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Programme (as of May 14 2014)


Summer School in Cognitive Science 2014

 

WEB SCIENCE AND THE MIND

 

JULY 7 - 18 2014

Universite du Quebec a Montreal

Montreal, Canada

www.summer14.isc.uqam.ca


Cognitive Science and Web Science have been converging in the study of cognition as: 


(i)
distributed within the brain,
(ii)
distributed between multiple minds
and
(iii)
distributed between minds and media.


The four themes of the Summer Institute are:

(1) Homologies and analogies between minds and databases
(2) Interactions between individual minds and distributed databases
(3) Interactions between multiple minds and distributed databases
(4) Analysis of organization and activity in minds and distributed databases



KATY BORNER, Indiana University
Humanexus: Envisioning Communication and Collaboration

LES CARR, University of Southampton
Web Impact on Society



SIMON DeDEO, Indiana University, Santa Fe Institute
Collective Memory in Wikipedia

SERGEY DOROGOVTSEV, Universidade de Aveiro
Explosive Percolation

JEAN-DANIEL FEKETE, INRIA
Visualizing Dynamic Interactions

FABIEN GANDON, INRIA  
Social and Semantic Web: Adding the Missing Links
 
LEE GILES, Pennsylvania State University
 Scholarly Big Data: Information Extraction and Data Mining

PETER GLOOR, MIT
Collaborative Innovation Networks
 
JENNIFER GOLBECK, University of Maryland
You Can't Hide: Predicting Personal Traits in Social Media

ROBERT GOLDSTONE,  Indiana University
Learning Along with Others



STEPHEN GRIFFIN, University of Pittsburgh
New Models of Scholarly Communication for Digital Scholarship

DAME WENDY HALL, University of Southampton
Web Science 



HARRY HALPIN, University of Edinburgh
Does the Web Extend the Mind - and Semantics?

JIAWEI HAN, University of Illinois  
Knowledge Mining in Heterogeneous Information Networks
 
JIM HENDLER, Rensselaer Polytechnic Institute,
The Data Web

TONY HEY, Microsoft Research Connections
Open Science and the Web
 
FRANCIS HEYLIGHEN, Vrije Universiteit Brussel
Towards a Global Brain: the Web as a Self-organizing, Distributed Intelligence
 
BRYCE HUEBNER, Georgetown University
Macrocognition: Situated versus Distributed
 
KAYVAN KOUSHA, U Wolverhampton
Web Impact Metrics for Research Assessment

VINCENT LARIVIERE, Universite de Montreal
 Scientific Interaction Before and Since the Web

YANG-YU LIU, Northeastern University
Controllability and Observability of Complex Systems

THOMAS MALONE, MIT
Collective Intelligence: What is it?  How can we measure it?  And increase it?

ADILSON MOTTER, Northeastern University
Bursts, Cascades, and Time Allocation

CAMERON NEYLON, Public Library of Science
Network Ready Research: The Role of Open Source and Open Thinking

TAKASHI NISHIKAWA, Northwestern University
Visual Analytics Approach for Discovering Network Structure Beyond Communities
 
FILIPPO RADICCHI, Indiana University,
Analogies between interconnected and clustered networks

MARK ROWLANDS, Indiana University
Extended Mentality: What It Is and Why It Matters
 
ROBERT RUPERT, U Colorado & Edinburgh
What is Cognition, and How Could it be Extended?
 
DEREK RUTHS, McGill University
Social Informatics

JUDITH SIMON, ITAS
Socio-Technical Epistemology

JEFF STIBEL, Dun & Bradstreet Credibility Corp
Web and Brain

JOHN SUTTON, Macquarie University
Transactive Memory and Distributed Cognitive Ecologies
 
PHIL TETLOW, IBM UK
Computational Models for Web Science
 
GEORG THEINER, Villanova University
Domains and Dimensions of Group Cognition
 
PETER TODD, Indiana University
Foraging in the World, Mind and Online





KATY BORNER, Indiana University, Department of Information and Library Science

 

Humanexus: Envisioning Communication and Collaboration


Overview: This presentation opens with a screening of Humanexus, an award-winning semi-documentary that visualizes human communication from the Stone Age to today and beyond. The film aims to make tangible the enormous changes in the quantity and quality of our collective knowledge and the impact of different media and distribution systems on knowledge exchange. It follows a presentation and discussion of recent collaborative work on scholarly communication and collaboration. Last but not least, everyone will be invited to explore the Information Visualization MOOC (for free or for IU credits) and to visit the Places & Spaces: Mapping Science exhibit on display at the summer school.

