Peer Production: A Modality of Collective Intelligence

Curated on Posted on by Stefaan Verhulst
New paper by Yochai Benkler, Aaron Shaw and Benjamin Mako Hill:  “Peer production is the most significant organizational innovation that has emerged from
Internet-mediated social practice and among the most a visible and important examples of collective intelligence. Following Benkler,  we define peer production as a form of open creation and sharing performed by groups online that: (1) sets and executes goals in a decentralized manner; (2) harnesses a diverse range of participant motivations, particularly non-monetary motivations; and (3) separates governance and management relations from exclusive forms of property and relational contracts (i.e., projects are governed as open commons or common property regimes and organizational governance utilizes combinations of participatory, meritocratic and charismatic, rather than proprietary or contractual, models). For early scholars of peer production, the phenomenon was both important and confounding for its ability to generate high quality work products in the absence of formal hierarchies and monetary incentives. However, as peer production has become increasingly established in society, the economy, and scholarship, merely describing the success of some peer production projects has become less useful. In recent years, a second wave of scholarship has emerged to challenge assumptions in earlier work; probe nuances glossed over by earlier framings of the phenomena; and identify the necessary dynamics, structures, and conditions for peer production success.
Peer production includes many of the largest and most important collaborative communities on the Internet….
Much of this academic interest in peer production stemmed from the fact that the phenomena resisted straightforward explanations in terms of extant theories of the organization and production of functional information goods like software or encyclopedias. Participants in peer production projects join and contribute valuable resources without the hierarchical bureaucracies or strong leadership structures common to state agencies or firms, and in the absence of clear financial incentives or rewards. As a result, foundationalresearch on peer production was focused on (1) documenting and explaining the organization and governance of peer production communities, (2) understanding the motivation of contributors to peer production, and (3) establishing and evaluating the quality of peer production’s outputs.
In the rest of this chapter, we describe the development of the academic literature on peer production in these three areas – organization, motivation, and quality.”

Making government simpler is complicated

Curated on Posted on by Stefaan Verhulst

Mike Konczal in The Washington Post: “Here’s something a politician would never say: “I’m in favor of complex regulations.” But what would the opposite mean? What would it mean to have “simple” regulations?

There are two definitions of “simple” that have come to dominate liberal conversations about government. One is the idea that we should make use of “nudges” in regulation. The other is the idea that we should avoid “kludges.” As it turns out, however, these two definitions conflict with each other —and the battle between them will dominate conversations about the state in the years ahead.

The case for “nudges”

The first definition of a “simple” regulation is one emphasized in Cass Sunstein’s recent book titled Simpler: The Future of Government (also see here). A simple policy is one that simply “nudges” people into one choice or another using a variety of default rules, disclosure requirements, and other market structures. Think, for instance, of rules that require fast-food restaurants to post calories on their menus, or a mortgage that has certain terms clearly marked in disclosures.

These sorts of regulations are deemed “choice preserving.” Consumers are still allowed to buy unhealthy fast-food meals or sign up for mortgages they can’t reasonably afford. The regulations are just there to inform people about their choices. These rules are designed to keep the market “free,” where all possibilities are ultimately possible, although there are rules to encourage certain outcomes.
In his book, however, Sunstein adds that there’s another very different way to understand the term “simple.” What most people mean when they think of simple regulations is a rule that is “simple to follow.” Usually a rule is simple to follow because it outright excludes certain possibilities and thus ensures others. Which means, by definition, it limits certain choices.

The case against “kludges”
This second definition of simple plays a key role in political scientist Steve Teles’ excellent recent essay, “Kludgeocracy in America.” For Teles, a “kludge” is a “clumsy but temporarily effective” fix for a policy problem. (The term comes from computer science.) These kludges tend to pile up over time, making government cumbersome and inefficient overall.
Teles focuses on several ways that kludges are introduced into policy, with a particularly sharp focus on overlapping jurisdictions and the related mess of federal and state overlap in programs. But, without specifically invoking it, he also suggests that a reliance on “nudge” regulations can lead to more kludges.
After all, non-kludge policy proposal is one that will be simple to follow and will clearly cause a certain outcome, with an obvious causality chain. This is in contrast to a web of “nudges” and incentives designed to try and guide certain outcomes.

Why “nudges” aren’t always simpler
The distinction between the two is clear if we take a specific example core to both definitions: retirement security.
For Teles, “one of the often overlooked benefits of the Social Security program… is that recipients automatically have taxes taken out of their paychecks, and, then without much effort on their part, checks begin to appear upon retirement. It’s simple and direct. By contrast, 401(k) retirement accounts… require enormous investments of time, effort, and stress to manage responsibly.”

