VoXup

Curated on Posted on by Stefaan Verhulst
Nesta: “Does your street feel safe? Would you like to change something in your neighbourhood? Is there enough for young people to do?
All basic questions, but how many local councillors have the time to put these issues to their constituents? A new web app aims to make it easier for councillors and council officers to talk to residents – and it’s all based around a series of simple questions.
Now, just a year after VoXup was created in a north London pub, Camden Council is using it to consult residents on its budget proposals.
One of VoXup’s creators, Peter Lewis, hit upon the idea after meeting an MP and being reminded of how hard it can be to get involved in decision-making….

Now VoXup is being used by Camden Council to engage with residents about its spending plans.
“They’ve got to cut a lot of money and they want to know which services people would prioritise,” Lewis explains.
“So we’ve created a custom community, and most popular topics have got about 200 votes. About 650 people have taken part at some level, and it’s only just begun. We’ve seen a lot of activity – of the people who look at the web page, almost half give an opinion on something.”

‘No need for smartphone app’
What does the future hold for VoXup? Lewis, who is working on the project full-time, says one thing the team won’t be doing is building a smartphone app.
“One of the things we thought about doing was creating a mobile app, but that’s been really unnecessary – we built VoXup as a responsive web app,” he says…. (More)”.

Turns Out the Internet Is Bad at Guessing How Many Coins Are in a Jar

Curated on Posted on by Stefaan Verhulst

Eric B. Steiner at Wired: “A few weeks ago, I asked the internet to guess how many coins were in a huge jar…The mathematical theory behind this kind of estimation game is apparently sound. That is, the mean of all the estimates will be uncannily close to the actual value, every time. James Surowiecki’s best-selling book, Wisdom of the Crowd, banks on this principle, and details several striking anecdotes of crowd accuracy. The most famous is a 1906 competition in Plymouth, England to guess the weight of an ox. As reported by Sir Francis Galton in a letter to Nature, no one guessed the actual weight of the ox, but the average of all 787 submitted guesses was exactly the beast’s actual weight….
So what happened to the collective intelligence supposedly buried in our disparate ignorance?
Most successful crowdsourcing projects are essentially the sum of many small parts: efficiently harvested resources (information, effort, money) courtesy of a large group of contributors. Think Wikipedia, Google search results, Amazon’s Mechanical Turk, and KickStarter.
But a sum of parts does not wisdom make. When we try to produce collective intelligence, things get messy. Whether we are predicting the outcome of an election, betting on sporting contests, or estimating the value of coins in a jar, the crowd’s take is vulnerable to at least three major factors: skill, diversity, and independence.
A certain amount of skill or knowledge in the crowd is obviously required, while crowd diversity expands the number of possible solutions or strategies. Participant independence is important because it preserves the value of individual contributors, which is another way of saying that if everyone copies their neighbor’s guess, the data are doomed.
Failure to meet any one of these conditions can lead to wildly inaccurate answers, information echo, or herd-like behavior. (There is more than a little irony with the herding hazard: The internet makes it possible to measure crowd wisdom and maybe put it to use. Yet because people tend to base their opinions on the opinions of others, the internet ends up amplifying the social conformity effect, thereby preventing an accurate picture of what the crowd actually thinks.)
What’s more, even when these conditions—skill, diversity, independence—are reasonably satisfied, as they were in the coin jar experiment, humans exhibit a whole host of other cognitive biases and irrational thinking that can impede crowd wisdom. True, some bias can be positive; all that Gladwellian snap-judgment stuff. But most biases aren’t so helpful, and can too easily lead us to ignore evidence, overestimate probabilities, and see patterns where there are none. These biases are not vanquished simply by expanding sample size. On the contrary, they get magnified.
Given the last 60 years of research in cognitive psychology, I submit that Galton’s results with the ox weight data were outrageously lucky, and that the same is true of other instances of seemingly perfect “bean jar”-styled experiments….”

