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Entropy
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Bayesian Statistics and Applied Probability for Games and Decisions
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Bayesian Statistics and Applied Probability for Games and Decisions

A special issue of Entropy (ISSN 1099-4300). This special issue belongs to the section "Information Theory, Probability and Statistics".

Deadline for manuscript submissions: closed (20 August 2022) | Viewed by 18559

Special Issue Editor

Dr. Tevfik Aktekin

E-Mail Website
Guest Editor
Department of Decision Sciences, Paul College of Business and Economics, University of New Hampshire, Durham, NH 03824, USA
Interests: Bayesian methods; stochastic processes; time series analysis; state space models; Markov chain Monte Carlo

Special Issue Information

Dear Colleagues,

The quantification of uncertainty as an aid in decision making has been an essential part of various fields and respective modern applications. As such, it has been a fertile area for multidisciplinary focused research due to the ease of access to computational capabilities. The objective of this Special Issue is to highlight recent advances in the analysis of games and decisions using Bayesian methods of inference or applied probability, more broadly defined. We anticipate these advances to stem mostly from modern computational aspects of decision making under uncertainty spanning across fields such as engineering, sciences, business, economics, and reliability/risk, among others. The Special Issue is designed to be interdisciplinary in nature and welcomes both novel methodological and application-focused research contributions across a myriad of fields. Some examples of areas of interest include (but are not limited to) sequential decision analysis, Bayesian information theory, state space time series analysis, stochastic volatility, Markov chain Monte Carlo methods, particle-based estimation, reliability and risk, multicriteria decision making, Bayesian networks, optimization, simulation-based decision theory, and multistage decision analysis.

Dr. Tevfik Aktekin
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Entropy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Bayesian
  • inference
  • decisions
  • games
  • Markov chain Monte Carlo
  • state space
  • sequential analysis
  • particle filtering
  • networks
  • simulation
  • dynamic systems

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Published Papers (3 papers)

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Research

24 pages, 866 KiB  
Article
Information Architecture for Data Disclosure
by Kurt A. Pflughoeft, Ehsan S. Soofi and Refik Soyer
Entropy 2022, 24(5), 670; https://doi.org/10.3390/e24050670 - 10 May 2022
Viewed by 2793
Abstract
Preserving confidentiality of individuals in data disclosure is a prime concern for public and private organizations. The main challenge in the data disclosure problem is to release data such that misuse by intruders is avoided while providing useful information to legitimate users for [...] Read more.
Preserving confidentiality of individuals in data disclosure is a prime concern for public and private organizations. The main challenge in the data disclosure problem is to release data such that misuse by intruders is avoided while providing useful information to legitimate users for analysis. We propose an information theoretic architecture for the data disclosure problem. The proposed framework consists of developing a maximum entropy (ME) model based on statistical information of the actual data, testing the adequacy of the ME model, producing disclosure data from the ME model and quantifying the discrepancy between the actual and the disclosure data. The architecture can be used both for univariate and multivariate data disclosure. We illustrate the implementation of our approach using financial data. Full article
(This article belongs to the Special Issue Bayesian Statistics and Applied Probability for Games and Decisions)
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22 pages, 4907 KiB  
Article
Smart Home IoT Network Risk Assessment Using Bayesian Networks
by Miguel Flores, Diego Heredia, Roberto Andrade and Mariam Ibrahim
Entropy 2022, 24(5), 668; https://doi.org/10.3390/e24050668 - 10 May 2022
Cited by 8 | Viewed by 2730
Abstract
A risk assessment model for a smart home Internet of Things (IoT) network is implemented using a Bayesian network. The directed acyclic graph of the Bayesian network is constructed from an attack graph that details the paths through which different attacks can occur [...] Read more.
A risk assessment model for a smart home Internet of Things (IoT) network is implemented using a Bayesian network. The directed acyclic graph of the Bayesian network is constructed from an attack graph that details the paths through which different attacks can occur in the IoT network. The parameters of the Bayesian network are estimated with the maximum likelihood method applied to a data set obtained from the simulation of attacks, in five simulation scenarios. For the risk assessment, inferences in the Bayesian network and the impact of the attacks are considered, focusing on DoS attacks, MitM attacks and both at the same time to the devices that allow the automation of the smart home and that are generally the ones that individually have lower levels of security. Full article
(This article belongs to the Special Issue Bayesian Statistics and Applied Probability for Games and Decisions)
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13 pages, 410 KiB  
Article
Chess AI: Competing Paradigms for Machine Intelligence
by Shiva Maharaj, Nick Polson and Alex Turk
Entropy 2022, 24(4), 550; https://doi.org/10.3390/e24040550 - 14 Apr 2022
Cited by 15 | Viewed by 10319
Abstract
Endgame studies have long served as a tool for testing human creativity and intelligence. We find that they can serve as a tool for testing machine ability as well. Two of the leading chess engines, Stockfish and Leela Chess Zero (LCZero), employ significantly [...] Read more.
Endgame studies have long served as a tool for testing human creativity and intelligence. We find that they can serve as a tool for testing machine ability as well. Two of the leading chess engines, Stockfish and Leela Chess Zero (LCZero), employ significantly different methods during play. We use Plaskett’s Puzzle, a famous endgame study from the late 1970s, to compare the two engines. Our experiments show that Stockfish outperforms LCZero on the puzzle. We examine the algorithmic differences between the engines and use our observations as a basis for carefully interpreting the test results. Drawing inspiration from how humans solve chess problems, we ask whether machines can possess a form of imagination. On the theoretical side, we describe how Bellman’s equation may be applied to optimize the probability of winning. To conclude, we discuss the implications of our work on artificial intelligence (AI) and artificial general intelligence (AGI), suggesting possible avenues for future research. Full article
(This article belongs to the Special Issue Bayesian Statistics and Applied Probability for Games and Decisions)
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