READINGS:

    Stipelman, Brooke A., Hall, Kara L., Zoss, Angela, Okamoto, Janet, Stokols, Dan, and Börner, Katy (submitted) Mapping the Impact of Transdisciplinary Research: A Visual Comparison of Investigator Initiated and Team Based Tobacco Use Research Publications. The Journal of Translational Medicine and Epidemiology.
    Bollen, Johan, David Crandall, Damion Junk, Ying Ding, and Katy Börner. 2014. From funding agencies to scientific agency: Collective allocation of science funding as an alternative to peer review. EMBO Reports 15 (1): 1-121.
    Mazloumian, Amin, Dirk Helbing, Sergi Lozano, Robert Light, and Katy Börner. 2013. Global Multi-Level Analysis of the 'Scientific Food Web'. Scientific Reports 3, 1167.
    Börner, Katy, Noshir S. Contractor, Holly J. Falk-Krzesinski, Stephen M. Fiore, Kara L. Hall, Joann Keyton, Bonnie Spring, Daniel Stokols, William Trochim, and Brian Uzzi. 2010. A Multi-Level Systems Perspective for the Science of Team Science. Science Translational Medicine 2 (49): 49(cm)24.

Relevant books:
    Börner, Katy, and David E. Polley. 2014. Visual Insights: A Practical Guide to Making Sense of Data. Cambridge, MA: The MIT Press.
    Scharnhorst, Andrea, Katy Börner, and Peter van den Besselaar, eds. 2012. Models of Science Dynamics: Encounters Between Complexity Theory and Information Science. Springer Verlag.
    Börner, Katy, Mike Conlon, Jon Corson-Rikert, and Ying Ding, eds. 2012. VIVO: A Semantic Approach to Scholarly Networking and Discovery. Morgan & Claypool Publishers LLC.
    Börner, Katy. 2010. Atlas of Science: Visualizing What We Know. The MIT Press.

        Humanexus

        Information Visualization MOOC

        Places & Spaces: Mapping Science exhibit


LES CARR, University of Southampton, Web Science

Web Impact on Society
[Overview to come]

READINGS:
    Hall, W., Tiropanis, T., Tinati, R., Booth, P., Gaskell, P., Hare, J., & Carr, L. (2013). The Southampton University Web Observatory.

    Shadbolt, N., Brody, T., Carr, L. and Harnad, S. (2006) The Open Research Web: A Preview of the Optimal and the Inevitable, in Jacobs, N., Eds. Open Access: Key Strategic, Technical and Economic Aspects. Chandos.

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. 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. Entropy, 15(11), 4933-4955.

 




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. Physical review letters, 105(25), 255701.
    Dorogovtsev, S. N., & Mendes, J. F. F. (2001).
Language as an evolving word web. 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. 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. 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, 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.
    Aussenac-Gilles, N., & Gandon, F. (2013).
From the knowledge acquisition bottleneck to the knowledge acquisition overflow: A brief French history of knowledge acquisition. Int. J. Hum.-Comput. Stud., 71(2), 157-165.



LEE GILES, Pennsylvania State University

 

Scholarly Big Data: Information Extraction and Data Mining


Overview: Collections of scholarly documents are usually not thought of as big data. However, large collections of scholarly documents often have many millions of publications, authors, citations, equations, figures, etc., and large scale related data and structures such as social networks, slides, data sets, etc. We discuss scholarly big data challenges, insights, methodologies and applications. We illustrate scholarly big data issues with examples of specialized search engines and recommendation systems based on the SeerSuite software. Using information extraction and data mining, we illustrate applications in such diverse areas as computer science, chemistry, archaeology, acknowledgements, citation recommendation, collaboration recommendation, and others.

READINGS:

    Khabsa, M & Giles, C.L. (2014) The Number of Scholarly Documents on the Web. PLOS ONE 10.1371/journal.pone.0093949
    Caragea, C., Wu, J., Ciobanu, A., Williams, K., Fernandez-Ramrez, J., Chen, H. H., ... & Giles, L. (2014).
CiteSeer x: A Scholarly Big Dataset. 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. 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. 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. International Journal of Organisational Design and Engineering, 2(2), 204-224.
    Gloor, P. (2010) Coolfarming - Turn Your Great Idea Into The Next Big Thing AMACOM, NY
    Gloor, P.  (2006) Swarm Creativity, Competitive Advantage Through Collaborative Innovation Networks. Oxford

 



JENNIFER GOLBECK, University of Maryland

 

You Can't Hide: Predicting Personal Traits in Social Media


Overview: People share a huge amount of personal information online. With over a billion people on social media, this is opening up new abilities for researchers to predict a range of personal attributes that reveal how we live, think, and interact, even as people may try to keep this information private. This presentation will cover the methods and results in this area and argue for the future science and policy these advances demand.


