The role of task difficulty in the effectiveness of collective intelligence


New article by Christian Wagner: “The article presents a framework and empirical investigation to demonstrate the role of task difficulty in the effectiveness of collective intelligence. The research contends that collective intelligence, a form of community engagement to address problem solving tasks, can be superior to individual judgment and choice, but only when the addressed tasks are in a range of appropriate difficulty, which we label the “collective range”. Outside of that difficulty range, collectives will perform about as poorly as individuals for high difficulty tasks, or only marginally better than individuals for low difficulty tasks. An empirical investigation with subjects randomly recruited online supports our conjecture. Our findings qualify prior research on the strength of collective intelligence in general and offer preliminary insights into the mechanisms that enable individuals and collectives to arrive at good solutions. Within the framework of digital ecosystems, the paper argues that collective intelligence has more survival strength than individual intelligence, with highest sustainability for tasks of medium difficulty”

A New Kind of Economy is Born – Social Decision-Makers Beat the "Homo Economicus"


A new paper by Dirk Helbing: “The Internet and Social Media change our way of decision-making. We are no longer the independent decision makers we used to be. Instead, we have become networked minds, social decision-makers, more than ever before. This has several fundamental implications. First of all, our economic theories must change, and second, our economic institutions must be adapted to support the social decision-maker, the “homo socialis”, rather than tailored to the perfect egoist, known as “homo economicus”….
Such developments will eventually create a participatory market society. “Prosumers”, i.e. co-producing consumers, the new “makers” movement, and the sharing economy are some examples illustrating this. Just think of the success of Wikipedia, Open Streetmap or Github. Open Streetmap now provides the most up-to-date maps of the world, thanks to more than 1 million volunteers.
This is just the beginning of a new era, where production and public engagement will more and more happen in a bottom up way through fluid “projects”, where people can contribute as a leaders (“entrepreneurs”) or participants. A new intellectual framework is emerging, and a creative and participatory era is ahead.
The paradigm shift towards participatory bottom-up self-regulation may be bigger than the paradigm shift from a geocentric to a heliocentric worldview. If we build the right institutions for the information society of the 21st century, we will finally be able to mitigate some very old problems of humanity. “Tragedies of the commons” are just one of them. After so many centuries, they are still plaguing us, but this needn’t be.”

Social media analytics for future oriented policy making


New paper by Verena Grubmüller, Katharina Götsch, and Bernhard Krieger: “Research indicates that evidence-based policy making is most successful when public administrators refer to diversified information portfolios. With the rising prominence of social media in the last decade, this paper argues that governments can benefit from integrating this publically available, user-generated data through the technique of social media analytics (SMA). There are already several initiatives set up to predict future policy issues, e.g. for the policy fields of crisis mitigation or migrant integration insights. The authors analyse these endeavours and their potential for providing more efficient and effective public policies. Furthermore, they scrutinise the challenges to governmental SMA usage in particular with regards to legal and ethical aspects. Reflecting the latter, this paper provides forward-looking recommendations on how these technologies can best be used for future policy making in a legally and ethically sound manner.”

Undefined By Data: A Survey of Big Data Definitions


Paper by Jonathan Stuart Ward and Adam Barker: “The term big data has become ubiquitous. Owing to shared origin between academia, industry and the media there is no single unified definition, and various stakeholders provide diverse and often contradictory definitions. The lack of a consistent definition introduces ambiguity and hampers discourse relating to big data. This short paper attempts to collate the various definitions which have gained some degree of traction and to furnish a clear and concise definition of an otherwise ambiguous term…
Despite the range and differences existing within each of the aforementioned definitions there are some points of similarity. Notably all definitions make at least one of the following assertions:
Size: the volume of the datasets is a critical factor.
Complexity: the structure, behaviour and permutations of the datasets is a critical factor.
Technologies: the tools and techniques which are used to process a sizable or complex dataset is a critical factor.
The definitions surveyed here all encompass at least one of these factors, most encompass two. An extrapolation of these factors would therefore postulate the following: Big data is a term describing the storage and analysis of large and or complex data sets using a series of techniques including, but not limited to: NoSQL, MapReduce and machine learning.”

Using Participatory Crowdsourcing in South Africa to Create a Safer Living Environment


New Paper by Bhaveer Bhana, Stephen Flowerday, and Aharon Satt in the International Journal of Distributed Sensor Networks: “The increase in urbanisation is making the management of city resources a difficult task. Data collected through observations (utilising humans as sensors) of the city surroundings can be used to improve decision making in terms of managing these resources. However, the data collected must be of a certain quality in order to ensure that effective and efficient decisions are made. This study is focused on the improvement of emergency and non-emergency services (city resources) through the use of participatory crowdsourcing (humans as sensors) as a data collection method (collect public safety data), utilising voice technology in the form of an interactive voice response (IVR) system.
The study illustrates how participatory crowdsourcing (specifically humans as sensors) can be used as a Smart City initiative focusing on public safety by illustrating what is required to contribute to the Smart City, and developing a roadmap in the form of a model to assist decision making when selecting an optimal crowdsourcing initiative. Public safety data quality criteria were developed to assess and identify the problems affecting data quality.
This study is guided by design science methodology and applies three driving theories: the Data Information Knowledge Action Result (DIKAR) model, the characteristics of a Smart City, and a credible Data Quality Framework. Four critical success factors were developed to ensure high quality public safety data is collected through participatory crowdsourcing utilising voice technologies.”

