Montreal Summer Institute on Web Science and the Mind
Katy Borner
katy at INDIANA.EDU
Mon May 12 23:32:18 EDT 2014
**
*
PRELIMINARY PROGRAM -- April 30^th 2014*
**
*Summer School in Cognitive Science 2014*
**
*/WEB SCIENCE AND THE MIND/*
**
*JULY 7^th to 18^th 2014*
*Universite du Quebec a Montreal*
*Montreal, Canada*
*www.summer14.isc.uqam.ca*
<http://users.ecs.soton.ac.uk/harnad/Temp/www.summer14.isc.uqam.ca>**
**
**
*- - - - - _MONDAY, JULY 7_ - - - - -
*
**
*9am to 12:30pm* Registration
*3pm***Welcoming Ceremony
*ROBERT PROULX*, Rector, UQAM
**
*3:15pm*Opening Session
*Web Science *//
*DAME WENDY HALL*, University of Southampton
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Hall>
//
**- - - - - *_TUESDAY, JULY 8_** - - - - -*
**
*9-10am*
*Web Philosophy*
*ALEXANDRE MONNIN*, Institut National de Recherche en Informatique et
Automatique (INRIA) Sophia Antipolis
**/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Monnin>
**
*10-11am*
*Web Semantics*//
*HARRY HALPIN*, University of Edinburgh, Institute of Communicating and
Collaborating Systems, School of Informatics
/Overview & Readings
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Halpin>/
*Coffee Break 11am to 11:30am*
**
*11:30-12:30*
*Web and Brain*
*JEFF STIBEL*, Dun & Bradstreet Credibility Corp
/Overview & Readings
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Stibel>
/
*Lunch time 12:30pm to 2pm*
**
*2-3pm*
*Towards a Global Brain: the Web as a Self-organizing, Distributed
Intelligence*
*FRANCIS HEYLIGHEN*, Vrije Universiteit Brussel, ECCO - Evolution,
Complexity and Cognition research group
//Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Heylighen> /
*2-4pm*
*Computational Models for Web Science*
*PHIL TETLOW*, IBM United Kingdom Limited
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Tetlow>
*Coffee Break 4pm to 4:30pm*
**
*4:30-5pm*
Summary and discussion of day's sessions
*5:30-8pm*
Poster Session and Cocktail
**
**
**- - - - - *_WEDNESDAY, JULY 9_** - - - - -*
**
*9-10am*
*Open Science and the Web*
*TONY HEY*, Microsoft Research Connections
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Hey><http://eprints.rclis.org/9202/1/heyhey_final_web.pdf>
**
*10-11am*
*Scientific Interaction Before and Since the Web*
*VINCENT LARIVIERE*, Universite du Quebec a Montreal
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Lariviere>
*Coffee Break 11am to 11:30am*
**
*11:30-12:30*
*Scholarly Big Data: Information Extraction and Data Mining*
*LEE GILES*, Pennsylvania State University
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Giles>
*Lunch time 12:30pm to 2pm*
**
*2-3pm*
*Web Impact Metrics for Research Assessment*
*KAYVAN KOUSHA*, University of University of, Statistical Cybermetrics
Research Group, School of Technology
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Kousha>
*2-4pm*
*TBA*
*Coffee Break 4pm to 4:30pm*
**
*4:30pm*
Summary and discussion of day's sessions
**
**
**- - - - - *_THURSDAY, JULY 10_** - - - - -*
**
*9-10am*
*Graphic Webs of Science*
*KATY BORNER*, Indiana University, Department of Information and Library
Science <http://ils.indiana.edu/>
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Borner>
*10 am to 11am*
*Visualizing Dynamic Interactions*
*JEAN-DANIEL FEKETE*, Institut National de Recherche en Informatique et
Automatique (INRIA) Saclay - ile-de-France
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Fekete>
*Coffee Break 11am to 11:30am*
**
*11:30-12:30*
*New Models of Scholarly Communication for Digital Scholarship
*
*STEPHEN GRIFFIN*, University of Pittsburgh, School of Information Science
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Griffin>
*Lunch time 12:30pm to 2pm*
**
*2-3pm*
*Network Ready Research: The Role of Open Source and Open Thinking*
*CAMERON NEYLON*, PLOS (Public Library of Science)
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Neylon><https://www.youtube.com/watch?v=Axr80qm6NHw>
**
*2-4pm*
*Collaborative Innovation Networks*
*PETER GLOOR*, MIT Center for Collective Intelligence
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Gloor>//
**
*Coffee Break 4pm to 4:30pm*
**
*4:30pm*
Summary and discussion of day's sessions
**
**
**- - - - - *_FRIDAY, JULY 11_** - - - - -*
**
*9-10am*
*Computational Models for Web Science*
*ROBERT GOLDSTONE*, Indiana University, Department of Psychology
/Overview & Readings
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Goldstone>
/
*10-11am*
*TBA*
*Coffee Break 11am to 11:30am*
**
*11:30-12:30*
*Social and Semantic Web: Adding the Missing Links***
*FABIEN GANDON*, INRIA Research Center of Sophia-Antipolis
**/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Gandon>**
**
*Lunch time 12:30pm to 2pm*
**
*2pm to 4pm*
Optional meetings for students
**
*Coffee Break 4pm to 4:30pm*
**
*4:30pm*
Summary and discussion of day's sessions
**
**
**- - - - - *_MONDAY, JULY 14_** - - - - -*
**
*9-10am*
*Explosive Percolation*
*SERGEY DOROGOVTSEV*, Universidade de Aveiro
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Dorogovstev>**
**
**
*10-11am*
*Bursts, Cascades, and Time Allocation*
*ADILSON MOTTER*, Northeastern University, Physics of Complex Systems
and Networks
/Overview & Readings
/ <http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Motter>
*Coffee Break 11am to 11:30am*
**
*11:30-12:30*
*Controllability and Observability of Complex Systems
**YANG-YU LIU*, Northeastern University, Center for Complex Network
Research, Physics Department
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Liu>
*Lunch time 12:30pm to 2pm*
**
*2-3pm*
*The Social Web
**JENNIFER GOLBECK*, University of Maryland
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Golbeck>
*2-4pm*
*Collective Memory in Wikipedia*
*SIMON DeDEO*, Indiana University, Santa Fe Institute
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Dedeo>
*Coffee Break 4pm to 4:30pm*
**
*4:30pm*
