# MINIMAL BOOKS OF RATIONALES

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1 MINIMAL BOOKS OF RATIONALES José Apesteguía Miguel A. Ballester D.T.2005/01

2 MINIMAL BOOKS OF RATIONALES JOSE APESTEGUIA AND MIGUEL A. BALLESTER Abstract. Kalai, Rubinstein, and Spiegler (2002) propose the rationalization of choice functions that violate the independence of irrelevant alternatives axiom through a collection (book) of linear orders (rationales). In this paper we present an algorithm which, for any choice function, gives (i) the minimal number of rationales that rationalizes the choice function, (ii) the composition of such rationales, and (iii) information on how choice problems are related to rationales. As in the classical case, this renders the information given by a choice function completely equivalent to that given by a minimal book of rationales. We also study the structure of several choice procedures that are prominent in the literature. Keywords: Rationalization, Independence of irrelevant alternatives, Order partition, Computational effort. 1. Introduction The classical account for the rationalization of choice functions states that the property known as independence of irrelevant alternatives 1 (IIA) is a necessary and sufficient condition for the existence of a linear order (i.e., a rationale) that is consistent with choice behavior. 2 In a seminal paper Kalai, Rubinstein, and Spiegler (2002) study the problem of rationalizing choice behavior when axiom IIA does not necessarily hold. Their proposal is to use a book of rationales, such that for every set A in the universal set of alternatives X, the choice c(a) is maximal in A for some page of the book. Clearly, there are multiple books that can rationalize a given choice function. The authors propose to Date: February, We thank Coralio Ballester and Jon Benito for their valuable comments. Financial support by the Spanish Commission of Science and Technology (CICYT, SEC ) is gratefully acknowledged. Universidad Publica de Navarra. Universitat Autonoma de Barcelona. 1 If an element x is chosen from a set, x should be also chosen from any subset that contains it. 2 See Arrow (1959); for reviews see Moulin (1985) and Suzumura (1983). 1

3 2 focus on those books that use the minimal number of rationales. Of course, in the classical case, that is, when IIA holds, the minimal book is composed of only one page. Kalai, Rubinstein, and Spiegler study the minimal number of rationales for the second-best and the median choice procedures. In this paper we offer an algorithm that, for any universal set of alternatives X and any choice function c on X, gives the minimal number of rationales that rationalizes c, and the composition of each of such rationales. Furthermore, the algorithm avoids any loss of information as the result of switching from choice behavior to the book of rationales. That is, given a book of rationales, we want which page to turn to in order to find the maximal element for a given choice set A. As in the classical setup, we want the choice function and the book of rationales to be completely equivalent in terms of behavioral information. Therefore, for any pair (X, c), it will be our aim to give: the minimal number of rationales that rationalizes c, the composition of such rationales, and information on how choice problems are associated to rationales. We will use the term minimal rationalization by multiple rationales (minimal RMR) of (X, c) to refer to a solution of these three points. We will start by showing (section 2) that the problem of finding a minimal RMR is equivalent to that of finding a certain type of minimal partition of a set associated to the space of all choice problems (subsets of X). In particular, we will concentrate on the set of choice problems of which there is no superset with the same chosen element. In a sense, these are the choice problems that make a difference, since the remaining choice problems can be derived from these sets and the application of the IIA property. The type of partition of such a space that we study directly addresses the third point in the above statement: the association between choice problems and rationales. That is, an element of the partition is equivalent to a rationale of the book, but, in the former case, there is detailed information on the choice problems being rationalized by the rationale. This constitutes a formalization of the intuition given by Kalai, Rubinstein, and Spiegler: the decision maker has in mind a partition of the space of choice problems, and applies a rationale to each element of the partition. It is as if each element of the partition constituted a state of the world that the decision maker needs to internalize in order to rationalize her behavior. The rest of the paper is organized as follows. Section 3 presents and analyzes the properties of the algorithm that we design. Section 4 studies several prominent choice procedures and attempts to reduce