Digital Participation – The Case of the Italian 'Dialogue with Citizens'


New paper by Gianluca Sgueo presented at Democracy and Technology – Europe in Tension from the 19th to the 21th Century – Sorbonne Paris, 2013: “This paper focuses on the initiative named “Dialogue With Citizens” that the Italian Government introduced in 2012. The Dialogue was an entirely web-based experiment of participatory democracy aimed at, first, informing citizens through documents and in-depth analysis and, second, designed for answering to their questions and requests. During the year and half of life of the initiative roughly 90.000 people wrote (approximately 5000 messages/month). Additionally, almost 200.000 participated in a number of public online consultations that the government launched in concomitance with the adoption of crucial decisions (i.e. the spending review national program).
From the analysis of this experiment of participatory democracy three questions can be raised. (1) How can a public institution maximize the profits of participation and minimize its costs? (2) How can public administrations manage the (growing) expectations of the citizens once they become accustomed to participation? (3) Is online participatory democracy going to develop further, and why?
In order to fully answer such questions, the paper proceeds as follows: it will initially provide a general overview of online public participation both at the central and the local level. It will then discuss the “Dialogue with Citizens” and a selected number of online public consultations lead by the Italian government in 2012. The conclusions will develop a theoretical framework for reflection on the peculiarities and problems of the web-participation.”

Mobile phone data are a treasure-trove for development


Paul van der Boor and Amy Wesolowski in SciDevNet: “Each of us generates streams of digital information — a digital ‘exhaust trail’ that provides real-time information to guide decisions that affect our lives. For example, Google informs us about traffic by using both its ‘My Location’ feature on mobile phones and third-party databases to aggregate location data. BBVA, one of Spain’s largest banks, analyses transactions such as credit card payments as well as ATM withdrawals to find out when and where peak spending occurs.This type of data harvest is of great value. But, often, there is so much data that its owners lack the know-how to process it and fail to realise its potential value to policymakers.
Meanwhile, many countries, particularly in the developing world, have a dearth of information. In resource-poor nations, the public sector often lives in an analogue world where piles of paper impede operations and policymakers are hindered by uncertainty about their own strengths and capabilities.Nonetheless, mobile phones have quickly pervaded the lives of even the poorest: 75 per cent of the world’s 5.5 billion mobile subscriptions are in emerging markets. These people are also generating digital trails of anything from their movements to mobile phone top-up patterns. It may seem that putting this information to use would take vast analytical capacity. But using relatively simple methods, researchers can analyse existing mobile phone data, especially in poor countries, to improve decision-making.
Think of existing, available data as low-hanging fruit that we — two graduate students — could analyse in less than a month. This is not a test of data-scientist prowess, but more a way of saying that anyone could do it.
There are three areas that should be ‘low-hanging fruit’ in terms of their potential to dramatically improve decision-making in information-poor countries: coupling healthcare data with mobile phone data to predict disease outbreaks; using mobile phone money transactions and top-up data to assess economic growth; and predicting travel patterns after a natural disaster using historical movement patterns from mobile phone data to design robust response programmes.
Another possibility is using call-data records to analyse urban movement to identify traffic congestion points. Nationally, this can be used to prioritise infrastructure projects such as road expansion and bridge building.
The information that these analyses could provide would be lifesaving — not just informative or revenue-increasing, like much of this work currently performed in developed countries.
But some work of high social value is being done. For example, different teams of European and US researchers are trying to estimate the links between mobile phone use and regional economic development. They are using various techniques, such as merging night-time satellite imagery from NASA with mobile phone data to create behavioural fingerprints. They have found that this may be a cost-effective way to understand a country’s economic activity and, potentially, guide government spending.
Another example is given by researchers (including one of this article’s authors) who have analysed call-data records from subscribers in Kenya to understand malaria transmission within the country and design better strategies for its elimination. [1]
In this study, published in Science, the location data of the mobile phones of more than 14 million Kenyan subscribers was combined with national malaria prevalence data. After identifying the sources and sinks of malaria parasites and overlaying these with phone movements, analysis was used to identify likely transmission corridors. UK scientists later used similar methods to create different epidemic scenarios for the Côte d’Ivoire.”

