arXiv subjects

Balázs R. Sziklai

Publications and source records attributed to Balázs R. Sziklai.

8 recordsLinked to original sources

arXiv2026

The efficacy-competitiveness frontier in the Swiss system

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.

physics.soc-ph↗

arXiv2025

Impact of AI Tools on Learning Outcomes: Decreasing Knowledge and Over-Reliance

Students at all levels of education are increasingly relying on generative artificial intelligence (AI) tools to complete assignments and achieve higher exam scores. However, it remains unclear how this reliance affects their motivation, their genuine understanding of the material, and the extent to which it substitutes for the process of knowledge acquisition. To investigate the impact of generative AI on learning outcomes, an experiment was conducted at Corvinus University of Budapest. In an operations research class, students were randomly assigned into two groups: one was permitted to use AI tools during classes and examinations, while the other was not. To ensure fairness, a compensation mechanism was introduced: students in the lower-performing group received point adjustments until the average performance of the two groups was equalized. Despite the organizers' best efforts to explain the design and to create equal opportunities for all participants, many students perceived the experiment as a major disruption. Although the experiment was approved by every relevant university authority -- including the Ethics Board, the Head of Department, the Program Director, and the Student Council -- students escalated their concerns to the media and eventually to the State Secretary for Higher Education of Hungary. As a result, the experiment had to be substantially revised before completion: on the final exam the test group was merged with the control group. Still, the data allowed us to draw decisive conclusions regarding the students' learning habits. Uncontrolled use of AI tools leads to disengaged students and low understanding of material. The extreme reactions of the students proved even more revealing than the data collected: generative AI tools have already become indispensable for students, raising fundamental questions about the validity of their learning process.

cs.CY↗

arXiv2025

rSRD: An R package for the Sum of Ranking Differences statistical procedure

Sum of Ranking Differences (SRD) is a relatively novel, non-para-metric statistical procedure that has become increasingly popular recently. SRD compares solutions via a reference by applying a rank transformation on the input and calculating the distance from the reference in $L_1$ norm. Although the computation of the test statistics is simple, validating the results is cumbersome -- at least by hand. There are two validation steps involved. Comparison of Ranks with Random Numbers, which is a permutation-test, and cross-validation combined with statistical testing. Both options impose computational difficulties albeit different ones. The rSRD package was devised to simplify the validation process by reducing both validation steps into single function calls. In addition, the package provides various useful tools including data preprocessing and plotting. The package makes SRD accessible to a wide audience as there are currently no other software options with such a comprehensive toolkit. This paper aims to serve as a guide for practitioners by offering a detailed presentation of the features.

math.ST↗

arXiv2022

Finding Early Adopters of Innovation in Social Network

Social networks play a fundamental role in the diffusion of innovation through peers' influence on adoption. Thus, network position including a wide range of network centrality measures have been used to describe individuals' affinity to adopt an innovation and their ability to propagate diffusion. Yet, social networks are assortative in terms of susceptibility and influence and in terms of network centralities as well. This makes the identification of influencers difficult especially since susceptibility and centrality does not always go hand in hand. Here we propose the Top Candidate algorithm, an expert recommendation method, to rank individuals based on their perceived expertise, which resonates well with the assortative nature of innovators and early adopters. Leveraging adoption data from two online social networks that are assortative in terms of adoption but represent different levels of assortativity of network centralities, we demonstrate that the Top Candidate ranking is more efficient in capturing early adopters than other widely used indices. Top Candidate nodes adopt earlier and have higher reach among innovators, early adopters and early majority than nodes highlighted by other methods. These results suggest that the Top Candidate method can identify good seeds for influence maximization campaigns on social networks.