Summary and discussion of day's sessions
**
**- - - - - *_TUESDAY, JULY 15_** - - - - -*
**
*9-10am*
*The Data Web*
*JIM HENDLER*, Rensselaer Polytechnic Institute, Department of Computer
Science
**
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Hendler>/Overview
& Readings
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Hendler>
/
*10-11am*
*Foraging in the World, Mind and Online
**PETER TODD*, Indiana University, Department of Psychological and Brain
Sciences
**/Overview & Readings
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Todd>
/
*Coffee Break 11am to 11:30am*
**
*11:30-12:30****
Macrocognition: Situated versus
Distributed*<http://www.sciencedirect.com/science/article/pii/S1389041713000259>
*BRYCE HUEBNER*, Georgetown University, Department of Philosophy**
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Huebner>
*Lunch time 12:30pm to 2pm*
**
*2-3pm*
***Visual Analytics for Discovering Network Structure Beyond Communities*
*TAKASHI NISHIKAWA*, Northwestern University, Physics & Astronomy
////Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Nishikawa>/
*2-4pm*
*Detecting Communities in Complex Networks: Role of Degree Correlations
**FILIPPO RADICCHI*, Indiana University, School of Informatics and
Computing
**/Overview & Readings
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Radicchi>
/
*Coffee Break 4pm to 4:30pm*
**
*4:30pm*
Summary and discussion of day's sessions
**
**- - - - - *_WEDNESDAY, JULY 16_** - - - - -*
**
*9-10am*
*TBA*//
**
*10-11am*
*Extended Mentality: What It Is and Why It Matters*
*MARK ROWLANDS*, Indiana University, Department of Psychological and
Brain Sciences
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Rowlands>__
*Coffee Break 11am to 11:30am*
**
*11:30-12:30*
*Transactive Memory and Distributed Cognitive Ecologies*
*JOHN SUTTON*, Macquarie University, Department of Cognitive Science
/Overview & Readings
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Sutton>
/
*Lunch time 12:30pm to 2pm*
**
*2-3pm*
*Knowledge Mining in Heterogeneous Information Networks*
*JIAWEY HAN*, University of Illinois at Urbana-Champaign, Department of
Computer Science
/Overview & Readings
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Han>
/
*2-4pm*
*What is Cognition, and How Could it be Extended?*
*ROBERT RUPERT*, University of Colorado, Department of Philosophy
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Rupert>
*Coffee Break 4pm to 4:30pm*
**
*4:30pm*
Summary and discussion of day's sessions
**
**- - - - - *_THURSDAY, JULY 17_** - - - - -*
**
*9-10am*
*TBA*
**
*10-11am*
*Collective Intelligence: What is it? How can we measure it? And
**increase it?*
*THOMAS MALONE*, MIT Sloan School of Management
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Malone>
*Coffee Break 11am to 11:30am*
**
*11:30-12:30*
*Socio-Technical Epistemology*
*JUDITH SIMON*, Institut fuer Technikfolgenabschuetzung und
Systemanalyse (ITAS)
/Overview & Readings
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Simon>
/
*Lunch time 12:30pm to 2pm*
**
*2-3pm*
*Domains and Dimensions of Group Cognition*
*GEORG THEINER*, Villanova University, Department of Philosophy
/Overview & Readings/
<http://users.ecs.soton.ac.uk/harnad/Temp/AbsPrelimProg3.htm#Theiner>
*
*
*2-4pm*
*TBA*//
*Coffee Break 4pm to 4:30pm*
**
*4:30pm*
Summary and discussion of day's sessions
**
**- - - - - *_FRIDAY, JULY 18_ - - - - -**
*
**
*Closing day*
**
*[Talks TBA]
*
**
***KATY BORNER*, Indiana University, Department of Information and
Library Science <http://ils.indiana.edu/>
*Graphic Webs of Science*
/[Overview to come]/
*READINGS:*
Light, R. P., Polley, D. E., & Borner, K. (2013). Open Data and Open
Code for Big Science of Science Studies
<http://ivl.cns.iu.edu/km/pub/2013-light-sdb-sci2-issi.pdf>. ISSI.
Chen, Y., Borner, K., & Fang, S. (2013). Evolving collaboration networks
in Scientometrics in 1978-2010: a micro-macro analysis
<http://ivl.cns.iu.edu/km/pub/2012-chen-evolving-scientometrics.pdf>.
/Scientometrics/, /95/(3), 1051-1070.
***SIMON DeDEO*, Indiana University, Santa Fe Institute
*Collective Memory in Wikipedia*
/[Overview to come]/
*READINGS:*
DeDeo, S. (2013). Collective Phenomena and Non-Finite State Computation
in a Human Social System
<http://dx.plos.org/10.1371/journal.pone.0075818.g002>. /PloS one/,
/8/(10), e75818.
Hooper, P. L., DeDeo, S., Caldwell Hooper, A. E., Gurven, M., & Kaplan,
H. S. (2013). Dynamical Structure of a Traditional Amazonian Social
Network <http://www.mdpi.com/1099-4300/15/11/4932/pdf>. /Entropy/,
/15/(11), 4932-4955.
***SERGEY DOROGOVTSEV*, Universidade de Aveiro
*Explosive Percolation*
/[Overview to come]/
*READINGS:*
da Costa, R. A., Dorogovtsev, S. N., Goltsev, A. V., & Mendes, J. F. F.
(2010). Explosive percolation transition is actually continuous
<http://arxiv.org/pdf/1009.2534>. /Physical review letters/, /105/(25),
255701.**
Dorogovtsev, S. N., & Mendes, J. F. F. (2001). Language as an evolving
word web
<http://www-personal.umich.edu/%7Echoucc/Language%20as%20an%20evolving%20word%20web.pdf>.
/Proceedings of the Royal Society of London. Series B: Biological
Sciences/, /268/(1485), 2603-2606.
***JEAN-DANIEL FEKETE*, Institut National de Recherche en Informatique
et Automatique (INRIA) Unite de Recherche Saclay - ile-de-France
*Visualizing Dynamic Interactions*
/[Overview to come]/
*READINGS:*
Wybrow, M., Elmqvist, N., Fekete, J. D., von Landesberger, T., van Wijk,
J. J., & Zimmer, B. (2014). Interaction in the Visualization of
Multivariate Networks
<http://hal.archives-ouvertes.fr/docs/00/97/43/35/PDF/04-Interaction.pdf>.
In /Multivariate Network Visualization/ (pp. 97-125). Springer
International Publishing.
Bach, B., Pietriga, E., & Fekete, J. D. (2014, April). Visualizing
Dynamic Networks with Matrix Cubes
<http://hal.inria.fr/docs/00/93/19/11/PDF/cubix_acc2.pdf>. In /SICCHI
Conference on Human Factors in Computing Systems (CHI)/.
