# WHICH SCORING RULE MAXIMIZES CONDORCET EFFICIENCY? 1. Introduction

Save this PDF as:

Size: px
Start display at page:

## Transcription

1 WHICH SCORING RULE MAXIMIZES CONDORCET EFFICIENCY? DAVIDE P. CERVONE, WILLIAM V. GEHRLEIN, AND WILLIAM S. ZWICKER Abstract. Consider an election in which each of the n voters casts a vote consisting of a strict preference ranking of the three candidates A, B, and C. In the limit as n, which scoring rule maximizes, under the assumption of Impartial Anonymous Culture (uniform probability distribution over profiles), the probability that the Condorcet candidate wins the election, given that a Condorcet candidate exists? We produce an analytic solution, which is not the Borda Count. Our result agrees with recent numerical results from two independent studies, and contradicts a published result of Van Newenhizen (199). 1. Introduction We wish to consider elections in which n voters select a winner from among the three candidates A, B, and C. We assume that each voter expresses, as his or her vote, a strict, complete, and transitive preference ranking of the candidates; in particular, no voter expresses indifference between any two candidates. Each voter chooses, then, from among six possible rankings: n 1 n n 3 n 4 n 5 n 6 A C C B B A B B A A C C C A B C A B Here, each n i is equal to the number of voters who express the associated ranking. Thus, any 6-tuple P = (n 1, n, n 3, n 4, n 5, n 6 ) of non-negative integers that sum to n tells us how many voters chose each of the rankings in a given election. Such a tuple is known as a profile. Numerous criteria have been suggested to help select which voting systems, among various systems for choosing winners from profiles, best reflect the cumulative will of the electorate. One of the most common of these is the Condorcet Criterion. If U and V are candidates, let U > C V indicate that candidate U defeats V in the pairwise majority election between these two (strictly more voters ranked U over V than ranked V over U); we ll use U C V for the corresponding weak relation. A candidate is the Condorcet winner if she defeats each other candidate in pairwise majority elections. Keywords: Condorcet efficiency, scoring systems, Borda count, Impartial Anonymous Culture, voting. Subject classifiction: C67. 1

2 DAVIDE P. CERVONE, WILLIAM V. GEHRLEIN, AND WILLIAM S. ZWICKER It is well known [3, 11] that a Condorcet winner need not exist. However, many feel that a reasonable voting system should elect the Condorcet winner whenever such a candidate exists; this is the Condorcet Winner Criterion. The cost of completely meeting this criterion can be high, however, because it forces us to sacrifice certain other desirable requirements [8]. So it has become common to consider, as a measure of partial fulfillment, the Condorcet efficiency of a voting system S, which is the conditional probability that S elects the Condorcet winner, given that a Condorcet winner exists. Condorcet efficiency depends, of course, on the underlying probability distribution describing the likelihood that various profiles are observed. Many such distributions have been considered, but the two most common assumptions are: Impartial Culture (IC): voters choose a preference ranking randomly and independently with probability 1/6 of choosing any particular ranking. Impartial Anonymous Culture (IAC): each possible preference profile is equally likely. 1 Further discussion of these two assumptions may be found in Berg [1], Gehrlein [4], and Stensholt [1]. In the general discussion that follows, we will assume that the underlying probability distribution is neutral that is, it treats the candidates symmetrically. Both IAC and IC are neutral. Our focus is on the Condorcet efficiency of scoring rules (also called weighted scoring rules). For three candidates, a scoring rule is determined by a vector α 1, α, α 3 of real-number scoring weights satisfying both α 1 α α 3 and α 1 < α 3. Each voter awards α 1 points to her bottom-ranked candidate, α points to her second-ranked candidate, and α 3 points to her most favored candidate. The winner is the candidate with the highest number of total points, and in fact the point totals determine a complete ranking of the candidates (with ties possible). It is has long been known that any order-preserving affine transform α 1, α, α 3 aα 1 + b, aα + b, aα 3 + b 1 To appreciate the difference between these two assumptions, note that under IC the probability distribution over profiles is not uniform; it has a peak at the central point (1/6, 1/6, 1/6, 1/6, 1/6, 1/6). As n increases, more and more of the area under the curve is concentrated very close to the central point, in such a way that the distribution approaches a spherically symmetric one with a huge spike at the center. (We use quotation marks here, because all the action is of course in a higher dimension.) Specifically, we assume that each permutation of the candidates leaves fixed the probability of any event described in terms of specific candidates.

3 WHICH SCORING RULE MAXIMIZES CONDORCET EFFICIENCY? 3 (where we require a > 0 and b to be real-number constants) produces a second vector of scoring weights that induces the identical voting system; for every profile, total score yields the same ranking of candidates using one vector as it does using its transform. Thus, we can normalize any vector so that α 1 = 1 and α 3 = 1. Under this normalization (others are possible) the voting system is completely determined by the middle scoring weight, which we term β and which satisfies 1 β 1. Two scoring systems are particularly worth noting in this connection: plurality voting (β = 1) and the Borda count (β = 0). Let us introduce a bit of notation here. Suppose for a moment that our profile P is fixed, and U and V are candidates. We ll use U β to denote the total of all points awarded to U using the scoring weights 1, β, 1, and we will write U > β V to mean U β > V β and U β V to mean U β V β. We are now ready to state the three questions that most naturally suggest themselves for study, given our line of inquiry. Question #1. Which value of β maximizes the probability that A > β B, given that A > C B? 3 (Interpretation: Which value of β maximizes the probability, for any two candidates, that the scoring system will rank them in the same order as pairwise majority?) Question #. Which value of β maximizes the probability that A > β B and A > β C, given that A > C B and A > C C? (Interpretation: Which value of β maximizes the probability that the scoring winner will equal the Condorcet winner, given that a Condorcet winner exists?) Question #3. Which value of β maximizes the probability that A > β B > β C, given that A > C B > C C and A > C C? 4 (Interpretation: Which value of β maximizes the probability that the scoring ranking will equal the Condorcet ranking, given that a Condorcet ranking exists?) These interpretations may seem, at first, to be too general, but note (for example, in question ) that neutrality implies that the probability that a 3 Here, as elsewhere, we are somewhat arbitrarily favoring strict inequalities over weaker ones. This choice is of little account, because for any reasonable probability distribution, as n the probability of a tie in either type of ordering (such as A C B and B C A) becomes vanishingly small. Discounting ties becomes automatic when we later reduce the problem to one of calculating volumes of polytopes, because the volume of such a region is the same regardless of whether the region includes the appropriate sections of its bounding hyperplanes or not, and it is exactly the points lying on these bounding hyperplanes that correspond to ties. 4 The last inequality is needed because > C may be intransitive; it would be redundant for > β.

4 4 DAVIDE P. CERVONE, WILLIAM V. GEHRLEIN, AND WILLIAM S. ZWICKER particular candidate is the scoring winner, given that that candidate is the Condorcet winner, is equal to the probability that the scoring winner is the Condorcet winner, given that a Condorcet winner exists. Question is the most important to the current study. Van Newenhizen [14] considered all three questions in an attempt to show that Borda Rule maximized all three of the associated probabilities under a family of probability distributions for voter profiles, including IAC. Question #4. Which value of β maximizes the probability that A > β C > β B, given that A > C B > C C and A > C C? The importance of question 4 is not obvious. As we will see, it helps to complete a package that makes the answer to question seem more reasonable and, with the benefit of hindsight, almost predictable. Gehrlein and Fishburn [6] consider question with the assumption of IC. The results of that study found the range of β values to maximize the associated probability for small n, to show that Borda Rule was not included in the range of those weights. However, as n under IC, it was shown that Borda Rule uniquely maximized the probability from question. Tataru and Merlin [13] use the assumption of IC as n to develop a representation for the probability that B > β A and C > β A, given that A > C B and A > C C, and show that it is the same as the representation for the probability that A > β B and A > β C, given that B > C A and C > C A. Their study begins with notions of geometric models of voting phenomena, as developed by Saari and Tataru [9], and then obtains probability representations by applying results of Schläfli [10]. Gehrlein [5] addresses the probability in question for small n under the assumption of IAC to find the range of β values to maximize that probability. Results do not suggest that Borda Rule will maximize the probability as n with IAC, as it did with IC. Lepelley, Pierron and Valognes [7] also present computational results for large n to suggest that Borda Rule does not maximize the probability from question as n with IAC. These observations are not in agreement with the results given in Van Newenhizen [14], as mentioned above. The current study considers the limiting case as n with the assumption of IAC, and develops closed-form representations for the probabilities, to resolve the inconsistencies in these observations. The rest of this paper is organized as follows. In section we explain how the problem may be reduced to one of pure geometry. Our main results are detailed answers to questions 1,, 3, and 4, that are stated without proof in section 3. Some subtleties of the problem, which seem to make the IAC case somewhat more complex than the IC case, are discussed in section 4. In section 5, we discuss the role of question 4 and of certain symmetry arguments in providing some intuition for the main results. It seems, however,