cs.SI↗

arXiv2022

The efficacy of tournament designs

Tournaments are a widely used mechanism to rank alternatives in a noisy environment. This paper investigates a fundamental issue of economics in tournament design: what is the best usage of limited resources, that is, how should the alternatives be compared pairwise to best approximate their true but latent ranking. We consider various formats including knockout tournaments, multi-stage championships consisting of round-robin groups followed by single elimination, and the Swiss-system. They are evaluated via Monte-Carlo simulations under six different assumptions on winning probabilities. Comparing the same pair of alternatives multiple times turns out to be an inefficacious policy. While seeding can increase the efficacy of the knockout and group-based designs, its influence remains marginal unless one has an unrealistically good estimation on the true ranking of the players. The Swiss-system is found to be the most accurate among all these tournament formats, especially in its ability to rank all participants. A possible explanation is that it does not eliminate a player after a single loss, while it takes the history of the comparisons into account. The results can be especially interesting for emerging esports, where the tournament designs are not yet solidified.

stat.AP↗

arXiv2022

Testing Rankings with Cross-Validation

This research investigates how to determine whether two rankings come from the same distribution. We evaluate three hybrid tests: Wilcoxon's, Dietterich's, and Alpaydin's statistical tests combined with cross-validation (CV), each operating with folds ranging from 5 to 10, thus altogether 18 variants. We have applied these tests in the framework of a popular comparative statistical test, the Sum of Ranking Differences that builds upon the Manhattan distance between the rankings. The introduced methodology is widely applicable from machine learning through social sciences. To compare these methods, we have followed an innovative approach borrowed from Economics. We designed nine scenarios for testing type I and II errors. These represent typical situations (that is, different data structures) that CV tests face routinely. The optimal CV method depends on the preferences regarding the minimization of type I/II errors, size of the input, and expected patterns in the data. The Wilcoxon method with eight folds proved to be the best for all three investigated input sizes. Although the Dietterich and Alpaydin methods are the best in type I situations, they fail badly in type II cases. We demonstrate our results on real-world data, borrowed from chess and chemistry. Overall we cannot recommend either Alpaydin or Dietterich as an alternative to Wilcoxon cross-validation.

stat.ME↗

arXiv2022

Location and audience selection for maximizing social influence

Viral marketing campaigns target primarily those individuals who are central in social networks and hence have social influence. Marketing events, however, may attract diverse audience. Despite the importance of event marketing, the influence of heterogeneous target groups is not well understood yet. In this paper, we define the Spreading Potential (SP) problem in which different sets of agents need to evaluated and compared based on their social influence. A typical application of SP is choosing locations for a series of marketing events. The SP problem is different from the well-known Influence Maximization (IM) problem in two aspects. Firstly, it deals with sets rather than nodes. Secondly, the sets are diverse, composed by a mixture of influential and ordinary agents. Thus, SP needs to assess the contribution of ordinary agents too, while IM only aims to find top spreaders. We provide a systemic test for ranking influence measures in the SP problem based on node sampling and on a novel statistical method, the Sum of Ranking Differences. Using a Linear Threshold diffusion model on an online social network, we evaluate seven network measures of social influence. We demonstrate that the statistical assessment of these influence measures is remarkably different in the SP problem, when low-ranked individuals are present, from the IM problem, when we focus on the algorithm's top choices exclusively.

cs.SI↗

arXiv2019

Modelling transfer profits as externalities in a cooperative game-theoretic model of natural gas networks

Existing cooperative game theoretic studies of bargaining power in gas pipeline systems are based on the so called characteristic function form (CFF). This approach is potentially misleading if some pipelines fall under regulated third party access (TPA). TPA, which is by now the norm in the EU, obliges the owner of a pipeline to transport gas for others, provided they pay a regulated transport fee. From a game theoretic perspective, this institutional setting creates so called "externalities," the description of which requires partition function form (PFF) games. In this paper we propose a method to compute payoffs, reflecting the power structure, for a pipeline system with regulated TPA. The method is based on an iterative flow mechanism to determine gas flows and transport fees for individual players and uses the recursive core and the minimal claim function to convert the PPF game back into a CFF game, which can be solved by standard methods. We illustrate the approach with a simple stylized numerical example of the gas network in Central Eastern Europe with a focus on Ukraine's power index as a major transit country.

econ.TH↗