READINGS:

    Golbeck, J. (2013). Analyzing the social web.
    Newnes.
Golbeck, J., Robles, C., Edmondson, M., & Turner, K. (2011, October). Predicting personality from twitter. 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
    Kosinski, M., Stillwell, D., & Graepel, T. (2013). Private traits and attributes are predictable from digital records of human behavior. Proceedings of the National Academy of Sciences, 110(15), 5802-5805.
    Golbeck, J., Robles, C., & Turner, K. (2011, May). Predicting personality with social media. In CHI'11 Extended Abstracts on Human Factors in Computing Systems (pp. 253-262). ACM..



ROBERT GOLDSTONE, Psychological and Brain Sciences, Indiana University

 

Learning Along with Others

Overview: We have developed internet-enabled experimental platforms to explore group patterns that emerge when people can see and imitate the solutions, innovations, and choices of their peers over several rounds.  Experiments and simulations show that there is a systematic relation between the difficulty of a problem search space and the optimal social network for transmitting solutions. With more complex search spaces, people imitate: prevalent options, options that become increasingly prevalent, high-scoring options, solutions similar to one’s own solution, and during the early stages of an extended search process.  Historical records of baby names show that naming choices are influenced by both the frequency of a name, and increasingly by its “momentum” in the recent past.

READINGS:

    Goldstone, R. L., Wisdom, T. N., Roberts, M. E., & Frey, S. (2013). Learning along with others. Psychology of Learning and Motivation, 58, 1-45.
    Wisdom, T. N., Song, X., & Goldstone, R. L. (2013).
Social Learning Strategies in Networked Groups. Cognitive science, 37(8), 1383-1425.
    Theiner, G., Allen, C., & Goldstone, R. L. (2010).
Recognizing group cognition. Cognitive Systems Research, 11(4), 378-395.   
    Frey, S., & Goldstone, R. L. (2013). Cyclic game dynamics driven by iterated reasoning. PLoS One, 8(2)
    Roberts, M. E., & Goldstone, R. L. (2011).  Adaptive Group Coordination and Role DifferentiationPLoS One, 6, 1-8.
    Gureckis, T. M., & Goldstone, R. L. (2009). How you named your child: Understanding the relationship between individual decision-making and collective outcomes. Topics in Cognitive Science, 1, 651-674.



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:

    Griffin, S (2014) Digital Scholarship, Scholarly Communication and New Roles for Libraries
    Griffin, S. et al (2014) The Denton Declaration: An Open Data Manifesto
    Calhoun, K (2014) Exploring Digital Libraries: Foundations, practice, prospects Facet Publishing London, UK

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.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. 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. IEEE Intelligent Systems, 28(2), 100-104.


HARRY HALPIN, University of Edinburgh, Institute of Communicating and Collaborating Systems, School of Informatics

 

Does the Web Extend the Mind - and Semantics?


OVERVIEW: Under what conditions does the Web count as a part of your own mind? We discuss the conditions upon which cognitive extension and integration can be upheld, and inspect these in light of the Web. We also argue that this ability to integrate the mind into media such as the Web is inherently social, insofar as it involves interaction with both technological scaffolding and other humans. Also, there are many cases where external media like the Web are not actually integrated cognitively, but simply serve as a way to co-ordinate intelligent problem-solving via distributed cognition. Yet distributed cognition should not be underestimated, as it can serve as a stepping stone to a wider kind of cognitive integration: collective intelligence. Finally, we inspect the impact of the Web — via phenomena like tagging, social media, and search engines — on traditional notions of language and semantics.


READINGS:
    Hui, Y., & Halpin, H. (2013). Collective individuation: the future of the social web. The Unlike Us Reader, 103-116
    Halpin, H., Robu, V., & Shepherd, H. (2007, May). The complex dynamics of collaborative tagging. In Proceedings of the 16th international conference on World Wide Web (pp. 211-220). ACM.
    Halpin, H (2013) Does the web extend the mind?. In: Proceedings of the 5th Annual ACM Web Science Conference (WebSci '13). ACM, New York, NY, USA, 139-147.

