Hogyan számszerűsíthető az ösztönzéskompatibilitás? Esettanulmány a sport világából

Minden sportbajnoksággal szemben alapvető elvárás a versenyzők megfelelő ösztönzése. A csalásbiztosságot az irodalom jellemzően bináris fogalomként kezeli, amely akadályozza az ösztönzéskompatibilitás és más kedvező tulajdonságok közötti átváltás feltárását. A cikk a csalás elleni védelem sérülésének számszerűsítésére tesz kísérletet a 2022-es labdarúgó-világbajnokság európai selejtezőjének példáján keresztül. Szimulációval becsüljük meg az eredményes manipuláció valószínűségét, majd megmutatjuk, hogy a csoportkör sorsolásához adott újabb korlátozó feltételek segítségével lényegében megszüntethető a hibás ösztönzés problémája. Ajánlásunk egyszerű, könnyen elfogadható, és nem növeli a szabályok bonyolultságát. Eredményeink révén javítható a sportbajnokságok igazságossága

Quantifying incentive (in)compatibility: A case study from sports

For every sports tournament, it is an important requirement to provide contestants with the appropriate incentives to perform. However, incentive compatibility is usually considered an all or nothing (binary) concept in the academic literature, that is, the rules are proved to be either strategy-proof or vulnerable to manipulation. Our paper aims to present a method for quantifying the violation of this theoretical property through the example of the European Qualifiers for the 2022 FIFA World Cup. Even though that competition is known to be incentive incompatible since lower-ranked Nations League group winners are interested in the success of their higher-ranked peers, the extent of the problem has remained unexplored until now. Computer simulations reveal that the threat of tanking can be substantially mitigated by adding a carefully chosen set of draw restrictions, which offers a justifiable and transparent solution to improve fairness. Sports governing bodies are encouraged to take our findings into account

Trust-based recommendation systems: an axiomatic approach

ABSTRACT High-quality, personalized recommendations are a key feature in many online systems. Since these systems often have explicit knowledge of social network structures, the recommendations may incorporate this information. This paper focuses on networks which represent trust and recommendations which incorporate trust relationships. The goal of a trust-based recommendation system is to generate personalized recommendations from known opinions and trust relationships. In analogy to prior work on voting and ranking systems, we use the axiomatic approach from the theory of social choice. We develop an natural set of five axioms which we desire any recommendation system exhibit. Then we show that no system can simultaneously satisfy all these axioms. We also exhibit systems which satisfy any four of the five axioms. Next we consider ways of weakening the axioms, which can lead to a unique recommendation system based on random walks. We consider other recommendation systems (personal page rank, majority of majorities, and min cut) and search for alternative axiomatizations which uniquely characterize these systems. Finally, we determine which of these systems are incentive compatible. This is an important property for systems deployed in a monetized environment: groups of agents interested in manipulating recommendations to make others share their opinion have nothing to gain from lying about their votes or their trust links

Foundations of mechanism design: a tutorial Part 1- Key concepts and classical results

Mechanism design, an important tool in microeconomics, has found widespread applications in modelling and solving decentralized design problems in many branches of engineering, notably computer science, electronic commerce, and network economics. Mechanism design is concerned with settings where a social planner faces the problem of aggregating the announced preferences of multiple agents into a collective decision when the agents exhibit strategic behaviour. The objective of this paper is to provide a tutorial introduction to the foundations and key results in mechanism design theory. The paper is in two parts. Part 1 focuses on basic concepts and classical results which form the foundation of mechanism design theory. Part 2 presents key advanced concepts and deeper results in mechanism design

Quantifying incentive compatibility of ranking systems

Reasoning about agent preferences on a set of alternatives, and the aggregation of such preferences into some social ranking is a fundamental issue in reasoning about multi-agent systems. When the set of agents and the set of alternatives coincide, we get the ranking systems setting. A famous type of ranking systems are page ranking systems in the context of search engines. Such ranking systems do not exist in empty space, and therefore agents ’ incentives should be carefully considered. In this paper we define three measures for quantifying the incentive compatibility of ranking systems. We apply these measures to several known ranking systems, such as PageRank, and prove tight bounds on the level of incentive compatibility under two basic properties: strong monotonicity and non-imposition. We also introduce two novel non-imposing ranking systems, one general, and the other for the case of systems with three participants. A full axiomatization is provided for the latter.