With ratings we judged "Good As L" by the ballot expectation under the lottery L. With rankings we might be tempted to base GAL on expected rank numbers... but that would be a huge mistake ... a repeat of the classic mistake of Borda ... treating rankings as interchangeable with ratings ... thereby losing clone independence. The way that has the best chance of yielding positive results is to mark alternative X as GAL ("Good as L") iff it is more likely that L will pick an alternative ranked below (behind) X than one ranked above (before) X. For each alternative X, let GAL(X) be the percentage of ballots on which X is marked GAL. Then for each ballot beta, let c(beta) be the "chosen" alternative X, among those marked GAL on beta, with the greatest value of GAL(X)*beta(X), where beta(X) is one or 1/2, respectively depending on whether or not X is ranked (Equal)Top. Finally, let L'(X) be the percentage of ballots beta such that c(beta) = X. How about the "weakly better" relation on lotteries in this ranking context? In the ratings context we made use of dot products to define this relation, an expedient that is unavailable in the context of mere rankings... Let's say that L' is weakly better than L as far as ballot beta is concerned if the set of alternatives marked as GAL' on ballot beta contains the set marked as GAL. If this is true for all beta, and strict containment holds for at least one beta, then L' is weakly better than L. In our next message we talk more about entropy.... (TO BE CONT'D) Forest El lun., 4 de oct. de 2021 8:09 p. m., Forest Simmons < forest.simmons21@gmail.com> escribió: > This is the promised continuation that suggests how one can put to good > use the cardinal ratings we defined in our previous message. > > The following steps constitute a way of transforming a non-proportional > lottery L into a proportional one L', given sincere cardinal ratings as > described in our previous message. > > If we are not mistaken, optimal rational strategy for certain methods (as > suggested at the end of this message) that incorporate this transformation > will elicit from rational voters sincere ratings of that kind. > > Let L be the input lottery. On each ballot mark GAL for "Good As L" next > to each alternative whose ballot rating is not less than that ballot's > lottery expectation. > > [If the ballot ratings and lottery probabilities are given in vector form, > then the ballot's lottery expectation is the dot product of the two > vectors.] > > For each alternative X, let GAL(X) be the percentage of ballots on which > X is marked GAL. Then for each ballot beta, let c(beta) be the "chosen" > alternative X, among those marked GAL on beta, with the greatest value of > GAL(X)*beta(X), where beta(X) is ballot beta's rating of X. > > So each ballot beta invests its share of the probability in a candidate > who has support from other ballots (as attested by the factor GAL(X)) and > is also rated relatively high by beta's voter (as attested by the factor > beta(X)). Thus the cooperative viability and the individual voter's > estimate of desirability have equal weight in this product. > > Finally, let L'(X) be the percentage of ballots beta such that c(beta) = X. > > Thus we see how the output lottery L' is determined by the input lottery L. > > If a subset S of the ballots rate only candidate X above zero, then each > of those ballots will choose X, i.e. for each beta in S, the choice c(beta) > will be X, which entails that L'(X) is at least #S/N, where N is the total > number of ballots and #S is the cardinality of the subset S. > > Therefore L' is a fair (proportional) lottery even if L is not. > > Suppose that for every ballot beta, the dot product beta•L is no greater > than the dot product beta•L', and that for at least one ballot beta (L' - > L)•beta>0. Then we can say that L' is at least weakly better than L. > > One method based on this lottery improvement transformation is to > initialize L as the random favorite "benchmark" lottery. Then iterate the > transformation L ---> L' until L' is no longer weakly better than L. At > that point the last improved L is the winning lottery ... i.e. the one used > to pick a single winner, or the one used to apportion seats among parties > in a multi winner party list context ... as the case may be. > > Another way to use this L ---> L' transformation is to solicit nominations > for L from all voters, candidates, and other interested (non-bot) parties. > Then choose by random ballot from among the corresponding L' lotteries that > are tied for minimum entropy. > > In our next message let's see how well we can mimic these results using > only ordinal ballots ... to be continued... > > FWS > > El jue., 30 de sep. de 2021 9:51 p. m., Forest Simmons < > forest.simmons21@gmail.com> escribió: > >> Here are some of my thoughts about determining sincere ratiings with the >> help of sincere rankings ... ratings adequate for use in lottery methods: >> >> We set up a system of equations (to be solved iteratively) whose >> solutions are the desired ratings. >> >> First assign Top and Bottom ranked (or truncated) candidates the >> respective boundary values of 100 and zero percent. >> >> >> Each remaining candidate Y is interior to the ranks, i.e. ranked between >> two neighbors X and Z. We use the lower case variables x, y, and z to >> represent the ratings (whether given or to be determined) of the respective >> candidates X, Y, and Z. >> >> For each interior Y adjust parameters p and q (while keeping p + q = >> 100%) interactively until the user is indifferent between the lotteries p*X >> + q*Z and 100%Y, where X and Z are adjacent to Y in the ranking. >> >> Then set y = p*x + q*z . >> >> Having done this for each interior Y, we now have a system of equations >> >> {y = p*x+q*a | Y is ranked consecutively between X and Z} >> >> which together with the previously mentioned boundary conditions are >> sufficient to uniquely determine the desired ratings. >> >> In fact, an approximate solution set for this system can be obtained by >> initializing all of the interior variables randomly and then iterating the >> set of equations (always respecting boundary conditions) until the >> variables converge (e.g.) to the accuracy of the math coprocessor, ... as >> long as you realize the accuracy of the actual ratings cannot exceed the >> accuracy of the p and q estimates provided by the user ... GIGO. >> >> The main purpose of the above verbiage is to show that there is a >> conceptually rigorous way to define meaningful ratings adequate for use in >> lottery methods without mention of "utilities." >> >> That said, forty plus years of assigning partial credit to student work >> has taught me some useful shortcuts. >> >> A problem that can be solved in n sinificant steps gets fraction k/n >> partial credit if the student successfully completes k steps before getting >> derailed. >> >> Similarly, a candidate gets rating k/n if she meets k out of your n >> equally important criteria. If not equally important, then includes weights. >> >> Sometimes the easiest way to assign partial credit is to ask yourself the >> question, "What is the probability that this student would successfully >> solve a typical problem of this kind on another similar test?" >> >> Similarly, you can ask what is the probability that this candidate would >> faithfully represent your position on issues of importance to you (weighted >> by importance)? >> >> List the candidates in order of these weighted probabilities, then >> subtract the smallest from all of them .... finally divide the resulting >> values by the largest of these. Note, however, that these normalization >> steps form an affine transformation so they are not necessary if your >> lottery method is invariant under affine transformations of the ballot >> ratings ... an indispensable requirement for a decent lottery method. >> >> I promise to show how to use these ratings ballots to make a lottery >> based, but completely deterministic, party list proportional representation >> method. >> >> How can that be? >> >> Here's the trick: the alternatives of the lottery method are the party >> lists themselves. Voters rate the lists rather than the separate candidates >> within the lists. Then the number of candidates contributed by a list is N >> times p, where p is the lottery probability of that list and where N is the >> number of seats to be filled by the election. >> >> If the lottery method is "random favorite party," then you get a basic >> party list method depending on how you round the N*p values to whole >> numbers. Note that this method is absolutely deterministic despite its use >> of lottery language to describe the distribution of winning candidates >> among the various party lists. >> >> But other proportional lottery methods (besides the benchmark >> random-favorite lottery) with significantly lower entropy can lead to less >> fragmentation and more potential for cooperation, without sacrificing >> proportional representation of minority groups. >> >> To be continued ... >> >> FWS >> >

