scholarly journals Learning And Predicting Individual Preferences In Multicriteria Decision Making With Neural Networks Vs. Utility Functions

2016 ◽  
Vol 15 (1) ◽  
pp. 15-20
Author(s):  
Dat-Dao Nguyen
Keyword(s):  
Neural Network ◽  
Decision Making ◽  
Utility Theory ◽  
Individual Preference ◽  
Predictive Ability ◽  
Multicriteria Decision Making ◽  
Universal Function ◽  
Multi Attribute Utility Theory ◽  
Function Approximator ◽  
And Behavior

This paper reports an empirical investigation into the performance of neural network technique vs. traditional utility theory-based method in capturing and predicting individual preference in multi-criteria decision making. As a universal function approximator, a neural network can assess individual utility function without imposing strong assumptions on functional form and behavior of the underlying data.  Results of this study show that in all cases, the predictive ability of neural network technique was comparable to the multi-attribute utility theory-based models. 

scholarly journals Chaos and complexity from quantum neural network. A study with diffusion metric in machine learning

2021 ◽  
Vol 2021 (4) ◽  
Author(s):  
Sayantan Choudhury ◽  
Ankan Dutta ◽  
Debisree Ray
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Quantum Chaos ◽  
Geometric Approach ◽  
Universal Function ◽  
Stochastic Gradient Descent ◽  
Generalization Capability ◽  
Steady State Condition ◽  
Quantum Neural Network ◽  
Function Approximator

Abstract In this work, our prime objective is to study the phenomena of quantum chaos and complexity in the machine learning dynamics of Quantum Neural Network (QNN). A Parameterized Quantum Circuits (PQCs) in the hybrid quantum-classical framework is introduced as a universal function approximator to perform optimization with Stochastic Gradient Descent (SGD). We employ a statistical and differential geometric approach to study the learning theory of QNN. The evolution of parametrized unitary operators is correlated with the trajectory of parameters in the Diffusion metric. We establish the parametrized version of Quantum Complexity and Quantum Chaos in terms of physically relevant quantities, which are not only essential in determining the stability, but also essential in providing a very significant lower bound to the generalization capability of QNN. We explicitly prove that when the system executes limit cycles or oscillations in the phase space, the generalization capability of QNN is maximized. Finally, we have determined the generalization capability bound on the variance of parameters of the QNN in a steady state condition using Cauchy Schwartz Inequality.

scholarly journals An Emergency Decision-Making Method for Urban Rainstorm Water-Logging: A China Study

2018 ◽  
Vol 10 (10) ◽  
pp. 3453 ◽  
Author(s):  
Jiyong Ding ◽  
Juefang Cai ◽  
Guangxiang Guo ◽  
Chen Chen
Keyword(s):  
Decision Making ◽  
Bayesian Analysis ◽  
Emergency Management ◽  
Utility Theory ◽  
Urban Traffic ◽  
Economic Losses ◽  
Water Logging ◽  
Multi Attribute Utility Theory ◽  
Emergency Decision Making ◽  
Emergency Decision

With the rapid development of the urbanization process, rainstorm water-logging events occur more frequently in big cities in China, which causes great impact on urban traffic safety and brings about severe economic losses. Water-logging has become a hot issue of widespread concern in China. As one kind of natural disasters and emergencies, rainstorm water-logging has the uncertainties of occurrence, development, and evolution. Thus, the emergency decision-making in rainstorm water-logging should be carried out in stages according to its development trend, which is very complicated. In this paper, an emergency decision-making method was proposed for urban water-logging with a hybrid use of dynamic network game technology, Bayesian analysis, and multi-attribute utility theory. The dynamic game process between “rainstorm water-logging” and “decision-making group” was established and the dynamic generation of emergency schemes was analyzed based on Bayesian analysis in various stages of water-logging. In terms of decision-making attributes, this paper mainly considered two goals, i.e., ensuring smooth traffic and controlling emergency cost. The multi-attribute utility theory was used to select the final scheme. An example analysis in Guangzhou of China showed that the method is more targeted and can achieve emergency management objectives more effectively when compared with traditional methods. Therefore, it can provide reference for the scientific decision-making of emergency management in urban rainstorm water-logging.

scholarly journals Penerapan Metode Multi Attribute Utility Theory (MAUT) Dalam Pemilihan Rumah Kost

2019 ◽  
Vol 3 (2) ◽  
pp. 243
Author(s):  
Rita Novita Sari ◽  
Ratna Sri Hayati
Keyword(s):  
Decision Making ◽  
Information Technology ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Utility Theory ◽  
Weighted Sums ◽  
Multi Attribute Utility Theory ◽  
Boarding House ◽  
Selection Of

The development of information technology is currently developing rapidly. The use of information technology is very broad in various fields of life. The choice of boarding house is one of the things that is not easy to do. The problem often faced when choosing a boarding house is that boarders find it difficult to get information about boarding houses. Decision support system is a method that helps in making decisions on a particular problem, where no one can make a definite decision. Multi Attribute Utility Theory (MAUT) is a method of decision making. MAUT is a method where looking for weighted sums of the same values in each utility in each attribute. By applying the MAUT method in the selection of boarding houses, it can give good suggestions or recommendations on boarding houses.

