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Non-convexity (economics)

In economics, non-convexity refers to violations of the convexity assumptions of elementary economics. Basic economics textbooks concentrate on consumers with convex preferences (that do not prefer extremes to in-between values) and convex budget sets and on producers with convex production sets; for convex models, the predicted economic behavior is well understood.[1][2] When convexity assumptions are violated, then many of the good properties of competitive markets need not hold: Thus, non-convexity is associated with market failures,[3][4] where supply and demand differ or where market equilibria can be inefficient.[1][4][5][6][7][8] Non-convex economies are studied with nonsmooth analysis, which is a generalization of convex analysis.[8][9][10][11]

Contents

  • Demand with many consumers 1
  • Supply with few producers 2
  • Contemporary economics 3
    • Optimization over time 3.1
    • Nonsmooth analysis 3.2
  • See also 4
  • Notes 5
  • References 6
  • External links 7

Demand with many consumers

If a preference set is non-convex, then some prices determine a budget-line that supports two separate optimal-baskets. For example, we can imagine that, for zoos, a lion costs as much as an eagle, and further that a zoo's budget suffices for one eagle or one lion. We can suppose also that a zoo-keeper views either animal as equally valuable. In this case, the zoo would purchase either one lion or one eagle. Of course, a contemporary zoo-keeper does not want to purchase half of an eagle and half of a lion (or a griffin)! Thus, the zoo-keeper's preferences are non-convex: The zoo-keeper prefers having either animal to having any strictly convex combination of both.

When consumer preferences have concavities, then the linear budgets need not support an equilibrium: Consumers can jump between two separate allocations (of equal utility).

When the consumer's preference set is non-convex, then (for some prices) the consumer's demand is not connected; A disconnected demand implies some discontinuous behavior by the consumer, as discussed by Harold Hotelling:

If indifference curves for purchases be thought of as possessing a wavy character, convex to the origin in some regions and concave in others, we are forced to the conclusion that it is only the portions convex to the origin that can be regarded as possessing any importance, since the others are essentially unobservable. They can be detected only by the discontinuities that may occur in demand with variation in price-ratios, leading to an abrupt jumping of a point of tangency across a chasm when the straight line is rotated. But, while such discontinuities may reveal the existence of chasms, they can never measure their depth. The concave portions of the indifference curves and their many-dimensional generalizations, if they exist, must forever remain in unmeasurable obscurity.[12]

The difficulties of studying non-convex preferences were emphasized by Herman Wold[13] and again by Paul Samuelson, who wrote that non-convexities are "shrouded in eternal darkness ...",[14] according to Diewert.[15]

When convexity assumptions are violated, then many of the good properties of competitive markets need not hold: Thus, non-convexity is associated with market failures, where supply and demand differ or where market equilibria can be inefficient.[1] Non-convex preferences were illuminated from 1959 to 1961 by a sequence of papers in The Journal of Political Economy (JPE). The main contributors were Farrell,[16] Bator,[17] Koopmans,[18] and Rothenberg.[19] In particular, Rothenberg's paper discussed the approximate convexity of sums of non-convex sets.[20] These JPE-papers stimulated a paper by Lloyd Shapley and Martin Shubik, which considered convexified consumer-preferences and introduced the concept of an "approximate equilibrium".[21] The JPE-papers and the Shapley–Shubik paper influenced another notion of "quasi-equilibria", due to Robert Aumann.[22][23]

Non-convex sets have been incorporated in the theories of general economic equilibria,.[24] These results are described in graduate-level textbooks in microeconomics,[25] general equilibrium theory,[26] game theory,[27] mathematical economics,[28] and applied mathematics (for economists).[29] The Shapley–Folkman lemma establishes that non-convexities are compatible with approximate equilibria in markets with many consumers; these results also apply to production economies with many small firms.[30]

