Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2610.01997

The efficacy-competitiveness frontier in the Swiss system

László Csató·Balázs R. Sziklai
View original source ↗Read source document ↗Export metadata ↓

Abstract

An ideal tournament is both efficacious and attractive: it selects the best player as the winner, but only at the end of the tournament in order to maintain suspense as long as possible. We identify and explore the trade-off between these criteria in Swiss-system chess team tournaments via simulations. Organising more rounds is shown to improve efficacy but increase the probability of a stakeless last round. The 2008 reform in the Chess Olympiad ranking is found to be a questionable move, as the previous rule based on board points determines the efficacy-competitiveness frontier, regardless of whether two or three points are awarded for winning a match. Combining board points and match points is better than using match points only. Our results can help optimise the design of the Swiss system, which, besides chess, is also widely used in e-sports.

Explore related subjects

physics.soc-phstat.AP

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

László Csató, Balázs R. Sziklai. 2026-10-01. The efficacy-competitiveness frontier in the Swiss system. https://arxiv.org/abs/2610.01997

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

Optimality as a Generative Principle for Network Structure

Most real-world networks have evolved or been engineered to optimize some function, yet a unified framework for studying optimal networks across domains is lacking. We introduce GradNet, an AI-enabled framework that treats network topology as a continuously differentiable object, enabling the design and study of networks that optimize structural and dynamical objectives amenable to automatic differentiation under realistic constraints. We derive general optimality conditions, including an equimarginal principle for linear budgets, that make optimized networks analytically tractable. Canonical network features emerge spontaneously from constrained optimization: maximizing Kuramoto synchronization under coupling budgets yields sparse, bipartite, frequency-disassortative networks; minimizing social tension in opinion dynamics reproduces the factional split in Zachary's karate club; and maximizing communication capacity in spatial quantum networks under distance-dependent costs recovers minimum spanning trees. GradNet thus serves both as a network design tool scalable beyond $10^5$ nodes and as a scientific probe of structure-function relationships.

physics.soc-ph↗
arXiv2026

Community-Centers Identify Robust Biomarker in High-Dimensional, Low-Sample-Size Gene Expression Data

High-dimensional, low-sample-size bulk gene expression data poses a fundamental challenge in transcriptomics, which typically includes tens of thousands of genes but relatively few samples, leading to overfitting and unstable selection for key features as biomarkers. We propose a regression by community centers (RCC) framework tailored for such data. RCC converts gene expression data into a feature proximity network and leverages the phase transition of the giant connected component to identify a critical distance threshold that yields a sparse yet maximally informative network representation, indicated by a minimum normalized shortest compression length. Intuitively, at the criticality, the network balances fragmentation and over-connectivity, allowing meaningful gene-gene associations communities to emerge while filtering noises, such that community-centers capture the most informative and non-redundant signals. These representative genes are then fed to a simple ordinary least squares (OLS) model for downstream predictions. Across five bulk gene expression datasets, RCC displays exceptional prediction performance, clearly outperforming benchmark methods, remains robust to missing data and noises, and does not rely on external biological knowledge. These results suggest that network-based representations provide an effective and general framework for high-dimensional, low-sample-size prediction tasks beyond transcriptomics and illustrate how network-science ideas can support robust learning in data-scarce regimes.

physics.soc-ph↗
arXiv2026

Unbeatable imitation of a friend

Imitation sometimes achieves success in multi-agent situations even though it is very simple. In repeated games, success of imitation has been characterized by unbeatability against other agents. Previous studies specified conditions under which imitation is unbeatable against co-players in repeated symmetric games, and clarified that the existence of unbeatable imitation is strongly related to the existence of payoff-controlling strategies, called zero-determinant strategies. However, these results were on the situations in which one repeated game is played by one group of players. It was pointed out that imitation of other players in the same group and imitation of other players in the same role in other groups generally result in different outcomes. Here, we investigate the existence condition of unbeatable imitation in the latter situations, described by two concurrent repeated games played by two different groups of players. We find that the existence condition of unbeatable imitation is stronger than the existence condition of unbeatable zero-determinant strategies, whereas both are very limited. Furthermore, we also find that adding exploration can make an imitation strategy unbeatable even in games where imitation without exploration can be beaten. Our findings suggest a strong relation between unbeatable imitation and payoff control even in the two concurrent repeated game situations.

physics.soc-ph↗