Is Additive Utility Function Always a Sufficient Method in the Project Prioritization Process? Bridge Management Perspective

2019 ◽  
Vol 2673 (10) ◽  
pp. 284-294 ◽  
Author(s):  
Onur Kalan ◽  
Abdullah Kurkcu ◽  
Kaan Ozbay
Keyword(s):  
Decision Making ◽  
Utility Theory ◽  
Functional Form ◽  
Utility Functions ◽  
Management Systems ◽  
Decision Making Process ◽  
Bridge Management ◽  
Policy Makers ◽  
Hypothetical Scenario ◽  
Multi Attribute Utility Theory

The prioritization of maintenance activities in bridges has great importance in bridge asset management systems as they are mentioned in MAP-21. One of the most commonly used prioritization methodologies in bridge management systems is multi-attribute utility theory process. In this study, the problem is defined as using the additive functional form in this process without testing additive independence (AI) assumption, which is one of the properties of multi-attribute utility theory. This study aims to emphasize the strength of the use of multiplicative functional forms when the multiplicative form is proven to be more appropriate by AI assumption test. To demonstrate this vital point, mathematical expressions are derived for the feasible regions of indifference curves. Then, the optimum region for both additive and multiplicative approaches are calculated using these analytical expressions to demonstrate the difference between the two relation to maximizing utility. This comparison is aimed at preventing suboptimal decisions because of the use of the additive approach when the multiplicative approach is more representative of the actual decision-making process. The relevance of this claim is also demonstrated using a simple hypothetical scenario. Findings of the paper provide valuable insights to decision makers and policy makers about the importance of choosing the most appropriate functional form for utility functions employed in a prioritization. We hope that policy makers at state departments of transportation will use the comparative analysis of the effect of utility functions on the final project selection process presented in this paper as part of their routine decision-making process.

INTEGRATING VISUALIZATION AND MULTI-ATTRIBUTE UTILITY THEORY FOR ONLINE PRODUCT SELECTION

2007 ◽  
Vol 06 (04) ◽  
pp. 723-750 ◽  
Author(s):  
CHUREE THEETRANONT ◽  
PETER HADDAWY ◽  
DONYAPRUETH KRAIRIT
Keyword(s):  
Decision Making ◽  
Product Space ◽  
Utility Theory ◽  
Visualization Tool ◽  
Product Selection ◽  
Selection Tool ◽  
Number Of Factors ◽  
Multi Attribute Utility Theory ◽  
Search And Selection ◽  
Decision Making Behavior

Effectively selling products online is a challenging task. Today's product domains often contain a dizzying variety of brands and models with highly complex sets of characteristics. This paper addresses the problem of supporting product search and selection in domains containing large numbers of alternatives with complex sets of features. A number of online shopping websites provide product choice assistance by making direct use of Multi-Attribute Utility Theory (MAUT). While the MAUT approach is appealing due to its solid theoretical foundations, there are several reasons that it does not fit well with people's decision making behavior. This paper presents an approach designed to better fit with people's natural decision making process. The system is called VMAP for Visualizing Multi-Attribute Preferences. VMAP provides on one screen both a multi-attribute preference tool (MAP-tool) and a product visualization tool (V-tool). The product visualization tool displays the set of available products, with each product displayed as a point in a 3D attribute space. By viewing the product space, users can gain an overview of the range of available products, as well as an understanding of the relationships between their attributes. The MAP-tool integrates expression of preferences and filter conditions, which are then immediately reflected in the V-tool display. In this way, the user can immediately see the consequences of his expressed preferences on the product space. The VMAP system is evaluated on a number of factors by comparing users' subjective ratings of the system to those of a more traditional MAUT product selection tool. The results show that while VMAP is somewhat more difficult to use than a traditional MAUT product selection tool, it provides better flexibility, provides the ability to more effectively explore the product domain, and produces more confidence in the selected product.

IP Core Based Hardware Implementation of Multi-Layer Perceptrons on FPGAs: A Parallel Approach

2012 ◽  
Vol 433-440 ◽  
pp. 5647-5653 ◽  
Author(s):  
Xiao Jun Li ◽  
Lin Li
Keyword(s):  
Neural Network ◽  
Hardware Implementation ◽  
Research Work ◽  
Universal Function ◽  
Multi Layer Perceptron ◽  
The Mean ◽  
Ip Cores ◽  
Artificial Neural Network Ann ◽  
Function Approximator ◽  
Mean Time

There’re many models derived from the famous bio-inspired artificial neural network (ANN). Among them, multi-layer perceptron (MLP) is widely used as a universal function approximator. With the development of EDA and recent research work, we are able to use rapid and convenient method to generate hardware implementation of MLP on FPGAs through pre-designed IP cores. In the mean time, we focus on achieving the inherent parallelism of neural networks. In this paper, we firstly propose the hardware architecture of modular IP cores. Then, a parallel MLP is devised as an example. At last, some conclusions are made.

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