scholarly journals Computing Bayes-Nash Equilibria in Combinatorial Auctions with Verification

2020 ◽  
Vol 69 ◽  
pp. 531-570
Author(s):  
Vitor Bosshard ◽  
Benedikt Bünz ◽  
Benjamin Lubin ◽  
Sven Seuken

We present a new algorithm for computing pure-strategy ε-Bayes-Nash equilibria (ε-BNEs) in combinatorial auctions. The main innovation of our algorithm is to separate the algorithm’s search phase (for finding the ε-BNE) from the verification phase (for computing the ε). Using this approach, we obtain an algorithm that is both very fast and provides theoretical guarantees on the ε it finds. Our main contribution is a verification method which, surprisingly, allows us to upper bound the ε across the whole continuous value space without making assumptions about the mechanism. Using our algorithm, we can now compute ε-BNEs in multi-minded domains that are significantly more complex than what was previously possible to solve. We release our code under an open-source license to enable researchers to perform algorithmic analyses of auctions, to enable bidders to analyze different strategies, and many other applications.

2021 ◽  
pp. 1-14
Author(s):  
Bruno Yun ◽  
Srdjan Vesic ◽  
Nir Oren

In this paper we describe an argumentation-based representation of normal form games, and demonstrate how argumentation can be used to compute pure strategy Nash equilibria. Our approach builds on Modgil’s Extended Argumentation Frameworks. We demonstrate its correctness, showprove several theoretical properties it satisfies, and outline how it can be used to explain why certain strategies are Nash equilibria to a non-expert human user.


Author(s):  
Wei Hao Khoong

In this paper, we introduce deboost, a Python library devoted to weighted distance ensembling of predictions for regression and classification tasks. Its backbone resides on the scikit-learn library for default models and data preprocessing functions. It offers flexible choices of models for the ensemble as long as they contain the predict method, like the models available from scikit-learn. deboost is released under the MIT open-source license and can be downloaded from the Python Package Index (PyPI) at https://pypi.org/project/deboost. The source scripts are also available on a GitHub repository at https://github.com/weihao94/DEBoost.


2020 ◽  
pp. 3-39
Author(s):  
Bendix Carstensen

This chapter discusses how the best way to learn R is to use it. One should start by using it as a simple calculator, and keep on exploring what one gets back by inspecting the size, shape, and content of what one creates. R is available from CRAN, the Comprehensive R Archive Network. A nice interface to R is RStudio, which is a commercial product, but RStudio has a free open source license that allows one to have a very good and handy interface to R for free, including the possibility of writing reports using Rmarkdown, Sweave, or knitr. The chapter then looks at the two main graphics systems used in R: base graphics, which is an integral part of any R distribution, and ggplot2 (gg referring to grammar of graphics). Data from large epidemiological studies are often summarized in the form of frequency data, which record the frequency of all possible combinations of values of the variables in the study.


2014 ◽  
Vol 16 (03) ◽  
pp. 1450007
Author(s):  
BRANDON LEHR

This paper builds a model of efficiency wages with heterogeneous workers in the economy who differ with respect to their disutility of labor effort. In such an economy, two types of pure strategy symmetric Nash equilibria in firm wage offers can exist: a no-shirking equilibrium in which all workers exert effort while employed and a shirking equilibrium in which within each firm some workers exert effort while others shirk. The type of equilibrium that prevails in the economy depends crucially on the extent of heterogeneity among the workers and the equilibrium rate at which workers join firms from the unemployment pool.


2011 ◽  
Vol 214 (1) ◽  
pp. 91-98 ◽  
Author(s):  
J.M. Díaz-Báñez ◽  
M. Heredia ◽  
B. Pelegrín ◽  
P. Pérez-Lantero ◽  
I. Ventura

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