Aniket, Jeffrey V. Nickerson, Michael S. Bernstein, Elizabeth M. Gerber, Aaron Shaw, John Zimmerman, Matthew Lease, and John J. Horton. “The Future of Crowd Work.” In 16th ACM Conference on Computer Supported Cooperative Work (CSCW 2013), 2012. http://bit.ly/1c1GJD3.

  • In this paper, the authors discuss paid crowd work, which “offers remarkable opportunities for improving productivity, social mobility, and the global economy by engaging a geographically distributed workforce to complete complex tasks on demand and at scale.” However, they caution that, “it is also possible that crowd work will fail to achieve its potential, focusing on assembly-line piecework.”
  • The authors argue that seven key challenges must be met to ensure that crowd work processes evolve and reach their full potential:
    • Designing workflows
    • Assigning tasks
    • Supporting hierarchical structure
    • Enabling real-time crowd work
    • Supporting synchronous collaboration
    • Controlling quality

Madison, Michael J. “Commons at the Intersection of Peer Production, Citizen Science, and Big Data: Galaxy Zoo.” In Convening Cultural Commons, 2013. http://bit.ly/1ih9Xzm.

  • This paper explores a “case of commons governance grounded in research in modern astronomy. The case, Galaxy Zoo, is a leading example of at least three different contemporary phenomena. In the first place, Galaxy Zoo is a global citizen science project, in which volunteer non-scientists have been recruited to participate in large-scale data analysis on the Internet. In the second place, Galaxy Zoo is a highly successful example of peer production, some times known as crowdsourcing…In the third place, is a highly visible example of data-intensive science, sometimes referred to as e-science or Big Data science, by which scientific researchers develop methods to grapple with the massive volumes of digital data now available to them via modern sensing and imaging technologies.”
  • Madison concludes that the success of Galaxy Zoo has not been the result of the “character of its information resources (scientific data) and rules regarding their usage,” but rather, the fact that the “community was guided from the outset by a vision of a specific organizational solution to a specific research problem in astronomy, initiated and governed, over time, by professional astronomers in collaboration with their expanding universe of volunteers.”

Malone, Thomas W., Robert Laubacher and Chrysanthos Dellarocas. “Harnessing Crowds: Mapping the Genome of Collective Intelligence.” MIT Sloan Research Paper. February 3, 2009. https://bit.ly/2SPjxTP.

  • In this article, the authors describe and map the phenomenon of collective intelligence – also referred to as “radical decentralization, crowd-sourcing, wisdom of crowds, peer production, and wikinomics – which they broadly define as “groups of individuals doing things collectively that seem intelligent.”
  • The article is derived from the authors’ work at MIT’s Center for Collective Intelligence, where they gathered nearly 250 examples of Web-enabled collective intelligence. To map the building blocks or “genes” of collective intelligence, the authors used two pairs of related questions:
    • Who is performing the task? Why are they doing it?
    • What is being accomplished? How is it being done?
  • The authors concede that much work remains to be done “to identify all the different genes for collective intelligence, the conditions under which these genes are useful, and the constraints governing how they can be combined,” but they believe that their framework provides a useful start and gives managers and other institutional decisionmakers looking to take advantage of collective intelligence activities the ability to “systematically consider many possible combinations of answers to questions about Who, Why, What, and How.”

Mulgan, Geoff. “True Collective Intelligence? A Sketch of a Possible New Field.” Philosophy & Technology 27, no. 1. March 2014. http://bit.ly/1p3YSdd.

  • In this paper, Mulgan explores the concept of a collective intelligence, a “much talked about but…very underdeveloped” field.
  • With a particular focus on health knowledge, Mulgan “sets out some of the potential theoretical building blocks, suggests an experimental and research agenda, shows how it could be analysed within an organisation or business sector and points to possible intellectual barriers to progress.”
  • He concludes that the “central message that comes from observing real intelligence is that intelligence has to be for something,” and that “turning this simple insight – the stuff of so many science fiction stories – into new theories, new technologies and new applications looks set to be one of the most exciting prospects of the next few years and may help give shape to a new discipline that helps us to be collectively intelligent about our own collective intelligence.”

Sauermann, Henry and Chiara Franzoni. “Participation Dynamics in Crowd-Based Knowledge Production: The Scope and Sustainability of Interest-Based Motivation.” SSRN Working Papers Series. November 28, 2013. http://bit.ly/1o6YB7f.

  • In this paper, Sauremann and Franzoni explore the issue of interest-based motivation in crowd-based knowledge production – in particular the use of the crowd science platform Zooniverse – by drawing on “research in psychology to discuss important static and dynamic features of interest and deriv[ing] a number of research questions.”
  • The authors find that interest-based motivation is often tied to a “particular object (e.g., task, project, topic)” not based on a “general trait of the person or a general characteristic of the object.” As such, they find that “most members of the installed base of users on the platform do not sign up for multiple projects, and most of those who try out a project do not return.”
  • They conclude that “interest can be a powerful motivator of individuals’ contributions to crowd-based knowledge production…However, both the scope and sustainability of this interest appear to be rather limited for the large majority of contributors…At the same time, some individuals show a strong and more enduring interest to participate both within and across projects, and these contributors are ultimately responsible for much of what crowd science projects are able to accomplish.”

