Unbiased tournament selection

Tournament selection is a popular form of selection which is commonly used with genetic algorithms, genetic programming and evolutionary programming. However, tournament selection introduces a sampling bias into the selection process. We review analytic results and present empirical evidence that shows this bias has a significant impact on search performance. We introduce two new forms of unbiased tournament selection that remove or reduce sampling bias in tournament selection. Categories and Subject Descriptor

A Tournament of Judges?

We suggest a Tournament of Judges where the reward to the winner is elevation to the Supreme Court. Politics (and ideology) surely has a role to play in the selection of justices. However, the present level of partisan bickering has resulted in delays in judicial appointments as well as undermined the public\u27s confidence in the objectivity of justices selected through such a process. More significantly, much of the politicking is not transparent, often obscured with statements on a particular candidate\u27s merit - casting a taint on all those who make their way through the judicial nomination process. We argue that the benefits from introducing more (and objective) competition among judges are potentially significant and the likely damage to judicial independence negligible. Among the criteria that could be used are opinion publication rates, citations of opinions by other courts, citations by the Supreme Court, citations by academics, dissent rates, and speed of disposition of cases. Where political motivations drive the selection of an alternative candidate, our proposed system of objective criteria will make it more likely that such motivations are made transparent to the public. Just as important, a judicial tournament for selection to the Supreme Court will serve not only to select effective justices, but also to provide incentives to existing judges to exert effort

The correlation between inbreeding and performance in the Hanoverian sport horse : a thesis presented for the degree Master of Science in Animal Science at Massey University

The following content is not available due to proprietary nature. Please contact the author. "Chapter 12 Appendices and Annova – see CD attached.”The aim of this thesis was to examine the relationship between inbreeding and performance in the Hanoverian Sport Horse. A total of 84,724 hanoverian horses born between the years 1990 and 2009 were used for the study, of which 78,907 had their own performance records. Pedigree records were traced back as far as possible, with a maximum of 37 generations used. There was 100% completeness of pedigree up to the grandparent generation for all horses. The majority of horses (80%) had completeness of pedigree past the sixth generation. Inbreeding were calculated using two methods; the Meuwissen method and the van Raden Method. Both methods gave identical results (100% fit). As aquantitative measure of performance, the Integrated Estimated Breeding Value (iEBV), using both breed and competition results was used. The Evaluation was carried out using the BLUP (Best Linear Unbiased Prediction) Multitrait Repeatability Animal Model. Two different GLM were run with the inbreeding coefficient (IBC) modelled as either a continuous variable or as a fixed class of five differing levels of inbreeding (IBC=0.00; 0<IBC≤0.01: 0.01<IBC≤0.02; 0.02<IBC≤0.05; 0.05<IBC). Age and Sex were included as fixed effects within the model. All subgroups in both dressage and jumping data, with either fixed effect or linear covariate for the IBC, generated a similar result. Due to the large sample size there was a significant (p<0.001) relationship between inbreeding (IBC) and performance (iEBV). In dressage horses there was a significant positive relationship in all categories while in jumping horses there was a significant negative relationship in all catagories. However, the effect of inbreeding on iEBV explained only ±1% of the variance in the models. The models were simultaneously adjusted for the bias of the confounding factor of sex which also accounted for ±1% of the variance. The majority of variance in iEBV is due to the year cohort effect which accounts for ± 95%. The low level of inbreeding (±1.5%) and lack of biological effect on iEBV indicate that inbreeding is not a problem in the Hannoverian horse

Forecasts of relative performance in tournaments: evidence from the field

This paper uses a field experiment to investigate the quality of individuals' forecasts of relative performance in tournaments. We ask players in luck-based (poker) and skill-based (chess) tournaments to make point forecasts of rank. The main finding of the paper is that players' forecasts in both types of tournaments are biased towards overestimation of relative performance. However, the size of the biases found is not as large as the ones often reported in the psychology literature. We also find support for the "unskilled and unaware hypothesis" in chess: high skilled chess players make better forecasts than low skilled chess players. Finally, we find that chess players' forecasts of relative performance are not efficient.Tournaments; Rationality; Field Experiment

Simulation of an Optional Strategy in the Prisoner's Dilemma in Spatial and Non-spatial Environments

This paper presents research comparing the effects of different environments on the outcome of an extended Prisoner's Dilemma, in which agents have the option to abstain from playing the game. We consider three different pure strategies: cooperation, defection and abstinence. We adopt an evolutionary game theoretic approach and consider two different environments: the first which imposes no spatial constraints and the second in which agents are placed on a lattice grid. We analyse the performance of the three strategies as we vary the loner's payoff in both structured and unstructured environments. Furthermore we also present the results of simulations which identify scenarios in which cooperative clusters of agents emerge and persist in both environments.Comment: 12 pages, 8 figures. International Conference on the Simulation of Adaptive Behavio

Fighting Collusion in Tournaments

This paper proposes a new approach of fighting collusion in tournaments which sheds light on the principle of divide and conquer: the principal can benefit from manipulating information revelation, by which he brings asymmetric information between the agents and thus creates a distortion of efficiency in the coalition. We employ a simple tournament setting where, due to perfect collusion, the efficient effort levels are impossible to be implemented through simple mechanisms. We propose a sophisticated mechanism with a biased promotion rule that allows the principal to manipulate the revelation of information and make asymmetric information between the agents, which brings trade-offs between rent-extraction and distortion of efficiency into the coalition. We show that, it is possible to implement efficient effort levels under the sophisticated mechanism. JEL Classification: C72, D82collusion; tournament

Metric-Based Model Selection For Time-Series Forecasting

Metric-based methods, which use unlabeled data to detect gross differences in behavior away from the training points, have recently been introduced for model selection, often yielding very significant improvements over alternatives (including cross-validation). We introduce extensions that take advantage of the particular case of time-series data in which the task involves prediction with a horizon "h". The ideas are (i) to use at "t" the "h" unlabeled examples that precede "t" for model selection, and (ii) take advantage of the different error distributions of cross-validation and the metric methods. Experimental results establish the effectiveness of these extensions in the context of feature subset selection. Les méthodes métriques, et qui utilisent des données non-étiquetées pour détecter les différences brutes pour les comportements loin des pointes d'entrainement, ont été récemment introduites pour la sélection de modèles, apportant une amélioration dans beaucoup de cas (incluant la validation croisée). Nous présentons des prolongements à ces méthodes qui prennent avantage du cas particulier des séries temporelles pour lesquelles la tâche consiste en une prédiction avec un horizon "h". Les idées sont (i) d'utiliser au temps "t" les "h" exemples non-étiquetés qui précèdent "t", et (ii) profiter des différentes distributions d'erreur de validation croisée et de méthodes métriques. Des résultats expérimentaux établissent l'efficacité de ces prolongements dans le contexte de la sélection d'un sous-ensemble de caractéristiques.Unlabeled data, model selection, time-series, Données non-étiquetées, sélection de modèles, séries temporelles