5 WHICH SCORING RULE MAXIMIZES CONDORCET EFFICIENCY? 5 that these ideas are unlikely to yield short proofs that substitute fully for the detailed geometrical analyses and proofs, which appear in section 6.. Geometrization If we divide each of the vote counts, n i, that appears in our vote profile P = (n 1, n, n 3, n 4, n 5, n 6 ) by the number of votes, n, we obtain a normalized profile P = (x 1, x, x 3, x 4, x 5, x 6 ) of numbers x i = n i /n that represent the fractions of voters who favor each of the six strict preference rankings. Each of the relationships we require can be expressed as easily in terms of the x i as in terms of the n i. For example, A > C B if, and only if, and A > β B if, and only if, x 1 x + x 3 x 4 x 5 + x 6 > 0 (H 1a ) (1 β)x 1 (1 + β)x + (1 + β)x 3 (1 β)x 4 + ( x 5 + x 6 ) > 0. (H 1b ) Note that each x i 0 and x i = 1, so the normalized profiles correspond to those points in 5, the 5-simplex in R 6, for which each coordinate is a rational number that may be written with denominator n. These points form a regular lattice in 5, in which each point spaced away from the boundary has 6 5 = 30 nearest neighbors. Under IAC, our probability measure puts equal weight on each of these lattice points inside 5. In figure 1 we have sketched the analogous situation for the 3-simplex in R 3, showing first how 3 sits inside R 3 and then illustrating the lattice for the case n = 5 (for which each point spaced away from the boundary has 3 = 6 nearest neighbors.) (0,0,1) P C = (1/3,1/3,1/3) (0,0,1) (0,1,0) (/5, 1/5,/5) (1,0,0) (1,0,0) (0,1,0) Figure 1. The -simplex,, in three-space, containing the central point P C = (1/3, 1/3, 1/3) is shown on the left. On the right we show the lattice points (for n = 5) within this simplex. The entire region 5 in R 6 is the graph of the solution set of the following system T 0 of linear equations and inequalities: x 1 + x + x 3 + x 4 + x 5 + x 6 = 1 x 1 0, x 0, x 3 0, x 4 0, x 5 0, and x 6 0. (T 0 )

6 6 DAVIDE P. CERVONE, WILLIAM V. GEHRLEIN, AND WILLIAM S. ZWICKER Now consider the region we shall call R 1 (because it is the principal region of interest for question 1), defined as the graph of the solution set of the system T 1 consisting of T 0 together with inequalities H 1a and H 1b, and also consider region R 1a similarly defined in terms of the system T 1a consisting of T 0 with only inequality H 1a added in. It is evident that the probability for question 1 (of our scoring system ranking of two candidates being in agreement with the pairwise majority outcome) is given by the following expression: lim n number of lattice points in region R 1 number of lattice points in region R 1a Because our regions are not complicated (they are solution sets of a linear system) this limit agrees with 5 the ratio where by volume we intend 5-volume. volume of region R 1 volume of region R 1a (1) Furthermore, changes in β do not affect the volume of region R 1a, so the value of β maximizing ratio (1) is the same as that maximizing the volume of R 1. Thus, our original question 1 can be translated as follows: Question #1 [volume equivalent form]. What value of β maximizes the volume of region R 1? We can say more about these inequalities. Let M be the hyperplane containing 5, which has equation x 1 + x + x 3 + x 4 + x 5 + x 6 = 1. We will primarily be concerned with what goes on within this plane. Consider the equality corresponding to inequality H 1a : x 1 x + x 3 x 4 x 5 + x 6 = 0. (P 1a ) This hyperplane, P 1a, intersected with M, forms a hyperplane M 1a. Note that M 1a passes through the central point P C of 5, and that the solution of the original inequality, H 1a, (again, taken within M ) forms a half-space within M. (For notational purposes, if we have a half-space in six-space given by the inequality H x, its bounding hyperplane will be the equality denoted P x, which intersects with M to form a 5-dimensional hyperplane, M x.) The equality corresponding to inequality H 1b, (1 β)x 1 (1 + β)x + (1 + β)x 3 (1 β)x 4 + ( x 5 + x 6 ) = 0, (P 1b ) yields a second hyperplane M 1b (β) that also passes through the central point P C of 5. Thus R 1 consists of a wedge cut out of 5 by a pair of hyperplanes that pass through the middle of 5. The first question can be rephrased informally, then, as follows: 5 The necessary background for a proof of this assertion is given in [].

7 WHICH SCORING RULE MAXIMIZES CONDORCET EFFICIENCY? 7 Question #1 [informal version]. As β varies from 1 to +1, the plane M 1b (β) swivels about the central point of the simplex. For which β does this plane, together with the fixed plane M 1a, cut the largest wedge R 1 out of 5? A schematic depiction, using in place of 5, appears as figure. M 1a η R 1 M 1b Figure. A fixed line M 1a and a moving line M 1b cut a region R 1 from the -simplex that is largest when the angle η between the lines is largest. Each of the other questions can be similarly expressed. The translation of the second question is as follows: Question # [informal version]. As β varies from 1 to +1, the planes M b1 (β) and M b (β) both swivel about the central point of the simplex. For which β do these two planes, together with the two fixed planes M a1 and M a, cut the largest wedge R out of 5? The equations of the half-spaces (in six-space that induce these planes in M ) together with the associated inequalities in terms of > C and > β, are as follow: (A > C B) x 1 x + x 3 x 4 x 5 + x 6 > 0 (H a1 ) (A > C C) x 1 x x 3 + x 4 x 5 + x 6 > 0 (H a ) (A > β B) (1 β)x 1 (1 + β)x + (1 + β)x 3 (1 β)x 4 + ( x 5 + x 6 ) > 0 (H b1 ) (A > β C) (x 1 x ) (1 β)x 3 + (1 + β)x 4 (1 + β)x 5 + (1 β)x 6 > 0 (H b ) Each of the questions 3 and 4 give rise to three fixed hyperplanes (from the inequalities A > C B, B > C C, and A > C C) and two moving hyperplanes (from A > β B and B > β C, for question 3, and from A > β C and C > β B for question 4). The equations for these are given in section 6.

8 8 DAVIDE P. CERVONE, WILLIAM V. GEHRLEIN, AND WILLIAM S. ZWICKER 3. Statements of Main Results Theorem 1. Consider elections using scoring systems for which there are three candidates and n voters, and for which a vote consists of a strict preference ranking of the candidates. Assume that all profiles are equally likely (IAC). Then for any two candidates, the limit, as n, of the value of β maximizing the probability that the scoring system ranks these two in the same order as they are ranked by pairwise majority is 0; that is, the Borda count maximizes the limiting probability in this context. This number is the unique value of β in [ 1, 1] for which the function F 1 given below satisfies F 1 (β) = 0. The function F 1 (β) defined on the interval [ 1, 1] whose value is the limiting conditional probability described above (i.e., the ratio of the 5-volume of region R 1 to that of region R 1a ) is given by the following formula: { F1 ( β)/v 1 for 1 β 0 F 1 (β) = F 1 (β)/v 1 for 0 β 1 where F 1 (β) = β 47β 11β 3 + 4β 4 (1 + β)(3 β)(3 + β) and V 1 = 6/40 is the volume of region R 1a. This function is symmetric about the y axis (β = 0), and its graph appears as figure 3. Although the formula presented here was computed using the method described in section 6, it was duplicated independently by the second-named author using an algebraic partitioning of the space enclosed by the hyperplanes Figure 3. The graph of the function F 1(β), which gives the conditional probability of question 1. It is symmetric about the y axis and has a unique maximum at β = 0. Theorem. Consider elections using scoring systems for which there are three candidates and n voters, and for which a vote consists of a strict preference ranking of the candidates. Assume that all profiles are equally likely (IAC). Then the limit, as n, of the value of β maximizing the probability that the scoring winner of the election is the Condorcet winner, given

