|
on Sports and Economics |
| By: | Stuart Baumann; Carl Singleton |
| Abstract: | Boxing has a long-standing problem with biased judging, impacting both professional and Olympic bouts. ''Robberies'', where boxers are widely seen as being denied rightful victories, threaten to drive fans and athletes away from the sport. To tackle this problem, we propose a minimalist adjustment in how boxing is scored: the winner would be decided by the majority of round-by-round victories according to the judges, rather than relying on the judges' overall bout scores. This approach, rooted in social choice theory and utilising majority rule and middlemost aggregation functions, creates a coordination problem for partisan judges and attenuates their influence. Our model analysis and simulations demonstrate the potential to significantly decrease the likelihood of a partisan judge swaying the result of a bout. |
| Date: | 2024–02 |
| URL: | http://d.repec.org/n?u=RePEc:arx:papers:2402.06594&r=spo |
| By: | Johannes Carow (Johannes Gutenberg University Mainz); Niklas M. Witzig (Johannes Gutenberg University Mainz) |
| Abstract: | We study the impact of time pressure on strategic risk-taking of professional chess players. We propose a novel machine-learning-based measure for the degree of strategic risk of a single chess move and apply this measure to the 2013-2023 FIDE Chess World Cups that allow for plausibly exogenous variation in thinking time. Our results indicate that time pressure leads chess players to opt for more risk-averse moves. We additionally provide correlational evidence for strategic loss aversion, a tendency for risky moves after a mistake/ in a disadvantageous position. This suggests that high-proficiency decision-makers in highstake situations react to time pressure and contextual factors more broadly. We discuss the origins and implication of this finding in our setting. |
| Keywords: | Chess, Risk, Time Pressure, Loss Aversion, Machine Learning |
| JEL: | C26 C45 D91 |
| Date: | 2024–02–22 |
| URL: | http://d.repec.org/n?u=RePEc:jgu:wpaper:2404&r=spo |
| By: | Paolo Viappiani (Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres, LAMSADE - Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision - Université Paris Dauphine-PSL - PSL - Université Paris sciences et lettres - CNRS - Centre National de la Recherche Scientifique, CNRS - Centre National de la Recherche Scientifique) |
| Abstract: | Scoring rules are a popular method for aggregating rankings; they are frequently used in many settings, including social choice, information retrieval and sports. Scoring rules are parametrized by a vector of weights (the scoring vectors), one for each position, and declare as winner the candidate that maximizes the score obtained when summing up the weights corresponding to the position of each voter. It is well known that properly setting the weights is a crucial task, as different candidates can win with different scoring vectors. In this paper, we provide several methods to identify the winner considering all possible weights. We first propose VolumetricTop, a rule that ranks alternatives based on the hyper-polytope representing the set of weights that give the alternative the highest score, and provide a detailed analysis of the rule from the point-of-view of social choice theory. In order to overcome some of its limitations, we then propose two other methods: Volumetric-runoff, a rule that iteratively eliminates the alternative associated with the smallest region until a winner is found, and Volumetric-tournament, where alternatives are matched in pairwise comparisons; we provide several insights about these rules. Finally we provide some test cases of rank aggregation using the proposed methods. |
| Date: | 2024 |
| URL: | http://d.repec.org/n?u=RePEc:hal:journl:hal-04440153&r=spo |