1. Algorithmic Game Theory and Internet Computing Vijay V. Vazirani Georgia Tech Combinatorial Algorithms for Convex Programs (Capturing Market Equilibria and Nash Bargaining Solutions)

2. What is Economics? ‘‘Economics is the study of the use of scarce resources which have alternative uses.’’ Lionel Robbins (1898 – 1984)

3. How are scarce resources assigned to alternative uses?

4. Prices!

5. How are scarce resources assigned to alternative uses? Prices Parity between demand and supply

6. How are scarce resources assigned to alternative uses? Prices Parity between demand and supply equilibrium prices

7. Do markets even admit equilibrium prices?

8. General Equilibrium Theory Occupied center stage in Mathematical Economics for over a century Do markets even admit equilibrium prices?

9. Arrow-Debreu Theorem, 1954 Celebrated theorem in Mathematical Economics Established existence of market equilibrium under very general conditions using a theorem from topology - Kakutani fixed point theorem.

10. Do markets even admit equilibrium prices?

11. Easy if only one good!

12. Supply-demand curves

13. Do markets even admit equilibrium prices? What if there are multiple goods and multiple buyers with diverse desires and different buying power?

14. Irving Fisher, 1891 Defined a fundamental market model

17. linear utilities

18. For given prices, find optimal bundle of goods

19. Several buyers with different utility functions and moneys.

20. Several buyers with different utility functions and moneys. Find equilibrium prices.

22. “Stock prices have reached what looks like a permanently high plateau”

23. Irving Fisher, October 1929

26. Arrow-Debreu Theorem, 1954 Celebrated theorem in Mathematical Economics Established existence of market equilibrium under very general conditions using a theorem from topology - Kakutani fixed point theorem. Highly non-constructive!

27. An almost entirely non-algorithmic theory! General Equilibrium Theory

28. The new face of computing

29. New markets defined by Internet companies, e.g.,  Google  eBay  Yahoo!  Amazon Massive computing power available. Need an inherenltly-algorithmic theory of markets and market equilibria. Today’s reality

30. Combinatorial Algorithm for Linear Case of Fisher’s Model Devanur, Papadimitriou, Saberi & V., 2002 Using the primal-dual paradigm

31. Combinatorial algorithm Conducts an efficient search over a discrete space. E.g., for LP: simplex algorithm vs ellipsoid algorithm or interior point algorithms.

32. Combinatorial algorithm Conducts an efficient search over a discrete space. E.g., for LP: simplex algorithm vs ellipsoid algorithm or interior point algorithms. Yields deep insights into structure.

33. No LP’s known for capturing equilibrium allocations for Fisher’s model

34. No LP’s known for capturing equilibrium allocations for Fisher’s model Eisenberg-Gale convex program, 1959

35. No LP’s known for capturing equilibrium allocations for Fisher’s model Eisenberg-Gale convex program, 1959 Extended primal-dual paradigm to solving a nonlinear convex program

36. Linear Fisher Market B = n buyers, money m i for buyer i G = g goods, w.l.o.g. unit amount of each good : utility derived by i on obtaining one unit of j Total utility of i, Find market clearing prices.

37. Eisenberg-Gale Program, 1959

38. prices p j

39. Why remarkable? Equilibrium simultaneously optimizes for all agents. How is this done via a single objective function?

40. Theorem If all parameters are rational, Eisenberg-Gale convex program has a rational solution!  Polynomially many bits in size of instance

41. Theorem If all parameters are rational, Eisenberg-Gale convex program has a rational solution!  Polynomially many bits in size of instance Combinatorial polynomial time algorithm for finding it.

42. Theorem If all parameters are rational, Eisenberg-Gale convex program has a rational solution!  Polynomially many bits in size of instance Combinatorial polynomial time algorithm for finding it. Discrete space

43. Idea of algorithm primal variables: allocations dual variables: prices of goods iterations: execute primal & dual improvements Allocations Prices (Money)

44. How are scarce resources assigned to alternative uses? Prices Parity between demand and supply

47. Yin & Yang

49. Nash bargaining game, 1950 Captures the main idea that both players gain if they agree on a solution. Else, they go back to status quo. Complete information game.

50. Example Two players, 1 and 2, have vacation homes:  1: in the mountains  2: on the beach Consider all possible ways of sharing.