9 WHICH SCORING RULE MAXIMIZES CONDORCET EFFICIENCY? 9 that a Condorcet winner exists, is β = (which is correct to five decimal places); in particular, the Borda count does not maximize the limiting probability in this context. This number is the unique value of β in [ 1, 1] for which the function F given below satisfies F (β) = 0. The function F (β) defined on the interval [ 1, 1] whose value is the limiting conditional probability described above (i.e., the ratio of the 5-volumes of the corresponding regions R and R a ) is given by the following formula: { F (β)/v for 1 β 0 F (β) = F + (β)/v for 0 β 1 where ( 6 F (β) = β β 115β 3 155,50 (1 β) 3 (9 β ) β β 5 + 8β 6 + β 7 (1 β) 3 (9 β ) and ( 6 F + (β) = β β β β 4 155,50 (1 + β) 3 (9 β )(1 + 3β) β β β 7 + 1β 8 (1 + β) 3 (9 β )(1 + 3β) and V = 6/384 is the volume of the region R a that is the intersection of the simplex 5 and the two fixed hyperplanes H a1 and H a. This function is not symmetric about the y axis (β = 0), nor is it symmetric about the line β = Its graph appears as figure 4. ) ) Figure 4. The graph of the function F (β), which gives the conditional probability of question. It is not symmetric and has a maximum at approximately β =

10 10 DAVIDE P. CERVONE, WILLIAM V. GEHRLEIN, AND WILLIAM S. ZWICKER Theorem 3. Consider elections using scoring systems for which there are three candidates and n voters, and for which a vote consists of a strict preference ranking of the candidates. Assume that all profiles are equally likely (IAC). Then the limit, as n, of the value of β maximizing the probability that the ranking of candidates by total score agrees with the transitive ranking by pairwise majorities, given that the pairwise majorities do yield a transitive ranking, is 0; that is, the Borda count maximizes the limiting probability in this context. Zero is the unique value of β in [ 1, 1] for which the function F 3 given below satisfies F 3 (β) = 0. The function F 3 (β) defined on the interval [ 1, 1] whose value is the limiting conditional probability described above (i.e., the ratio of the 5-volumes of the corresponding regions R 3 and R 3a ) is given by the following formula: { F3 ( β)/v 3 for 1 β 0 F 3 (β) = F 3 (β)/v 3 for 0 β 1 where F 3 (β) = ( β β β β 4 311,040 (1 + β) 3 (9 β )(1 + 3β) + 596β β β 7 + 6β 8 (1 + β) 3 (9 β )(1 + 3β) where V 3 = 6/768. This function is symmetric about the y axis (β = 0), and its graph appears as figure 5. ) Figure 5. The graph of the function F 3(β), which gives the conditional probability of question 3. It is symmetric about the y axis and has a unique maximum at β = 0. Theorem 4. Consider elections using scoring systems for which there are three candidates and n voters, and for which a vote consists of a strict preference ranking of the candidates. Assume that all profiles are equally likely (IAC). Then the limit, as n, of the value of β maximizing the probability that the ranking of candidates by total score agrees, in terms of the top candidate, with the transitive ranking by pairwise majorities, but disagrees

11 WHICH SCORING RULE MAXIMIZES CONDORCET EFFICIENCY? 11 in terms of the ordering of the second and third ranked candidates, given that the pairwise majorities do yield a transitive ranking, is 1; that is, plurality voting maximizes the limiting probability in this context. The function F 4 (β) defined on the interval [ 1, 1] whose value is the limiting conditional probability described above (i.e., the ratio of the 5-volumes of the corresponding regions R 4 and R 4a ) is given by the following formula: { F 4 (β)/v 4 for 1 β 0 F 4 (β) = F 4 + (β)/v 4 for 0 β 1 where ( 6 F4 (β) = β β 6876β β 4 155,50 (1 β) 3 (9 β )(1 3β) + 667β5 893β 6 + 4β 7 6β 8 (1 β) 3 (9 β )(1 3β) ) and ( 6 F 4 + (β) = β + 115β β β 4 155,50 (1 + β) 3 (9 β )(1 + 3β) + 96β5 + 70β 6 41β 7 + 3β 8 (1 + β) 3 (9 β )(1 + 3β) ) and V 4 = 6/768. The graph of F 4 (β) is decreasing on the interval from 1 to and increasing on the interval from to 1. This function is not symmetric about the y axis (β = 0), nor is it symmetric about the line β = Its graph appears as figure Figure 6. The graph of the function F 4(β), which gives the conditional probability of question 4. It is not symmetric and has a maximum at β = 1.

12 1 DAVIDE P. CERVONE, WILLIAM V. GEHRLEIN, AND WILLIAM S. ZWICKER 4. The Arguments in Van Newenhizen [199] Much of the intuition behind our results arises from an understanding of what goes wrong in the approach of Van Newenhizen [14], here termed the basic approach, which is the natural one that might first suggest itself. Because question 1 entails only one fixed plane and one moving plane, we begin by considering this question. The basic approach considers the angle η between the two hyperplanes M 1a and M 1b (β) (see figure ), shows that when β = 0 the angle η is maximized, and concludes that when β = 0 the volume of the resulting wedge R 1 is also maximized. Figure certainly suggests that a larger value of η yields a wedge R 1 that strictly contains (as a set of points) the version of R 1 arising from a smaller η value. However, figure is a schematic representation of a more complex situation in a higher dimensional space. The two hyperplanes M 1a and M 1b (β) each contain the central point P C, so their intersection contains P C as well. In figure it appears as if P C is the only point in this intersection, from which it would follow that this intersection is fixed as β varies. In fact, the intersection, inside M, of these two 4-dimensional hyperplanes is itself a 3-dimensional object that swivels about P C as β varies. Consequently the R 1 for some β value corresponding to a certain angle η is likely to neither contain nor be contained by the R 1 for a different β value corresponding to a larger or smaller angle η. As β varies, some points are added to R 1 and other points are removed, as we show in section 6.4. Suppose, however, that the wedge R 1 were cut from a ball (solid sphere), rather than from a simplex, by a pair of hyperplanes passing through the center of the sphere. Then thanks to spherical symmetry, the largest volume would necessarily correspond to the largest angle η between the planes, regardless of whether or not each R 1 with a larger η contained as a subset each R 1 with a smaller η. For the actual case at hand, however, we are faced with the following plausible scenario: perhaps as β varies it happens that while η is decreasing the position of R 1 is swinging around in such a way that the portion of the wall of our simplex that forms part of the boundary of R 1 is becoming more distant from the central point. Let us sum up. The probability distribution under consideration is not spherically symmetric. Hence, it does not follow that the largest η yields the largest volume, unless one can show that the intersection of the hyperplanes is fixed as β varies. But this intersection is not fixed, so there is a gap in the basic approach. This same flaw is inherent in the application of these methods to questions # and #3, as well. In the case of questions #1 and #3, the result obtained by the basic approach turns out to be correct, so conceivably there is some way to patch the argument, rather than starting from scratch. This seems as if it would

13 WHICH SCORING RULE MAXIMIZES CONDORCET EFFICIENCY? 13 be difficult to do. In the case of question, the result obtained from the basic approach turns out to be incorrect. Could the flaw discussed in the previous paragraph explain why this approach yields the wrong answer to question? The answer is not immediately apparent. Because question (as well as #3) entails more than two planes, a second type of difficulty arises. To focus on this second issue, let us temporarily move the first issue off the table by ignoring the simplex (and all its bounding hyperplanes), and pretending instead that our probability distribution is the uniform distribution, inside M, over the solid ball centered at the point P C that is common to all remaining hyperplanes. The basic approach considers two of these angles and shows that these are each maximized when β = 0. But as there are several planes and more than two angles involved, the subtleties of higher dimensional geometry are such that it is not immediately clear which condition on these angles is the appropriate one, given our temporary assumption of spherical symmetry. Does β = 0 maximize the spherical variant of region R? To answer this, we look first at a three-dimensional analogue: two planes cutting a wedge out of a three-ball. If the planes pass through the center of the ball, their intersection forms a line, L, passing through antipodal points on the sphere, and the wedge is in the shape of an orange slice. The volume of this slice is maximized when the area of the piece of orange peel on the slice is maximized. This area is in the form of a spherical wedge known as a lune. Note that planes perpendicular to the line L cut the lune in circular arcs, with the longest arc for the plane through the center of the sphere (i.e., an equatorial arc). The lune of greatest area will be achieved by the two planes that form the longest equatorial arc. The corresponding higher-dimensional situation has four hyperplanes passing through the center of the 5-ball in the five-dimensional space M. They intersect in a line, L, and cut a five-dimensional wedge from the 5-ball. This wedge is maximized when the corresponding four-dimensional lune cut from the 4-sphere is maximized. The hyperplanes perpendicular to L slice the four-dimensional lune in three-dimensional spherical regions, in this case, spherical tetrahedra whose four faces correspond to the four original hyperplanes. Again, the largest tetrahedron is the equatorial one, and the volume of the five-dimensional wedge is maximized when the three-dimensional volume of this equatorial tetrahedron is greatest. The basic approach argues that the greatest volume occurs for β = 0 by looking at one angle between two of the four slicing hyperplanes and a second between the other two hyperplanes, but this does not seem sufficient justification for the claim. On the other hand, it turns out that for any two of the four hyperplanes, their maximal angle occurs when β = 0. Now the angles between the hyperplanes are actually the angles between the faces of