***FABIEN GANDON*, INRIA Research Center of Sophia-Antipolis
*Social and Semantic Web: Adding the Missing Links*
**
*/OVERVIEW:/*/ Since the mid-90s the Web re-opened in read-write mode
and, almost as a side effect, paved the way to numerous new social media
applications. Today, the Web is no longer perceived as a document system
but as a virtual place where persons and software interact in mixed
communities. These large scale interactions create many problems -- in
particular, reconciling the formal semantics of computer science (e.g.
logics, ontologies, typing systems, etc.) on which the Web architecture
is built, with the soft semantics of people (e.g. posts, tags, status,
etc.) on which the Web content is built. //Wimmics/
<http://wimmics.inria.fr/>/, among other research labs, studies methods,
models and algorithms to bridge formal semantics and social semantics on
the Web. We focus on the characterization of typed graph formalisms to
model and capture these different pieces of knowledge and hybrid
operators to process them jointly. This talk will describe the basics of
semantic web formalisms and introduce different initiatives using these
frameworks to represent reason and support social media and social
applications on the web./
*READINGS:*
Hasan, R., & Gandon, F. (2012). Explanation in the Semantic Web: a
survey of the state of the art
<http://hal.archives-ouvertes.fr/docs/00/70/22/77/PDF/RR-7974.pdf>.
Aussenac-Gilles, N., & Gandon, F. (2013). From the knowledge acquisition
bottleneck to the knowledge acquisition overflow: A brief French history
of knowledge acquisition
<http://www.researchgate.net/publication/234581482_from_the_knowledge_acquisition_bottleneck_to_the_knowledge_acquisition_overflow_A_brief_French_history_of_knowledge_acquisition/file/e0b49518aa6deb5f2a.pdf>.
/Int. J. Hum.-Comput. Stud./,/71/(2), 157-165.
***LEE GILES*, Pennsylvania State University
*Scholarly Big Data: Information Extraction and Data Mining*
/[Overview to come]/
*READINGS:*
Caragea, C., Wu, J., Ciobanu, A., Williams, K., Fernandez-Ramrez, J.,
Chen, H. H., ... & Giles, L. (2014). CiteSeer x: A Scholarly Big Dataset
<http://www.cse.unt.edu/%7Eccaragea/papers/ecir14.pdf>. In /Advances in
Information Retrieval/ (pp. 311-322). Springer International Publishing.
Flake, G. W., Lawrence, S., Giles, C. L., & Coetzee, F. M. (2002).
Self-organization and identification of web communities
<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.387.5730&rep=rep1&type=pdf>.
/Computer/, /35/(3), 66-70.
***PETER GLOOR*, MIT Center for Collective Intelligence
*Collaborative Innovation Networks*
*/OVERVIEW:/*/ Every disruptive innovation is not the result of a lone
inventor, but of a small group of likeminded individuals, working
together in close collaboration to get their cool idea off the ground.
They are leveraging the concept of swarm creativity, where this small
team - the Collaborative Innovation Network (COIN) - empowered by the
collaborative technologies of the Internet and social media, turns their
creative labor of love into a product that changes the way how we think,
work, or spend our day./
/This talk describes a series of ongoing projects at the MIT Center for
Collective Intelligence with the goal of analyzing the new idea creation
process through tracking human interaction patterns on three levels:/
/On the global level, macro- and microeconomic indicators such as the
valuation of companies and consumer indices, or election outcomes, are
predicted based on social media analysis on Twitter, Blogs, and
Wikipedia. On the organizational level, productivity and creativity of
companies and teams is measured through extracting 'honest signals' from
communication archives such as company e-mail. On the individual level,
individual and team creativity is analyzed through face-to-face
interaction with sociometric badges and personal e-mail logs. //
///
/The talk introduces the concept of coolhunting, finding new trends by
finding the trendsetters, and coolfarming, helping the trendsetters
getting their idea over the tipping point. The talk also presents the
concept of 'Virtual Mirroring', increasing individual and team
creativity by analyzing and optimizing five inter-personal interaction
variables of honest communication: 'strong leadership', 'rotating
leaders', 'balanced contribution', 'fast response', and 'honest sentiment.'/
*READINGS:*
Gloor, P. A., Krauss, J., Nann, S., Fischbach, K., & Schoder, D. (2009,
August). Web science 2.0: Identifying trends through semantic social
network analysis
<http://dspace.mit.edu/openaccess-disseminate/1721.1/59353>. In
/Computational Science and Engineering, 2009. CSE'09. International
Conference on/ (Vol. 4, pp. 215-222). IEEE.
Kleeb, R., Gloor, P. A., Nemoto, K., & Henninger, M. (2012). Wikimaps:
dynamic maps of knowledge
<http://www.atelier.net/sites/default/files/etude/wikimaps.pdf>.
/International Journal of Organisational Design and Engineering/,
/2/(2), 204-224.
/cci.mit.edu/pgloor <http://cci.mit.edu/pgloor>/
***JENNIFER GOLBECK*, University of Maryland
*The Social Web*
/[Overview to come]/
*READINGS:*
Golbeck, J. (2013). /Analyzing the social web/. Newnes.
Golbeck, J., Robles, C., Edmondson, M., & Turner, K. (2011, October).
Predicting personality from twitter
<http://www.cs.umd.edu/%7Egolbeck/pubs/Golbeck%20et%20al.%20-%202011%20-%20Predicting%20Personality%20from%20Twitter.pdf>.
In /Privacy, security, risk and trust (passat), 2011 ieee third
international conference on and 2011 ieee third international conference
on social computing (socialcom)/ (pp. 149-156). IEEE.
***ROBERT GOLDSTONE*, Indiana University, Department of Psychology
*Computational Models for Web Science*
/[Overview to come]/
*READINGS:*
Wisdom, T. N., Song, X., & Goldstone, R. L. (2013). Social Learning
Strategies in Networked Groups
<http://www.indiana.edu/%7Epcl/papers/imitationinnovation.pdf>.
/Cognitive science/, /37/(8), 1383-1425.
Theiner, G., Allen, C., & Goldstone, R. L. (2010). Recognizing group
cognition
<http://colinallen.dnsalias.org/Papers/Published/2010/2010-Theiner-etal-CogSys.pdf>.
/Cognitive Systems Research/, /11/(4), 378-395.
