Non-redistributive Second Welfare Theorems

  • Bundit Laekhanukit
  • Guyslain Naves
  • Adrian Vetta
Conference paper

DOI: 10.1007/978-3-642-35311-6_17

Part of the Lecture Notes in Computer Science book series (LNCS, volume 7695)
Cite this paper as:
Laekhanukit B., Naves G., Vetta A. (2012) Non-redistributive Second Welfare Theorems. In: Goldberg P.W. (eds) Internet and Network Economics. WINE 2012. Lecture Notes in Computer Science, vol 7695. Springer, Berlin, Heidelberg

Abstract

The second welfare theorem tells us that social welfare in an economy can be maximized at an equilibrium given a suitable redistribution of the endowments. We examine welfare maximization without redistribution. Specifically, we examine whether the clustering of traders into k submarkets can improve welfare in a linear exchange economy. Such an economy always has a market clearing ε-approximate equilibrium. As ε → 0, the limit of these approximate equilibria need not be an equilibrium but we show, using a more general price mechanism than the reals, that it is a “generalized equilibrium”. Exploiting this fact, we give a polynomial time algorithm that clusters the market to produce ε-approximate equilibria in these markets of near optimal social welfare, provided the number of goods and markets are constants. On the other hand, we show that it is NP-hard to find an optimal clustering in a linear exchange economy with a bounded number of goods and markets. The restriction to a bounded number of goods is necessary to obtain any reasonable approximation guarantee; with an unbounded number of goods, the problem is as hard as approximating the maximum independent set problem, even for the case of just two markets.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Bundit Laekhanukit
    • 1
  • Guyslain Naves
    • 2
  • Adrian Vetta
    • 3
  1. 1.School of Computer ScienceMcGill UniversityCanada
  2. 2.Laboratoire d’Informatique Fondamentale de MarseilleAix-Marseille UniversitésFrance
  3. 3.Department of Mathematics and Statistics and School of Computer ScienceMcGill UniversityCanada

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