Before the promised continuation .. first a correction on the "weakly better " relation on lotteries in the rankings context... I got the containment backwards ... in general a voter V prefers L' over L if L' gives more probability (than does L) to V's higher ranked alternatives than to V's lower ranked alternatives , which tends to raise the GAL' cutoff above the GAL cutoff ... so the set of alternatives marked GAL will contain the set marked GAL', the reverse of what I mistakenly said in my previous message. El mar., 5 de oct. de 2021 1:12 p. m., Forest Simmons < forest.simmons21@gmail.com> escribió: > With ratings we judged "Good As L" by the ballot expectation under the > lottery L. > > With rankings we might be tempted to base GAL on expected rank numbers... > but that would be a huge mistake ... a repeat of the classic mistake of > Borda ... treating rankings as interchangeable with ratings ... thereby > losing clone independence. > > The way that has the best chance of yielding positive results is to mark > alternative X as GAL ("Good as L") iff it is more likely that L will pick > an alternative ranked below (behind) X than one ranked above (before) X. > > For each alternative X, let GAL(X) be the percentage of ballots on which > X is marked GAL. Then for each ballot beta, let c(beta) be the "chosen" > alternative X, among those marked GAL on beta, with the greatest value of > GAL(X)*beta(X), where beta(X) is one or 1/2, respectively depending on > whether or not X is ranked (Equal)Top. > > Finally, let L'(X) be the percentage of ballots beta such that c(beta) = X. > > How about the "weakly better" relation on lotteries in this ranking > context? > > In the ratings context we made use of dot products to define this > relation, an expedient that is unavailable in the context of mere > rankings... > > Let's say that L' is weakly better than L as far as ballot beta is > concerned if the set of alternatives marked as GAL' on ballot beta contains > the set marked as GAL. > *** CORRECTION ... Should be... the set of alternatives marked as GAL on ballot beta contains the set marked as GAL'. > > If this is true for all beta, and strict containment holds for at least > one beta, then L' is weakly better than L. > > In our next message we talk more about entropy.... (TO BE CONT'D) > > Forest > > El lun., 4 de oct. de 2021 8:09 p. m., Forest Simmons < > forest.simmons21@gmail.com> escribió: > >> This is the promised continuation that suggests how one can put to good >> use the cardinal ratings we defined in our previous message. >> >> The following steps constitute a way of transforming a non-proportional >> lottery L into a proportional one L', given sincere cardinal ratings as >> described in our previous message. >> >> If we are not mistaken, optimal rational strategy for certain methods (as >> suggested at the end of this message) that incorporate this transformation >> will elicit from rational voters sincere ratings of that kind. >> >> Let L be the input lottery. On each ballot mark GAL for "Good As L" next >> to each alternative whose ballot rating is not less than that ballot's >> lottery expectation. >> >> [If the ballot ratings and lottery probabilities are given in vector >> form, then the ballot's lottery expectation is the dot product of the two >> vectors.] >> >> For each alternative X, let GAL(X) be the percentage of ballots on which >> X is marked GAL. Then for each ballot beta, let c(beta) be the "chosen" >> alternative X, among those marked GAL on beta, with the greatest value of >> GAL(X)*beta(X), where beta(X) is ballot beta's rating of X. >> > >> So each ballot beta invests its share of the probability in a candidate >> who has support from other ballots (as attested by the factor GAL(X)) and >> is also rated relatively high by beta's voter (as attested by the factor >> beta(X)). Thus the cooperative viability and the individual voter's >> estimate of desirability have equal weight in this product. >> >> Finally, let L'(X) be the percentage of ballots beta such that c(beta) = >> X. >> >> Thus we see how the output lottery L' is determined by the input lottery >> L. >> >> If a subset S of the ballots rate only candidate X above zero, then each >> of those ballots will choose X, i.e. for each beta in S, the choice c(beta) >> will be X, which entails that L'(X) is at least #S/N, where N is the total >> number of ballots and #S is the cardinality of the subset S. >> >> Therefore L' is a fair (proportional) lottery even if L is not. >> >> Suppose that for every ballot beta, the dot product beta•L is no greater >> than the dot product beta•L', and that for at least one ballot beta (L' - >> L)•beta>0. Then we can say that L' is at least weakly better than L. >> >> One method based on this lottery improvement transformation is to >> initialize L as the random favorite "benchmark" lottery. Then iterate the >> transformation L ---> L' until L' is no longer weakly better than L. At >> that point the last improved L is the winning lottery ... i.e. the one used >> to pick a single winner, or the one used to apportion seats among parties >> in a multi winner party list context ... as the case may be. >> >> Another way to use this L ---> L' transformation is to solicit >> nominations for L from