14 14 DAVIDE P. CERVONE, WILLIAM V. GEHRLEIN, AND WILLIAM S. ZWICKER the spherical tetrahedron, and it seems reasonable that the volume of this tetrahedron would be largest when its angles are largest. Thus the maximal volume for the spherical R region does appear to be at β = 0. The result is that although the reasoning is not sufficient, the conclusion reached by the basic approach is nonetheless correct in the case of a spherically symmetric region. Thus the original issue discussed above (namely that the region is not spherically symmetric, and so a larger wedge does not necessarily cut a larger volume) seems to be the fatal flaw in the basic approach. 5. Symmetry and the Role of Question #4 The detailed geometrical analysis in section 6 proves that, in the context of this paper, the Borda count is not the most Condorcet efficient among scoring systems, yet it is the best scoring system in the sense of questions #1 and #3. However, the material in section 6 does not provide a short and intuitively satisfying story explaining why things turn out this way, or why the value of β is closer to 1 than to 1 in the most Condorcet efficient scoring system. Does such a story exist? The answer seems to be Yes, to a limited extent. We begin with a short and self-contained argument explaining why the functions F 1 (β) and F 3 (β) must be symmetric about the line β = 0. (This argument does not rest on the formulae for these functions, which were derived through the longer geometrical analysis.) We discuss why the same argument does not apply to F (β). Of course, symmetry does not show that the maximum is located at β = 0 (because there might exist several maxima located symmetrically about the line β = 0). Then we turn to the role of question 4 in providing a plausibility argument for why the value of β is closer to 1 than to 1 in the most Condorcet efficient scoring system. Theorem 5. The functions F 1 (β) and F 3 (β) are each symmetric about the line β = 0. Proof. We ll show F 3 (β) is symmetric and leave the proof for F 1 (β) to the reader. Consider the probability p 1 that given that A > β B and B > β C () A > C B, B > C C and A > C C. (3) We ll show that p 1 is equal to the probability p that given that C > β B and B > β A, (4) C > C B, B > C A and C > C A. (5)

15 WHICH SCORING RULE MAXIMIZES CONDORCET EFFICIENCY? 15 For reasons we have already discussed, p 1 = F 3 (β) and p = F 3 ( β), so it will follow that F 3 (β) = F 3 ( β), as desired. Now we have and p 1 = Volume of Region R 3 Volume of Region R 3a p = Volume of Region R 3 Volume of Region R3a where region R 3a is defined by the inequalities in T 0 (for the simplex) together with (3), while for R 3 we add () as well. Similarly, region R3a is defined by the inequalities in T 0 (for the simplex) together with (5), while for R3 we add (4) as well. Next, consider the function G: 5 5 by G(x 1, x, x 3, x 4, x 5, x 6 ) = (x, x 1, x 4, x 3, x 6, x 5 ). As a map from 5 to 5, G is clearly a volume-preserving bijection, so it follows that p 1 = Volume of Region G(R 3) Volume of Region G(R 3a ) The regions G(R 3 ) and G(R 3a ) are described by the inequalities obtained by transforming the earlier inequalities () and (3) via G. It remains only, then, to show that these transformed inequalities are (4) and (5), and it will follow that G(R 3 ) = R 3 and G(R 3a) = R 3a, whence p 1 = p. As the effect of applying G to a profile is to have each voter flip his or her preference ranking upside down, it is immediate that applying G to any inequality of form U > C V transforms it into the inequality V > C U. Also, the total score U β (P ) for a candidate U under a profile P is easily seen to satisfy that U β (P ) = U β (G(P )) from which it follows that applying G to any inequality of form U > β V transforms it into the inequality V > β U. Now it is immediate that G transforms () into (4) and (3) into (5), which completes the proof. It is interesting to observe what happens when the above argument is applied to question, asking which value of β yields the most Condorcet efficient scoring system, or equivalently, which value of β maximizes the probability that A > β B and A > β C, given that A > C B and A > C C. Applying the same transform G to the question tells us that the question answer is the same as the value of β maximizing the probability that B > β A and C > β A, given that B > C A and C > C A. In this case, the transformed question is not equivalent to the original: it asks us which value of β maximizes the probability that the scoring loser is the Condorcet loser, given that a Condorcet loser exists. We cannot conclude that F (β) is symmetric. But the argument does yield some information when thus applied it tells us that if we were to plot

16 16 DAVIDE P. CERVONE, WILLIAM V. GEHRLEIN, AND WILLIAM S. ZWICKER Condorcet loser efficiency as a function of β, its graph would be the reflection across the line β = 0 of the graph of F (β). We turn next to a discussion of theorem. The most surprising result in this paper is certainly the fact that the Borda count (β = 0) is not most Condorcet efficient among scoring systems. Should we have expected this, and might we have guessed that efficiency is maximized with a negative β value? We offer a qualified yes, one certainly aided by hindsight, in what follows. Assume that A is the Condorcet candidate in an election with three candidates, so that A > C B and A > C C. Then there is no cycle, so there exists a Condorcet ranking. Ignoring the possibility of ties (see footnote 1), either Case 1: A > C B > C C with A > C C, or Case : A > C C > C B with A > C B. By symmetry, the probabilities of these two cases holding are equal. We will work on case 1; our reasoning applies just as well for case. The desired outcome that A is the scoring winner with A > β A > β C can happen in two ways. Either B and Case 1.1: A > C B > C C (with A > C C) and A > β B > β C, or Case 1.: A > C B > C C (with A > C C) and A > β C > β B. Given that we are in case 1, the probability that case 1.1 holds is maximized when β = 0 (Borda count); this is precisely what theorem 3 states. Theorem 4 tells us that the value of β maximizing the probability of case 1., given that case 1 holds, is β = 1 (plurality voting). We will return to theorem 4 in a moment. Now the probability that case 1 occurs (equivalently, the volume of a certain 5-dimensional region) is equal to the sum of the probabilities for the exclusive cases 1.1 and 1. (equivalently, the case 1 region is partitioned into the disjoint case 1.1 and 1. subregions, so that its volume is the sum of the volumes of these subregions). Given that one probability (or subregion volume) is maximized when β = 0 and the other is maximized when β = 1, it seems reasonable that the sum might be maximized by some compromise value of β between 1 and 0. We did wonder whether it is possible to bypass the computational geometry of section 6 by making the above argument rigorous. However, the directions that we explored seem to point towards showing that the volume of the region for question 4 steadily decreases as β varies from 1 to +1. This will not be possible to show, since, in fact, F 4 is increasing for β > , as illustrated in figure 7.