Gamifying Cancer Research Crowdsources the Race for the Cure

Curated on Posted on by Stefaan Verhulst

Jason Brick at PSFK: “Computer time and human hours are among of the biggest obstacles in the face of progress in the fight against cancer. Researchers have terabytes of data, but only so many processors and people with which to analyze it. Much like the SETI program (Search for Extra Terrestrial Intelligence), it’s likely that big answers are already in the information we’ve collected. They’re just waiting for somebody to find them.
Reverse the Odds, a free mobile game from Cancer Research UK, accesses the combined resources of geeks and gamers worldwide. It’s a simple app game, the kind you play in line at the bank or while waiting at the dentist’s office, in which you complete mini puzzles and buy upgrades to save an imaginary world.
Each puzzle of the game is a repurposing of cancer data. Players find patterns in the data — the exact kind of analysis grad students and volunteers in a lab look for — and the results get compiled by Cancer Research UK for use in finding a cure. Errors are expected and accounted for because the thousands of players expected will round out the occasional mistake….(More)”

The New Thing in Google Flu Trends Is Traditional Data

Curated on Posted on by Stefaan Verhulst

in the New York Times: “Google is giving its Flu Trends service an overhaul — “a brand new engine,” as it announced in a blog post on Friday.

The new thing is actually traditional data from the Centers for Disease Control and Prevention that is being integrated into the Google flu-tracking model. The goal is greater accuracy after the Google service had been criticized for consistently over-estimating flu outbreaks in recent years.

The main critique came in an analysis done by four quantitative social scientists, published earlier this year in an article in Science magazine, “The Parable of Google Flu: Traps in Big Data Analysis.” The researchers found that the most accurate flu predictor was a data mash-up that combined Google Flu Trends, which monitored flu-related search terms, with the official C.D.C. reports from doctors on influenza-like illness.

The Google Flu Trends team is heeding that advice. In the blog post, written by Christian Stefansen, a Google senior software engineer, wrote, “We’re launching a new Flu Trends model in the United States that — like many of the best performing methods in the literature — takes official CDC flu data into account as the flu season progresses.”

Google’s flu-tracking service has had its ups and downs. Its triumph came in 2009, when it gave an advance signal of the severity of the H1N1 outbreak, two weeks or so ahead of official statistics. In a 2009 article in Nature explaining how Google Flu Trends worked, the company’s researchers did, as the Friday post notes, say that the Google service was not intended to replace official flu surveillance methods and that it was susceptible to “false alerts” — anything that might prompt a surge in flu-related search queries.

Yet those caveats came a couple of pages into the Nature article. And Google Flu Trends became a symbol of the superiority of the new, big data approach — computer algorithms mining data trails for collective intelligence in real time. To enthusiasts, it seemed so superior to the antiquated method of collecting health data that involved doctors talking to patients, inspecting them and filing reports.

But Google’s flu service greatly overestimated the number of cases in the United States in the 2012-13 flu season — a well-known miss — and, according to the research published this year, has persistently overstated flu cases over the years. In the Science article, the social scientists called it “big data hubris.”

Open Access Button

Curated on Posted on by Stefaan Verhulst

About the Open Access Button: “The key functions of the Open Access Button are finding free research, making more research available and also advocacy. Here’s how each works.

Finding free papers

Research published in journals that require you to pay to read can sometimes be accessed free in other places. These other copies are often very similar to the published version, but may lack nice formatting or be a version prior to peer review. These copies can be found in research repositories, on authors websites and many other places because they’re archived. To find these versions we identify the paper a user needs and effectively search on Google Scholar and CORE to find these copies and link them to the users.

Making more research, or information about papers available

If a free copy isn’t available we aim to make one. This is not a simple task and so we have to use a few different innovative strategies. First, we email the author of the research and ask them to make a copy of the research available – once they do this we’ll send it to everyone who needs it. Second, we create pages for each paper needed which, if shared, viewed, and linked to an author could see and provide their paper on. Third, we’re building ways to find associated information about a paper such as the facts contained, comments from people who’ve read it, related information and lay summaries.