***STEPHEN GRIFFIN*, University of Pittsburgh, School of Information Science
*New Models of Scholarly Communication for Digital Scholarship*
*/OVERVIEW:/*///Contemporary research and scholarship increasingly uses
large-scale datasets and computationally intensive processing. Cultural
shifts in the scholarly community challenge long-standing of academic
institutions and call into question the efficacy and fairness of
traditional models of scholarly communication. Scholars are also calling
for greater authority in the publication of their works and rights
management. Agreement is growing on how best to manage and share
massive amounts of diverse and complex information objects. Open
standards and technologies allow interoperability across institutional
repositories. Content level interoperability based on semantic web and
linked open data standards is becoming more common. Information
research objects are increasingly thought of as social as well as data
objects - promoting knowledge creation and sharing and possessing
qualities that promote new forms of scholarly arrangements and
collaboration. This talk will present alternative paths for expanding
the scope and reach of digital scholarship and robust models of
scholarly communication necessary for full reporting. The overall goals
are to increase research productivity and impact, and to give scholars a
new type of intellectual freedom of expression./
*READINGS:*
Franzoni, C., & Sauermann, H. (2014). Crowd science: The organization of
scientific research in open collaborative projects
<http://scistarter.com/blog/wp-content/uploads/2013/04/SSRN-id2167538211.pdf>.
/Research Policy/, /43/(1), 1-20.**
http://www.openscholarship.org/jcms/c_5012/en/home
http://www.ischool.pitt.edu/scholarlycom/
http://www.sis.pitt.edu/~repwkshop (from Internet Archive Wayback
Machine) <http://www.sis.pitt.edu/%7Erepwkshop>
http://www.digitalhumanities.org/dhq/vol/3/1/000035/000035.html
http://en.wikipedia.org/wiki/E-Science
http://journalofdigitalhumanities.org/
http://chia.pitt.edu/
http://www.perseus.tufts.edu/hopper/
***DAME WENDY HALL*, University of Southampton
*Web Science */
[Overview to come]/
*READINGS:*
O'Hara, K., Contractor, N. S., Hall, W., Hendler, J. A., & Shadbolt, N.
(2013). Web Science: understanding the emergence of macro-level features
on the World Wide Web
<http://eprints.soton.ac.uk/360718/1/1800000017-O%27Hara-Vol4-WEB-017.pdf>.
/Foundations and Trends in Web Science/, /4/(2-3), 103-267.
Tiropanis, T., Hall, W., Shadbolt, N., De Roure, D., Contractor, N., &
Hendler, J. (2013). The Web Science Observatory
<http://eprints.soton.ac.uk/354604/1/TheWebScienceObservatory-postprint.pdf>.
/IEEE Intelligent Systems/, /28/(2), 100-104.
***HARRY HALPIN*, University of Edinburgh, Institute of Communicating
and Collaborating Systems, School of Informatics
*Web Semantics*
/[Overview to come]/
*READINGS:*
Hui, Y., & Halpin, H. (2013). Collective individuation: the future of
the social web
<http://digital-studies.org/wp/wp-content/uploads/2013/01/HuiYuk_and_HarryHalpin_FINAL_CollectiveIndividuation.pdf>.
/The Unlike Us Reader/, 103-116.
Halpin, H., Robu, V., & Shepherd, H. (2007, May). The complex dynamics
of collaborative tagging
<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.78.5341&rep=rep1&type=pdf>.
In /Proceedings of the 16th international conference on World Wide
Web/ (pp. 211-220). ACM.
***JIAWEY HAN*, University of Illinois at Urbana-Champaign, Department
of Computer Science
*Knowledge Mining in Heterogeneous Information Networks*
*/OVERVIEW:/*/ People and informational objects are interconnected,
forming gigantic, interconnected, integrated information networks. By
structuring these data objects into multiple types, such networks become
semi-structured heterogeneous information networks. Most real world
applications that handle big data, including interconnected social media
and social networks, medical information systems, online e-commerce
systems, or database systems, can be structured into typed,
semi-structured, heterogeneous information networks. For example, in a
medical care network, objects of multiple types, such as patients,
doctors, diseases, medication, and links such as visits, diagnosis, and
treatments are intertwined together, providing rich information and
forming heterogeneous information networks. Effective construction,
exploration and analysis of large-scale heterogeneous information
networks poses an interesting but critical challenge./
/In this talk, we present principles, methodologies and algorithms for
mining in heterogeneous social and information networks and show that
mining typed, heterogeneous networks is a promising research frontier in
data mining research. Departing from many existing network models that
view data as homogeneous graphs or networks, the semi-structured
heterogeneous information network model leverages the rich semantics of
typed nodes and links in a network and can uncover surprisingly rich
knowledge from interconnected data. This heterogeneous network modeling
will lead to the discovery of a set of new principles and methodologies
for mining and exploring interconnected data, such as rank-based
clustering and classification, meta path-based similarity search, and
meta path-based link/relationship prediction. We will also discuss our
recent progress on construction of quality semi-structured heterogeneous
information networks from unstructured data and point out some promising
research directions./
*READINGS:*
Han, J., & Wang, C. (2014). Mining Latent Entity Structures from Massive
Unstructured and Interconnected Data
<http://www.cs.uiuc.edu/%7Ehanj/pdf/sigmod14_jhan.pdf>.
Ren, X., Wang, Y., Yu, X., Yan, J., Chen, Z., & Han, J. (2014).
Heterogeneous Graph-Based Intent Learning with Queries, Web Pages and
Wikipedia Concepts <http://www.cs.uiuc.edu/%7Ehanj/pdf/wsdm14_xren.pdf>.
***JIM HENDLER*, Rensselaer Polytechnic Institute, Department of
Computer Science
*The Data Web*
/[Overview to come]/
*READINGS:*
Hendler, J., & Berners-Lee, T. (2010). From the Semantic Web to social
machines
<http://123seminarsonly.com/Seminar-Reports/009/64490827-Semantic-Web.pdf>:
A research challenge for AI on the World Wide Web. /Artificial
Intelligence/, /174/(2), 156-161.
Shadbolt, N., Hall, W., Hendler, J. A., & Dutton, W. H. (2013). Web
science: a new frontier
<http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3575572/>. /Philosophical
Transactions of the Royal Society A: Mathematical, Physical and
Engineering Sciences/, /371/(1987), 20120512.
Hendler, J. (2014). Big data meets computer science. /Journal of
Computing Sciences in Colleges/, /29/(6), 5-6.
