Parliamentary Voting Procedures: Agenda Control, Manipulation, and Uncertainty

Bredereck, Robert; Niedermeier, Rolf; Walsh, Toby

doi:10.1613/jair.5407

Cited by 3 publications

(3 citation statements)

References 26 publications

(54 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In other election problems, the natural, standard attack is to give integer linear program (ILP) formulations and use Lenstra's algorithm [49]. For instance, this has been applied to winner determination [2], control [36], possible winner [7,17], and lobbying [16] problems. This works because with m candidates there are at most m!…”

Section: Number Of Candidatesmentioning

confidence: 99%

Prices matter for the parameterized complexity of shift bribery

Bredereck

Faliszewski

Nichterlein

et al. 2016

Information and Computation

Self Cite

View full text Add to dashboard Cite

In the SHIFT BRIBERY problem, we are given an election (based on preference orders), a preferred candidate p, and a budget. The goal is to ensure that p wins by shifting p higher in some voters' preference orders. However, each such shift request comes at a price (depending on the voter and on the extent of the shift) and we must not exceed the given budget. We study the parameterized computational complexity of SHIFT BRIBERY with respect to a number of parameters (pertaining to the nature of the solution sought and the size of the election) and several classes of price functions. When we parameterize SHIFT BRIBERY by the number of affected voters, then for each of our voting rules (Borda, Maximin, Copeland) the problem is W[2]-hard. If, instead, we parameterize by the number of positions by which p is shifted in total, then the problem is fixed-parameter tractable for Borda and Maximin, and is W[1]-hard for Copeland. If we parameterize by the budget for the cost of shifting, then the results depend on the price function class. We also show that SHIFT BRIBERY tends to be tractable when parameterized by the number of voters, but that the results for the number of candidates are more enigmatic.

show abstract

Section: Number Of Candidatesmentioning

confidence: 99%

Prices matter for the parameterized complexity of shift bribery

Bredereck

Faliszewski

Nichterlein

et al. 2016

Information and Computation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Given that preference and ranking problems are ubiquitous, computational models for solving such problems can improve prediction and lead to new insights. For example, in voting theory and social choice, Bredereck, Chen, Niedermeier, and Walsh (2017) use computational methods to analyze several parliamentary voting procedures.…”

Section: Behavioral Datamentioning

confidence: 99%

Advances in Machine Learning for the Behavioral Sciences

Kliegr

Bahník

Fürnkranz

2019

American Behavioral Scientist

View full text Add to dashboard Cite

The areas of machine learning and knowledge discovery in databases have considerably matured in recent years. In this article, we briefly review recent developments as well as classical algorithms that stood the test of time. Our goal is to provide a general introduction into different tasks such as learning from tabular data, behavioral data, or textual data, with a particular focus on actual and potential applications in behavioral sciences. The supplemental appendix to the article also provides practical guidance for using the methods by pointing the reader to proven software implementations. The focus is on R, but we also cover some libraries in other programming languages as well as systems with easy-to-use graphical interfaces.

show abstract

“…Fixed-parameter tractability captures the scalability of algorithms to large inputs as long as the problem parameters remain small. Several key problems that arise in AI have been studied in terms of their fixed-parameter tractability, including Planning (Bäckström et al 2012), SAT and CSP (Bessière et al 2008;Gaspers et al 2014), Computational Social Choice (Bredereck et al 2017), Machine Learning (Ganian et al 2018), and Argumentation (Dvorák, Ordyniak, and Szeider 2012).…”

Section: Introductionmentioning

confidence: 99%

Parameterized Complexity of Small Decision Tree Learning

Ordyniak

Szeider

2021

AAAI

View full text Add to dashboard Cite

We study the NP-hard problem of learning a decision tree (DT) of smallest depth or size from data. We provide the first parameterized complexity analysis of the problem and draw a detailed parameterized complexity map for the natural parameters: size or depth of the DT, maximum domain size of all features, and the maximum Hamming distance between any two examples. Our main result shows that learning DTs of smallest depth or size is fixed-parameter tractable (FPT) parameterized by the combination of all three of these parameters. We contrast this FPT-result by various hardness results that underline the algorithmic significance of the considered parameters.

show abstract

Parliamentary Voting Procedures: Agenda Control, Manipulation, and Uncertainty

Cited by 3 publications

References 26 publications

Prices matter for the parameterized complexity of shift bribery

Prices matter for the parameterized complexity of shift bribery

Advances in Machine Learning for the Behavioral Sciences

Parameterized Complexity of Small Decision Tree Learning

Contact Info

Product

Resources

About