17 WHICH SCORING RULE MAXIMIZES CONDORCET EFFICIENCY? Figure 7. The graph of F 4(β) appears to be decreasing, but there is a small rise to the right of β = , shown here with an exaggerated scale on the vertical axis. 6. Proofs of Theorems 1 4 In this section, we give the details of the calculations that lead to the formulae for the volumes given in section 3. The method used is described in general terms in section 6.1. To carry out the process, the combinatorial structure of the region whose volume is to be computed must be determined; this is done in detail in section 6. for the region R 1 associated with theorem 1. This result is used in section 6.3 to compute the volume of R 1, and the fact that the maximum volume occurs at β = 0 is verified in section 6.4. The data needed to perform similar computations for the regions R, R 3 and R 4 are given in section 6.5, but the details are left to the reader The Volume of a Convex Region. One way to determine the volume of a region is to break it into smaller pieces, whose volumes can be computed more readily, and then add up the results. In our case, since the region is convex, we can select one of its vertices and decompose the region into a collection of pyramids having this vertex as their apex and the various faces of the region as their bases. We need only consider the faces that don t contain the selected vertex, as the volume will be zero if the base contains the apex of the pyramid. A three dimensional example would be the decomposition of a cube into three congruent square-based pyramids with apexes directly above a corner of their square bases (figure 8); the three pyramids meet along a long diagonal of the cube. The volume of a pyramid in dimension n is given by 1 nv h where V is the (n 1)-dimensional volume of the base, and h is the height of the apex above the base. In two dimensions, this reduces to the usual formula 1 bh for the area of a triangle of base b and height h, and in three dimensions to the formula 1 3Ah for the volume of a pyramid (or cone) of height h whose base has area A. To use this formula in general, however, we need to be able to compute the (n 1)-volume of the base. In our case, the bases are faces of a convex object and so are themselves convex, hence we can

18 18 DAVIDE P. CERVONE, WILLIAM V. GEHRLEIN, AND WILLIAM S. ZWICKER Figure 8. A cube can be divided into three square-based pyramids having a common apex. compute their volumes by breaking them into pyramids in dimension n 1 and adding up the volumes of these pyramids exactly as we did with the original region. This generates a recursive procedure for computing the volume of the region. The base case for the recursion is the -dimensional case, where the pyramid is a triangle, and the base is a line segment; the volume of the base is then just the length of this line segment, which can be computed using the distance formula. v Figure 9. A vector perpendicular to the base can be obtained by subtracting the projection of the vector onto the plane of the base. To use the volume formula for a pyramid, we also need to know the height of the apex over the hyperplane containing the base. One way to get this is the following: consider the vector v, from a point in the hyperplane (say a vertex of the base) to the apex (figure 9); subtract from v the projection of v onto the base hyperplane to obtain a vector perpendicular to the base that points to the apex. The length of this perpendicular vector is the height, h. If we know orthogonal unit vectors that span the base hyperplane, then the projection of v can be computed as the sum of the projections onto the orthogonal basis vectors. Notice that in finding the height, we compute a vector perpendicular to the base hyperplane, so we can extend the orthonormal basis for the base to an orthonormal basis for one dimension higher (i.e., for the space containing the pyramid). This fits in nicely with our recursive procedure, since the pyramid may be part of the base of a higher-dimensional pyramid whose volume we are computing, and we d need this enlarged basis to get the height of that larger pyramid. (The process is

19 WHICH SCORING RULE MAXIMIZES CONDORCET EFFICIENCY? 19 really just Grahm-Schmidt orthonormalization with volume computations mixed in.) In this way, the volume of a convex polyhedral object in any dimension can be computed effectively, provided the structure of its faces is known. We turn now to our specific region and analyze its shape more carefully. 6.. The Structure of the Region R 1. To describe the region we need some terminology. For each i = 1,..., 6, define P i to be the hyperplane x i = 0; recall that M is the plane x i = 1. Suppose we let u i be the point in R 6 that has a 1 in the i-th coordinate and 0 s everywhere else. Note that the P i and M intersect to form a regular 5-simplex in six-space having the u i as its vertices. (The corresponding object in three-space is the equilateral triangle having vertices (1, 0, 0), (0, 1, 0) and (0, 0, 1), lying in the plane x + y + z = 1 shown in figure 1.) The region R 1 is the intersection of this 5-simplex with two additional halfspaces, H 1a and H 1b defined in section, having P 1a and P 1b as their bounding hyperplanes Note that the hyperplane P 1a cuts off a section of 5 that does not depend on β, while the hyperplane P 1b then cuts off another section that does depend on β. Recall that R 1a is the result of intersecting 5 with the halfspace H 1a, so R 1 is then the result of intersecting R 1a with H 1b. To compute the volume of R 1, we look more closely at how these intersections occur. First, let N 1a = 1, 1, 1, 1, 1, 1, a vector normal to the bounding hyperplane, P 1a, for H 1a. Let x = (x 1, x, x 3, x 4, x 5, x 6 ) be an arbitrary point in six-space; then x will lie in the halfspace H 1a provided N 1a x 0. In particular, we can check the six vertices u i of the 5-simplex to see which vertices are cut off by P 1a. Since u i is all zeros except for a 1 in the i-th coordinate, N 1a u i is just the i-th coordinate of N 1a ; when this is positive, the vertex remains within the region, otherwise it is cut off. From this, we see that vertices u 1, u 3, and u 6 are in R 1a, while u, u 4 and u 5 are cut off. Let S + = {u 1, u 3, u 6 } and S = {u, u 4, u 5 }. New vertices are formed for R 1a when the hyperplane P 1a intersects one of the edges of the 5-simplex. This occurs when the two vertices of the edge are on opposite sides of the hyperplane; that is, when one vertex is in the set S + and the other is in S. Since the 5-simplex has an edge for each pair of its vertices, this means that there are nine new vertices created by slicing the simplex by this hyperplane. We can compute the locations of these vertices by parameterizing the points along the edge with vertices p 0 and p 1 by p t = p 0 + t(p 1 p 0 ) and solving for the t where N 1a p t = 0; i.e., N 1a (p 0 + t(p 1 p 0 )) = 0, or t = N 1a p 0 /(N 1a p 0 N 1a p 1 ). Since p 0 and p 1 each are one of the u i, N 1a p i is always one of the coordinates of N 1a, so will be 1 or 1; but since N 1a p 0 and N 1a p 1 must be of opposite sign (the vertices are on opposite sides of the hyperplane), it must be that

20 0 DAVIDE P. CERVONE, WILLIAM V. GEHRLEIN, AND WILLIAM S. ZWICKER N 1a p 0 = N 1a p 1. So t = N 1a p 0 /(N 1a p 0 + N 1a p 0 ) = 1/, no matter which edge we consider. Thus each of the nine edges are cut exactly in half, and the new vertices formed in R 1a are v 1 = 1 (1, 1, 0, 0, 0, 0) v 3 = 1 (0, 1, 1, 0, 0, 0) v 6 = 1 (0, 1, 0, 0, 0, 1) = 1 (1, 0, 0, 1, 0, 0) v 34 = 1 (0, 0, 1, 1, 0, 0) 6 = 1 (0, 0, 0, 1, 0, 1) v 15 = 1 (1, 0, 0, 0, 1, 0) v 35 = 1 (0, 0, 1, 0, 1, 0) 6 = 1 (0, 0, 0, 0, 1, 1) where v ij is the vertex half-way between u i and u j. The fact that we have three vertices on each side of the hyperplane, and that the edges between them are cut exactly in half, suggests that there may be a symmetry involved here, and indeed that is the case; the two halves of the 5-simplex are congruent, so P 1a divides the simplex exactly in half. To see this algebraically, we can give a transformation that maps the hyperplanes that form one half onto those that form the other half. The system T 0 together with H 1a intersects to form R 1a ; if we introduce halfspace H 1a as x 1 x + x 3 x 4 x 5 + x 6 0, then T 0 together with H 1a intersect to form the other half of the 5-simplex. The halfspace H 1a can be rewritten as x 1 + x x 3 + x 4 + x 5 x 6 0; then exchanging x 1 with x, x 3 with x 4, and x 5 with x 6 in all the hyperplanes converts T 0 and H 1a into T 0 and H 1a, hence this also transforms one half into the other. This exchange represents the composition of three reflections; it is a rigid motion, and so is shape preserving. To form the final region of interest, R 1, we must now slice R 1a by P 1b, the hyperplane for H 1b. As above, we first determine which vertices of R 1a are inside and which outside H 1b, and then compute the positions of the new vertices obtained from slicing the edges whose vertices are on opposite sides of P 1b. Our job is made more complicated since P 1b changes with β. Note, however, that the two R 1 regions obtained for β and β are congruent: if β is substituted for β in H 1b, and if we exchange x 1 with x 3 and x with x 4, the result is the original H 1b, while this exchange leaves H 1a and the original 5-simplex unchanged. Thus, by symmetry, we need only be concerned with β in the range 0 β 1. (Note the close connection between these arguments and the ones in theorem 5.) Let N 1b = 1 β, (1 + β), 1 + β, (1 β),,, a normal vector for the hyperplane P 1b bounding the halfspace H 1b. Then when we intersect R 1a with H 1b, a point x of R 1a remains in R 1 if, and only if, N 1b x 0. We can compute N 1b x for each vertex in R 1a and determine the values of β (if any) for which the vertex still lies in the region. For example, u 1 is a vertex of R 1a, and N 1b u 1 = 1 β, so N 1b u 1 0 only when 1 β. This is true for all β in our range of interest, so u 1 remains in R 1 for all β. On the other hand, v 1 is a vertex of R 1a, but N 1b v 1 0 only when