Yet 401(k)s are the ultimately fantasy laboratory for nudge enthusiasts. A whole cottage industry has grown up around figuring out ways to default people into certain contributions, on designing the architecture of choices of investments, and trying to effortlessly and painlessly guide people into certain savings.
Each approach emphasizes different things. If you want to focus your energy on making people better consumers and market participations, expanding our government’s resources and energy into 401(k)s is a good choice. If you want to focus on providing retirement security directly, expanding Social Security is a better choice.
The first is “simple” in that it doesn’t exclude any possibility but encourages market choices. The second is “simple” in that it is easy to follow, and the result is simple as well: a certain amount of security in old age is provided directly. This second approach understands the government as playing a role in stopping certain outcomes, and providing for the opposite of those outcomes, directly….

Why it’s hard to create “simple” regulations
Like all supposed binaries this is really a continuum. Taxes, for instance, sit somewhere in the middle of the two definitions of “simple.” They tend to preserve the market as it is but raise (or lower) the price of certain goods, influencing choices.
And reforms and regulations are often most effective when there’s a combination of these two types of “simple” rules.
Consider an important new paper, “Regulating Consumer Financial Products: Evidence from Credit Cards,” by Sumit Agarwal, Souphala Chomsisengphet, Neale Mahoney and Johannes Stroebel. The authors analyze the CARD Act of 2009, which regulated credit cards. They found that the nudge-type disclosure rules “increased the number of account holders making the 36-month payment value by 0.5 percentage points.” However, more direct regulations on fees had an even bigger effect, saving U.S. consumers $20.8 billion per year with no notable reduction in credit access…..
The balance between these two approaches of making regulations simple will be front and center as liberals debate the future of government, whether they’re trying to pull back on the “submerged state” or consider the implications for privacy. The debate over the best way for government to be simple is still far from over.”

Google’s flu fail shows the problem with big data

Curated on Posted on by Stefaan Verhulst

Adam Kucharski in The Conversation: “When people talk about ‘big data’, there is an oft-quoted example: a proposed public health tool called Google Flu Trends. It has become something of a pin-up for the big data movement, but it might not be as effective as many claim.
The idea behind big data is that large amount of information can help us do things which smaller volumes cannot. Google first outlined the Flu Trends approach in a 2008 paper in the journal Nature. Rather than relying on disease surveillance used by the US Centers for Disease Control and Prevention (CDC) – such as visits to doctors and lab tests – the authors suggested it would be possible to predict epidemics through Google searches. When suffering from flu, many Americans will search for information related to their condition….
Between 2003 and 2008, flu epidemics in the US had been strongly seasonal, appearing each winter. However, in 2009, the first cases (as reported by the CDC) started in Easter. Flu Trends had already made its predictions when the CDC data was published, but it turned out that the Google model didn’t match reality. It had substantially underestimated the size of the initial outbreak.
The problem was that Flu Trends could only measure what people search for; it didn’t analyse why they were searching for those words. By removing human input, and letting the raw data do the work, the model had to make its predictions using only search queries from the previous handful of years. Although those 45 terms matched the regular seasonal outbreaks from 2003–8, they didn’t reflect the pandemic that appeared in 2009.
Six months after the pandemic started, Google – who now had the benefit of hindsight – updated their model so that it matched the 2009 CDC data. Despite these changes, the updated version of Flu Trends ran into difficulties again last winter, when it overestimated the size of the influenza epidemic in New York State. The incidents in 2009 and 2012 raised the question of how good Flu Trends is at predicting future epidemics, as opposed to merely finding patterns in past data.
In a new analysis, published in the journal PLOS Computational Biology, US researchers report that there are “substantial errors in Google Flu Trends estimates of influenza timing and intensity”. This is based on comparison of Google Flu Trends predictions and the actual epidemic data at the national, regional and local level between 2003 and 2013
Even when search behaviour was correlated with influenza cases, the model sometimes misestimated important public health metrics such as peak outbreak size and cumulative cases. The predictions were particularly wide of the mark in 2009 and 2012:

Original and updated Google Flu Trends (GFT) model compared with CDC influenza-like illness (ILI) data. PLOS Computational Biology 9:10
Click to enlarge

Although they criticised certain aspects of the Flu Trends model, the researchers think that monitoring internet search queries might yet prove valuable, especially if it were linked with other surveillance and prediction methods.
Other researchers have also suggested that other sources of digital data – from Twitter feeds to mobile phone GPS – have the potential to be useful tools for studying epidemics. As well as helping to analysing outbreaks, such methods could allow researchers to analyse human movement and the spread of public health information (or misinformation).
Although much attention has been given to web-based tools, there is another type of big data that is already having a huge impact on disease research. Genome sequencing is enabling researchers to piece together how diseases transmit and where they might come from. Sequence data can even reveal the existence of a new disease variant: earlier this week, researchers announced a new type of dengue fever virus….”