JIAWEI 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:

    Yizhou Sun and Jiawei Han (2012) Mining Heterogeneous Information Networks: Principles and Methodologies, Morgan & Claypool Publishers

    Chi Wang, Marina Danilevsky, Jialu Liu, Nihit Desai, Heng Ji, and Jiawei Han, Constructing Topical Hierarchies in Heterogeneous Information Networks, Proc. 2013 IEEE Int. Conf.on Data Mining (ICDM'13), Dallas, TX, Dec. 2013



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: 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. 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?. Data-Intensive Science, 115.
    Hey, T. (2010).
The next scientific revolution. 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://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. 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. 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?. Review of Philosophy and Psychology, 1(2), 225-243.

Huebner, B. (2011). Genuinely collective emotions. 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. 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 Wolverhampton

 

Web Impact Metrics for Research Assessment

[Overview to come]

READINGS:

    Thelwall, M., Vaughan, L., & Bjorneborn, L. (2005). Webometrics. ARIST, 39(1), 81-135.
    Kousha, K., & Thelwall, M. (2007).
Google Scholar citations and Google Web/URL citations: A multidiscipline exploratory analysis. Journal of the American Society for Information Science and Technology, 58(7), 1055-1065.



VINCENT LARIVIERE, Universite de 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. 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. 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. 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. Nature, 473(7346), 167-173.
    Zhao, C., Wang, W. X., Liu, Y. Y., & Slotine, J. J. (2014).
Universal Symmetry in Complex Network Control. 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. Communications of the ACM, 55(5), 41-43.
    Malone, T. W., Laubacher, R., & Dellarocas, C. (2010).
The collective intelligence genome. IEEE Engineering Management Review, 38(3), 38.


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. 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. PLoS One, 4(11), e7678.

    Motter, A. E. (2010).
Nonlinear dynamics: Spontaneous synchrony breaking. 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. PLoS biology, 9(12), e1001195.
    Whyte, A., & Pryor, G. (2011).
Open science in practice: Researcher perspectives and participation. 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. Scientific reports, 1.
    


FILIPPO RADICCHI, Indiana University, Center for Complex Networks and Systems Research, School

of Informatics and Computing

 

Analogies between interconnected and clustered networks


Overview: In this talk, I will illustrate how spectral methods can be used to determine common properties shared by interconnected networks and graphs with community structure. In particular, I will show that degree correlations play a fundamental role for the characterization of the structural phases of these systems.

READINGS:

    Radicchi, F (2014) A paradox in community detection  EPL 106, 38001
    Radicchi, F (2014) Driving interconnected networks to supercriticality Phys. Rev. X 4, 021014
    Radicchi, F (2013) Detectability of communities in heterogeneous networks Phys. Rev. E 88, 010801(R)


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. 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, U Colorado & U Edinburgh, 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. Philosophical studies, 152(3), 427-436.
    Rupert, R. D. (2009). Cognitive systems and the extended mind. Oxford University Press.
    Rupert, R. (2013) Memory, Natural Kinds, and Cognitive Extension; or, Martians Don’t Remember, and Cognitive Science Is Not about Cognition, Review of Philosophy and Psychology 4, 1 (2013): 25–47


DEREK RUTHS, McGill University

Social Informatics

[Overview to come]nishi

READINGS:

    Ruths, D. A., Nakhleh, L., Iyengar, M. S., Reddy, S. A., & Ram, P. T. (2006). Hypothesis generation in signaling networks. Journal of Computational Biology, 13(9), 1546-1557.
   
J. Ruths and D. Ruths. “Control Profiles of Complex Networks” Science



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 Socioepistemological Framework for Scientific Publishing. 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 (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. 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. 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

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. Cognitive Systems Research, 11(4), 378-395.
    Theiner, G., & O'Connor, T. (2010). 5
The Emergence of Group Cognition. Emergence in science and philosophy, 6, 78.

    Theiner, G. (2013)
The 'Symbol Un-Grounding Problem'

 

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. Psychological review, 119(2), 431.
    Hills, T.T.,     Todd, P.M., and Goldstone, R.L. (2008).  Search in external and internal spaces: Evidence for generalized cognitive search processesPsychological Science, 19(8), 802-808.
    Wilke, A., Todd, P.M., and Hutchinson, J.M.C. (2009).  Fishing for the right words: Decision rules for human foraging behavior in external and internal search tasksCognitive Science, 33, 497-529.