4 the computational effort required by the algorithm. Finally, section 5 concludes and relates our work to recent developments in the literature Rationalization by Multiple Rationales and Complete Preorder Partitions Let X be a finite set of objects. We denote by P (X) and P 2 (X) the set of all non-empty subsets of X and the set of all non-empty subsets of X with cardinality greater than or equal to 2, respectively. A choice function c on X assigns to every A P (X) a unique element c(a) A. Definition 2.1 (Kalai, Rubinstein, and Spiegler). A K-tuple of strict preference relations ( k ) k=1,...,k on X is a rationalization by multiple rationales (RMR) of c if for every A, the element c(a) is k -maximal in A for some k. For convenience, hereafter we will use the term minimal RMR of (X, c) to refer to a complete characterization of a minimal book of rationales. That is, a minimal RMR will give the composition of each of the orderings of a minimal book of rationales that explains choice behavior, and information on how choice problems are associated to rationales. We are now in a position to introduce an equivalent problem to that of finding the minimal RMR of (X, c): finding a minimal complete preorder partition of a set associated to P (X). Let us first specify what we mean by a complete preorder partition. Definition 2.2. A partition of set V, {V p } p=1,...,p, is said to be a complete preorder partition (CPP) according to the binary relation if, for every class V p, the restriction of to V p is a complete preorder. It is said to be a minimal CPP if any other CPP according to has at least P classes. Although it is possible to define a minimal CPP problem over the space P (X), we provide a more compact formulation that simplifies further analysis and computations. Definition 2.3. Given (X, c) a subset S P 2 (X) is said to be c- maximal if for all T X, with S T, it is the case that c(t ) c(s). Denote the family of c-maximal sets by M c. We concentrate on the set of c-maximal sets, all other sets in P (X) being trivially associated to at least one c-maximal set. In section 4.1 we will study the composition of M c for several choice procedures.

5 4 An obvious candidate for an RMR of (X, c) is the construction of M c rationales with the respective chosen elements of the different c- maximal sets dominating the pertinent rationale. However, it is easy to see that we can do better. That is, elements of M c can be placed together, sharing a rationale. The following binary relation aims to capture this flavor. Definition 2.4. Define the binary relation R on M c by ARB if and only if c(a) B \ {c(b)}, A, B M c. We can now state the main result of this section in terms of the following proposition. Proposition 2.5. Solving for a minimal RMR problem in (X, c) is equivalent to solving for a minimal CPP problem in (M c, R). Furthermore, the minimal number of rationales and classes coincide. Proof: Consider a pair (X, c). We first prove that any CPP of (M c, R) with P classes can be associated to an RMR of (X, c) with P rationales. Suppose that {V p } p=1,...,p is a CPP of M c according to R. For each class V p consider the following binary relation p on X: x p y if and only if x = c(a), y = c(b), A, B V p and ARB. Clearly, since (V p, R) is a complete preorder set, p is a preorder on X. Now, if there are several different elements x 1,..., x t such that x i p x j and x j p x i for all i, j = 1,..., t, eliminate some pairs in such a way that the resulting binary relation is a linear order on x 1,..., x t. Complete p so that the resulting order, say p, constitutes a linear order where the remaining pairs of p after the above-mentioned eliminated pairs are the top elements of p. We will show that ( p) p=1,...,p is an RMR of c. Take any choice set D. By definition, there exists a maximal set, B M c such that c(d) = c(b), D B. We prove that the rationale, say p, associated to the class in which B is contained, say V p, rationalizes the election c(d). Notice that x p c(b) = c(d) is only possible if x = c(a) c(b), A M c and ARB. But in this case, by definition, c(a) B \{c(b)} and hence, c(a) D \{c(d)}. Since c(a) c(d), it is the case that c(a) D and therefore the rationalization is possible. We now show the reverse implication, namely, that any RMR of (X, c) with P rationales can be associated to a CPP of (M c, R) with, at most, P classes. Let ( p ) p=1,...,p be an RMR of c. Consider a mapping (there may be several) f : M c {1,..., P } such that f(a) = j if and only if j rationalizes the choice in A. Of the partitions of M c induced by such a map, we consider the one that contains, at most, P

