tial ranking of these classes, which can be seen as a
quantification of more familiar heuristic notions of
“complexity.” This answers the open question of
Brunnermeier and Oehmke. 9
3. It can be difficult for regulatory bodies to control the
above-mentioned cherry picking because the cherry
picking can be difficult to detect ex ante. In some
models the cherry picking seems undetectable even
ex post. Both these remain true even in a fully transparent market where all transactions occur on a public
exchange. It also implies that verifying the existence of
the lemon cost due to computational complexity in
historical data (in other words, an empirical test of our
paper) may prove difficult, especially given that the
market has not been fully transparent.
4. arora, S. and Barak, B. Computational
Complexity: A Modern Approach.
Cambridge university Press, 2009.
5. arora, S., Barak, B., Brunnermeier, M.,
Ge, r. Computational complexity and
information asymmetry in financial
products. In The First Symposium
on Innovations in Computer Science,
ICS 2010, Tsinghua university Press,
Beijing, 2010, 49–65.
6. Bernardo, a.e., Cornell, B. The
valuation of complex derivatives by
major investment firms: empirical
evidence. J. Finance 52 (1997),
785–798.
7. Bollobás, B. Random Graphs, 2nd edn.
Cambridge university Press, 2001.
8. Brunnermeier, M.K. Deciphering the
liquidity and credit crunch 2007–08.
J. Econ. Perspect. 23( 1) (2009), 77–100.
9. Brunnermeier, M. K., oehmke, M.
Complexity in financial markets.
Working Paper. Princeton university.
10. Coval, J., Jurek, J., Stafford, e.
The economics of structured
finance. J. Econ. Perspect. 23( 1)
(2009), 3–25.
11. DeMarzo, P. M. The pooling and
tranching of securities: a model of
informed intermediation. Rev. Financ.
Stud. 18( 1) (2005), 1–35.
12. Duffie, D. Innovations in credit risk
transfer: Implications for financial
stability. BIS Working Papers.
13. Kahneman, D. Maps of bounded
rationality: Psychology for behavioral
economics. Am. Econ. Rev. 93( 5)
(2003), 1449–1475.
14. Simon, H.a. Bounded rationality
and organizational learning.
Organ. Sci. 2( 1) (1991),
125–134.
In sum, our approach of combining insights from computer
science with economic questions allows one to formally
study phenomena, such as complexity and bounded rationality, that are of first-order importance but were difficult
to capture in formal economic models. These new insights
should help shape future regulation and the post-2008
financial architecture.
Sanjeev Arora ( arora@cs.princeton.edu),
Department of Computer Science,
Center for Computational Intractability,
Princeton university, Princeton, nJ.
Markus Brunnermeier (markus@
princeton.edu), Department of economics,
Bendheim Center for Finance,
Princeton university, Princeton, nJ.
Boaz Barak ( boaz@microsoft.com),
Microsoft research new england,
Princeton university, Princeton, nJ.
Rong Ge ( rongge@cs.princeton.edu),
Department of Computer Science, Center
for Computational Intractability,
Princeton university, Princeton, nJ.
References
1. akerlof, G.a. The market for “lemons”:
Quality uncertainty and the market
mechanism. Q. J. Econ. 84( 3) (1970),
488–500.
2. alon, n., Spencer, J. H. The
Probabilistic Method, 3rd edn. Wiley,
Hoboken, nJ, 2008.
© 2011 aCM 0001-0782/11/05 $10.00
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