Advocacy

Unfortunately the Open Access Button can only do so much, and isn’t a perfect or long term solution to this problem. The data and stories collected by the Button are used to help make the changes required to really solve this issue. We also support campaigns and grassroots advocates with this at openaccessbutton.org/action..”

Social Collective Intelligence

Curated on Posted on by Stefaan Verhulst

New book edited by Daniele Miorandi, Vincenzo Maltese, Michael Rovatsos, Anton Nijholt, and James Stewart: “The book focuses on Social Collective Intelligence, a term used to denote a class of socio-technical systems that combine, in a coordinated way, the strengths of humans, machines and collectives in terms of competences, knowledge and problem solving capabilities with the communication, computing and storage capabilities of advanced ICT.
Social Collective Intelligence opens a number of challenges for researchers in both computer science and social sciences; at the same time it provides an innovative approach to solve challenges in diverse application domains, ranging from health to education and organization of work.
The book will provide a cohesive and holistic treatment of Social Collective Intelligence, including challenges emerging in various disciplines (computer science, sociology, ethics) and opportunities for innovating in various application areas.
By going through the book the reader will gauge insight and knowledge into the challenges and opportunities provided by this new, exciting, field of investigation. Benefits for scientists will be in terms of accessing a comprehensive treatment of the open research challenges in a multidisciplinary perspective. Benefits for practitioners and applied researchers will be in terms of access to novel approaches to tackle relevant problems in their field. Benefits for policy-makers and public bodies representatives will be in terms of understanding how technological advances can support them in supporting the progress of society and economy…”

Journey tracking app will use cyclist data to make cities safer for bikes

Curated on Posted on by Stefaan Verhulst

Springwise: “Most cities were never designed to cater for the huge numbers of bikes seen on their roads every day, and as the number of cyclists grows, so do the fatality statistics thanks to limited investment in safe cycle paths. While Berlin already crowdsources bikers’ favorite cycle routes and maps them through the Dynamic Connections platform, a new app called WeCycle lets cyclists track their journeys, pooling their data to create heat maps for city planners.
Created by the UK’s TravelAI transport startup, WeCycle taps into the current consumer trend for quantifying every aspect of life, including journey times. By downloading the free iOS app, London cyclists can seamlessly create stats each time they get on their bike. They app runs in the background and uses the device’s accelerometer to smartly distinguish walking or running from cycling. They can then see how far they’ve traveled, how fast they cycle and every route they’ve taken. Additionally, the app also tracks bus and car travel.
Anyone that downloads the app agrees that their data can be anonymously sent to TravelAI, creating an accurate and real-time information resource. It aims to create tools such as heat maps and behavior monitoring for cities and local authorities to learn more about how citizens are using roads to better inform their transport policies.
WeCycle follows in the footsteps of similar apps such as Germany’s Radwende and the Toronto Cycling App — both released this year — in taking a popular trend and turning into data that could help make cities a safer place to cycle….Website: www.travelai.info“

Crowdteaching: Supporting Teaching as Designing in Collective Intelligence Communities

Curated on Posted on by Stefaan Verhulst

Paper by Mimi Recker, Min Yuan, and Lei Ye in the International Review of Research in Open and Distant Learning: “The widespread availability of high-quality Web-based content offers new potential for supporting teachers as designers of curricula and classroom activities. When coupled with a participatory Web culture and infrastructure, teachers can share their creations as well as leverage from the best that their peers have to offer to support a collective intelligence or crowdsourcing community, which we dub crowdteaching. We applied a collective intelligence framework to characterize crowdteaching in the context of a Web-based tool for teachers called the Instructional Architect (IA). The IA enables teachers to find, create, and share instructional activities (called IA projects) for their students using online learning resources. These IA projects can further be viewed, copied, or adapted by other IA users. This study examines the usage activities of two samples of teachers, and also analyzes the characteristics of a subset of their IA projects. Analyses of teacher activities suggest that they are engaging in crowdteaching processes. Teachers, on average, chose to share over half of their IA projects, and copied some directly from other IA projects. Thus, these teachers can be seen as both contributors to and consumers of crowdteaching processes. In addition, IA users preferred to view IA projects rather than to completely copy them. Finally, correlational results based on an analysis of the characteristics of IA projects suggest that several easily computed metrics (number of views, number of copies, and number of words in IA projects) can act as an indirect proxy of instructionally relevant indicators of the content of IA projects.”

