scholarly journals Bayesian Networks for Decision-Making and Causal Analysis under Uncertainty in Aviation

2019 ◽  
Author(s):  
Rosa Maria Arnaldo Valdés ◽  
V. Fernando Gómez Comendador ◽  
Alvaro Rodriguez Sanz ◽  
Eduardo Sanchez Ayra ◽  
Javier Alberto Pérez Castán ◽  
...  
Keyword(s):  
Decision Making ◽  
Bayesian Networks ◽  
Causal Analysis

scholarly journals Using Bayesian Networks to Assist Decision-Making in Syndromic Surveillance

2016 ◽  
Vol 8 (1) ◽  
Author(s):  
Felipe J. Colón-González ◽  
Iain Lake ◽  
Gary Barker ◽  
Gillian E. Smith ◽  
Alex J. Elliot ◽  
...  
Keyword(s):  
Decision Making ◽  
Bayesian Networks ◽  
Syndromic Surveillance

A decision-making framework to model environmental flow requirements in oasis areas using Bayesian networks

2016 ◽  
Vol 540 ◽  
pp. 1209-1222 ◽  
Author(s):  
Jie Xue ◽  
Dongwei Gui ◽  
Ying Zhao ◽  
Jiaqiang Lei ◽  
Fanjiang Zeng ◽  
...  
Keyword(s):  
Decision Making ◽  
Bayesian Networks ◽  
Environmental Flow ◽  
Decision Making Framework

scholarly journals Behavioral Implications of Causal Misperceptions

2020 ◽  
Vol 12 (1) ◽  
pp. 81-106
Author(s):  
Ran Spiegler
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Bayesian Networks ◽  
Causal Inference ◽  
Graphical Models ◽  
Computational Analysis ◽  
Decision Makers ◽  
Equilibrium Models ◽  
Economic Applications ◽  
Basic Concepts

This review presents an approach to modeling decision making under misspecified subjective models. The approach is based on the idea that decision makers impose subjective causal interpretations on observed correlations, and it borrows basic concepts and tools from the statistics and artificial intelligence literatures on Bayesian networks. While these background literatures used Bayesian networks as a platform for normative and computational analysis of probabilistic and causal inference, in the framework proposed here graphical models represent causal misperceptions and help analyze their behavioral implications. I show how this approach sheds light on earlier equilibrium models with nonrational expectations and demonstrate its scope of economic applications.

PATHWAY REGULATORY ANALYSIS IN THE CONTEXT OF BAYESIAN NETWORKS USING THE COEFFICIENT OF DETERMINATION

2011 ◽  
Vol 19 (04) ◽  
pp. 651-682 ◽  
Author(s):  
CHEN ZHAO ◽  
IVAN IVANOV ◽  
MICHAEL L. BITTNER ◽  
EDWARD R. DOUGHERTY
Keyword(s):  
Decision Making ◽  
Bayesian Networks ◽  
Network Structure ◽  
Signal Transmission ◽  
Coefficient Of Determination ◽  
Tree Model ◽  
Cellular Decision Making ◽  
Sufficient Detail ◽  
Regulatory Analysis

To effectively intervene when cells are trapped in pathological modes of operation it is necessary to build models that capture relevant network structure and include characterization of dynamical changes within the system. The model must be of sufficient detail that it facilitates the selection of intervention points where pathological cell behavior arising from improper regulation can be stopped. What is known about this type of cellular decision-making is consistent with the general expectations associated with any kind of decision-making operation. If the result of a decision at one node is serially transmitted to other nodes, resetting their states, then the process may suffer from mechanistic inefficiencies of transmission or from blockage or activation of transmission through the action of other nodes acting on the same node. A standard signal-processing network model, Bayesian networks, can model these properties. This paper employs a Bayesian tree model to characterize conditional pathway logic and quantify the effects of different branching patterns, signal transmission efficiencies and levels of alternate or redundant inputs. In particular, it characterizes master genes and canalizing genes within the quantitative framework. The model is also used to examine what inferences about the network structure can be made when perturbations are applied to various points in the network.

scholarly journals Causal analysis of customer needs in the banking system by applying fuzzy group decision making

2011 ◽  
Vol 5 (21) ◽  
pp. 8417-8431
Author(s):  
Toloie Eshlaghy Abbas ◽  
Afshar Kazemi Mohammadali ◽  
Radfar Reza ◽  
Abdolmohammadi Nasibe
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Group Decision ◽  
Banking System ◽  
Causal Analysis ◽  
Customer Needs ◽  
Fuzzy Group ◽  
Fuzzy Group Decision Making
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