6 classes. Let {A t s} t=1,2,...,t be the c-maximal sets associated to class s, and without loss of generality assume that satisfy 5 c(a i s) s c(a j s) implies i < j We only need to prove that the restriction of R to this class is a complete preorder. We initially prove that A 1 s R-dominates the other sets in class s. Rationalization requires that c(a 1 s) should not be present in A j s \ {c(a j s)}, j = 2,..., T, which is merely the definition of R. This can be repeated for the rest of the class, to conclude that a CPP with at most P classes can be defined. Hence, given a minimal CPP of m classes we can give a minimal RMR of m rationales, and vice-versa. Therefore, solving for a minimal RMR problem in (X, c) is equivalent to solving for a minimal CPP problem in (M c, R). As the proof details, a rationale is equivalent to a class of the partition. That is, the proof shows how to construct a rationale from a class of the partition, and vice-versa. Further, the composition of the classes in the partition provides the information needed to relate choice problems with rationales. The equivalence between the two problems means that, throughout the rest of the paper, we will often refer indistinctly to either of the two. 3. The Algorithm The algorithm that we present below starts by focusing on the family M c and the binary relation R. Then, it simply tries to group elements of M c on the basis of R, in as few disjoint sets as possible. This creates a partition of M c into complete preordered subsets, where each subset is to be viewed as a rationale that can be computed following the proof of Proposition 2.5. It will then be proved that the algorithm does, indeed, give the minimal CPP of (M c, R), and consequently the minimal RMR of (X, c). For the sake of simplicity, we now present the crucial steps of the algorithm. More specific details concerning each of these steps are available upon request. The algorithm makes use of linear orders < over M c, interpreting A < B by A is analyzed before B.

7 6 Algorithm 1. For any (X, c): (1) Compute M c associated to c, and R on M c. (2) For each possible linear order < on M c, apply the following subprocess. (Denote by A 1,..., A k the sets in M c ordered according to <.) (a) Define a class C 1 and include A 1 in such a class. (b) If A 1 RA 2, include A 2 in C 1. If not, define the class C 2 and include A 2 in that class. (c) Consider A n M c and let C 1,..., C p be the classes defined after considering A 1,..., A n 1. Identify the classes C j for which ARA n for all A C j. Call these classes, admissible classes for A n. Select one of these admissible classes and include A n in that class. If no admissible class can be identified, define C p+1 and include A n in that class. Iterate this process until n = k. (3) Select one of the linear orders < over M c that gives a minimal partition. Notice that the rule, according to which a c-maximal set A is associated to a particular admissible class C j from the set of admissible classes, has not been specified in the algorithm (see point 2c in Algorithm 1). In the next proposition we show that the algorithm determines a minimal CPP irrespective of the particular selection rule from the set of admissible classes. Proposition 3.1. For every choice function c Algorithm 1 generates a minimal CPP. Proof: We will show that, given a minimal CPP of (M c, R) with m classes, there is a specific linear order < on M c that generates a (possibly different) CPP with, at most, m classes. Let {C 1,..., C m } be a minimal CPP. Denote by C j i, i = 1,..., m, j = 1,..., j(i), the j-best c- maximal set in C i according to R, breaking indifferences as desired. We will show that the following ordering of the elements of M c generates a CPP with at most m classes: C j 1 i 1 < C j 2 i 2 if and only if i 1 < i 2 or i 1 = i 2, j 1 < j 2 To see this, we will show that Algorithm 1 situates any c-maximal set C j i in a class h, with h i. We prove this by double induction, both on i and j. We initially see it for i = 1. It is obvious for C 1 1. Then, if it is true for j = 1,..., n 1 we will prove that it is also true for C n 1. Since the elements in C 1 are ordered according to R, it must be that C j 1RC n 1 for all j = 1,..., n 1