***TONY HEY*, Microsoft Research Connections
*Open Science and the Web*
*/OVERVIEW:/*/ Turing award winner, Jim Gray, envisioned a world where
all research literature and all research data were online and
interoperable. He believed that such a distributed, global digital
library could significantly increase the research "information velocity"
and improve the scientific productivity of researchers. The last decade
has seen significant progress in the move towards open access to
scholarly research publications and the removal of barriers to access
and re-use. But barrier-free access to the literature alone only
scratches the surface of what the revolution of data intensive science
promises. Recently, in the US, the White House has called for federal
agencies to make all research outputs (publications and data) openly
available. But in order to make this effort effective, researchers need
better tools to capture and curate their data, and Jim Gray called for
'letting 100 flowers bloom' when it came to research data tools.
Universities have the opportunity and obligation to cultivate the next
regeneration of professional data scientists who can help define, build,
manage, and preserve the necessary data infrastructure. This talk will
cover some of the recent progress made in open access and open data, and
will discuss some of the opportunities ahead./
*READINGS:*
Fox, G., Hey, T., & Trefethen, A. (2013). Where Does All the Data Come
From
<http://grids.ucs.indiana.edu/ptliupages/publications/Where%20does%20all%20the%20data%20come%20from%20v7.pdf>?.
/Data-Intensive Science/, 115.
Hey, T. (2010). The next scientific revolution
<http://i2ge.com/wp-content/uploads/2012/01/Next-Scientific-Revolution.pdf>.
/Harv Bus Rev/, /88/(11), 56-63.
The Fourth Paradigm: Data-Intensive Scientific Discovery Book 2009
http://research.microsoft.com/en-us/collaboration/fourthparadigm/default.aspx
<http://research.microsoft.com/en-us/collaboration/fourthparadigm/default.aspx%20>
http://eprints.rclis.org/9202/1/heyhey_final_web.pdf
***FRANCIS HEYLIGHEN*, Vrije Universiteit Brussel, ECCO - Evolution,
Complexity and Cognition research group
*Towards a Global Brain: the Web as a Self-organizing, Distributed
Intelligence*
**
*/OVERVIEW:/*/ Distributed intelligence is an ability to solve problems
and process information that is not localized inside a single person or
computer, but that emerges from the coordinated interactions between a
large number of people and their technological extensions. The Internet
and in particular the World-Wide Web form a nearly ideal substrate for
the emergence of a distributed intelligence that spans the planet,
integrating the knowledge, skills and intuitions of billions of people
supported by billions of information-processing devices. This
intelligence becomes increasingly powerful through a process of
self-organization in which people and devices selectively reinforce
useful links, while rejecting useless ones. This process can be modeled
mathematically and computationally by representing individuals and
devices as agents, connected by a weighted directed network along which
"challenges" propagate. Challenges represent problems, opportunities or
questions that must be processed by the agents to extract benefits and
avoid penalties. Link weights are increased whenever agents extract
benefit from the challenges propagated along it. My research group is
developing such a large-scale simulation environment in order to better
understand how the web may boost our collective intelligence. The
anticipated outcome of that process is a "global brain", i.e. a nervous
system for the planet that would be able to tackle both global and
personal problems./
*READINGS:*
Heylighen, F. (2014). Return to Eden? Promises and Perils on the Road to
a Global Superintelligence
<http://pespmc1.vub.ac.be/Papers/BrinkofSingularity.pdf>. /The End of
the Beginning: Life, Society and Economy on the Brink of the
Singularity, B. Goertzel and T. Goertzel, Eds/.
Heylighen, F. (2013). Self-organization in Communicating Groups: the
emergence of coordination, shared references and collective intelligence
<http://cogprints.org/7265/1/barcelona-languageso.pdf>. In /Complexity
Perspectives on Language, Communication and Society/ (pp. 117-149).
Springer Berlin Heidelberg.
***BRYCE HUEBNER*, Georgetown University, Department of Philosophy
*Macrocognition: Situated versus Distributed*
*/OVERVIEW:/*/ 'Macrocognition' has two distinct, but closely related
meanings. Cacciabue and Hollnagel (1995) introduced it to denote the
study of cognition in realistic tasks, where people interact with
various forms of environmental and social scaffolding; Klein and
colleagues also used it to understand how people manage uncertainty and
make sense of real world environments. I introduced a second use
(Huebner 2014) as shorthand for system-level cognition implemented by
integrated networks of specialized computational mechanisms, whether in
individuals or groups. Macrocognition has one sense that's closer to
'situated or extended cognition' and another that's closer to
'distributed or collective cognition' but they are often conflated.
There are important differences between the hypothesis of collective
cognition (HCC) and the hypothesis of extended cognition (HEC). Recent
work on situated and collective memory and philosophical approaches to
coordination and planning suggest that HCC is more plausible if we
abandon HEC in favor of an 'ontologically thinner' approach to situated
cognition. There is a form of collective planning distinct from the
planning that relies on web-based technologies and other forms of social
scaffolding. Distinguishing two forms of macrocognition, one situated
the other distributed, can help us to make sense of a number of
theoretically and empirically interesting phenomena./
*READINGS:*
Cacciabue, P. C., & Hollnagel, E. (1995). Simulation of cognition:
Applications (pp. 55-73). Lawrence Erlbaum Associates.
Huebner, B., Bruno, M., & Sarkissian, H. (2010). What does the nation of
China think about phenomenal states?
<http://www.philosophyandreligion.msstate.edu/faculty/pdf/HuebnerBrunoSarkissian-ChinaPhenomenal-RPP-2010.pdf>.
/Review of Philosophy and Psychology/, /1/(2), 225-243.
Huebner, B. (2011). Genuinely collective emotions
<http://www9.georgetown.edu/faculty/lbh24/GenuinelyCollectiveEmotions.pdf>.
/European Journal for Philosophy of Science/, /1/(1), 89-118.
Huebner, B. (2014). /Macrocognition: A Theory of Distributed Minds and
Collective Intentionality/. Oxford University Press.
Klein, G., Ross, K. G., Moon, B. M., Klein, D. E., Hoffman, R. R., &
Hollnagel, E. (2003). Macrocognition
<http://cmapsinternal.ihmc.us/rid=1H1V9P5VL-1FR35RD-HKV/Macrocognition-IEEE2003.pdf>.
/Intelligent Systems, IEEE/, 18(3), 81-85.
http://brycehuebner.weebly.com/
http://www.sciencedirect.com/science/article/pii/S1389041713000259
**
***KAYVAN KOUSHA*, University of University of, Statistical Cybermetrics
Research Group, School of Technology
*Web Impact Metrics for Research Assessment*
/[Overview to come]/
*READINGS:*
Thelwall, M., Vaughan, L., & Bjorneborn, L. (2005). Webometrics
<http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.118.5694&rep=rep1&type=pdf>.