Schmitt-Sands, Catherine E. and Richard J. Smith. “Prospects for Online Crowdsourcing of Social Science Research Tasks: A Case Study Using Amazon Mechanical Turk.” SSRN Working Papers Series. January 9, 2014. http://bit.ly/1ugaYja.

  • In this paper, the authors describe an experiment involving the nascent use of Amazon’s Mechanical Turk as a social science research tool. “While researchers have used crowdsourcing to find research subjects or classify texts, [they] used Mechanical Turk to conduct a policy scan of local government websites.”
  • Schmitt-Sands and Smith found that “crowdsourcing worked well for conducting an online policy program and scan.” The microtasked workers were helpful in screening out local governments that either did not have websites or did not have the types of policies and services for which the researchers were looking. However, “if the task is complicated such that it requires ongoing supervision, then crowdsourcing is not the best solution.”

Shirky, Clay. Here Comes Everybody: The Power of Organizing Without Organizations. New York: Penguin Press, 2008. https://bit.ly/2QysNif.

  • In this book, Shirky explores our current era in which, “For the first time in history, the tools for cooperating on a global scale are not solely in the hands of governments or institutions. The spread of the Internet and mobile phones are changing how people come together and get things done.”
  • Discussing Wikipedia’s “spontaneous division of labor,” Shirky argues that the process is like, “the process is more like creating a coral reef, the sum of millions of individual actions, than creating a car. And the key to creating those individual actions is to hand as much freedom as possible to the average user.”

Silvertown, Jonathan. “A New Dawn for Citizen Science.” Trends in Ecology & Evolution 24, no. 9 (September 2009): 467–471. http://bit.ly/1iha6CR.

  • This article discusses the move from “Science for the people,” a slogan adopted by activists in the 1970s to “’Science by the people,’ which is “a more inclusive aim, and is becoming a distinctly 21st century phenomenon.”
  • Silvertown identifies three factors that are responsible for the explosion of activity in citizen science, each of which could be similarly related to the crowdsourcing of skills by governing institutions:
    • “First is the existence of easily available technical tools for disseminating information about products and gathering data from the public.
    • A second factor driving the growth of citizen science is the increasing realisation among professional scientists that the public represent a free source of labour, skills, computational power and even finance.
    • Third, citizen science is likely to benefit from the condition that research funders such as the National Science Foundation in the USA and the Natural Environment Research Council in the UK now impose upon every grantholder to undertake project-related science outreach. This is outreach as a form of public accountability.”

Szkuta, Katarzyna, Roberto Pizzicannella, David Osimo. “Collaborative approaches to public sector innovation: A scoping study.” Telecommunications Policy. 2014. http://bit.ly/1oBg9GY.

  • In this article, the authors explore cases where government collaboratively delivers online public services, with a focus on success factors and “incentives for services providers, citizens as users and public administration.”
  • The authors focus on six types of collaborative governance projects:
    • Services initiated by government built on government data;
    • Services initiated by government and making use of citizens’ data;
    • Services initiated by civil society built on open government data;
    • Collaborative e-government services; and
    • Services run by civil society and based on citizen data.
  • The cases explored “are all designed in the way that effectively harnesses the citizens’ potential. Services susceptible to collaboration are those that require computing efforts, i.e. many non-complicated tasks (e.g. citizen science projects – Zooniverse) or citizens’ free time in general (e.g. time banks). Those services also profit from unique citizens’ skills and their propensity to share their competencies.”

The Promise of a New Internet

Curated on Posted on by Stefaan Verhulst

Adrienne Lafrance in the Atlantic: “People tend to talk about the Internet the way they talk about democracy—optimistically, and in terms that describe how it ought to be rather than how it actually is.

This idealism is what buoys much of the network neutrality debate, and yet many of what are considered to be the core issues at stake—like payment for tiered access, for instance—have already been decided. For years, Internet advocates have been asking what regulatory measures might help save the open, innovation-friendly Internet.
But increasingly, another question comes up: What if there were a technical solution instead of a regulatory one? What if the core architecture of how people connect could make an end run on the centralization of services that has come to define the modern net?
It’s a question that reflects some of the Internet’s deepest cultural values, and the idea that this network—this place where you are right now—should distribute power to people. In the post-NSA, post-Internet-access-oligopoly world, more and more people are thinking this way, and many of them are actually doing something about it.
Among them, there is a technology that’s become a kind of shorthand code for a whole set of beliefs about the future of the Internet: “mesh networking.” These words have become a way to say that you believe in a different, freer Internet.
*  *  *
Mesh networks promise the things we already expect but don’t always get from the Internet: they’re fast, reliable, and relatively inexpensive. But before we get into the particulars of what this alternate Internet might look like, a quick refresher on how the one we have works:
Your computer is connected to an Internet service provider like Comcast, which sends packets of your data (the binary stuff of emails, tweets, Facebook status updates, web addresses, etc.) back and forth across the network. The packets that move across the Internet encounter a series of checkpoints including routers and servers along the paths your data travels. You can’t control these paths or these checkpoints, so your data is subject to all kinds of security threats like hackers and snooping NSA agents.
So the idea behind mesh networking is to skip those checkpoints and cut out the middleman service provider whenever possible. This can work when each device in a network connects to the other devices, rather than each device connecting to the ISP.
It helps to visualize it. The image on the left shows a network built around a centralized hub, like the Internet as we know it. The image on the right is what a mesh network looks like:

