[sigCR] Fwd: [SIG-IRList] Inconsistency Robustness 2014: Call for Participation

Tue Jun 12 09:18:16 EDT 2012

FYI

Joseph T. Tennis
Assistant Professor
The Information School
University of Washington

Reviews Editor, Knowledge Organization

jtennis at u.washington.edu<mailto:jtennis at u.washington.edu>
faculty.washington.edu/jtennis<http://faculty.washington.edu/jtennis>

Begin forwarded message:

From: <jack.g.conrad at thomsonreuters.com<mailto:jack.g.conrad at thomsonreuters.com>>
Subject: [SIG-IRList] Inconsistency Robustness 2014: Call for Participation
Date: June 11, 2012 4:36:40 AM PDT
To: <IRList at lists.shef.ac.uk<mailto:IRList at lists.shef.ac.uk>>
Cc: <hewitt at concurrency.biz<mailto:hewitt at concurrency.biz>>

That’s right -- this really is for 2014, with the first deadlines occurring mid-next year.  The idea is that this is such a new, nonstandard topic that people may
wish to start thinking about it from scratch, and will thus appreciate some time.   --Jack

__________________________________
Call for Participation
Inconsistency Robustness 2014
Stanford University
3-4 days Summer 2014
_____________________________

Inconsistency robustness is information system performance in the face of continually pervasive inconsistencies---a shift from the previously dominant paradigms of inconsistency denial and inconsistency elimination attempting to sweep them under the rug.

Illustrative Issues
In fact, inconsistencies are pervasive throughout our information infrastructure and they affect one another. Consequently, an interdisciplinary approach is needed. For example (in no particular order):
· Computational linguistics relies on human-annotated data to train machine learners. Inconsistency among the human annotators must be carefully managed (otherwise, the annotations are useless in computation). How can this annotation process be made scalable?
• What are the limitations in the ability of a many-core computer software system to measure and diagnose its own performance?
• How to deal with the strategic inconsistency between classical microeconomics (i.e. individual economic transactions lead to generally desirable outcomes) and Keynesian macroeconomics (i.e. fraud, externalities, and monetary instabilities require government regulation)?
• Addiction is a huge health problem in which inconsistencies abound. For example, Step 1 in Twelve Step programs of recovery is that addicts admit that they are powerless over their addiction.
• In teaching situations (e.g. with infants, avatars, or robots), how does a teacher realize that they need to help correct a learner and how does a learner realize what correction is needed?
• Is privacy protection inconsistent with preventing terrorism?
• How do appellate courts reconcile inconsistent decisions of lower courts?
• If interlocutors in the same organization hold inconsistent positions, how do they negotiate? If the interlocutors are in separate organizations with overlapping concerns, how are the negotiations different?
• Is the existence of an observer-independent objective view of reality inconsistent with the laws of physics?
• What kind of regulation is consistent with innovation?
• How are inconsistencies in law related to inconsistencies in science?
• What are foundations for robust reasoning in pervasively inconsistent theories?
• Does the human brain mediate inconsistencies among its constituent parts?
In each case, inconsistencies need to be precisely identified and their consequences explored.

Innovation
Inconsistency robustness differs from previous paradigms based on belief revision, probability, and uncertainty as follows:
· Belief revision: Large information systems are continually, pervasively inconsistent and there is no way to revise them to attain consistency.
• Probability and fuzzy logic: In large information systems, there are typically several ways to calculate probability.  Often the result is that the probability is both close to 0% and close to 100%!
· Uncertainty: Resolving uncertainty to determine truth is not a realistic goal in large information systems.

Particulars
This interdisciplinary symposium is conducted under the auspices of the International Society for Inconsistency Robustness (http://www.isir.ws<http://www.isir.ws/> <http://<http:/>www.isir.ws/<http://www.isir.ws/>> ). The symposium is broadly based on theory and practice, addresses fundamental issues in inconsistency robustness is the follow-on symposium to Inconsistency Robustness 2011 (http://www.robust11.org/).
     Submissions will be refereed by the program committee and those accepted will be published in the proceedings. The symposium program will consist of presentations of accepted refereed submissions, panel
discussions, and invited lectures that will be video-recorded, edited, and posted on the Internet after the end of the symposium.
     Topics of Interest include: affect and sentiment, argumentation, authority and accountability, classifications
and ontologies, collaboration, design, discretion, education, efficiency, finance, history, innovation, judgment, language, organizational management, prediction, provenance, risk management, repair, social structure, scalability, and timeliness.