/ARIST/, /39/(1), 81-135.
Kousha, K., & Thelwall, M. (2007). Google Scholar citations and Google
Web/URL citations: A multi‐discipline exploratory analysis
<http://eprints.rclis.org/7641/1/google.pdf>. /Journal of the American
Society for Information Science and Technology/, /58/(7), 1055-1065.
*
*
*
****VINCENT LARIVIERE*, Universite du Quebec a Montreal
*Scientific Interaction Before and Since the Web*
/[Overview to come]/
*READINGS:*
Wallace, M. L., Lariviere, V., & Gingras, Y. (2012). A small world of
citations? The influence of collaboration networks on citation practices
<http://dx.plos.org/10.1371/journal.pone.0033339.g005>. /PloS one/,
/7/(3), e33339.
Lariviere, V., Gingras, Y., & Archambault, E. (2006). Canadian
collaboration networks: A comparative analysis of the natural sciences,
social sciences and the humanities
<http://www.chss.uqam.ca/Portals/0/docs/Canadian_Networks_Final.pdf>.
/Scientometrics/,/68/(3), 519-533.
Bollen, J., Van de Sompel, H., Hagberg, A., & Chute, R. (2009). A
principal component analysis of 39 scientific impact measures
<http://dx.plos.org/10.1371/journal.pone.0006022>. /PloS one/, /4/(6),
e6022.
*
*
***YANG-YU LIU*, Northeastern University, Center for Complex Network
Research, Physics Department
*Controllability and Observability of Complex Systems*
*/OVERVIEW:/*/ The ultimate proof of our understanding of complex
systems is reflected in our ability to control them. Although control
theory offers mathematical tools for steering engineered systems towards
a desired state, a framework to control complex systems is lacking. In
this talk I will show that many dynamic properties of complex systems
can studied be quantitatively, via a combination of tools from control
theory, network science and statistical physics. In particular, I will
focus on two dual concepts, i.e. controllability and observability, of
general complex systems. Controllability concerns our ability to drive
the system from any initial state to any final state within finite time,
while observability concerns the possibility of deducing the system's
internal state from observing its input-output behavior. I will show
that by exploring the underlying network structure of complex systems
one can determine the driver (or sensor) nodes that with time-dependent
inputs (or measurements) will enable us to fully control (or observe)
the whole system. /
*READINGS:*
Liu, Y. Y., Slotine, J. J., & Barabasi, A. L. (2011). Controllability of
complex networks
<http://leonidzhukov.net/hse/2011/seminar/papers/nature10011.pdf>.
/Nature/, /473/(7346), 167-173.
Zhao, C., Wang, W. X., Liu, Y. Y., & Slotine, J. J. (2014). Universal
Symmetry in Complex Network Control
<http://arxiv.org/pdf/1403.0041.pdf>. /arXiv preprint arXiv:1403.0041/.
*
*
***THOMAS MALONE*, MIT Sloan School of Management
*Collective Intelligence: What is it? How can we measure it? And how
can we increase it?*
*/OVERVIEW:/*/ This talk will describe how the same statistical
techniques used to measure intelligence in individuals can be used to
measure the "collective intelligence" of groups. We find that, just as
with individuals, a single statistical factor can predict the
performance of a group on a wide range of different tasks. This factor
is only weakly correlated with the group members' individual
intelligence. It is, however, correlated with the group members' social
perceptiveness, conversational behavior, and gender./
/The talk will also include brief overviews of other work to increase
collective intelligence by: (a) combining predictions from humans and
computers, (b) mapping the "genome" of collective intelligence, and (c)
harnessing the collective intelligence of thousands of people around the
world to develop proposals for what to do about global climate change./
*READINGS:*
Bernstein, A., Klein, M., & Malone, T. W. (2012). Programming the global
brain <http://dspace.mit.edu/openaccess-disseminate/1721.1/75216>.
/Communications of the ACM/, /55/(5), 41-43.
Malone, T. W., Laubacher, R., & Dellarocas, C. (2010). The collective
intelligence genome
<http://www.scob.alaska.edu/afef/CollectiveIntel.pdf>. /IEEE Engineering
Management Review/, /38/(3), 38.
*
****ALEXANDRE MONNIN*, Institut National de Recherche en Informatique et
Automatique (INRIA) Sophia Antipolis
*Web Philosophy*
/[Overview to come]/
*READINGS:*
Monnin, A., & Halpin, H. (2013). Toward a Philosophy of the Web:
Foundations and Open Problems
<http://media.johnwiley.com.au/product_data/excerpt/8X/11187001/111870018X-1.pdf>.
/Philosophical Engineering: Toward a Philosophy of the Web/, 1-20.
Monnin, A. (2013). /Vers une philosophie du Web: le Web comme
devenir-artefact de la philosophie/
<http://hal.inria.fr/docs/00/87/91/47/PDF/Vers_une_philosophie_du_Web_thA_se_version_finale_de_publication.pdf>/ (entre
URIs, tags, ontologie (s) et ressources)/ (Doctoral dissertation,
Universite Pantheon-Sorbonne-Paris I).
***ADILSON MOTTER*, Northeastern University, Physics of Complex Systems
and Networks
*Bursts, Cascades, and Time Allocation*
**
*/OVERVIEW:/*/ In this talk, I will present recent results on three
distinct but related problems concerning Web Science and the Mind:
bursts in the temporal distribution of words, cascading dynamics in
diverse network systems, and human allocation of time. In each case I
will discuss key properties, the principles governing these properties,
and opportunities their modeling offers for monitoring and controlling
complex behavior./
*READINGS:*
Cornelius, S. P., Kath, W. L., & Motter, A. E. (2013). Realistic control
of network dynamics <http://arxiv.org/pdf/1307.0015>. /Nature
communications/, /4/.
Altmann, E. G., Pierrehumbert, J. B., & Motter, A. E. (2009). Beyond
word frequency: Bursts, lulls, and scaling in the temporal distributions
of words
<http://www.plosone.org/article/info%3Adoi%2F10.1371%2Fjournal.pone.0007678#pone-0007678-g003>.
/PLoS One/, /4/(11), e7678.
Motter, A. E. (2010). Nonlinear dynamics: Spontaneous synchrony breaking
<http://arxiv.org/pdf/1003.2465.pdf?origin=publication_detail>. /Nature
Physics/, /6/(3), 164-165.