Prizes and Productivity: How Winning the Fields Medal Affects Scientific Output


New NBER working paper by George J. Borjas and Kirk B. Doran: “Knowledge generation is key to economic growth, and scientific prizes are designed to encourage it. But how does winning a prestigious prize affect future output? We compare the productivity of Fields medalists (winners of the top mathematics prize) to that of similarly brilliant contenders. The two groups have similar publication rates until the award year, after which the winners’ productivity declines. The medalists begin to “play the field,” studying unfamiliar topics at the expense of writing papers. It appears that tournaments can have large post-prize effects on the effort allocation of knowledge producers.”

The Contours of Crowd Capability


New paper by Prashant Shukla and John Prpi: “The existence of dispersed knowledge has been a subject of inquiry for more than six decades. Despite the longevity of this rich research tradition, the “knowledge problem” has remained largely unresolved both in research and practice, and remains “the central theoretical problem of all social science”. However, in the 21st century, organizations are presented with opportunities through technology to potentially benefit from the dispersed knowledge problem to some extent. One such opportunity is represented by the recent emergence of a variety of crowd-engaging information systems (IS).
In this vein, Crowdsourcing  is being widely studied in numerous contexts, and the knowledge generated from these IS phenomena is well-documented. At the same time, other organizations are leveraging dispersed knowledge by putting in place IS-applications such as Predication Markets to gather large sample-size forecasts from within and without the organization. Similarly, we are also observing many organizations using IS-tools such as “Wikis” to access the knowledge of dispersed populations within the boundaries of the organization. Further still, other organizations are applying gamification techniques to accumulate Citizen Science knowledge from the public at large through IS.
Among these seemingly disparate phenomena, a complex ecology of crowd- engaging IS has emerged, involving millions of people all around the world generating knowledge for organizations through IS. However, despite the obvious scale and reach of this emerging crowd-engagement paradigm, there are no examples of research (as far as we know), that systematically compares and contrasts a large variety of these existing crowd-engaging IS-tools in one work. Understanding this current state of affairs, we seek to address this significant research void by comparing and contrasting a number of the crowd-engaging forms of IS currently available for organizational use.

To achieve this goal, we employ the Theory of Crowd Capital as a lens to systematically structure our investigation of crowd-engaging IS. Employing this parsimonious lens, we first explain how Crowd Capital is generated through Crowd Capability in organizations. Taking this conceptual platform as a point of departure, in Section 3, we offer an array of examples of IS currently in use in modern practice to generate Crowd Capital. We compare and contrast these emerging IS techniques using the Crowd Capability construct, therein highlighting some important choices that organizations face when entering the crowd- engagement fray. This comparison, which we term “The Contours of Crowd Capability”, can be used by decision-makers and researchers alike, to differentiate among the many extant methods of Crowd Capital generation. At the same time, our comparison also illustrates some important differences to be found in the internal organizational processes that accompany each form of crowd-engaging IS. In section 4, we conclude with a discussion of the limitations of our work.”

From Crowd-Sourcing Potholes to Community Policing


New paper by Manik Suri (GovLab): “The tragic Boston Marathon bombing and hair-raising manhunt that ensued was a sobering event. It also served as a reminder that emerging “civic technologies” – platforms and applications that enable citizens to connect and collaborate with each other and with government – are more important today than ever before. As commentators have noted, local police and federal agents utilized a range of technological platforms to tap the “wisdom of the crowd,” relying on thousands of private citizens to develop a “hive mind” that identified two suspects within a record period of time.
In the immediate wake of the devastating attack on April 15th, investigators had few leads. But within twenty-four hours, senior FBI officials, determined to seek “assistance from the public,” called on everyone with information to submit all media, tips, and leads related to the Boston Marathon attack. This unusual request for help yielded thousands of images and videos from local Bostonians, tourists, and private companies through technological channels ranging from telephone calls and emails to Flickr posts and Twitter messages. In mere hours, investigators were able to “crowd-source” a tremendous amount of data – including thousands of images from personal cameras, amateur videos from smart phones, and cell-tower information from private carriers. Combing through data from this massive network of “eyes and ears,” law enforcement officials were quickly able to generate images of two lead suspects – enabling a “modern manhunt” to commence immediately.
Technological innovations have transformed our commercial, political, and social realities. These advances include new approaches to how we generate knowledge, access information, and interact with one another, as well as new pathways for building social movements and catalyzing political change. While a significant body of academic research has focused on the role of technology in transforming electoral politics and social movements, less attention has been paid to how technological innovation can improve the process of governance itself.
A growing number of platforms and applications lie at this intersection of technology and governance, in what might be termed the “civic technology” sector. Broadly speaking, this sector involves the application of new information and communication technologies – ranging from robust social media platforms to state-of-the-art big data analysis systems – to address public policy problems. Civic technologies encompass enterprises that “bring web technologies directly to government, build services on top of government data for citizens, and change the way citizens ask, get, or need services from government.” These technologies have the potential to transform governance by promoting greater transparency in policy-making, increasing government efficiency, and enhancing citizens’ participation in public sector decision-making.