Supply with few producers

Non-convexity is important under oligopolies and especially monopolies.[8] Concerns with large producers exploiting market power initiated the literature on non-convex sets, when Piero Sraffa wrote about on firms with increasing returns to scale in 1926,[31] after which Harold Hotelling wrote about marginal cost pricing in 1938.[32] Both Sraffa and Hotelling illuminated the market power of producers without competitors, clearly stimulating a literature on the supply-side of the economy.[33]

Contemporary economics

Recent research in economics has recognized non-convexity in new areas of economics. In these areas, non-convexity is associated with market failures, where equilibria need not be efficient or where no equilibrium exists because supply and demand differ.[1][4][4][5][6][7][8] Non-convex sets arise also with environmental goods (and other externalities),[6][7] and with market failures,[3] and public economics.[5][34] Non-convexities occur also with information economics,[35] and with stock markets[8] (and other incomplete markets).[36][37] Such applications continued to motivate economists to study non-convex sets.[1]

Optimization over time

The previously mentioned applications concern non-convexities in finite-dimensional vector spaces, where points represent commodity bundles. However, economists also consider dynamic problems of optimization over time, using the theories of differential equations, dynamic systems, stochastic processes, and functional analysis: Economists use the following optimization methods:

In these theories, regular problems involve convex functions defined on convex domains, and this convexity allows simplifications of techniques and economic meaningful interpretations of the results.[43][44][45] In economics, dynamic programing was used by Martin Beckmann and Richard F. Muth for work on monetary policy, fiscal policy, taxation, economic growth, search theory, and labor economics.[49] Dixit & Pindyck used dynamic programming for capital budgeting.[50] For dynamic problems, non-convexities also are associated with market failures,[51] just as they are for fixed-time problems.[52]

Nonsmooth analysis

Economists have increasingly studied non-convex sets with nonsmooth analysis, which generalizes convex analysis. Convex analysis centers on convex sets and convex functions, for which it provides powerful ideas and clear results, but it is not adequate for the analysis of non-convexities, such as increasing returns to scale.[53] "Non-convexities in [both] production and consumption ... required mathematical tools that went beyond convexity, and further development had to await the invention of non-smooth calculus": For example, Clarke's differential calculus for Lipschitz continuous functions, which uses Rademacher's theorem and which is described by Rockafellar & Wets (1998)[54] and Mordukhovich (2006),[9] according to Khan (2008).[10] Brown (1995, pp. 1967–1968) wrote that the "major methodological innovation in the general equilibrium analysis of firms with pricing rules" was "the introduction of the methods of non-smooth analysis, as a [synthesis] of global analysis (differential topology) and [of] convex analysis." According to Brown (1995, p. 1966), "Non-smooth analysis extends the local approximation of manifolds by tangent planes [and extends] the analogous approximation of convex sets by tangent cones to sets" that can be non-smooth or non-convex.[11][55]

See also

Notes

  1. ^ a b c d e  
  2. ^ Green, Jerry; Heller, Walter P. (1981). "1 Mathematical analysis and convexity with applications to economics". In  
  3. ^ a b Salanié, Bernard (2000). "7 Nonconvexities". Microeconomics of market failures (English translation of the (1998) French Microéconomie: Les défaillances du marché (Economica, Paris) ed.). Cambridge, MA: MIT Press. pp. 107–125.  
  4. ^ a b c d Salanié (2000, p. 36)
  5. ^ a b c Pages 63–65:  
  6. ^ a b c Starrett, David A. (1972). "Fundamental nonconvexities in the theory of externalities". Journal of Economic Theory 4 (2). pp. 180–199.  
  7. ^ a b c Pages 106, 110–137, 172, and 248:  
  8. ^ a b c d e Page 1:  )
  9. ^ a b Chapter 8 "Applications to economics", especially Section 8.5.3 "Enter nonconvexity" (and the remainder of the chapter), particularly page 495:

     