//
***CAMERON NEYLON*, PLOS (Public Library of Science)
*Network Ready Research: The Role of Open Source and Open Thinking*
*/OVERVIEW:/*/ The highest principle of network architecture design is
interoperability. Metcalfe's Law says a network's value can scale as
some exponent of the number of connections. Our job in building networks
is to ensure that those connections are as numerous, operational, and
//easy to create as possible. Informatics is a science of networks: of
physical interactions, genetic control, degree of similarity, or
ecological interactions, amongst many others. Informatics is also
amongst the most networked of research communities and amongst the most
open in the sharing of research papers, research data, tools, and even
research in process in online conversations and writing. Lifting our
gaze from the networks we work on to the networks we occupy is a
challenge. Our human networks are messy and contingent and our machine
networks clogged with things we can't use, even if we could access them.
What principles can we apply to build our research to make the most of
the network infrastructure we have around us. Where are the pitfalls and
the opportunities? What will it take to configure our work so as to
enable "network ready research"?/
*READINGS:*
Molloy, J. C. (2011). The open knowledge foundation: open data means
better science <http://dx.plos.org/10.1371/journal.pbio.1001195.g001>.
/PLoS biology/, /9/(12), e1001195.
Whyte, A., & Pryor, G. (2011). Open science in practice: Researcher
perspectives and participation
<http://ijdc.net/index.php/ijdc/article/viewFile/173/241>.
/International Journal of Digital Curation/, /6/(1), 199-213.**
http://cameronneylon.net/blog/fork-merge-and-crowd-sourcing-data-curation/
https://www.youtube.com/watch?v=Axr80qm6NHw
**
***TAKASHI NISHIKAWA*, Northwestern University, Physics & Astronomy
*Visual Analytics Approach for Discovering Network Structure Beyond
Communities*
*/OVERVIEW: /*/To understand the formation, evolution, and function of
complex systems, it is crucial to understand the internal organization
of their interaction networks. Partly due to the impossibility of
visualizing large complex networks, resolving network structure remains
a challenging problem. In this talk, I will describe an approach that
overcomes this difficulty by combining the visual pattern recognition
ability of humans with the high processing speed of computers to develop
an exploratory method for discovering groups of nodes characterized by
common network properties, including but not limited to communities of
densely connected nodes. Without any prior information about the nature
of the groups, the method simultaneously identifies the number of
groups, the group assignment, and the properties that define these
groups. The results of applying our method to real networks suggest
that most group structures lurk undiscovered in the fast-growing
inventory of social, biological, and technological networks of
scientific interest./
*READINGS:*
Nishikawa, T., & Motter, A. E. (2011). Discovering network structure
beyond communities
<http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3240966/>. /Scientific
reports/, /1/.
Nishikawa, T., & Motter, A. E. (2010). Network synchronization landscape
reveals compensatory structures, quantization, and the positive effect
of negative interactions
<http://www.pnas.org/content/107/23/10342.full>. /Proceedings of the
National Academy of Sciences/, 107(23), 10342-10347.
***FILIPPO RADICCHI*, Indiana University, Center for Complex Networks
and Systems Research, School
of Informatics and Computing
*Detecting Communities in Complex Networks: The Role of Degree Correlations*
/[Overview to come]/
*READINGS:*
Radicchi, F., Castellano, C., Cecconi, F., Loreto, V., & Parisi, D.
(2004). Defining and identifying communities in networks
<http://www.pnas.org/content/101/9/2658.long>. /Proceedings of the
National Academy of Sciences of the United States of America/, /101/(9),
2658-2663.
Radicchi, F., & Castellano, C. (2013). Analysis of bibliometric
indicators for individual scholars in a large data set
<http://arxiv.org/pdf/1304.1267.pdf>. /Scientometrics/, /97/(3), 627-637.
***MARK ROWLANDS*, Indiana University, Department of Psychological and
Brain Sciences
*Extended Mentality: What It Is and Why It Matters*
*/OVERVIEW:/*/ Does it matter if (some) mental processes extend into the
subjects's environment. The notion of mattering is an elliptical one:
something matters only to someone and in some way. A tacit assumption in
the recent debate is that the question of whether mental processes
extend should be decided by way of its implications for cognitive
science. The persons to whom it matters and who should be charged with
adjudicating the issue are, accordingly, cognitive scientists and
philosophers of cognitive science. I shall argue against this
assumption. What is really at stake is a philosophical vision of the
nature of mentality that can, to a considerable extent, be
elaborated independently of developments in cognitive science./
*READINGS:*
Rowlands, M. (2009). Enactivism and the extended mind
<http://demo.sheruyasodha.com.np/uploads/Topoi_28_%5B2009%5D_53-62.pdf>.
/Topoi/, /28/(1), 53-62.
Rowlands, M. (2009). Extended cognition and the mark of the cognitive.
/Philosophical Psychology/, /22/(1), 1-19.
Rowlands, M. (2010). /The new science of the mind/. Mit Press.
*
*
***ROBERT RUPERT*, University of Colorado, Department of Philosophy
*What is Cognition, and How Could it be Extended?*
*/OVERVIEW:/*/ Cognition is the overarching natural kind or property
that distinctively contributes to the production of the proprietary
phenomenon investigated by cognitive science, that is, intelligent
behavior. On the ground, cognitive-scientific practice relies most
fundamentally on modeling. Taken together, these two observations
suggest a way to identify what it is for a process or state to be
cognitive: abstract from the variety of forms of successful
cognitive-scientific modeling. The central theoretical construct of
cognitive science, the one common to all successful forms of
cognitive-scientific modeling, is the relatively persisting, integrated
system that moves through the world managing the agent's interaction
with the environment when the agent behaves intelligently. I
characterize the relevant form of integration more precisely, then ask
(1) whether humans currently function as components in cognitive systems
that include more than individual humans and (2) whether the idea of an
integrated system can help us to decide whether to count as cognitive
the processes occurring in creatures other than humans./
*READINGS:*
Rupert, R. D. (2011). Cognitive systems and the supersized mind
<http://hci.ucsd.edu/102a/readings/SSMSymp/Rupert.pdf>. /Philosophical
studies/, /152/(3), 427-436.
Rupert, R. D. (2009). /Cognitive systems and the extended mind/. Oxford
University Press.
**
***JUDITH SIMON*, Institut fuer Technikfolgenabschuetzung und
Systemanalyse (ITAS)
*Socio-Technical Epistemology*
/[Overview to come]/
*READINGS:*
Simon, J. (2010). The entanglement of trust and knowledge on the Web.
/Ethics and Information Technology/, /12/(4), 343-355.