8 and hence, the algorithm situates C1 n in the first class C 1. Suppose this fact is true for C i, i = 1,..., r 1. We will prove it for C r. Obviously, Cr 1 is situated, by the algorithm, in the r-th class, or before. If this is true for Cr, j j = 1,..., n 1, we show it is true for Cr n. But, since all the c-maximal sets have been situated in C r or in a previous class, the class Cr (which may be empty) constructed by Algorithm 1, is contained in the original one, that is, in C r. Thus, if Cr n was dominated by all the c-maximal sets Cr h, with h < n, in C r, it is also dominated by all c-maximal sets in Cr, when the time comes to consider Cr n. Hence, this subset may be included in class Cr or in a previous one, and the proof is concluded. Algorithm 1 is very general, since it does not need to specify a particular selection rule from the set of admissible classes in order to provide a minimal RMR, and also because it has a very primitive way of identifying the set of admissible classes for every A M c. In section 4.1, when we study the second-best choice procedure, we will extend the algorithm in order to expand the set of admissible classes. Notice that, in order to determine a minimal RMR of a particular choice procedure, we need to analyze all the possible linear orders < over the set M c. Depending on the size of the set M c, this task may require a heavy computational effort. 3 For this reason, we proceed within the following section to analyze two different ways of reducing this effort. One is to study specific choice procedures and to analyze the manageability of the associated M c sets. The other is to investigate general properties on the set of all linear orders over M c that might reduce the number of linear orders that needs to be considered in order to obtain a minimal RMR. 4. Specific choice procedures and linear orders In this section, we first study three choice procedures that present very different levels of complexity and, consequently, require very different degrees of computational effort: the rational, the second-best, and the median choice procedures. We then approach the problem of providing properties on the set of linear orders over M c that reduce the 3 We conjecture that the problem of finding a minimal RMR for every (X, c) is, in the language of theoretical computer science, an NP-complete problem (for an excellent introduction to the theory of NP-completeness see Garey and Johnson, 1979). We have proved that our problem is in NP, that to partition a directed graph into complete and transitive subgraphs (PICTSG) is an NP-complete problem, and that our problem is a subproblem of PICTSG. We can provide details upon request. 7

9 8 number of cases that need to be considered in order to give a minimal RMR Some Specific Choice Procedures. We begin by studying a prominent case, the rational procedure (that is, when property IIA holds). We will observe that rational behavior implies an important contraction of M c with respect to P (X), thus lending simplicity to our algorithm. We, therefore, will be able to give not only the composition of the set M c, but also the linear order on M c that generates a minimal RMR. Then, we proceed to analyze the second-best and the median procedures. Although they show similar features (choice is made in positional terms, according to a linear order over the alternatives), their complexity is rather dissimilar. While the second-best procedure generates a set M c with a relatively small number of elements (polynomial in terms of X ), the median procedure generates an exponential number (thus, it does not constitute a significant reduction in size with respect to P (X)). The simplicity of the second-best procedure allows us to give an exact description of the linear order on M c that generates the minimal book. In the case of the median procedure, we will give the composition of M c The Rational Procedure. By the rational procedure we mean a pair (X, c) such that c satisfies the IIA property. Proposition 4.1. Let (X, c) be a rational procedure. Then, M c = X 1, and a concrete order < of the family M c is provided so that steps 2a, 2b, 2c of Algorithm 1 give a CP P with only one class. Proof: It is easy to see that for every family of sets X j = {A X : A = X j}, j = 1,..., n 2, there is a unique set A j with c(x), c(a i ) / A j, i < j. Then, M c is composed of set X and the collection of sets A j, j = 1,..., n 2. Order M c by the cardinality of its members, from bigger to smaller. By construction, it is the case that XRA j, j = 1,..., n 2, and that A i RA j, i < j. Hence, the algorithm integrates all members of M c in a single class, C 1, in X 1 steps The Second-Best Procedure. Let be a linear order on X. The second-best procedure states that for every A X, c selects the secondbest element in A according to. Starting with Sen (1993), this procedure has gained the attention of economists (see Baigent and Gaertner, 1996; Gaertner and Xu, 1999a; McFadden, 1999). Kalai, Rubinstein and Spiegler (2002) show that the minimal number of rationales for