Making regulations easier to use

Curated on Posted on by Stefaan Verhulst

at the Consumer Financial Protection Bureau (CFPB): “We write rules to protect consumers, but what actually protects consumers is people: advocates knowing what rights people have, government agencies’ supervision and enforcement staff having a clear view of what potential violations to look out for; and responsible industry employees following the rules.
Today, we’re releasing a new open source tool we built, eRegulations, to help make regulations easier to understand. Check it out: consumerfinance.gov/eregulations
One thing that’s become clear during our two years as an agency is that federal regulations can be difficult to navigate. Finding answers to questions about a regulation is hard. Frequently, it means connecting information from different places, spread throughout a regulation, often separated by dozens or even hundreds of pages. As a result, we found people were trying to understand regulations by using paper editions, several different online tools to piece together the relevant information, or even paid subscription services that still don’t make things easy, and are expensive.
…
Here’s hoping that even more people who work with regulations will have the same reaction as this member of our bank supervision team:
 “The eRegulations site has been very helpful to my work. It has become my go-to resource on Reg. E and the Official Interpretations. I use it several times a week in the course of completing regulatory compliance evaluations. My prior preference was to use the printed book or e-CFR, but I’ve found the eRegulations (tool) to be easier to read, search, and navigate than the printed book, and more efficient than the e-CFR because of the way eRegs incorporates the commentary.”
New rules about international money transfers – also called “remittances” –  in Regulation E will take effect on October 28, 2013, and you can now use the eRegulations tool to check out the regulation.

We need your help

There are two ways we’d love your help with our work to make regulations easier to use. First, the tool is a work in progress.  If you have comments or suggestions, please write to us at [email protected]. We read every message and would love to hear what you think.
Second, the tool is open source, so we’d love for other agencies, developers, or groups to use it and adapt it. And remember, the first time a citizen developer suggested a change to our open source software, it was to fix a typo (thanks again, by the way!), so no contribution is too small.”

Global Collective Intelligence in Technological Societies

Curated on Posted on by Stefaan Verhulst
Paper by Juan Carlos Piedra Calderón and Javier Rainer in the International Journal of Artificial Intelligence and Interactive Multimedia: “The big influence of Information and Communication Technologies (ICT), especially in area of construction of Technological Societies has generated big
social changes. That is visible in the way of relating to people in different environments. These changes have the possibility to expand the frontiers of knowledge through sharing and cooperation. That has meaning the inherently creation of a new form of Collaborative Knowledge. The potential of this Collaborative Knowledge has been given through ICT in combination with Artificial Intelligence processes, from where is obtained a Collective Knowledge. When this kind of knowledge is shared, it gives the place to the Global Collective Intelligence”.

Are We Puppets in a Wired World?

Curated on Posted on by Stefaan Verhulst

Sue Halpern in The New York Review of Books: “Also not obvious was how the Web would evolve, though its open architecture virtually assured that it would. The original Web, the Web of static homepages, documents laden with “hot links,” and electronic storefronts, segued into Web 2.0, which, by providing the means for people without technical knowledge to easily share information, recast the Internet as a global social forum with sites like Facebook, Twitter, FourSquare, and Instagram.
Once that happened, people began to make aspects of their private lives public, letting others know, for example, when they were shopping at H+M and dining at Olive Garden, letting others know what they thought of the selection at that particular branch of H+M and the waitstaff at that Olive Garden, then modeling their new jeans for all to see and sharing pictures of their antipasti and lobster ravioli—to say nothing of sharing pictures of their girlfriends, babies, and drunken classmates, or chronicling life as a high-paid escort, or worrying about skin lesions or seeking a cure for insomnia or rating professors, and on and on.
The social Web celebrated, rewarded, routinized, and normalized this kind of living out loud, all the while anesthetizing many of its participants. Although they likely knew that these disclosures were funding the new information economy, they didn’t especially care…
The assumption that decisions made by machines that have assessed reams of real-world information are more accurate than those made by people, with their foibles and prejudices, may be correct generally and wrong in the particular; and for those unfortunate souls who might never commit another crime even if the algorithm says they will, there is little recourse. In any case, computers are not “neutral”; algorithms reflect the biases of their creators, which is to say that prediction cedes an awful lot of power to the algorithm creators, who are human after all. Some of the time, too, proprietary algorithms, like the ones used by Google and Twitter and Facebook, are intentionally biased to produce results that benefit the company, not the user, and some of the time algorithms can be gamed. (There is an entire industry devoted to “optimizing” Google searches, for example.)
But the real bias inherent in algorithms is that they are, by nature, reductive. They are intended to sift through complicated, seemingly discrete information and make some sort of sense of it, which is the definition of reductive.”
Books reviewed:

The End of Hypocrisy

Curated on Posted on by Stefaan Verhulst

New paper by Henry Farrell and Martha Finnemore in Foreign Affairs: “The U.S. government seems outraged that people are leaking classified materials about its less attractive behavior. It certainly acts that way: three years ago, after Chelsea Manning, an army private then known as Bradley Manning, turned over hundreds of thousands of classified cables to the anti-secrecy group WikiLeaks, U.S. authorities imprisoned the soldier under conditions that the UN special rapporteur on torture deemed cruel and inhumane. The Senate’s top Republican, Mitch McConnell, appearing on Meet the Press shortly thereafter, called WikiLeaks’ founder, Julian Assange, “a high-tech terrorist.””
More recently, following the disclosures about U.S. spying programs by Edward Snowden, a former National Security Agency analyst, U.S. officials spent a great deal of diplomatic capital trying to convince other countries to deny Snowden refuge. And U.S. President Barack Obama canceled a long-anticipated summit with Russian President Vladimir Putin when he refused to comply.
Despite such efforts, however, the U.S. establishment has often struggled to explain exactly why these leakers pose such an enormous threat. Indeed, nothing in the Manning and Snowden leaks should have shocked those who were paying attention…
The deeper threat that leakers such as Manning and Snowden pose is more subtle than a direct assault on U.S. national security: they undermine Washington’s ability to act hypocritically and get away with it. Their danger lies not in the new information that they reveal but in the documented confirmation they provide of what the United States is actually doing and why…”

What the Government Does with Americans’ Data

Curated on Posted on by Stefaan Verhulst

New paper from the Brennan Center for Justice: “After the attacks of September 11, 2001, the government’s authority to collect, keep, and share information about Americans with little or no basis to suspect wrongdoing dramatically expanded. While the risks and benefits of this approach are the subject of intense debate, one thing is certain: it results in the accumulation of large amounts of innocuous information about law-abiding citizens. But what happens to this data? In the search to find the needle, what happens to the rest of the haystack? For the first time in one report, the Brennan Center takes a comprehensive look at the multiple ways U.S. intelligence agencies collect, share, and store data on average Americans. The report, which surveys across five intelligence agencies, finds that non-terrorism related data can be kept for up to 75 years or more, clogging national security databases and creating opportunities for abuse, and recommends multiple reforms that seek to tighten control over the government’s handling of Americans’ information.”

Open Data and Open Government: Rethinking Telecommunications Policy and Regulation

Curated on Posted on by Stefaan Verhulst

New paper by Ewan Sutherland: “While attention has been given to the uses of big data by network operators and to the provision of open data by governments, there has been no systematic attempt to re-examine the regulatory systems for telecommunications. The power of public authorities to access the big data held by operators could transform regulation by simplifying proof of bias or discrimination, making operators more susceptible to behavioural remedies, while it could also be used to deliver much finer granularity of decision making. By opening up data held by government and its agencies to enterprises, think tanks and research groups it should be possible to transform market regulation.“

The small-world effect is a modern phenomenon

Curated on Posted on by Stefaan Verhulst

New paper by Seth A. Marvel, Travis Martin, Charles R. Doering, David Lusseau, M. E. J. Newman: “The “small-world effect” is the observation that one can find a short chain of acquaintances, often of no more than a handful of individuals, connecting almost any two people on the planet. It is often expressed in the language of networks, where it is equivalent to the statement that most pairs of individuals are connected by a short path through the acquaintance network. Although the small-world effect is well-established empirically for contemporary social networks, we argue here that it is a relatively recent phenomenon, arising only in the last few hundred years: for most of mankind’s tenure on Earth the social world was large, with most pairs of individuals connected by relatively long chains of acquaintances, if at all. Our conclusions are based on observations about the spread of diseases, which travel over contact networks between individuals and whose dynamics can give us clues to the structure of those networks even when direct network measurements are not available. As an example we consider the spread of the Black Death in 14th-century Europe, which is known to have traveled across the continent in well-defined waves of infection over the course of several years. Using established epidemiological models, we show that such wave-like behavior can occur only if contacts between individuals living far apart are exponentially rare. We further show that if long-distance contacts are exponentially rare, then the shortest chain of contacts between distant individuals is on average a long one. The observation of the wave-like spread of a disease like the Black Death thus implies a network without the small-world effect.”