all voters, candidates, and other interested >> (non-bot) parties. Then choose by random ballot from among the >> corresponding L' lotteries that are tied for minimum entropy. >> >> In our next message let's see how well we can mimic these results using >> only ordinal ballots ... to be continued... >> >> FWS >> >> El jue., 30 de sep. de 2021 9:51 p. m., Forest Simmons < >> forest.simmons21@gmail.com> escribió: >> >>> Here are some of my thoughts about determining sincere ratiings with the >>> help of sincere rankings ... ratings adequate for use in lottery methods: >>> >>> We set up a system of equations (to be solved iteratively) whose >>> solutions are the desired ratings. >>> >>> First assign Top and Bottom ranked (or truncated) candidates the >>> respective boundary values of 100 and zero percent. >>> >>> >>> Each remaining candidate Y is interior to the ranks, i.e. ranked between >>> two neighbors X and Z. We use the lower case variables x, y, and z to >>> represent the ratings (whether given or to be determined) of the respective >>> candidates X, Y, and Z. >>> >>> For each interior Y adjust parameters p and q (while keeping p + q = >>> 100%) interactively until the user is indifferent between the lotteries p*X >>> + q*Z and 100%Y, where X and Z are adjacent to Y in the ranking. >>> >>> Then set y = p*x + q*z . >>> >>> Having done this for each interior Y, we now have a system of equations >>> >>> {y = p*x+q*a | Y is ranked consecutively between X and Z} >>> >>> which together with the previously mentioned boundary conditions are >>> sufficient to uniquely determine the desired ratings. >>> >>> In fact, an approximate solution set for this system can be obtained by >>> initializing all of the interior variables randomly and then iterating the >>> set of equations (always respecting boundary conditions) until the >>> variables converge (e.g.) to the accuracy of the math coprocessor, ... as >>> long as you realize the accuracy of the actual ratings cannot exceed the >>> accuracy of the p and q estimates provided by the user ... GIGO. >>> >>> The main purpose of the above verbiage is to show that there is a >>> conceptually rigorous way to define meaningful ratings adequate for use in >>> lottery methods without mention of "utilities." >>> >>> That said, forty plus years of assigning partial credit to student work >>> has taught me some useful shortcuts. >>> >>> A problem that can be solved in n sinificant steps gets fraction k/n >>> partial credit if the student successfully completes k steps before getting >>> derailed. >>> >>> Similarly, a candidate gets rating k/n if she meets k out of your n >>> equally important criteria. If not equally important, then includes weights. >>> >>> Sometimes the easiest way to assign partial credit is to ask yourself >>> the question, "What is the probability that this student would successfully >>> solve a typical problem of this kind on another similar test?" >>> >>> Similarly, you can ask what is the probability that this candidate would >>> faithfully represent your position on issues of importance to you (weighted >>> by importance)? >>> >>> List the candidates in order of these weighted probabilities, then >>> subtract the smallest from all of them .... finally divide the resulting >>> values by the largest of these. Note, however, that these normalization >>> steps form an affine transformation so they are not necessary if your >>> lottery method is invariant under affine transformations of the ballot >>> ratings ... an indispensable requirement for a decent lottery method. >>> >>> I promise to show how to use these ratings ballots to make a lottery >>> based, but completely deterministic, party list proportional representation >>> method. >>> >>> How can that be? >>> >>> Here's the trick: the alternatives of the lottery method are the party >>> lists themselves. Voters rate the lists rather than the separate candidates >>> within the lists. Then the number of candidates contributed by a list is N >>> times p, where p is the lottery probability of that list and where N is the >>> number of seats to be filled by the election. >>> >>> If the lottery method is "random favorite party," then you get a basic >>> party list method depending on how you round the N*p values to whole >>> numbers. Note that this method is absolutely deterministic despite its use >>> of lottery language to describe the distribution of winning candidates >>> among the various party lists. >>> >>> But other proportional lottery methods (besides the benchmark >>> random-favorite lottery) with significantly lower entropy can lead to less >>> fragmentation and more potential for cooperation, without sacrificing >>> proportional representation of minority groups. >>> >>> To be continued ... >>> >>> FWS >>> >>