### Section 1.1. Introduction to R n

The Calculus of Functions of Several Variables Section. Introduction to R n Calculus is the study of functional relationships and how related quantities change with each other. In your first exposure to

### Solving Simultaneous Equations and Matrices

Solving Simultaneous Equations and Matrices The following represents a systematic investigation for the steps used to solve two simultaneous linear equations in two unknowns. The motivation for considering

### The Not-Formula Book for C1

Not The Not-Formula Book for C1 Everything you need to know for Core 1 that won t be in the formula book Examination Board: AQA Brief This document is intended as an aid for revision. Although it includes

### Chapter 15 Introduction to Linear Programming

Chapter 15 Introduction to Linear Programming An Introduction to Optimization Spring, 2014 Wei-Ta Chu 1 Brief History of Linear Programming The goal of linear programming is to determine the values of

### Notes on polyhedra and 3-dimensional geometry

Notes on polyhedra and 3-dimensional geometry Judith Roitman / Jeremy Martin April 23, 2013 1 Polyhedra Three-dimensional geometry is a very rich field; this is just a little taste of it. Our main protagonist

### Interactive Math Glossary Terms and Definitions

Terms and Definitions Absolute Value the magnitude of a number, or the distance from 0 on a real number line Additive Property of Area the process of finding an the area of a shape by totaling the areas

### GRADES 7, 8, AND 9 BIG IDEAS

Table 1: Strand A: BIG IDEAS: MATH: NUMBER Introduce perfect squares, square roots, and all applications Introduce rational numbers (positive and negative) Introduce the meaning of negative exponents for

### Recall that two vectors in are perpendicular or orthogonal provided that their dot

Orthogonal Complements and Projections Recall that two vectors in are perpendicular or orthogonal provided that their dot product vanishes That is, if and only if Example 1 The vectors in are orthogonal

### Current Standard: Mathematical Concepts and Applications Shape, Space, and Measurement- Primary

Shape, Space, and Measurement- Primary A student shall apply concepts of shape, space, and measurement to solve problems involving two- and three-dimensional shapes by demonstrating an understanding of:

### Geometrical symmetry and the fine structure of regular polyhedra

Geometrical symmetry and the fine structure of regular polyhedra Bill Casselman Department of Mathematics University of B.C. cass@math.ubc.ca We shall be concerned with geometrical figures with a high

### Arrangements And Duality

Arrangements And Duality 3.1 Introduction 3 Point configurations are tbe most basic structure we study in computational geometry. But what about configurations of more complicated shapes? For example,

### Tangent and normal lines to conics

4.B. Tangent and normal lines to conics Apollonius work on conics includes a study of tangent and normal lines to these curves. The purpose of this document is to relate his approaches to the modern viewpoints

### Thnkwell s Homeschool Precalculus Course Lesson Plan: 36 weeks

Thnkwell s Homeschool Precalculus Course Lesson Plan: 36 weeks Welcome to Thinkwell s Homeschool Precalculus! We re thrilled that you ve decided to make us part of your homeschool curriculum. This lesson

### Adding vectors We can do arithmetic with vectors. We ll start with vector addition and related operations. Suppose you have two vectors

1 Chapter 13. VECTORS IN THREE DIMENSIONAL SPACE Let s begin with some names and notation for things: R is the set (collection) of real numbers. We write x R to mean that x is a real number. A real number

### Construction and Properties of the Icosahedron

Course Project (Introduction to Reflection Groups) Construction and Properties of the Icosahedron Shreejit Bandyopadhyay April 19, 2013 Abstract The icosahedron is one of the most important platonic solids

### Numerical Analysis Lecture Notes

Numerical Analysis Lecture Notes Peter J. Olver 5. Inner Products and Norms The norm of a vector is a measure of its size. Besides the familiar Euclidean norm based on the dot product, there are a number

### FCAT Math Vocabulary

FCAT Math Vocabulary The terms defined in this glossary pertain to the Sunshine State Standards in mathematics for grades 3 5 and the content assessed on FCAT in mathematics. acute angle an angle that

### Math Content

2012-2013 Math Content PATHWAY TO ALGEBRA I Unit Lesson Section Number and Operations in Base Ten Place Value with Whole Numbers Place Value and Rounding Addition and Subtraction Concepts Regrouping Concepts

### Common Core Unit Summary Grades 6 to 8

Common Core Unit Summary Grades 6 to 8 Grade 8: Unit 1: Congruence and Similarity- 8G1-8G5 rotations reflections and translations,( RRT=congruence) understand congruence of 2 d figures after RRT Dilations

### RIT scores between 191 and 200

Measures of Academic Progress for Mathematics RIT scores between 191 and 200 Number Sense and Operations Whole Numbers Solve simple addition word problems Find and extend patterns Demonstrate the associative,

### Axiom A.1. Lines, planes and space are sets of points. Space contains all points.

73 Appendix A.1 Basic Notions We take the terms point, line, plane, and space as undefined. We also use the concept of a set and a subset, belongs to or is an element of a set. In a formal axiomatic approach

### Geometry Chapter 1 Vocabulary. coordinate - The real number that corresponds to a point on a line.

Chapter 1 Vocabulary coordinate - The real number that corresponds to a point on a line. point - Has no dimension. It is usually represented by a small dot. bisect - To divide into two congruent parts.

### SCISSORS CONGRUENCE EFTON PARK

SCISSORS CONGRUENCE EFTON PARK 1. Scissors Congruence in the Plane Definition 1.1. A polygonal decomposition of a polygon P in the plane is a finite set {P 1, P,..., P k } of polygons whose union is P

### We call this set an n-dimensional parallelogram (with one vertex 0). We also refer to the vectors x 1,..., x n as the edges of P.

Volumes of parallelograms 1 Chapter 8 Volumes of parallelograms In the present short chapter we are going to discuss the elementary geometrical objects which we call parallelograms. These are going to

### NEW MEXICO Grade 6 MATHEMATICS STANDARDS

PROCESS STANDARDS To help New Mexico students achieve the Content Standards enumerated below, teachers are encouraged to base instruction on the following Process Standards: Problem Solving Build new mathematical

### REVISED GCSE Scheme of Work Mathematics Higher Unit 6. For First Teaching September 2010 For First Examination Summer 2011 This Unit Summer 2012

REVISED GCSE Scheme of Work Mathematics Higher Unit 6 For First Teaching September 2010 For First Examination Summer 2011 This Unit Summer 2012 Version 1: 28 April 10 Version 1: 28 April 10 Unit T6 Unit

### CAMI Education linked to CAPS: Mathematics

- 1 - TOPIC 1.1 Whole numbers _CAPS Curriculum TERM 1 CONTENT Properties of numbers Describe the real number system by recognizing, defining and distinguishing properties of: Natural numbers Whole numbers

### A BRIEF GUIDE TO ABSOLUTE VALUE. For High-School Students

1 A BRIEF GUIDE TO ABSOLUTE VALUE For High-School Students This material is based on: THINKING MATHEMATICS! Volume 4: Functions and Their Graphs Chapter 1 and Chapter 3 CONTENTS Absolute Value as Distance

### Geometry Notes PERIMETER AND AREA

Perimeter and Area Page 1 of 57 PERIMETER AND AREA Objectives: After completing this section, you should be able to do the following: Calculate the area of given geometric figures. Calculate the perimeter

### Number Sense and Operations

Number Sense and Operations representing as they: 6.N.1 6.N.2 6.N.3 6.N.4 6.N.5 6.N.6 6.N.7 6.N.8 6.N.9 6.N.10 6.N.11 6.N.12 6.N.13. 6.N.14 6.N.15 Demonstrate an understanding of positive integer exponents

### MATH 095, College Prep Mathematics: Unit Coverage Pre-algebra topics (arithmetic skills) offered through BSE (Basic Skills Education)

MATH 095, College Prep Mathematics: Unit Coverage Pre-algebra topics (arithmetic skills) offered through BSE (Basic Skills Education) Accurately add, subtract, multiply, and divide whole numbers, integers,

### Vectors 2. The METRIC Project, Imperial College. Imperial College of Science Technology and Medicine, 1996.

Vectors 2 The METRIC Project, Imperial College. Imperial College of Science Technology and Medicine, 1996. Launch Mathematica. Type

