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{\em Distortion} is a well-established notion for quantifying the loss of social welfare that may occur in voting. As voting rules take as input only ordinal information, they are essentially forced to neglect the exact values the agents…

Computer Science and Game Theory · Computer Science 2024-10-15 Ioannis Caragiannis , Karl Fehrs

Using Kolmogorov Game Derandomization, upper bounds of the Kolmogorov complexity of deterministic winning players against deterministic environments can be proved. This paper gives improved upper bounds of the Kolmogorov complexity of such…

Computational Complexity · Computer Science 2024-05-28 Samuel Epstein

The algorithmic Markov condition states that the most likely causal direction between two random variables X and Y can be identified as that direction with the lowest Kolmogorov complexity. Due to the halting problem, however, this notion…

Machine Learning · Computer Science 2017-02-23 Kailash Budhathoki , Jilles Vreeken

There seems to be an upper limit to predicting the outcome of matches in (semi-)professional sports. Recent work has proposed that this is due to chance and attempts have been made to simulate the distribution of win percentages to identify…

Applications · Statistics 2015-08-21 Albrecht Zimmermann

The assumption that voters' preferences share some common structure is a standard way to circumvent NP-hardness results in social choice problems. While the Kemeny ranking problem is NP-hard in the general case, it is known to become easy…

Discrete Mathematics · Computer Science 2022-06-13 Bruno Escoffier , Olivier Spanjaard , Magdaléna Tydrichová

The Coalitional Manipulation (CM) problem has been studied extensively in the literature for many voting rules. The CM problem, however, has been studied only in the complete information setting, that is, when the manipulators know the…

Artificial Intelligence · Computer Science 2015-05-01 Palash Dey , Neeldhara Misra , Y. Narahari

The Condorcet criterion (CC) is a classical and well-accepted criterion for voting. Unfortunately, it is incompatible with many other desiderata including participation (Par), half-way monotonicity (HM), Maskin monotonicity (MM), and…

Theoretical Economics · Economics 2022-08-22 Lirong Xia

Complexity theory is a useful tool to study computational issues surrounding the elicitation of preferences, as well as the strategic manipulation of elections aggregating together preferences of multiple agents. We study here the…

Artificial Intelligence · Computer Science 2012-04-18 Toby Walsh

In automata theory, while determinisation provides a standard route to solving many common problems in automata theory, some weak forms of nondeterminism can be dealt with in some problems without costly determinisation. For example, the…

Formal Languages and Automata Theory · Computer Science 2026-05-29 Thomas A. Henzinger , Keya Prakash , K. S. Thejaswini

The subcube partition model of computation is at least as powerful as decision trees but no separation between these models was known. We show that there exists a function whose deterministic subcube partition complexity is asymptotically…

Computational Complexity · Computer Science 2016-05-20 Robin Kothari , David Racicot-Desloges , Miklos Santha

We study a ranking and selection problem of learning from choice-based feedback with dynamic assortments. In this problem, a company sequentially displays a set of items to a population of customers and collects their choices as feedback.…

Machine Learning · Computer Science 2025-01-03 Junwen Yang , Yifan Feng

State-of-the-art results in typical classification tasks are mostly achieved by unexplainable machine learning methods, like deep neural networks, for instance. Contrarily, in this paper, we investigate the application of rule learning…

Machine Learning · Computer Science 2024-03-11 Albert Nössig , Tobias Hell , Georg Moser

We study the parameterized complexity of winner determination problems for three prevalent $k$-committee selection rules, namely the minimax approval voting (MAV), the proportional approval voting (PAV), and the Chamberlin-Courant's…

Computer Science and Game Theory · Computer Science 2023-06-13 Yongjie Yang , Jianxin Wang

We study the computational complexity of the popular board game backgammon. We show that deciding whether a player can win from a given board configuration is NP-Hard, PSPACE-Hard, and EXPTIME-Hard under different settings of known and…

Computational Complexity · Computer Science 2021-12-15 R. Teal Witter

We study the computational complexity of an important property of simple, regular and weighted games, which is decisiveness. We show that this concept can naturally be represented in the context of hypergraph theory, and that decisiveness…

Computer Science and Game Theory · Computer Science 2013-07-10 Andreas Polyméris , Fabián Riquelme

In this paper, we take a statistical decision-theoretic viewpoint on social choice, putting a focus on the decision to be made on behalf of a system of agents. In our framework, we are given a statistical ranking model, a decision space,…

Artificial Intelligence · Computer Science 2016-03-15 Hossein Azari Soufiani , David C. Parkes , Lirong Xia

The main idea of the {\em distance rationalizability} approach to view the voters' preferences as an imperfect approximation to some kind of consensus is deeply rooted in social choice literature. It allows one to define ("rationalize")…

Computer Science and Game Theory · Computer Science 2010-09-03 Edith Elkind , Piotr Faliszewski , Arkadii Slinko

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…

General Economics · Economics 2024-06-28 Stuart Baumann , Carl Singleton

There is a striking relationship between a three hundred years old Political Science theorem named "Condorcet's jury theorem" (1785), which states that majorities are more likely to choose correctly when individual votes are often correct…

Machine Learning · Computer Science 2020-02-17 Hanan Shteingart , Eran Marom , Igor Itkin , Gil Shabat , Michael Kolomenkin , Moshe Salhov , Liran Katzir

The existence of adversarial examples points to a basic weakness of deep neural networks. One of the most effective defenses against such examples, adversarial training, entails training models with some degree of robustness, usually at the…

Machine Learning · Computer Science 2023-10-05 Matan Levi , Aryeh Kontorovich