4,153 research outputs found
Mathematical practice, crowdsourcing, and social machines
The highest level of mathematics has traditionally been seen as a solitary
endeavour, to produce a proof for review and acceptance by research peers.
Mathematics is now at a remarkable inflexion point, with new technology
radically extending the power and limits of individuals. Crowdsourcing pulls
together diverse experts to solve problems; symbolic computation tackles huge
routine calculations; and computers check proofs too long and complicated for
humans to comprehend.
Mathematical practice is an emerging interdisciplinary field which draws on
philosophy and social science to understand how mathematics is produced. Online
mathematical activity provides a novel and rich source of data for empirical
investigation of mathematical practice - for example the community question
answering system {\it mathoverflow} contains around 40,000 mathematical
conversations, and {\it polymath} collaborations provide transcripts of the
process of discovering proofs. Our preliminary investigations have demonstrated
the importance of "soft" aspects such as analogy and creativity, alongside
deduction and proof, in the production of mathematics, and have given us new
ways to think about the roles of people and machines in creating new
mathematical knowledge. We discuss further investigation of these resources and
what it might reveal.
Crowdsourced mathematical activity is an example of a "social machine", a new
paradigm, identified by Berners-Lee, for viewing a combination of people and
computers as a single problem-solving entity, and the subject of major
international research endeavours. We outline a future research agenda for
mathematics social machines, a combination of people, computers, and
mathematical archives to create and apply mathematics, with the potential to
change the way people do mathematics, and to transform the reach, pace, and
impact of mathematics research.Comment: To appear, Springer LNCS, Proceedings of Conferences on Intelligent
Computer Mathematics, CICM 2013, July 2013 Bath, U
Crime applications and social machines: crowdsourcing sensitive data
The authors explore some issues with the United Kingdom (U.K.) crime reporting and recording systems which currently produce Open Crime Data. The availability of Open Crime Data seems to create a potential data ecosystem which would encourage crowdsourcing, or the creation of social machines, in order to counter some of these issues. While such solutions are enticing, we suggest that in fact the theoretical solution brings to light fairly compelling problems, which highlight some limitations of crowdsourcing as a means of addressing Berners-Lee’s “social constraint.” The authors present a thought experiment – a Gendankenexperiment - in order to explore the implications, both good and bad, of a social machine in such a sensitive space and suggest a Web Science perspective to pick apart the ramifications of this thought experiment as a theoretical approach to the characterisation of social machine
Social Machines
The term ‘social machine’ has recently been coined to refer to Web-based systems that support a variety of socially-relevant processes. Such systems (e.g., Wikipedia, Galaxy Zoo, Facebook, and reCAPTCHA) are progressively altering the way a broad array of social activities are performed, ranging from the way we communicate and transmit knowledge, establish romantic partnerships, generate ideas, produce goods and maintain friendships. They are also poised to deliver new kinds of intelligent processing capability by virtue of their ability to integrate the complementary contributions of both the human social environment and a global nexus of distributed computational resources. This chapter provides an overview of recent research into social machines. It examines what social machines are and discusses the kinds of social machines that currently exist. It also presents a range of issues that are the focus of current research attention within the Web Science community
Human Computation and Convergence
Humans are the most effective integrators and producers of information,
directly and through the use of information-processing inventions. As these
inventions become increasingly sophisticated, the substantive role of humans in
processing information will tend toward capabilities that derive from our most
complex cognitive processes, e.g., abstraction, creativity, and applied world
knowledge. Through the advancement of human computation - methods that leverage
the respective strengths of humans and machines in distributed
information-processing systems - formerly discrete processes will combine
synergistically into increasingly integrated and complex information processing
systems. These new, collective systems will exhibit an unprecedented degree of
predictive accuracy in modeling physical and techno-social processes, and may
ultimately coalesce into a single unified predictive organism, with the
capacity to address societies most wicked problems and achieve planetary
homeostasis.Comment: Pre-publication draft of chapter. 24 pages, 3 figures; added
references to page 1 and 3, and corrected typ
The social in the platform trap: Why a microscopic system focus limits the prospect of social machines
“Filter bubble”, “echo chambers”, “information diet” – the metaphors to describe today’s information dynamics on social media platforms are fairly diverse. People use them to describe the impact of the viral spread of fake, biased or purposeless content online, as witnessed during the recent race for the US presidency or the latest outbreak of the Ebola virus (in the latter case a tasteless racist meme was drowning out any meaningful content). This unravels the potential envisioned to arise from emergent activities of human collectives on the World Wide Web, as exemplified by the Arab Spring mass movements or digital disaster response supported by the Ushahidi tool suite
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