### A Non-Linear Schema Theorem for Genetic Algorithms

A Non-Linear Schema Theorem for Genetic Algorithms William A Greene Computer Science Department University of New Orleans New Orleans, LA 70148 bill@csunoedu 504-280-6755 Abstract We generalize Holland

### THE FUNDAMENTAL THEOREM OF ARBITRAGE PRICING

THE FUNDAMENTAL THEOREM OF ARBITRAGE PRICING 1. Introduction The Black-Scholes theory, which is the main subject of this course and its sequel, is based on the Efficient Market Hypothesis, that arbitrages

### 13 Solutions for Section 6

13 Solutions for Section 6 Exercise 6.2 Draw up the group table for S 3. List, giving each as a product of disjoint cycles, all the permutations in S 4. Determine the order of each element of S 4. Solution

### Applied Algorithm Design Lecture 5

Applied Algorithm Design Lecture 5 Pietro Michiardi Eurecom Pietro Michiardi (Eurecom) Applied Algorithm Design Lecture 5 1 / 86 Approximation Algorithms Pietro Michiardi (Eurecom) Applied Algorithm Design

### KEANSBURG SCHOOL DISTRICT KEANSBURG HIGH SCHOOL Mathematics Department. HSPA 10 Curriculum. September 2007

KEANSBURG HIGH SCHOOL Mathematics Department HSPA 10 Curriculum September 2007 Written by: Karen Egan Mathematics Supervisor: Ann Gagliardi 7 days Sample and Display Data (Chapter 1 pp. 4-47) Surveys and

### Coordinate Coplanar Distance Formula Midpoint Formula

G.(2) Coordinate and transformational geometry. The student uses the process skills to understand the connections between algebra and geometry and uses the oneand two-dimensional coordinate systems to

### Factoring Patterns in the Gaussian Plane

Factoring Patterns in the Gaussian Plane Steve Phelps Introduction This paper describes discoveries made at the Park City Mathematics Institute, 00, as well as some proofs. Before the summer I understood

### ARE211, Fall2012. Contents. 2. Linear Algebra Preliminary: Level Sets, upper and lower contour sets and Gradient vectors 1

ARE11, Fall1 LINALGEBRA1: THU, SEP 13, 1 PRINTED: SEPTEMBER 19, 1 (LEC# 7) Contents. Linear Algebra 1.1. Preliminary: Level Sets, upper and lower contour sets and Gradient vectors 1.. Vectors as arrows.

### Intersection of an Oriented Box and a Cone

Intersection of an Oriented Box and a Cone David Eberly Geometric Tools, LLC http://www.geometrictools.com/ Copyright c 1998-2016. All Rights Reserved. Created: January 4, 2015 Contents 1 Introduction

### In mathematics, there are four attainment targets: using and applying mathematics; number and algebra; shape, space and measures, and handling data.

MATHEMATICS: THE LEVEL DESCRIPTIONS In mathematics, there are four attainment targets: using and applying mathematics; number and algebra; shape, space and measures, and handling data. Attainment target

### Algebra Unpacked Content For the new Common Core standards that will be effective in all North Carolina schools in the 2012-13 school year.

This document is designed to help North Carolina educators teach the Common Core (Standard Course of Study). NCDPI staff are continually updating and improving these tools to better serve teachers. Algebra

### Geometry Course Summary Department: Math. Semester 1

Geometry Course Summary Department: Math Semester 1 Learning Objective #1 Geometry Basics Targets to Meet Learning Objective #1 Use inductive reasoning to make conclusions about mathematical patterns Give

### Pythagoras Theorem and its Generalizations

Paradox Issue 2, 2014 Page 15 Pythagoras Theorem and its Generalizations Pythagoras theorem is probably the most famous theorem in Euclidean geometry. In its most familiar form, it states that in a right-angled

### 8 th Grade Math Curriculum/7 th Grade Advanced Course Information: Course 3 of Prentice Hall Common Core

8 th Grade Math Curriculum/7 th Grade Advanced Course Information: Course: Length: Course 3 of Prentice Hall Common Core 46 minutes/day Description: Mathematics at the 8 th grade level will cover a variety

### 1. Determine all real numbers a, b, c, d that satisfy the following system of equations.

altic Way 1999 Reykjavík, November 6, 1999 Problems 1. etermine all real numbers a, b, c, d that satisfy the following system of equations. abc + ab + bc + ca + a + b + c = 1 bcd + bc + cd + db + b + c

Content Strand: Number and Numeration Understand the Meanings, Uses, and Representations of Numbers Understand Equivalent Names for Numbers Understand Common Numerical Relations Place value and notation

### Biggar High School Mathematics Department. National 5 Learning Intentions & Success Criteria: Assessing My Progress

Biggar High School Mathematics Department National 5 Learning Intentions & Success Criteria: Assessing My Progress Expressions & Formulae Topic Learning Intention Success Criteria I understand this Approximation

### Lecture 1 Newton s method.

Lecture 1 Newton s method. 1 Square roots 2 Newton s method. The guts of the method. A vector version. Implementation. The existence theorem. Basins of attraction. The Babylonian algorithm for finding

### North Carolina Math 2

Standards for Mathematical Practice 1. Make sense of problems and persevere in solving them. 2. Reason abstractly and quantitatively 3. Construct viable arguments and critique the reasoning of others 4.

### α = u v. In other words, Orthogonal Projection

Orthogonal Projection Given any nonzero vector v, it is possible to decompose an arbitrary vector u into a component that points in the direction of v and one that points in a direction orthogonal to v

### Planar Tree Transformation: Results and Counterexample

Planar Tree Transformation: Results and Counterexample Selim G Akl, Kamrul Islam, and Henk Meijer School of Computing, Queen s University Kingston, Ontario, Canada K7L 3N6 Abstract We consider the problem

### Euclidean Geometry. We start with the idea of an axiomatic system. An axiomatic system has four parts:

Euclidean Geometry Students are often so challenged by the details of Euclidean geometry that they miss the rich structure of the subject. We give an overview of a piece of this structure below. We start

### 1 Polyhedra and Linear Programming

CS 598CSC: Combinatorial Optimization Lecture date: January 21, 2009 Instructor: Chandra Chekuri Scribe: Sungjin Im 1 Polyhedra and Linear Programming In this lecture, we will cover some basic material

### Geometry of Vectors. 1 Cartesian Coordinates. Carlo Tomasi

Geometry of Vectors Carlo Tomasi This note explores the geometric meaning of norm, inner product, orthogonality, and projection for vectors. For vectors in three-dimensional space, we also examine the

Content Strand: Number and Numeration Understand the Meanings, Uses, and Representations of Numbers Understand Equivalent Names for Numbers Understand Common Numerical Relations Place value and notation

### CS268: Geometric Algorithms Handout #5 Design and Analysis Original Handout #15 Stanford University Tuesday, 25 February 1992

CS268: Geometric Algorithms Handout #5 Design and Analysis Original Handout #15 Stanford University Tuesday, 25 February 1992 Original Lecture #6: 28 January 1991 Topics: Triangulating Simple Polygons

### Pre-Algebra 2008. Academic Content Standards Grade Eight Ohio. Number, Number Sense and Operations Standard. Number and Number Systems

Academic Content Standards Grade Eight Ohio Pre-Algebra 2008 STANDARDS Number, Number Sense and Operations Standard Number and Number Systems 1. Use scientific notation to express large numbers and small

### Lesson List

2016-2017 Lesson List Table of Contents Operations and Algebraic Thinking... 3 Number and Operations in Base Ten... 6 Measurement and Data... 9 Number and Operations - Fractions...10 Financial Literacy...14

### You know from calculus that functions play a fundamental role in mathematics.

CHPTER 12 Functions You know from calculus that functions play a fundamental role in mathematics. You likely view a function as a kind of formula that describes a relationship between two (or more) quantities.

