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Today's Topics:<br>
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   1.  AI Lunch -- David Kurokawa -- February 7 (Adams Wei Yu)<br>
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Message: 1<br>
Date: Fri, 3 Feb 2017 05:41:29 -0500<br>
From: Adams Wei Yu <<a href="mailto:weiyu@cs.cmu.edu">weiyu@cs.cmu.edu</a>><br>
To: <a href="mailto:ai-seminar-announce@cs.cmu.edu">ai-seminar-announce@cs.cmu.edu</a><br>
Subject: [AI Seminar] AI Lunch -- David Kurokawa -- February 7<br>
Message-ID:<br>
        <CABzq7eoxKD2DJhTC1vC717dj_<wbr>5xKeTa02=<a href="mailto:1bYepnY8pvzaqf2A@mail.gmail.com">1bYepnY8pvzaqf2A@<wbr>mail.gmail.com</a>><br>
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Dear faculty and students,<br>
<br>
We look forward to seeing you Next Tuesday, February 7, at noon in NSH 3305<br>
for AI lunch. To learn more about the seminar and lunch, please visit<br>
the AI Lunch webpage <<a href="http://www.cs.cmu.edu/~aiseminar/" rel="noreferrer" target="_blank">http://www.cs.cmu.edu/~<wbr>aiseminar/</a>>.<br>
<br>
On Tuesday, David Kurokawa <<a href="http://www.cs.cmu.edu/~dkurokaw/" rel="noreferrer" target="_blank">http://www.cs.cmu.edu/~<wbr>dkurokaw/</a>> will give a<br>
talk titled ?Fairness Notions in the Indivisible Good Setting: Comparisons<br>
and their Approximations?.<br>
<br>
*Abstract:*<br>
<br>
Fair division of indivisible goods is the study of allocating a set of<br>
discrete goods among several interested parties. Often in such settings a<br>
desired allocation is hoped to satisfy some notion of fairness.  In this<br>
talk we investigate several such notions studied in the literature: maximin<br>
share guarantee (MMS), pairwise maximin share guarantee (PMMS), and<br>
envy-freeness up to any good (EFX).<br>
<br>
We begin by first defining MMS and exploring the pros and cons of it as the<br>
benchmark for fairness in the setting. We then define PMMS and EFX and<br>
demonstrate their potential as answers to these shortcomings of MMS. We<br>
further demonstrate a hierarchical nature between these and relevant<br>
notions from the literature --- namely envy-freeness (EF) and envy-freeness<br>
up to one good (EF1) as well as give approximation existence results. We<br>
close by examining the age-old method of drafting (such as in American<br>
sports leagues) and show that there exist far fairer approaches to this<br>
problem.<br>
<br>
This is joint work with Ariel Procaccia and Junxing Wang.<br>
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End of ai-seminar-announce Digest, Vol 71, Issue 1<br>
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