Simon, J. (2010). A Socio‐epistemological Framework for Scientific
Publishing
<http://moodle.tau.ac.il/2012/pluginfile.php/393107/mod_resource/content/1/Simon%202010.pdf>.
/Social Epistemology/, /24/(3), 201-218.
*
*
***JEFF STIBEL*, Dun & Bradstreet Credibility Corp
*Web and Brain***
/[Overview to come]/
*READINGS:*
Stibel, J. M. (2013). /Wired for Thought: How the Brain Is Shaping the
Future of the Internet/. Harvard Business Press.
Stibel, J. (2013). /Breakpoint: Why the Web Will Implode, Search Will be
Obsolete, and Everything Else You Need to Know about Technology is in
Your Brain/. Macmillan.
Smith, N. The beginning of the end for the Internet
<http://digital-library.theiet.org/deliver/fulltext/et/8/9/20130934.pdf?itemId=/content/journals/10.1049/et.2013.0934&mimeType=pdf> (Stibel
Book Interview)
***JOHN SUTTON*, Macquarie University, Department of Cognitive Science
*Transactive Memory and Distributed Cognitive Ecologies*
*/OVERVIEW:/*/ Does the internet alter the way we remember? What
understanding of memory makes sense in light of our rich interactions
with technologies and with other people? This presentation introduces
theoretical and empirical work on distributed cognitive ecologies as a
framework for addressing web science and the mind. It surveys recent
accounts of the effect of new technologies on human memory, with a focus
on transactive memory theory. It embeds recent empirical findings on the
ways we remember in conjunction with each other and with online systems
in a broader picture of socially distributed remembering. In place of
metaphysical concerns about extended cognition and popular worries about
the erosion of natural memory, it suggests a number of rich research
possibilities for integrating the cognitive and social sciences./
*READINGS:*
Michaelian, K., & Sutton, J. (2013). Distributed cognition and memory
research: History and current directions
<http://kmichaelian.bilkent.edu.tr/offprints/2013RoPP-distributed.pdf>.
/Review of Philosophy and Psychology/, /4/(1), 1-24.
Sutton, J., Harris, C. B., Keil, P. G., & Barnier, A. J. (2010). The
psychology of memory, extended cognition, and socially distributed
remembering
<http://www.johnsutton.net/PCS_Sutton_Harris_Keil_Barnier.pdf>.
/Phenomenology and the cognitive sciences/, /9/(4), 521-560.**
http://www.johnsutton.net/Sutton_CHSC.pdf
http://www.johnsutton.net/PCS_Sutton_Harris_Keil_Barnier.pdf
http://www.wjh.harvard.edu/~wegner/pdfs/science.1207745.full.pdf
<http://www.wjh.harvard.edu/%7Ewegner/pdfs/science.1207745.full.pdf>
***PHIL TETLOW*, IBM United Kingdom Limited
*Computational Models for Web Science*
**
*/OVERVIEW:/*/ Web Science has matured considerably in recent years but
we still don't really know where our train is heading. For fundamental
research to work it has to be based on three principles: (1) invariance:
an idea should translate across multiple frames of reference and
applications; (2) causality: some recognisable change should be evident
from an idea's application in any given frame of reference; (3)
singularity of metric: the effectiveness of any idea should be
measurable using a context-free. This presentation will describe early
work done on applying the Invariance, Causality, Metric (ICM) framework
to Web Science and its implications for other areas of study such as
complexity theory, systems design and public safety./
*READINGS:*
Tetlow, P. (2012). /Understanding Information and Computation: From
Einstein to Web Science/. Gower Publishing, Ltd..
Tetlow, P. D. (2007). /The Web's awake: An introduction to the field of
Web Science and the concept of Web life/. John Wiley & Sons.
***GEORG THEINER*, Villanova University, Department of Philosophy
*Domains and Dimensions of Group Cognition*
/[Overview to come]/
*READINGS:*
Theiner, G., Allen, C., & Goldstone, R. L. (2010). Recognizing group
cognition
<http://colinallen.dnsalias.org/Papers/Published/2010/2010-Theiner-etal-CogSys.pdf>.
/Cognitive Systems Research/, /11/(4), 378-395.
Theiner, G., & O'Connor, T. (2010). 5 The Emergence of Group Cognition
<http://www.indiana.edu/%7Escotus/files/EmergGrpCognition.pdf>.
/Emergence in science and philosophy/, /6/, 78.
Theiner, G. (2013) The 'Symbol Un-Grounding Problem'
<http://avant.edu.pl/wp-content/uploads/Folder_Trends2013.pdf#page=36>
//
***PETER TODD*, Indiana University, Department of Psychological and
Brain Sciences
*Foraging in the World, Mind and Online*
*/OVERVIEW:/*/ How do we decide when to search for something better and
when to stick with what we've got? People, like other organisms, must
adaptively trade off between exploring and exploiting their environment
to obtain the resources they need. This applies to whatever space they
are searching: whether the external spatial world, looking for patches
of food; the social environment, looking for mates or friends; the
internal mental environment, looking for concepts in memory; or the
online environment, looking for information on the Web. Common
underlying mechanisms may be used to address the explore/exploit
tradeoff in each of these domains. People use similar heuristic
strategies to decide when to keep looking and when to give up searching
for resources in patches in space (e.g., for fish in a pond), in memory
(e.g., for words in a category), and online (e.g., for useful Web
pages), as predicted by optimal foraging theory. Moreover, the
connections between search in these domains may have deep evolutionary
roots, built on the same underlying mechanisms, as indicated by studies
showing that search in an external domain can prime subsequent search
strategies in an internal domain. In this talk, I will describe how new
studies are uncovering these connections between spatial search and
information search (as described in Cognitive Search: Evolution,
Algorithms, and the Brain, Todd, Hills, and Robbins, eds.; MIT Press,
2012)./
*READINGS:*
Hills, T. T., Jones, M. N., & Todd, P. M. (2012). Optimal foraging in
semantic memory
<http://www.researchgate.net/publication/221827967_Optimal_foraging_in_semantic_memory/file/79e4150a225159aaf9.pdf>.
/Psychological review/, /119/(2), 431.
Todd, P. M., Hills, T. T., & Robbins, T. W. (Eds.). (2012). /Cognitive
search: Evolution, algorithms, and the brain/. MIT Press.
Sporns, O. (2011). The human connectome: a complex network
<http://www.researchgate.net/publication/49770658_The_human_connectome_a_complex_network/file/9fcfd51095acd0d148.pdf>.
/Annals of the New York Academy of Sciences/, /1224/(1), 109-125.
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