Important dates
· May 30, 2013: Due date for extended abstracts (at least 4 pages and no more than 6 excluding references in at least 10 pt. font) and panel proposals. A submission for which there is no extended abstract is subject to summary rejection.
• September 30, 2013: Due date of full technical papers and revised panel proposals. A hard limit on size will not be imposed because the proceedings will be produced electronically. So anything up to about 25K words would be possible.  Of course, the length must be suitable to the subject matter.
    Refereed proceedings will be published in archival form. In addition, a PDF of the entire proceedings will be available to the public online without charge. Authors will retain copyright subject to the ability of Stanford to freely distribute.
• February 28, 2014: Notification of acceptance, conditional acceptance, or non-acceptance.

Submissions should be made via the EasyChair website at:
https://www.easychair.org/conferences/?conf=ir14
Inquiries may be addressed to Carl Hewitt <mailto:hewitt at concurrency.biz> .

Program Committee (additions pending)
The current program committee is as follows (affiliations are listed only for the purpose of identification):

Eric Winsberg, University of South Florida Philosophy
Mary-Anne Williams, Sydney Innovation Lab
Richard Waldinger, SRI
David Ungar, IBM
Mario Tokoro, Sony CSL
Mike Travers, SRI
Harry Surden, University of Colorado Law
Martha Russell, Stanford Media X
Jeff Rulifson, Oracle
Neil Rubens, Electro-Communications Information Systems
Dave Ripley, University of Connecticut Philosophy
Greg Restall, Melbourne Philosophy
Graham Priest, Melbourne Philosophy
David Olson, Boston College Law
Peter Neumann, SRI
Ike Nassi, UC Santa Cruz
Fanya S. Montalvo, ISIR
Mark Musen, Stanford Biomedical Informatics
Annemarie Mol, University of Amsterdam Philosophy
J. J. Meyer, University of Utrecht Information and CS
Hugo Mercier, University of Neuchâtel
Erik Meijer, Microsoft
Pankaj Mehra, Whodini
Henry Lieberman, MIT Media Lab
Alan Karp, HP
Eric Jui-Yi Kao, Stanford CS
David Israel, SRI
Thomas F Icard III, Stanford Philosophy
Mike Huhns, South Carolina Electrical & Computer Engineering
Robert Hoehndorf, Cambridge Genetics
Carl Hewitt (chair)
Elihu M. Gerson, Tremont Research Institute
Blaine Garst, ISIR
Anne Gardner, ISIR
Jean-Gabriel Ganascia, University Pierre et Marie Curie
Stefania Fusco, University of New Hampshire Law
Dan Flickinger, Stanford CSLI
Ron Dolin, Stanford CodeX and Media X
Gilad Bracha, Google
Alan Bundy, University of Edinburgh Informatics
Geoffrey Bowker, UC Irvine Informatics
Francesco Berto, Aberdeen Philosophy
Emily Bender, University of Washington Linguistics
Gil Alterovitz, MIT EECS and Harvard Medical School

Background
There are many examples of practical inconsistency robustness including the following:
• Our economy relies on large software systems that have tens of thousands of known inconsistencies (often called “bugs”) along with tens of thousands more that have yet to be pinned down even though their symptoms are sometimes obvious.
• Physics has progressed in the face of numerous inconsistencies including the ongoing decades-long inconsistency between its two most fundamental theories (general relativity and quantum mechanics).
• Decision makers commonly ask for the case against as well as the case for proposed findings and action plans in corporations, governments, and judicial systems.

Inconsistency robustness stands to become a more central theme for computation. The basic argument is that because inconsistency is continually pervasive in large information systems, the issue of inconsistency
robustness must be addressed! And the best way to address the issue is computationally. Inconsistency
robustness is both an observed phenomenon and a desired feature:
• It is an observed phenomenon because large information systems are required to operate in an environment of pervasive inconsistency. How are they doing?
• It is a desired feature because we need to improve the performance of large information systems.

Previous Conference Proceedings:  http://www.robust11.org<http://www.robust11.org/>
Electronic version of Call: http://call.ir14.org<http://call.ir14.org/> <http://call.ir14.org/>

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