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2 24 Monotone comparative statics Advanced Math Rather than a complete discussion of monotone comparative statics, today’s lecture should be considered an introduction to lattices, monotone comparative statics and a reader’s guide to Milgrom and Shannon
Before defining a lattice graphically, it is useful to get an idea of what one is graphically. The following two graphs show lattices in two dimensions and one dimension, in turn: More carefully, a lattice is a set in which the meet and join are defined, and in which the meet and joint of all pairs of points within the set are also within the set. A complete lattice is a set in which the meet and join of all as many as all points within the set is both defined and an element of the lattice. The function meet of A and B is denoted: A^B And the joing of A and B is denoted AVB It is useful to consider the idea of meet and join by considering sets (which do satisfy the definition of a lattice.) For sets, the meet is defined by the intersection: A∩B And the join is played defined by the union: AUB Similarly, the real numbers R is a lattice, as is R^2. For R, meet is the min of two numbers and joint is the max. For R^2 , the simplest definition of meet of A=(A 1 ,A 2 ) and B=(B 1 ,B 2 ), A^B, is: (Min(A 1 ,B 1 ), Min(A 1 ,B 1 ))
X 1
X 2
X
and the simplest definition of join of A and B, AVB, is (Max(A 1 ,B 1 ), Max(A 1 ,B 1 )) In other words, a lattice is an order relationship. Not all points in the lattice are explicitly ordered (see X and X’ in the following diagram). However, for all points in the lattice, there is a point within the lattice that is the ‘max’ of two points (the join), and the ‘min’ of the two (the meet.) Thus, while not a complete ordering, a lattice does give a partial ordering. (X→^ is the notation for vector.) The use of the lattice will be for comparative statics. Similar to calculus comparative statics, monotone comparative statics will ask the question, ‘if we change this parameter, how much will that change?’ However, unlike comparative statics through calculus, monotone comparative statics will be able to answer the question for even discrete changes. A sublattice is defined as a ‘portion of a lattice in which the meet and join of all points within the portion is also in the portion.’ A sublattice is defined by the lattice and the divisors between the elements within the portion from the members of the lattice that is outside the lattice. The paper includes the proof that any portion of a lattice that is separated from the rest by lines that are increasing is a sublattice. Let’s draw a few examples to get the idea. A cross of two sets on the axis is a lattice; this is used in the following examples. In the following example, the points are the elements of the lattice, while the dashed lines are the separator. It is easy to see that all meets and joins of points within the sublattice (between the lines) are also within the sublattice:
X→VX→’
X→^X→^ X→
X→’
X 1
X 2
X 2
X 1
When the sets S and S’ are overlapping, it is a bit more subtle. The set S is the set of points within the sublattice defined by the dashed lines, but above the heavy dotted line. The set S’ is the set of points within the sublattice defined by the dashed lines, but below the heavy solid line: The portions of S and S’ that are not elements of the union of S and S’ are follows exactly as the example above. The portions of S and S’ that are elements of S∩S’ are only slightly more sophisticated. The meet and join of many of the points within the union are in both S and S’. The important element is that they are in the S or S’ necessary for the condition to hold. You can create a similar graph of strongly ordered sets in one dimension as well. The set below the solid line is S’, while the set above the dashed line is S: Note that all points in S below other points in S must also be in S’. Otherwise there will be no ordering. Theorem 3: Spence Mirlese condition: This was discussed in the last lecture. Theorem 4: Monotonicity theorem : this is the denoument of the paper. The meaning of this theorem is a bit more sophisticated than the first three, and thus we cover it in a bit more detail. We begin with the exact words of the theorem:
S’
S
S
S
S’
X 2
X 1
S’
X
Let f be a function from XxT→Reals where X is a lattice T is a partially ordered set, and S⊂X Then argmaxXεSf(x,t) is monotone nondecreasing in (t,s) Iff f is quasi supermodular in x and f satisfies the single crossing property in (x;t) {xεX, tεT} There are two elements of the theorem that bear special consideration. The first is ‘f(x,t) is monotone nondecreasing in (t,s). This phrase means two things:
- Increasing t increases the argmax value of f
- Increasing the set of possible x’s chosen over does not decrease the argmax. Quasi supermodularity is a weaker form of supermodularity. Supermodularity is defined as: f(xVy)+f(x^y)≥f(x)+f(y) which leads to: f(xVy)-f(y)≥f(x)-f(x^y) This condition implies quasi supermodularity. Definition: f(•) is quasi-supermodular if: i. f(x)≥f(x^y) → f(xVy)≥f(y) ii. f(x)>f(x^y) → f(xVy)>f(y) As you can see, the comparison of f(•) over the points is the same for both supermodularity and quasi- supermodularity (See the points in R^2 below.) The definition of single crossing in x,t follows the same general pattern we considered in the last lecture. Definition: f(x,t) is single crossing in (x,t) if: For all x which are both: xεS where S≥sS’ where S’ is the set containing only y (i.e. x is ‘bigger than y’) x is in the preferred set compared to y (f(x,t)≥f(y,t) if t’>t then f(x,t’)≥f(y,t). For all x which are both:
X→VX→’
X→^X→ X→
X→’
The proof for necessity is a bit messier. This also is a two-part proof, but the method for the two parts is essentially the same. The first part (which we look at) is showing that f(•) must be quasi-supermodular. We begin with S’≥sS, and we write possible combinations that lead to the argmanx in each set: y x y y x y y x x y x y y x y y x xVy x xVy xVy x y x y x xVy x xVy xVy S S S S S S x y xVy xVy x xVy x y x x y y y y x ' ' ' The larger versions of the letters are the proposed argmaxes. Each set of four corresponds to the four possible argmax combinations that are within S and S’. However, three of these options do not satisfy the ordering of the sets: y x y y x y y x x y x y y x y y x xVy x xVy xVy x y x y x xVy x xVy xVy S S S S S S x y xVy xVy x xVy x y x x y y y y x ' ' ' However, you will note that all of the remaining possibilities satisfy the definition of quasi supermodularity. The method is the same for showing that the f(•) must be characterized by single crossing. However, the definitions of the rows are (t t’) rather than (S S’) as in the table above. Each box contains: X X Y Y The pattern of smaller versus larger elements (proposed combinations of maxima) is the same. After crossing out the combinations that do not satisfy the ordering of the set, you find the definition of single crossing must be satisfied.