10 the second-best procedure is equal to log 2 X rounded off to the higher integer. In order to give the minimal RMR of the second-best procedure in a simpler and more intuitive formulation, we need to expand the set of admissible classes, and to specify an admissible class-selection rule. Definition 4.2. The class C = {C 1,..., C j } is admissible-2 for a set A M c if either (1) C p RA for all p = 1,..., j, or (2) There exists a twin set C i of A in C, i.e., c(c i ) = c(a) and for all C p, p = 1,..., j, C p RA C p RC i and ARC p C i RC p. Definition 4.2 expands the set of admissible classes of A M c by also considering those classes where there is a c-maximal set B with the same choice as A and A has the same relation with the rest of the sets in the class as B has. On the basis of Definition 4.2, we can now define a version of Algorithm 1 that we will refer to as Algorithm 2. Further, we can obtain the analogous result to Proposition 3.1, that we state below without proof. Proposition 4.3. For every choice function c Algorithm 2 generates a minimal CPP. We adopt a specific selection rule of associated classes. Definition 4.4. Select the associated class of A M c as follows: (Let C i precede C j if C i was created before C j and write i < j.) (1) Identify among the admissible classes those in which there is a set B with C(B) = C(A). (2) If several are identified, select the first among them. If none is identified, select the first among the admissible classes. The above rule tries to group a c-maximal set with other c-maximal sets of the same choice. We are now in a position to present the characterization of the second-best procedure. Proposition 4.5. Let (X, c) be a second-best procedure. Then, M c = X ( X 1)/2, and a concrete order < of the family M c is provided so that steps 2a, 2b, 2c of Algorithm 2, implemented by Definition 4.4, give a CPP with log 2 X, rounded off to the higher integer, classes. Proof: Let, without loss of generality X = {1, 2,..., n}, with 1 2 n 1 n. The proof consists in three inductive steps. In the first, we define the family M c and state its cardinality, in the second, 9

11 10 we specify the particular order of the elements of M c, and in the last, we show that, for such an ordering of M c, steps 2a, 2b, 2c of Algorithm 2, implemented by Definition 4.4, generate a minimal CPP. Step 1: We first characterize the set M c. If X = {1, 2} the only c-maximal set is {1, 2}. Suppose that for certain X = {1,..., n}, Mc n = {A k } k=1,...,k. Consider the set X {n + 1}. It can be easily shown that, given A k in Mc n, A k = A k {n + 1} Mc n+1. Consider, furthermore, the additional sets {B r } r=1,...,n, B r = {r, n + 1} with choice n + 1. Obviously, these sets belong to Mc n+1. It is not hard to see that these are all the sets that compose Mc n+1. Hence, we can inductively obtain the cardinality of M c as follows. When X = {1, 2}, Mc 2 = 1, and Mc n+1 = Mc n + n. Hence, it is easy to see that Mc n = (n 1) = n (n 1)/2, as stated in the proposition. Step 2: We now describe a particular ordering of M c. If X = {1, 2}, the issue is trivial. Suppose that, for certain X = {1,..., n}, M c = {A k } k=1,...,k is, without loss of generality, ordered according to the natural order of positive integers, and that this order generates the minimal CPP of (M c, R). In this case, establish the following order for Mc n+1. First, put {A k } k=1,...,k. We now proceed to describe the order of sets {B r } r=1,...,n. To this end, we need to identify three parameters: t, s, and p. Parameter t gives information on the number of classes (equivalently, number of rationales); s gives information on the number of classes needed to rationalize all choice sets {B r } r=1,...,n ; p gives information on which particular classes are selected to rationalize all n + 1 choices. 4 Determine the positive integer t such that n+1 (2 t, 2 t+1 ]. Calculate the positive integer s, 0 s t such that n + 1 (2 t + ( t ( 0) t ) s 1, 2 t + ( ) ( t t ( s 1) + t s) ]. Compute the number p = (n + 1) (2 t + ( ) ( t t s 1) ). Consider the set T = {2 t 1, 2 t 1 + 2,..., 2 t 1}. Now consider all possible sets of s elements that can be formed from the elements of set T. Establish a leximax ordering over the latter sets. (Where leximax is, A L B if and only if the largest number in A\B is greater than the largest number in B \ A). Select the set that occupies position p in the leximax ordering. Denote this set by {x 1,..., x s }, where the elements are ordered in descending order with respect to the positive integers that they represent. 4 The following reasoning is only valid for t > 2. The ordering for t = 1, 2 is almost trivial.

