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<p class=MsoNormal><span style='font-size:9.0pt;font-family:"Arial","sans-serif";
color:blue'>Rong Ge will present his research seminar/general exam on Tuesday
May 18 at 2PM in Room 402. <o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:9.0pt;font-family:"Arial","sans-serif";
color:blue'>The members of his committee are: Sanjeev Arora (advisor),
Boaz Barak, and Ken Steiglitz. <o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:9.0pt;font-family:"Arial","sans-serif";
color:blue'>Everyone is invited to attend his talk, and those faculty wishing
to remain for the oral exam following <o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:9.0pt;font-family:"Arial","sans-serif";
color:blue'>are welcome to do so. His abstract and reading list follow
below.<o:p></o:p></span></p>
<p class=MsoNormal><span style='font-size:9.0pt;font-family:"Arial","sans-serif";
color:blue'>------------------------------------------------<o:p></o:p></span></p>
<p class=MsoNormal style='margin-bottom:12.0pt'>Title: Computational Complexity
and Information Asymmetry in Financial Products<br>
<br>
Abstract:<br>
<br>
Traditional economics argues that financial derivatives, like CDOs and
CDSs, ameliorate the negative costs imposed by asymmetric information. This is
because<br>
securitization via derivatives allows the informed party to find buyers for less
information-sensitive part of the cash flow stream of an asset (e.g., a
mortgage) and retain the remainder. In this paper we show that this viewpoint
may need to be revised once computational complexity is brought into the
picture. Using methods<br>
from theoretical computer science this paper shows that derivatives can
actually amplify the costs of asymmetric information instead of reducing them.
Note that<br>
computational complexity is only a small departure from full rationality since
even highly sophisticated investors are boundedly rational due to a lack of
requisite computational resources.<br>
<br>
Reading List:<br>
<br>
Books:<br>
<br>
Sanjeev Arora and Boaz Barak. Computational Complexity: A Modern Approach.
Cambridge University Press, 2009.<br>
<br>
Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, and Clifford Stein.
Introduction to Algorithms, Third Edition. The MIT Press, 2009.<br>
<br>
Papers:<br>
<br>
Benny Applebaum, Boaz Barak, and Avi Wigderson. Public key cryptography from
different assumptions. Available from the authors' web pages. To appear
in STOC'10.<br>
<br>
Aditya Bhaskara, Moses Charikar, Eden Chlamtac, Uri Feige, and Aravindan
Vijayaraghavan. Detecting high log-density: An o(n^{1/4})-approximation for
densest<br>
k-subgraph. Manuscript in preparation, to appear in STOC'10.<br>
<br>
U. Feige, D. Peleg, and G. Kortsarz. The dense k-subgraph problem.
Algorithmica, 29(3):410-421, 2001.<br>
<br>
S. Khot. Ruling out PTAS for graph min-bisection, densest subgraph and
bipartite clique. In FOCS, pages 136-145, 2004.<br>
<br>
G.A. Akerlof. The market for "lemons": quality uncertainty and the
market mechanism. The quarterly journal of economics, pages 488-500, 1970.<br>
<br>
P.M. DeMarzo. The pooling and tranching of securities: A model of informed
intermediation. Review of Financial Studies, 18(1):1-35, 2005.<br>
<br>
M.K. Brunnermeier. Deciphering the 2007-08 liquidity and credit crunch. Journal
of Economic Perspectives, 23(1):77-100, 2008<br>
<br>
<o:p></o:p></p>
<p class=MsoNormal><o:p> </o:p></p>
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