  10. ^ a b Khan, M. Ali (2008). "Perfect competition". In Durlauf, Steven N.; Blume, Lawrence E., ed. The New Palgrave Dictionary of Economics (Second ed.). Palgrave Macmillan.  
  11. ^ a b Brown, Donald J. (1991). "36 Equilibrium analysis with non-convex technologies". In  
  12. ^ Hotelling (1935, p. 74):  
  13. ^ Pages 231 and 239 (Figure 10 a–b: Illustration of lemma 5 [page 240]):  

    Exercise 45, page 146:  

  14. ^ Samuelson (1950, pp. 359–360):
    It will be noted that any point where the indifference curves are convex rather than concave cannot be observed in a competitive market. Such points are shrouded in eternal darkness—unless we make our consumer a monopsonist and let him choose between goods lying on a very convex "budget curve" (along which he is affecting the price of what he buys). In this monopsony case, we could still deduce the slope of the man's indifference curve from the slope of the observed constraint at the equilibrium point.
     

    For the epigraph to their seventh chapter, "Markets with non-convex preferences and production" presenting Starr (1969), Arrow & Hahn (1971, p. 169) quote John Milton's description of the (non-convex) Serbonian Bog in Paradise Lost (Book II, lines 592–594):

    A gulf profound as that Serbonian Bog
    Betwixt Damiata and Mount Casius old,
    Where Armies whole have sunk.
  15. ^ Diewert (1982, pp. 552–553): Diewert, W. E. (1982). "12 Duality approaches to microeconomic theory". In  
  16. ^ Farrell, M. J. (August 1959). "The Convexity assumption in the theory of competitive markets".  
  17. ^ Bator, Francis M. (October 1961a). "On convexity, efficiency, and markets". The Journal of Political Economy 69 (5): 480–483.  
  18. ^  

    Koopmans (1961, p. 478) and others—for example, Farrell (1959, pp. 390–391) and Farrell (1961a, p. 484), Bator (1961, pp. 482–483), Rothenberg (1960, p. 438), and Starr (1969, p. 26)—commented on Koopmans (1957, pp. 1–126, especially 9–16 [1.3 Summation of opportunity sets], 23–35 [1.6 Convex sets and the price implications of optimality], and 35–37 [1.7 The role of convexity assumptions in the analysis]):

     

  19. ^ Rothenberg (1960, p. 447): Rothenberg, Jerome (October 1960). "Non-convexity, aggregation, and Pareto optimality". The Journal of Political Economy 68 (5): 435–468.  )
  20. ^ Arrow & Hahn (1980, p. 182)
  21. ^ Shapley & Shubik (1966, p. 806):  
  22. ^ Aumann (1966, pp. 1–2):   Aumann (1966) builds on two papers: Aumann (1964, 1965)

     

     

  23. ^ Taking the convex hull of non-convex preferences had been discussed earlier by Wold (1943b, p. 243) and by Wold & Juréen (1953, p. 146), according to Diewert (1982, p. 552).
  24. ^ Pages 392–399 and page 188:  

    Pages 52–55 with applications on pages 145–146, 152–153, and 274–275:  

    Theorem C(6) on page 37 and applications on pages 115-116, 122, and 168:  

  25. ^  

    Page 628:  

  26. ^ Page 169 in the first edition: Starr, Ross M. (2011). "8 Convex sets, separation theorems, and non-convex sets in RN". General equilibrium theory: An introduction (Second ed.). Cambridge: Cambridge University Press.  

    In Ellickson, page xviii, and especially Chapter 7 "Walras meets Nash" (especially section 7.4 "Nonconvexity" pages 306–310 and 312, and also 328–329) and Chapter 8 "What is Competition?" (pages 347 and 352): Ellickson, Bryan (1994). Competitive equilibrium: Theory and applications. Cambridge University Press. p. 420.  

