Double Elimination Tournament Research Articles

Affect and Performance Across Double-Elimination Tournaments: A Field Study of Super Smash Bros Competitors

Affect and Performance Across Double-Elimination Tournaments: A Field Study of Super Smash Bros Competitors

Single- and Double-Elimination Tournaments under Psychological Momentum

Abstract This paper studies the effects of “psychological momentum” on strategic behavior in single- and double-elimination tournaments. We show that in presence of both positive and negative momentum a single-elimination tournament elicits a higher total effort than that of a double-elimination tournament if and only if the positive momentum is insignificant and the negative momentum is significant. Regardless of momentum effects, a single-elimination tournament elicits a higher average effort per match than a double-elimination tournament does.

Fixing balanced knockout and double elimination tournaments

Balanced knockout tournaments are one of the most common formats for sports competitions, and are also used in elections and decision-making. We consider the computational problem of finding the optimal draw for a particular player in such a tournament. The problem has generated considerable research within AI in recent years. We prove that checking whether there exists a draw in which a player wins is NP-complete, thereby settling an outstanding open problem. Our main result has a number of interesting implications on related counting and approximation problems. We present a memoization-based algorithm for the problem that is faster than previous approaches. Moreover, we highlight two natural cases that can be solved in polynomial time. All of our results also hold for the more general problem of counting the number of draws in which a given player is the winner. Finally, we show that our main NP-completeness result extends to a variant of balanced knockout tournaments called double-elimination tournaments.

Elimination Tournament Design under Psychological Momentum

In multi-round elimination tournaments, players experience We re-consider the design of elimination tournament plan, by taking into account of various types of psychological momentum. The results show that if there is negative psychological momentum, either persistent or not, a double-elimination tournament elicits a higher total effort than a single-elimination tournament if and only if the negative psychological shocks from a player's failures in preceding rounds do not affect this player's mental status significantly in the following rounds. Similarly, if positive momentum is non-persistent, a double-elimination tournament elicits a higher total effort if and only if psychological shocks are not significant. However, if positive psychological momentum is persistent, then a double-elimination tournament always dominates a single-elimination tournament.

A double-elimination-tournament-based competitive co-evolutionary artificial neural network classifier

This paper presents a competitive co-evolutionary (ComCoE) that engages a double elimination tournament (DET) to evolve artificial neural networks (ANNs) for undertaking data classification problems. The proposed model performs a global search by a ComCoE approach to find near optimal solutions. During the global search process, two populations of different ANNs compete and fitness evaluation of each ANN is made in a subjective manner based on their participations throughout a DET which promotes competitive interactions among individual ANNs. The adaptation and fitness evaluation processes drive the global search for a more competent ANN classifier. A winning ANN is identified from the global search. Then, the Scaled Conjugate Backpropagation algorithm, which is a local search, is performed to further train the winning ANN to obtain a precise solution. The performance of the proposed classification model is evaluated rigorously; its performance is compared with the baseline ANNs of the proposed model as well as other classifiers. The results indicate that the proposed model could construct an ANN which could produce high classification accuracy rates with a compact network structure.

Prize and incentives in double-elimination tournaments

I examine a game-theoretical model of two variants of double-elimination tournaments, and derive the equilibrium behavior of symmetric players and the optimal prize allocation assuming a designer aims to maximize total effort. I compare these theoretical properties to the well-known single-elimination tournament.

Prize and Incentives in Double-Elimination Tournaments

I examine a game-theoretical model of two variants of double-elimination tournaments, and derive the equilibrium behavior of symmetric players and the optimal prize allocation assuming a designer aims to maximize total effort. I compare these theoretical properties to the well-known single-elimination tournament.

Single- and double-elimination all-pay tournaments

Tournaments consisting of iterative matches are a common mechanism for determining how to allocate a prize. For this reason it is important to understand the behavioral as well as the theoretical properties of different tournament structures. Given that laboratory experiments have consistently found high levels of overbidding in contests, one might suspect that double-elimination tournaments would generate substantially more total investment than single-elimination tournaments despite the two types of tournaments generating theoretically equivalent expected aggregate investment. This paper reports a set of laboratory experiments designed to test this comparison. The results indicate that aggregate investment is similar between the two tournaments, despite behavioral differences in bidding strategies.

Comparison of many-objective evolutionary algorithms using performance metrics ensemble

In this study, we have thoroughly researched on performance of six state-of-the-art Multiobjective Evolutionary Algorithms (MOEAs) under a number of carefully crafted many-objective optimization benchmark problems. Each MOEA apply different method to handle the difficulty of increasing objectives. Performance metrics ensemble exploits a number of performance metrics using double elimination tournament selection and provides a comprehensive measure revealing insights pertaining to specific problem characteristics that each MOEA could perform the best. Experimental results give detailed information for performance of each MOEA to solve many-objective optimization problems. More importantly, it shows that this performance depends on two distinct aspects: the ability of MOEA to address the specific characteristics of the problem and the ability of MOEA to handle high-dimensional objective space.