12 Now, to complete the ordering of the c-maximal sets write the set {2 t, n + 1}, followed by sets {x 1, n + 1},..., {x s, n + 1}. The remaining sets follow in any order. Step 3: We now show that the application of steps 2a, 2b, and 2c of Algorithm 2, implemented by Definition 4.4, over this ordering on M c generates a minimal CPP. This is obviously true for X = {1, 2}. If it is true for X = {1,..., n} we will prove that it is also true when adding n + 1. Let {C i } i=1,...,p denote the family of classes of X = {1,..., n}, arranged in order of appearance. Clearly, {A k } k=1,...,k generates the same CPP as {A k } k=1,...,k = Mc n, and hence we concentrate on c-maximal sets {B r } r=1,...,n. We consider two cases. If n+1 = 2 t +1 for some positive integer t, a new class C P +1 is created to which set {2 t, n+1} is associated. This is so because by construction there are c-maximal sets with choice 2 t in every C i, i = 1,..., P (note that t = s when n + 1 = 2 t ) Then, given the selection rule in Definition 4.4, all {B r } r=1,...,n are associated to class C P +1. Now, it is easy to see that P +1 = t+1 = log 2 (n+1) = log 2 (2 t +1) rounded off to the higher integer. Hence, Kalai, Rubinstein and Spiegler s (2002) Proposition 3 guarantees that this is the minimal CPP. On the other hand, if n t + 1 for any positive integer t, then we show that no new class is created, and hence the result follows. Set {2 t, n + 1} can only be associated to class C P, since, by construction, all other classes contain c-maximal sets A such that c(a) = {2 t }. Further, the ordering on Mc n+1 has been designed in such a way that, by the selection rule in Definition 4.4, sets {x 1, n + 1},..., {x s, n + 1} are associated to the classes C i1,..., C is where C ij is the first class in which x j is not selected for any c-maximal set in the class. Finally, the remaining subsets do not generate any disturbance since they can be associated to one of the classes C i1,..., C is. This completes the proof of the proposition The Median Procedure. Gaertner and Xu (1999b) provide a characterization of the median choice procedure, and Kalai, Rubinstein, and Spiegler (2002) give lower and upper bounds for the minimal number of rationales required to rationalize it. The basic idea of such a rule is to select, from those subsets with an odd number of alternatives, the element that is situated in an intermediate position with respect to an original linear order over the alternatives. When considering subsets with an even number of alternatives, we adopt the rule of selecting the first intermediate element.