  27. ^ Theorem 1.6.5 on pages 24–25: Ichiishi, Tatsuro (1983). Game theory for economic analysis. Economic theory, econometrics, and mathematical economics. New York: Academic Press, Inc. [Harcourt Brace Jovanovich, Publishers]. pp. x+164.  
  28. ^ Pages 127 and 33–34:  
  29. ^ Pages 93–94 (especially example 1.92), 143, 318–319, 375–377, and 416: Carter, Michael (2001). Foundations of mathematical economics. Cambridge, MA: MIT Press. pp. xx+649.  

    Page 309: Moore, James C. (1999). Mathematical methods for economic theory: Volume I. Studies in economic theory 9. Berlin: Springer-Verlag. pp. xii+414.  

    Pages 47–48: Florenzano, Monique; Le Van, Cuong (2001). Finite dimensional convexity and optimization. Studies in economic theory 13. in cooperation with Pascal Gourdel. Berlin: Springer-Verlag. pp. xii+154.  

  30. ^ Economists have studied non-convex sets using advanced mathematics, particularly  
  31. ^  
  32. ^  
  33. ^ Pages 5–7: Quinzii, Martine (1992). Increasing returns and efficiency (Revised translation of (1988) Rendements croissants et efficacité economique. Paris: Editions du Centre National de la Recherche Scientifique ed.). New York: Oxford University Press. pp. viii+165.  
  34. ^ Starrett discusses non-convexities in his textbook on public economics (pages 33, 43, 48, 56, 70–72, 82, 147, and 234–236): Starrett, David A. (1988). Foundations of public economics. Cambridge economic handbooks. Cambridge: Cambridge University Press. 
  35. ^  
  36. ^ Page 270:  )
  37. ^ Magille (Quinzii, Section 31 "Partnerships", p. 371): Magill, Michael; Quinzii, Martine (1996). "6 Production in a finance economy". The Theory of incomplete markets. Cambridge, Massachusetts: MIT Press. pp. 329–425. 
  38. ^ Ramsey, F. P. (1928). "A Mathematical Theory of Saving".  
  39. ^ Hotelling, Harold (1931). "The Economics of Exhaustible Resources".  
  40. ^ Adda, Jerome; Cooper, Russell (2003), Dynamic Economics, MIT Press 
  41. ^ Howard, Ronald A. (1960). Dynamic Programming and Markov Processes. The M.I.T. Press. 
  42. ^ Sethi, S. P.;   Slides are available at http://www.utdallas.edu/~sethi/OPRE7320presentation.html
  43. ^ Troutman, John L. (1996). With the assistance of William Hrusa, ed. Variational calculus and optimal control: Optimization with elementary convexity. Undergraduate Texts in Mathematics (Second ed.). New York: Springer-Verlag. pp. xvi+461.  
  44. ^ Craven, B. D. (1995). Control and optimization. Chapman and Hall Mathematics Series. London: Chapman and Hall, Ltd. pp. x+193.  
  45. ^ Vinter, Richard (2000). Optimal control. Systems & Control: Foundations & Applications. Boston, MA: Birkhäuser Boston, Inc. pp. xviii+507.  
  46. ^ Beckmann, Martin; Muth, Richard F. (1954). "On the solution to the fundamental equation of inventory theory". Cowles Commission Discussion Paper 2116. 
  47. ^  
  48. ^  
  49. ^  
  50. ^  
  51. ^ Dasgupta & Heal (1979, pp. 96–97, 285, 404, 420, 422, and 429)
  52. ^ Dasgupta & Heal (1979, pp. 51, 64–65, 87, and 91–92)
  53. ^ Heal (1999, p. 4 in preprint):  
  54. ^  
  55. ^  

References

  •  
  • Crouzeix, J.-P. (2008). "Quasi-concavity". In Durlauf, Steven N.;  
  •  
  • Diewert, W. E. (1982). "12 Duality approaches to microeconomic theory". In  
  • Green, Jerry; Heller, Walter P. (1981). "1 Mathematical analysis and convexity with applications to economics". In  
  •  
  •  
  •  

External links

 

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