51. Utilities derived jointly : convex + compact feasible set

52. Disagreement point = status quo utilities Disagreement point =

53. Nash bargaining problem = (S, c) Disagreement point =

54. Nash bargaining Q: Which solution is the “right” one?

55. Solution must satisfy 4 axioms: Paretto optimality Invariance under affine transforms Symmetry Independence of irrelevant alternatives

56. Thm: Unique solution satisfying 4 axioms

59. Generalizes to n-players Theorem: Unique solution

60. Linear Nash Bargaining (LNB) Feasible set is a polytope defined by linear constraints Nash bargaining solution is optimal solution to convex program:

61. Q: Compute solution combinatorially in polynomial time?

62. Game-theoretic properties of LNB games Chakrabarty, Goel, V., Wang & Yu, 2008:  Fairness  Efficiency (Price of bargaining)  Monotonicity

63. Insights into markets V., 2005: spending constraint utilities (Adwords market) Megiddo & V., 2007: continuity properties V. & Yannakakis, 2009: piecewise-linear, concave utilities Nisan, 2009: Google’s auction for TV ads

65. How should they exchange their goods?

66. State as a Nash bargaining game S = utility vectors obtained by distributing goods among players

67. Special case: linear utility functions S = utility vectors obtained by distributing goods among players

68. Convex program for ADNB

69. Theorem (V., 2008) If all parameters are rational, solution to ADNB is rational!  Polynomially many bits in size of instance

70. Theorem (V., 2008) If all parameters are rational, solution to ADNB is rational!  Polynomially many bits in size of instance Combinatorial polynomial time algorithm for finding it.

71. Flexible budget markets Natural variant of linear Fisher markets ADNB flexible budget markets Primal-dual algorithm for finding an equilibrium

72. How is primal-dual paradigm adapted to nonlinear setting?

73. Fundamental difference between LP’s and convex programs Complementary slackness conditions: involve primal or dual variables, not both. KKT conditions: involve primal and dual variables simultaneously.

74. KKT conditions

76. Primal-dual algorithms so far (i.e., LP-based) Raise dual variables greedily. (Lot of effort spent on designing more sophisticated dual processes.)

77. Primal-dual algorithms so far Raise dual variables greedily. (Lot of effort spent on designing more sophisticated dual processes.)  Only exception: Edmonds, 1965: algorithm for max weight matching.

78. Primal-dual algorithms so far Raise dual variables greedily. (Lot of effort spent on designing more sophisticated dual processes.)  Only exception: Edmonds, 1965: algorithm for max weight matching. Otherwise primal objects go tight and loose. Difficult to account for these reversals -- in the running time.

79. Our algorithm Dual variables (prices) are raised greedily Yet, primal objects go tight and loose  Because of enhanced KKT conditions

80. Our algorithm Dual variables (prices) are raised greedily Yet, primal objects go tight and loose  Because of enhanced KKT conditions New algorithmic ideas needed!

81. Nonlinear programs with rational solutions! Open

82. Nonlinear programs with rational solutions! Solvable combinatorially!! Open

83. Primal-Dual Paradigm Combinatorial Optimization (1960’s & 70’s): Integral optimal solutions to LP’s

84. Exact Algorithms for Cornerstone Problems in P Matching (general graph) Network flow Shortest paths Minimum spanning tree Minimum branching

85. Primal-Dual Paradigm Combinatorial Optimization (1960’s & 70’s): Integral optimal solutions to LP’s Approximation Algorithms (1990’s): Near-optimal integral solutions to LP’s

86. Primal-Dual Paradigm Combinatorial Optimization (1960’s & 70’s): Integral optimal solutions to LP’s Approximation Algorithms (1990’s): Near-optimal integral solutions to LP’s WGMV 1992

87. Approximation Algorithms set cover facility location Steiner tree k-median Steiner network multicut k-MST feedback vertex set scheduling...

88. Primal-Dual Paradigm Combinatorial Optimization (1960’s & 70’s): Integral optimal solutions to LP’s Approximation Algorithms (1990’s): Near-optimal integral solutions to LP’s Algorithmic Game Theory (New Millennium): Rational solutions to nonlinear convex programs

90. Primal-Dual Paradigm Combinatorial Optimization (1960’s & 70’s): Integral optimal solutions to LP’s Approximation Algorithms (1990’s): Near-optimal integral solutions to LP’s Algorithmic Game Theory (New Millennium): Rational solutions to nonlinear convex programs Approximation algorithms for convex programs?!

92. Goel & V., 2009: ADNB with piecewise-linear, concave utilities

93. Convex program for ADNB

94. Eisenberg-Gale Program, 1959

95. Common generalization

96. Is it meaningful? Can it be solved via a combinatorial, polynomial time algorithm?

97. Common generalization Is it meaningful? Nonsymmetric ADNB Kalai, 1975: Nonsymmetric bargaining games  w i : clout of player i.

98. Common generalization Is it meaningful? Nonsymmetric ADNB Kalai, 1975: Nonsymmetric bargaining games  w i : clout of player i. Algorithm

102. Open Can Fisher’s linear case or ADNB be captured via an LP?