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 ( [e-mail]), 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 ( [e-mail]), Microsoft research new england, Princeton university, Princeton, nJ.

Rong Ge ( [e-mail]), 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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