Social Choice, Optimal Inference and Figure Skating

We approach the social choice problem as one of optimal statistical inference. If individual voters or judges observe the true order ona set of alternatives with error, then it is possible to use the set of individual rankings to make probability statements about the correct social order. Given the posterior distribution for orders and a suitably chosen loss function, an optimal order is one that minimises expected posterior loss. The paper develops a statistical model describing the behaviour of judges, and discusses Markov Chain Monte Carlo estimation. We also discuss criteria for choosing the appropriate loss functions. We apply our methods to a well-known problem: determining the correct ranking for figure skaters competing at the Olympic Games.Vote aggregation, ranking rules, figure skating, Bayesian methods, optimal inference, Markov Chain Monte Carlo

Statistical Comparison of Aggregation Rules for Votes

If individual voters observe the true ranking on a set of alternatives with error, then the social choice problem, that is, the problem of aggregating their observations, is one of statistical inference. This study develops a statistical methodology that can be used to evaluate the properties of a given or aggregation rule. These techniques are then applied to some well-known rules.Vote aggregation, ranking rules, figure skating, maximum likelihood, optimal inference, Monte Carlo, Kemeny, Borda

Statistical Comparison of Aggregation Rules for Votes

If individual voters observe the true ranking on a set of alternatives with error, then the social choice problem, that is, the problem of aggregating their observations, is one of statistical inference. This study develops a statistical methodology that can be used to evaluate the properties of a given voting or aggregation rule. These techniques are then applied to some well-known rules

Aggregation of Rankings: a Brief Review of Distance-Based Rules

Some researchers have addressed the problem of aggregating individual preferences or rankings by seeking a ranking that is closest to the individual rankings. Their methods differ according to the notion of distance that they use. The best known method of this sort is due to Kemeny. The first part of this paper offers a brief survey of some of these methods. Another way of approaching the aggregation of rankings is as a problem of optimal statistical inference, in which an expected loss is minimised. This approach requires a loss function, a concept closely related the notion of distance between rankings. The second part of this paper examines two classes of parametric functions and proposes one class for the optimal statistical inference problem.Vote aggregation, ranking rules, distance, loss function, maximum likelihood, optimal inference, Kemeny

Does Transparency Reduce Favoritism and Corruption? Evidence from the Reform of Figure Skating Judging

Transparency is usually thought to reduce favoritism and corruption by facilitating monitoring by outsiders, but there is concern it can have the perverse effect of facilitating collusion by insiders. In response to vote trading scandals in the 1998 and 2002 Olympics, the International Skating Union (ISU) introduced a number of changes to its judging system, including obscuring which judge issued which mark. The stated intent was to disrupt collusion by groups of judges, but this change also frustrates most attempts by outsiders to monitor judge behavior. I find that the "compatriot-judge effect", which aggregates favoritism (nationalistic bias from own-country judges) and corruption (vote trading), actually increased slightly after the reforms.

The Calculus of Committee Composition

Modern institutions face the recurring dilemma of designing accurate evaluation procedures in settings as diverse as academic selection committees, social policies, elections, and figure skating competitions. In particular, it is essential to determine both the number of evaluators and the method for combining their judgments. Previous work has focused on the latter issue, uncovering paradoxes that underscore the inherent difficulties. Yet the number of judges is an important consideration that is intimately connected with the methodology and the success of the evaluation. We address the question of the number of judges through a cost analysis that incorporates the accuracy of the evaluation method, the cost per judge, and the cost of an error in decision. We associate the optimal number of judges with the lowest cost and determine the optimal number of judges in several different scenarios. Through analytical and numerical studies, we show how the optimal number depends on the evaluation rule, the accuracy of the judges, the (cost per judge)/(cost per error) ratio. Paradoxically, we find that for a panel of judges of equal accuracy, the optimal panel size may be greater for judges with higher accuracy than for judges with lower accuracy. The development of any evaluation procedure requires knowledge about the accuracy of evaluation methods, the costs of judges, and the costs of errors. By determining the optimal number of judges, we highlight important connections between these quantities and uncover a paradox that we show to be a general feature of evaluation procedures. Ultimately, our work provides policy-makers with a simple and novel method to optimize evaluation procedures

Drawing on a Sculpted Space of Actions: Educating for Expertise while Avoiding a Cognitive Monster

Avoiding a Cognitive MonsterAlthough expertise is usually considered as a positive outcome of education and practice in domains as varied as sports, science, music and politics, there are also concerns about negative effects of expertise. Since expertise is facilitated largely by implicit, automatic cognitive and brain processes, it can also lead to undesirable consequences in the form of stereotypical, discriminatory or inflexible responses. Indeed, such responses can at times even be inconsistent with the explicit and intentional choices of an expert. Explaining this phenomenon, it is argued that an expert’s performance can be considered as selecting in a given situation a preferred option for action from a ‘Sculpted Space of Actions’ (Keestra, 2014), which contains more, more complex and better differentiated action representations than a beginner’s space of actions. Integrating this account with the cognitive neuroscientific theory of Predictive Processing, it is argued how mitigating undesirable effects of expertise depends upon awareness and control of the processes involved. Education should therefore provide for insights in and techniques for controlling the cognitive and brain processes that constitute expertise

The influence of independent local parties on spending: Evidence from Dutch municipalities

Do independent local parties make different decisions on municipal finances compared to their national counterparts? In this paper, we empirically analyze whether independent local parties affect public finances in Dutch municipalities. Using a matching strategy, we compare municipalities that are similar in their observable characteristics except for the presence of an independent local party majority in the municipal council. We provide evidence that shows that municipalities with independent local majorities indeed differ in terms of local spending, specifically they spend more on categories of Local Public Administration, Public Health and Environment and Culture and Recreation which are arguably more local-oriented. We extend our analysis by looking at the local effects of local independent majorities. Using a regression kink design, we find consistent results if we look at the changes that take place once the majority share of the seats in the municipal council has been reached by independent local parties