### Session 9 Solids. congruent regular polygon vertex. cross section edge face net Platonic solid polyhedron

Key Terms for This Session Session 9 Solids Previously Introduced congruent regular polygon vertex New in This Session cross section edge face net Platonic solid polyhedron Introduction In this session,

### Applications of Trigonometry

chapter 6 Tides on a Florida beach follow a periodic pattern modeled by trigonometric functions. Applications of Trigonometry This chapter focuses on applications of the trigonometry that was introduced

### Overview Mathematical Practices Congruence

Overview Mathematical Practices Congruence 1. Make sense of problems and persevere in Experiment with transformations in the plane. solving them. Understand congruence in terms of rigid motions. 2. Reason

### 3. Linear Programming and Polyhedral Combinatorics

Massachusetts Institute of Technology Handout 6 18.433: Combinatorial Optimization February 20th, 2009 Michel X. Goemans 3. Linear Programming and Polyhedral Combinatorics Summary of what was seen in the

### Foundation. Scheme of Work. Year 10 September 2016-July 2017

Foundation Scheme of Work Year 10 September 016-July 017 Foundation Tier Students will be assessed by completing two tests (topic) each Half Term. PERCENTAGES Use percentages in real-life situations VAT

### OPRE 6201 : 2. Simplex Method

OPRE 6201 : 2. Simplex Method 1 The Graphical Method: An Example Consider the following linear program: Max 4x 1 +3x 2 Subject to: 2x 1 +3x 2 6 (1) 3x 1 +2x 2 3 (2) 2x 2 5 (3) 2x 1 +x 2 4 (4) x 1, x 2

### Section P.9 Notes Page 1 P.9 Linear Inequalities and Absolute Value Inequalities

Section P.9 Notes Page P.9 Linear Inequalities and Absolute Value Inequalities Sometimes the answer to certain math problems is not just a single answer. Sometimes a range of answers might be the answer.

### Middle Grades Mathematics 5 9

Middle Grades Mathematics 5 9 Section 25 1 Knowledge of mathematics through problem solving 1. Identify appropriate mathematical problems from real-world situations. 2. Apply problem-solving strategies

### Geometry of Linear Programming

Chapter 2 Geometry of Linear Programming The intent of this chapter is to provide a geometric interpretation of linear programming problems. To conceive fundamental concepts and validity of different algorithms

### 6 EXTENDING ALGEBRA. 6.0 Introduction. 6.1 The cubic equation. Objectives

6 EXTENDING ALGEBRA Chapter 6 Extending Algebra Objectives After studying this chapter you should understand techniques whereby equations of cubic degree and higher can be solved; be able to factorise

### Polynomial Degree and Finite Differences

CONDENSED LESSON 7.1 Polynomial Degree and Finite Differences In this lesson you will learn the terminology associated with polynomials use the finite differences method to determine the degree of a polynomial

### ALGEBRA I A PLUS COURSE OUTLINE

ALGEBRA I A PLUS COURSE OUTLINE OVERVIEW: 1. Operations with Real Numbers 2. Equation Solving 3. Word Problems 4. Inequalities 5. Graphs of Functions 6. Linear Functions 7. Scatterplots and Lines of Best

### Centroid: The point of intersection of the three medians of a triangle. Centroid

Vocabulary Words Acute Triangles: A triangle with all acute angles. Examples 80 50 50 Angle: A figure formed by two noncollinear rays that have a common endpoint and are not opposite rays. Angle Bisector:

### NCTM Curriculum Focal Points for Grade 5. Everyday Mathematics, Grade 5

NCTM Curriculum Focal Points and, Grade 5 NCTM Curriculum Focal Points for Grade 5 Number and Operations and Algebra: Developing an understanding of and fluency with division of whole numbers Students

### I. Solving Linear Programs by the Simplex Method

Optimization Methods Draft of August 26, 2005 I. Solving Linear Programs by the Simplex Method Robert Fourer Department of Industrial Engineering and Management Sciences Northwestern University Evanston,

### Sequences with MS

Sequences 2008-2014 with MS 1. [4 marks] Find the value of k if. 2a. [4 marks] The sum of the first 16 terms of an arithmetic sequence is 212 and the fifth term is 8. Find the first term and the common

### Everyday Mathematics CCSS EDITION CCSS EDITION. Content Strand: Number and Numeration

CCSS EDITION Overview of -6 Grade-Level Goals CCSS EDITION Content Strand: Number and Numeration Program Goal: Understand the Meanings, Uses, and Representations of Numbers Content Thread: Rote Counting

### 15.062 Data Mining: Algorithms and Applications Matrix Math Review

.6 Data Mining: Algorithms and Applications Matrix Math Review The purpose of this document is to give a brief review of selected linear algebra concepts that will be useful for the course and to develop

### Lecture 3: Linear Programming Relaxations and Rounding

Lecture 3: Linear Programming Relaxations and Rounding 1 Approximation Algorithms and Linear Relaxations For the time being, suppose we have a minimization problem. Many times, the problem at hand can

### Math 215 HW #6 Solutions

Math 5 HW #6 Solutions Problem 34 Show that x y is orthogonal to x + y if and only if x = y Proof First, suppose x y is orthogonal to x + y Then since x, y = y, x In other words, = x y, x + y = (x y) T

### Linear Algebra Notes for Marsden and Tromba Vector Calculus

Linear Algebra Notes for Marsden and Tromba Vector Calculus n-dimensional Euclidean Space and Matrices Definition of n space As was learned in Math b, a point in Euclidean three space can be thought of

### Figure 2.1: Center of mass of four points.

Chapter 2 Bézier curves are named after their inventor, Dr. Pierre Bézier. Bézier was an engineer with the Renault car company and set out in the early 196 s to develop a curve formulation which would

### The Australian Curriculum Mathematics

The Australian Curriculum Mathematics Mathematics ACARA The Australian Curriculum Number Algebra Number place value Fractions decimals Real numbers Foundation Year Year 1 Year 2 Year 3 Year 4 Year 5 Year

### Solutions to Homework 10

Solutions to Homework 1 Section 7., exercise # 1 (b,d): (b) Compute the value of R f dv, where f(x, y) = y/x and R = [1, 3] [, 4]. Solution: Since f is continuous over R, f is integrable over R. Let x

### PRACTICAL GEOMETRY SYMMETRY AND VISUALISING SOLID SHAPES NCERT

UNIT 12 PRACTICAL GEOMETRY SYMMETRY AND VISUALISING SOLID SHAPES (A) Main Concepts and Results Let a line l and a point P not lying on it be given. By using properties of a transversal and parallel lines,

### Orthogonal Projections

Orthogonal Projections and Reflections (with exercises) by D. Klain Version.. Corrections and comments are welcome! Orthogonal Projections Let X,..., X k be a family of linearly independent (column) vectors

### Higher Education Math Placement

Higher Education Math Placement Placement Assessment Problem Types 1. Whole Numbers, Fractions, and Decimals 1.1 Operations with Whole Numbers Addition with carry Subtraction with borrowing Multiplication

### Math 0980 Chapter Objectives. Chapter 1: Introduction to Algebra: The Integers.

Math 0980 Chapter Objectives Chapter 1: Introduction to Algebra: The Integers. 1. Identify the place value of a digit. 2. Write a number in words or digits. 3. Write positive and negative numbers used

### 1.7 Graphs of Functions

64 Relations and Functions 1.7 Graphs of Functions In Section 1.4 we defined a function as a special type of relation; one in which each x-coordinate was matched with only one y-coordinate. We spent most

### Greater Nanticoke Area School District Math Standards: Grade 6

Greater Nanticoke Area School District Math Standards: Grade 6 Standard 2.1 Numbers, Number Systems and Number Relationships CS2.1.8A. Represent and use numbers in equivalent forms 43. Recognize place

### Geometry and Measurement

The student will be able to: Geometry and Measurement 1. Demonstrate an understanding of the principles of geometry and measurement and operations using measurements Use the US system of measurement for

### 5 =5. Since 5 > 0 Since 4 7 < 0 Since 0 0

a p p e n d i x e ABSOLUTE VALUE ABSOLUTE VALUE E.1 definition. The absolute value or magnitude of a real number a is denoted by a and is defined by { a if a 0 a = a if a

### CAMI Education linked to CAPS: Mathematics

- 1 - TOPIC 1.1 Whole numbers _CAPS curriculum TERM 1 CONTENT Mental calculations Revise: Multiplication of whole numbers to at least 12 12 Ordering and comparing whole numbers Revise prime numbers to

### Maths curriculum information

Maths curriculum information Year 7 Learning outcomes Place value, add and subtract Multiply and divide Geometry Fractions Algebra Percentages & pie charts Topics taught Place value (inc decimals) Add

### 2.3 Convex Constrained Optimization Problems

42 CHAPTER 2. FUNDAMENTAL CONCEPTS IN CONVEX OPTIMIZATION Theorem 15 Let f : R n R and h : R R. Consider g(x) = h(f(x)) for all x R n. The function g is convex if either of the following two conditions