Think of it this way: With a mesh network, each device is like a mini cell phone tower. So instead of having multiple devices rely on a single, centralized hub; multiple devices rely on one another. And with information ricocheting across the network more unpredictably between those devices, the network as a whole is harder to take out.
“You end up with a network that is much harder to disrupt,” said Stanislav Shalunov, co-founder of Open Garden, a startup that develops peer-to-peer and mesh networking apps. “There is no single point where you can unplug and expect that there will be a large impact.”
Plus, a mesh network forms itself based on an algorithm—which again reduces opportunities for disruption. “There is no human intervention involved, even from the users of the devices and certainly not from any administrative entity that needs to arrange the topology of this network or how people are connected or how the network is used,” Shalunov told me. “It is entirely up to the people participating and the software that runs this network to make everything work.”

Your regular old smartphone already has the power to connect to other smartphones without being hooked up to the Internet through a traditional carrier. All you need is the radio frequency of your phone’s bluetooth connection, and you can send and receive data over a mesh network from anyone in relatively close proximity—say, a person in the same neighborhood or office building. (Mesh networks can also be built around cheap wireless routers or roof antennae.)…
For now, there’s no nationwide device-to-device mesh network. So if you want to communicate with someone across the country,  someone—but not everyone—in the mesh network will need to be connected to the Internet through a traditional provider. That’s true locally, too, if you want the mesh network hooked up to the rest of the Internet. Mesh networks are more reliable in a crowd because devices can rely on one another—rather than each device trying to ping the same overburdened cell phone tower. “The important thing is we can use any of the Internet connections that anybody in that mesh network is connected to,” Shalunov said. “So maybe you are connected to AT&T and I am connected to Comcast and my phone is on Verizon and there is a Sprint subscriber nearby. If any of these will let the traffic through, all of it will get through.”
* * *
Mesh networks have been around, at least theoretically, for at least as long as the Internet has existed…”

How NYC Open Data and Reddit Saved New Yorkers Over $55,000 a Year

Curated on Posted on by Stefaan Verhulst

IQuantNY: “NYC generates an enormous amount of data each year, and for the most part, it stays behind closed doors.  But thanks to the Open Data movement, signed into law by Bloomberg in 2012 and championed over the last several years by Borough President Gale Brewer, along with other council members, we now get to see a small slice of what the city knows. And that slice is growing.
There have been some detractors along the way; a senior attorney for the NYPD said in 2012 during a council hearing that releasing NYPD data in csv format was a problem because they were “concerned with the integrity of the data itself” and because “data could be manipulated by people who want ‘to make a point’ of some sort”.  But our democracy is built on the idea of free speech; we let all the information out and then let reason lead the way.
In some ways, Open Data adds another check and balance into government: its citizens.  I’ve watched the perfect example of this check work itself out over the past month.  You may have caught my post that used parking ticket data to identify the fire hydrant in New York City that was generating the most income for the city in the form of fines: $33,000 a year.  And on the next block, the second most profitable hydrant was generating $24,000 a year.  That’s two consecutive blocks with hydrants generating over $55,000 a year. But there was a problem.  In my post, I laid out why these two parking spots were extremely confusing and basically seemed like a trap; there was a wide “curb extension” between the street and the hydrant, making it appear like the hydrant was not by the street.  Additionally, the DOT had painted parking spots right where you would be fined if you parked.
Once the data was out there, the hydrant took on a life of its own.  First, it raised to the top of the nyc sub-reddit.  That is basically one way that the internet voted that this is in-fact “interesting”.  And that is how things go from small to big. From there, it travelled to the New York Observer, which was able to get a comment from the DOT. After that, it appeared in the New York Post, the post was republished in Gothamist and finally it even went global in the Daily Mail.
I guess the pressure was on the DOT at this point, as each media source reached out for comment, but what struck me was their response to the Observer:

“While DOT has not received any complaints about this location, we will review the roadway markings and make any appropriate alterations”

Why does someone have to complain in order for the DOT to see problems like this?  In fact, the DOT just redesigned every parking sign in New York because some of the old ones were considered confusing.  But if this hydrant was news to them, it implies that they did not utilize the very strongest source of measuring confusion on our streets: NYC parking tickets….”