13 12 We show here that the cardinality of M c for the median procedure is not polynomially bounded in terms of X, and thus does not admit a significant contraction of set P (X). Proposition 4.6 characterizes M c when N is odd, being the case when N is even analogous. Proposition 4.6. Let (X, c) be a median procedure. Then, M c is not polynomially bounded in terms of X = N. When N is odd, the number of elements is: M c = j=(n 1)/2 j=1 [ ( ) ( ((N 1)/2)+j 2j 1 + ((N 1)/2)+j ) 2j ] Proof: Suppose that N is an odd number. First, we proceed to calculate the number of sets in M c for which some element in {(N + 1)/2 k} k=1,2,...,(n 1)/2 is the choice. Notice that, for any of these elements to be chosen in a c-maximal set, two conditions must be fulfilled: the element must be in the c-maximal set, and for every (N + 1)/2 k there are exactly (N + 1)/2 k 1 elements in the c-maximal set before (N + 1)/2 k. That is, all the elements 1, 2,..., (N + 1)/2 k are in the set, and this constitutes exactly half the cardinality of the c-maximal set. This is equivalent to extracting exactly 2k 1 elements from a set of (N 1)/2 + k, i.e., ( ) (N 1)/2+k 2k 1. This constitutes the first summation. Secondly, we proceed to calculate the number of sets in M c for which some element in {(N + 1)/2 + k} k=1,...,(n 1)/2 1 is the choice. 5 By reasoning similar to above, for every (N +1)/2+k it is sufficient to consider the number of sets for which all the elements (N + 1)/2 + k,..., N are in the set, constituting half the cardinality of the set plus one, or equivalently, to consider the possible extractions of 2k elements from a set of (N 1)/2 + k, i.e., ( ) (N 1)/2+k 2k. The c-maximal set {1, 2,..., N} in which (N + 1)/2 is selected completes the list, and, since 1 = ( N N), it may be considered within the second summation. Clearly, the value of M c is not polynomially bounded in terms of X. A similar reasoning applies for N being even, concluding the proof. Hence, the task of computing a minimal CPP for the median procedure still remains complicated. This divergence between the secondbest and the median procedures was also present in the work of Kalai, Rubinstein, and Spiegler (2002), where the possibility of providing the exact minimal number of rationales in the case of the second-best was accompanied with a bounded solution in the case of the median procedure. 5 Notice that the element N is never selected except in the singleton {N} which does not belong to M c by definition.

14 4.2. Reducing the set of linear orders over M c to be considered. We start by giving a very partial answer to the question of reducing the number of linear orders over M c that must be analyzed in order to obtain a minimal RMR. Proposition 4.7 shows that it is sufficient to evaluate the linear orders over M c that situate X (this is always an element of M c ) as the first choice problem. Proposition 4.7. A reformulation of Algorithm 1 (or 2) that only considers linear orders over M c starting with set X generates a minimal CPP. Proof: We proceed as in the proof of Proposition 3.1, considering a minimal CPP {C 1,..., C m }. Note, however, that the selected ordering of classes in that proof was irrelevant, while, here, we will suppose that X C 1. We only need to prove that X = C1, 1 since, in this case, the ordering that starts with X and follows as stated in the proof of the cited proposition, generates a CPP with, at most, m classes. Consider any A C 1. Since A M c it is the case that c(a) c(x). Therefore, c(a) X \ {c(x)} and it is not the case that ARX. But as long as C 1 endowed with R is a complete preorder, it must be that XRA, implying X = C1 1 and concluding the proof. The above result is due to the fact that the binary relation R is intimately linked to the set inclusion partial order. It is not difficult to observe that, for every A, B M c, A B implies that it is not true that ARB, which was the key point in the previous proof. Thus, the set X must be the top element within its class. Notice that this was the case of the specific linear orders < given for the rational and second-best procedures. More importantly, a scrutiny of those solutions shows that the linear orders were in fact extensions of the set inclusion partial order. This, in conjunction with the link of R with could lead us to venture that we only need to analyze linear orders over M c that constitute extensions of the partial order. This would considerably reduce the number of linear orders to be analyzed, and hence the computational effort. Unfortunately, this conjecture is not correct, as the next proposition shows. Proposition 4.8. The set of linear orders over M c that extend the set inclusion partial order does not necessarily generate a minimal CPP of (M c, R). Proof: Let X = {1, 2, 3, 4, 5}. Consider the choice function given by c({1, 3, 4, 5}) = 4, c({2, 4, 5}) = 5 and, otherwise, c(a) = i, such that i in A and i < j for all j in A. It is not hard to see that M c 13

