Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead

In this paper we propose a novel supervised learning approach for training Artificial Neural Networks (ANNs) to evaluate chess positions. The method that we present aims to train different ANN architectures to understand chess positions similarly to how highly rated human players do. We investigate the capabilities that ANNs have when it comes to pattern recognition, an ability that distinguishes chess grandmasters from more amateur players. We collect around 3,000,000 different chess positions played by highly skilled chess players and label them with the evaluation function of Stockfish, one of the strongest existing chess engines. We create 4 different datasets from scratch that are used for different classification and regression experiments. The results show how relatively simple Multilayer Perceptrons (MLPs) outperform Convolutional Neural Networks (CNNs) in all the experiments that we have performed. We also investigate two different board representations, the first one representing if a piece is present on the board or not, and the second one in which we assign a numerical value to the piece according to its strength. Our results show how the latter input representation influences the performances of the ANNs negatively in almost all experiments

Learning to Evaluate Chess Positions with Deep Neural Networks and Limited Lookahead

In this paper we propose a novel supervised learning approach for training Artificial Neural Networks (ANNs) to evaluate chess positions. The method that we present aims to train different ANN architectures to understand chess positions similarly to how highly rated human players do. We investigate the capabilities that ANNs have when it comes to pattern recognition, an ability that distinguishes chess grandmasters from more amateur players. We collect around 3,000,000 different chess positions played by highly skilled chess players and label them with the evaluation function of Stockfish, one of the strongest existing chess engines. We create 4 different datasets from scratch that are used for different classification and regression experiments. The results show how relatively simple Multilayer Perceptrons (MLPs) outperform Convolutional Neural Networks (CNNs) in all the experiments that we have performed. We also investigate two different board representations, the first one representing if a piece is present on the board or not, and the second one in which we assign a numerical value to the piece according to its strength. Our results show how the latter input representation influences the performances of the ANNs negatively in almost all experiments

Essays in the Economics of Tournaments

This dissertation consists of three essays that focus on tournaments, incentives, and performance. The first essay presents evidence of cheating that took place in online examinations during COVID-19 lockdowns and proposes two solutions with and without a camera for the cheating problem based on the experience accumulated by online chess communities over the past two decades. The best implementable solution is a uniform online exam policy where a camera capturing each students computer screen and room is a requirement. The second essay investigates the superstar effect using observations from chess tournaments. Superstars exist in many places. In competition, they might intimidate others, forcing their peers to exert less effort. On the other hand, superstars might encourage others because their participation in a competition encourages everybody to “step up” their game. We analyze direct and indirect effects of a superstar on their peers by analyzing six different chess superstars between 1962-2019. The results suggest that the direct superstar effect is always negative, but the indirect superstar effect depends on the intensity of the superstar. If the skill gap between the superstar and the rest is small, there is a positive peer effect. However, when the skill gap is large, the indirect effect is negative. The third essay examines the impact of expectations on performance. Traditionally, the red corner has been designated for the favorite fighter in fighting sports. The fighter in the red corner could gain a mental edge over their opponent, with their announcement as the favorite before the fight. This essay asks Can corner assignment itself change the odds of wins and losses? Using more than 5,000 fights that took place as part of Ultimate Fighting Championship (UFC) between 1993 and 2020, we identify fighters who were assigned to the red corner, but should have been assigned to the blue corner per their performance record. The results show that underdog fighters who get assigned to the red corner perform significantly better even against stronger opposition, suggesting performance gains with salient expectations