Performance Metric Ensemble for Multiobjective Evolutionary Algorithms

Evolutionary algorithms have been successfully exploited to solve multiobjective optimization problems. In the literature, a heuristic approach is often taken. For a chosen benchmark problem with specific problem characteristics, the performance of multiobjective evolutionary algorithms (MOEAs) is evaluated via some heuristic chosen performance metrics. The conclusion is then drawn based on statistical findings given the preferable choices of performance metrics. The conclusion, if any, is often indecisive and reveals no insight pertaining to which specific problem characteristics the underlying MOEA could perform the best. In this paper, we introduce an ensemble method to compare MOEAs by combining a number of performance metrics using double elimination tournament selection. The double elimination design allows characteristically poor performance of a quality algorithm to still be able to win it all. Experimental results show that the proposed metric ensemble can provide a more comprehensive comparison among various MOEAs than what could be obtained from a single performance metric alone. The end result is a ranking order among all chosen MOEAs, but not quantifiable measures pertaining to the underlying MOEAs.

Single- and Double-Elimination All-Pay Tournaments

Tournaments consisting of iterative matches are a common mechanism for determining how to allocate a prize. For this reason it is important to understand the behavioral as well as the theoretical properties of different tournament structures. Given that laboratory experiments have consistently found high levels of overbidding in contests, one might suspect that double-elimination tournaments would generate substantially more total investment than single-elimination tournaments despite the two types of tournaments generating theoretically equivalent expected aggregate investment. This paper reports a set of laboratory experiments designed to test this comparison. The results indicate that aggregate investment is similar between the two tournaments, despite behavioral differences in bidding strategies.

The structure, efficacy, and manipulation of double-elimination tournaments

A double-elimination tournament is a competition where no participant is eliminated until they have lost two matches. It is structured as two single-elimination tournaments: the winner bracket and the loser bracket. Players who lose once in the winner bracket are mapped to positions in the loser bracket, according to a mapping called the link function. Surprisingly, although the same structure of the winner and loser brackets is used universally, there is no standard definition of the link function. By investigating several design goals, we show that the functions used in practice are not optimal. We propose a similar function that is optimal with respect to our design goals. In order to demonstrate some of the possible research questions about double-elimination tournaments, we address the manipulability of the outcome of a double-elimination tournament. We show that they are vulnerable to manipulation by a coalition of players who can improve their chance of winning by throwing matches, a phenomenon recently observed in Olympic Badminton. We also discuss the computational complexity of manipulation by a tournament organizer (agenda control) in two settings: by changing the player seeding in the winner bracket, or by picking the mapping of losers to the loser bracket. We provide algorithms, hardness proofs, and we formulate open problems for future research.

A More Fitting Analogy How Does One Aptly Characterize Natural Selection?

Research Article| March 01 2007 A More Fitting Analogy How Does One Aptly Characterize Natural Selection? Douglas Allchin Douglas Allchin Search for other works by this author on: This Site PubMed Google Scholar The American Biology Teacher (2007) 69 (3): 174–176. https://doi.org/10.2307/4452127 Views Icon Views Article contents Figures & tables Video Audio Supplementary Data Peer Review Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Cite Icon Cite Search Site Citation Douglas Allchin; A More Fitting Analogy How Does One Aptly Characterize Natural Selection?. The American Biology Teacher 1 March 2007; 69 (3): 174–176. doi: https://doi.org/10.2307/4452127 Download citation file: Ris (Zotero) Reference Manager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentThe American Biology Teacher Search This content is only available via PDF. Copyright National Association of Biology Teachers Article PDF first page preview Close Modal You do not currently have access to this content.

Double-Elimination Tournaments: Counting and Calculating

Abstract Tournaments are popularly used in sporting events to select a champion. They are also used in experiments where paired comparison procedures are needed. Knockout tournaments are most useful when the number of treatments is too large to use the more well-known round-robin tournament. The mathematical and statistical literature does not address questions such as how one should seed teams in a tournament or which tournament structures are appropriate. For double-elimination (DE) tournaments even basic questions such as how many structures there are remain unanswered. This article addresses some fundamental questions concerning DE tournaments, including the number of DE tournaments and the probabilities of teams winning a DE tournament. Edwards gives many results about single-elimination (SE) tournaments, such as the probability of winning an SE tournament and a notation for labeling and counting them. In this article I develop similar results for the DE tournaments. The results given apply to an arb...

Double-Elimination Tournaments: Counting and Calculating

Double-Elimination Tournaments: Counting and Calculating

An evaluation of three adaptive hearing aid selection strategies.

Paired-comparison judgments of intelligibility of speech in noise were obtained from eight hearing-impaired subjects on a large number of hearing aids simulated by a digital master hearing aid. The hearing aids which comprised a 5 X 5 matrix differed systematically in the amount of low-frequency and high-frequency gain provided. A comparison of three adaptive strategies for determining optimum hearing aid frequency-gain characteristics (an iterative round robin, a double elimination tournament, and a modified simplex procedure) revealed convergence on the same or similar hearing aids for most subjects. Analysis revealed that subjects for whom all three procedures converged on the same hearing aid showed a single pronounced peak in the response surface, while a broader peak was evident for the subjects for whom the three procedures identified similar hearing aids. The modified simplex procedure was found to be most efficient and the iterative round robin least efficient.