15 14 is composed by A 1 = X, A 2 = {2, 3, 4, 5}, A 3 = {3, 4, 5} and the sets A 4 = {1, 3, 4, 5} and A 5 = {2, 4, 5}. Any ordering of M c that respects the inclusion partial order leads to a CPP with 3 classes. However, there are orderings that generate a CPP with 2 classes, which is the minimal number. Let us consider, for instance, the ordering A 1, A 3, A 5, A 2, A 4. This generates the CPP conformed by classes {A 1, A 3, A 5 } and {A 2, A 4 }. Note that a direct corollary of Proposition 4.8 rejects another natural candidate for reducing the set of linear orders to be considered: to order sets in M c according to their cardinality. Hence, beyond the result in Proposition 4.7, the task of restricting the set of < over M c remains open. 5. Conclusions In this paper we have elaborated on the issue of rationalizing choice functions that do not necessarily satisfy the IIA axiom by using multiple rationales, a notion originally proposed by Kalai, Rubinstein, and Spiegler (2002). It has been our aim to provide a complete description of a minimal book of rationales for every choice function. That is, we give the composition of each of the rationales composing a minimal number of rationales that rationalizes choice behavior, and information on how choice problems are associated to rationales. Such a characterization of the problem renders the information given by a choice function completely equivalent to that given by a minimal book of rationales. This feature, which was present in the classical case, is one that we explicitly wanted to restore when IIA does not hold. A systematic inquiry into the nature of minimal books of rationales may, we believe, constitute a valuable tool for the ultimate goal of understanding the idiosyncrasies of behavior. For example, understanding why the decision-maker opens one page or another of the book in order to rationalize a choice problem may significantly help to advance understanding of the nature of individual behavior. We will finish by relating our work to recent developments in the literature. Salant (2003) studies two computational aspects of choice when the IIA axiom does not necessarily hold: the amount of memory choice behavior requires, and the computational power needed for the computation of choice. He shows that the rational procedure is favored by these considerations. Manzini and Mariotti (2004) study the nature of choice functions that can be rationalized by sequentially applying a fixed set of asymmetric binary relations. Interestingly, they show that

16 choice procedures like the second-best or median cannot be sequentially rationalized. Finally, Ok (2004) provides an axiomatic characterization of choice correspondences that satisfy axiom IIA. References [1] Arrow, K. (1959), Rational Choice Functions and Orderings, Economica, 26: [2] Baigent, N. and W. Gaertner (1996), Never Choose the Uniquely Largest: A Characterization, Economic Theory, 8: [3] Gaertner, W. and Y. Xu (1999a), On the Structure of Choice under Different External References, Economic Theory, 14: [4] Gaertner, W. and Y. Xu (1999b), On Rationalizability of Choice Functions: A Characterization of the Median, Social Choice and Welfare, 16: [5] Garey, M.R. and D.S. Johnson (1979), Computers and Intractability: A Guide to the Theory of NP-Completeness. Freeman. [6] Kalai, G., A. Rubinstein, and R. Spiegler (2002), Rationalizing Choice Functions by Multiple Rationales, Econometrica, 70: [7] Manzini, P. and M. Mariotti (2004), Rationalizing Boundedly Rational Choice: Sequential Rationalizability and Rational Shortlist Methods, mimeo, Queen Mary, University of London. [8] McFadden, D.L. (1999), Rationality for Economists, Journal of Risk and Uncertainty, 19: [9] Moulin, H. (1985), Choice Functions Over a Finite Set: A Summary, Social Choice and Welfare, 2: [10] Ok, E.A. (2004), Independence of Irrelevant Alternatives and Individual Choice, mimeo, New York University. [11] Salant, Y. (2003), Limited Computational Resources Favor Rationality, mimeo, Hebrew University. [12] Sen, A. (1993), Internal Consistency of Choice, Econometrica, 61: [13] Suzumura, K. (1983), Rational Choice, Collective Decisions, and Social Welfare. Cambridge (U.K.): Cambridge University Press. 15

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