Forget The Wisdom of Crowds; Neurobiologists Reveal The Wisdom Of The Confident

Curated on Posted on by Stefaan Verhulst

Emerging Technology From the arXiv: “Way back in 1906, the English polymath Francis Galton visited a country fair in which 800 people took part in a contest to guess the weight of a slaughtered ox. After the fair, he collected the guesses and calculated their average which turned out to be 1208 pounds. To Galton’s surprise, this was within 1 per cent of the true weight of 1198 pounds.
This is one of the earliest examples of a phenomenon that has come to be known as the wisdom of the crowd. The idea is that the collective opinion of a group of individuals can be better than a single expert opinion.
This phenomenon is commonplace today on websites such as Reddit in which users vote on the importance of particular stories and the most popular are given greater prominence.
However, anyone familiar with Reddit will know that the collective opinion isn’t always wise. In recent years, researchers have spent a significant amount of time and effort teasing apart the factors that make crowds stupid. One important factor turns out to be the way members of a crowd influence each other.
It turns out that if a crowd offers a wide range of independent estimates, then it is more likely to be wise. But if members of the crowd are influenced in the same way, for example by each other or by some external factor, then they tend to converge on a biased estimate. In this case, the crowd is likely to be stupid.
Today, Gabriel Madirolas and Gonzalo De Polavieja at the Cajal Institute in Madrid, Spain, say they found a way to analyse the answers from a crowd which allows them to remove this kind of bias and so settle on a wiser answer.
The theory behind their work is straightforward. Their idea is that some people are more strongly influenced by additional information than others who are confident in their own opinion. So identifying these more strongly influenced people and separating them from the independent thinkers creates two different groups. The group of independent thinkers is then more likely to give a wise estimate. Or put another way, ignore the wisdom of the crowd in favour of the wisdom of the confident.
So how to identify confident thinkers. Madirolas and De Polavieja began by studying the data from an earlier set of experiments in which groups of people were given tasks such as to estimate the length of the border between Switzerland and Italy, the correct answer being 734 kilometres.
After one task, some groups were shown the combined estimates of other groups before beginning their second task. These experiments clearly showed how this information biased the answers from these groups in their second tasks.
Madirolas and De Polavieja then set about creating a mathematical model of how individuals incorporate this extra information. They assume that each person comes to a final estimate based on two pieces of information: first, their own independent estimate of the length of the border and second, the earlier combined estimate revealed to the group. Each individual decides on a final estimate depending on the weighting they give to each piece of information.
Those people who are heavily biased give a strong weighting to the additional information whereas people who are confident in their own estimate give a small or zero weighting to the additional information.
Madirolas and De Polavieja then take each person’s behaviour and fit it to this model to reveal how independent their thinking has been.
That allows them to divide the groups into independent thinkers and biased thinkers. Taking the collective opinion of the independent thinkers then gives a much more accurate estimate of the length of the border.
“Our results show that, while a simple operation like the mean, median or geometric mean of a group may not allow groups to make good estimations, a more complex operation taking into account individuality in the social dynamics can lead to a better collective intelligence,” they say.
…
Ref: arxiv.org/abs/1406.7578 : Wisdom of the Confident: Using Social Interactions to Eliminate the Bias in Wisdom of the Crowds”