Collective Memory in Wikipedia

SIMON DeDEO
Indiana University
Santa Fe Institute 

VIDEO

OVERVIEW: In an analysis of range of social systems, from online collaboration in Wikipedia to revolutionary activity in the Arab Spring, we find a common structure to social reasoning that crucially involves the formation of long-term memories and dispositions. No individual member serves as the system memory or reasoner; these dispositions are, instead, collective states of the group as a whole. The underlying computational structure appears to make use of at least one (formally) unbounded resource. We provide a game theoretic account of group-level strategies based on a simple belief-formation mechanism, and show the challenges that arise in connecting these group level phenomena to the beliefs and desires of the underlying individuals.

READINGS:

    DeDeo, S. (2013). Collective Phenomena and Non-Finite State Computation in a Human Social System. PloS one, 8(10), e75818. 

      Klingenstein, Sara, Tim Hitchcock, Simon DeDeo (2014) The civilizing process in London's Old Bailey. Proceedings of the National Academy of Sciences

    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.
    DeDeo, S (2014) Group Minds and the Case of Wikipedia

The Semantic Web: the inside story

JIM HENDLER
Rensselaer Polytechnic Institute
Department of Computer Science 

VIDEO

OVERVIEW:  In this talk I look at the Semantic Web idea of adding knowledge to the Web in ways compatible with machine processing.  Emerging in the late 90s, and growing since then,the languages , usage and uptake of semantic technologies has been increasing.  I'll discuss the genesis of this idea, some key steps in its history, and current usage. I also proposes challenges: Having far surpassed the original vision, how do we continue to use and grow the semantic web?

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.

Foraging in the World, Mind and Online

PETER TODD
Indiana University
Department of Psychological and Brain Sciences

VIDEO

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 processes.  Psychological 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 tasks.  Cognitive Science, 33, 497-529.

Macrocognition: Situated versus Distributed

BRYCE HUEBNER
Georgetown University
Department of Philosophy 

VIDEO

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:

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

Visual Analytics for Discovering Network Structure Beyond Communities

TAKASHI NISHIKAWA
Northwestern University
Physics & Astronomy

VIDEO

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, 151
    Keim, D., Andrienko, G., Fekete, J. D., Görg, C., Kohlhammer, J., & Melançon, G. (2008). Visual analytics: Definition, process, and challenges (pp. 154-175). Springer Berlin Heidelberg.
    Federico, P., Aigner, W., Miksch, S., Windhager, F., & Zenk, L. (2011. A visual analytics approach to dynamic social networks. Proceedings of the 11th International Conference on Knowledge Management and Knowledge Technologies. ACM.
    Li, K., Guo, L., Faraco, C., Zhu, D., Chen, H., Yuan, Y., ... & Liu, T. (2012). Visual analytics of brain networks. NeuroImage, 61(1), 82-97.

Analogies between interconnected and clustered networks

FILIPPO RADICCHI
Indiana University
School of Informatics and Computing

VIDEO

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) 

Web Semantics

HARRY HALPIN

University of Edinburgh
School of Informatics

VIDEO

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.