Stochastic modeling and analysis of drivers’ decision making

2007 ◽  
Author(s):  
Shun Taguchi ◽  
Shogo Sekizawa ◽  
Shinkichi Inagaki ◽  
Tatsuya Suzuki ◽  
Soichiro Hayakawa ◽  
...  
Keyword(s):  
Decision Making ◽  
Stochastic Modeling ◽  
Modeling And Analysis

Modified ridge and other regularization criteria: A brief review on meaningful regression models

2021 ◽  
Vol 16 (3) ◽  
pp. 225-227
Author(s):  
Stan Lipovetsky
Keyword(s):  
Decision Making ◽  
Regression Models ◽  
Latent Class ◽  
Marketing Research ◽  
Latent Class Models ◽  
Response Models ◽  
Modeling And Analysis ◽  
Regularization Parameters ◽  
Lp Metric ◽  
Class Models

The work describes a series of techniques designed to obtain regression models resistant to multicollinearity and having some other features needed for meaningful results. These models include enhanced ridge-regressions with several regularization parameters, regressions by data segments and by levels of the dependent variable, latent class models, unitary response, models, orthogonal and equidistant regressions, minimization in Lp-metric, and other criteria and models. All the approaches have been practically implemented in various projects and found useful for decision making in economics, management, marketing research, and other fields requiring data modeling and analysis.

Hierarchical Bayesian Parameter Estimation for Modeling and Analysis of User Affective Influence

2013 ◽  
Author(s):  
Feng Zhou ◽  
Jianxin (Roger) Jiao
Keyword(s):  
Decision Making ◽  
Interior Design ◽  
Computational Models ◽  
Human Perception ◽  
Model Parameters ◽  
Hierarchical Bayesian ◽  
Affective States ◽  
Modeling And Analysis ◽  
Human Decision

Traditional user experience (UX) models are mostly qualitative in terms of its measurement and structure. This paper proposes a quantitative UX model based on cumulative prospect theory. It takes a decision making perspective between two alternative design profiles. However, affective elements are well-known to have influence on human decision making, the prevailing computational models for analyzing and simulating human perception on UX are mainly cognition-based models. In order to incorporate both affective and cognitive factors in the decision making process, we manipulate the parameters involved in the cumulative prospect model to show the affective influence. Specifically, three different affective states are induced to shape the model parameters. A hierarchical Bayesian model with a technique called Markov chain Monte Carlo is used to estimate the parameters. A case study of aircraft cabin interior design is illustrated to show the proposed methodology.

scholarly journals Modeling and Analysis of New Products Diffusion on Heterogeneous Networks

2014 ◽  
Vol 2014 ◽  
pp. 1-12 ◽  
Author(s):  
Shuping Li ◽  
Zhen Jin
Keyword(s):  
Decision Making ◽  
Numerical Analysis ◽  
Power Law ◽  
Heterogeneous Networks ◽  
New Products ◽  
Positive Equilibrium ◽  
Modeling And Analysis ◽  
Scale Free ◽  
Scale Free Networks ◽  
Law Degree

We present a heterogeneous networks model with the awareness stage and the decision-making stage to explain the process of new products diffusion. If mass media is neglected in the decision-making stage, there is a threshold whether the innovation diffusion is successful or not, or else it is proved that the network model has at least one positive equilibrium. For networks with the power-law degree distribution, numerical simulations confirm analytical results, and also at the same time, by numerical analysis of the influence of the network structure and persuasive advertisements on the density of adopters, we give two different products propagation strategies for two classes of nodes in scale-free networks.

Introduction

2015 ◽  
Author(s):  
Habib Ammari ◽  
Elie Bretin ◽  
Josselin Garnier ◽  
Hyeonbae Kang ◽  
Hyundae Lee ◽  
...  
Keyword(s):  
Optimal Control ◽  
Wave Propagation ◽  
Elastic Wave ◽  
Stochastic Modeling ◽  
Physical Parameters ◽  
Vibration Testing ◽  
Elastic Structures ◽  
Modeling And Analysis ◽  
Elastic Inclusions ◽  
Statistical Approaches

This book is about recent mathematical, numerical and statistical approaches for elasticity imaging of inclusions and cracks with waves at zero, single or multiple non-zero frequencies. It considers important developments in asymptotic imaging, stochastic modeling, and analysis of both deterministic and stochastic elastic wave propagation phenomena and puts them together in a coherent way. It gives emphasis on deriving the best possible imaging functionals for small inclusions and cracks in the sense of stability and resolution. For imaging extended elastic inclusions, the book develops accurate optimal control methodologies and examines the effect of uncertainties of the geometric or physical parameters on their stability and resolution properties. It also presents an asymptotic framework for vibration testing and a method for identifying, locating, and estimating inclusions and cracks in elastic structures by measuring their modal characteristics.

The Full Knowledge of Big Data in the Integration of Inter-Organizational Information

Web Services ◽  
2019 ◽  
pp. 803-821
Author(s):  
Thiago Poleto ◽  
Victor Diogho Heuer de Carvalho ◽  
Ana Paula Cabral Seixas Costa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Making Process ◽  
Data Repositories ◽  
Modeling And Analysis ◽  
Incremental Change ◽  
Working Together ◽  
Full Knowledge ◽  
Using Data

Big Data is a radical shift or an incremental change for the existing digital infrastructures, that include the toolset used to aid the decision making process such as information systems, data repositories, formal modeling, and analysis of decisions. This work aims to provide a theoretical approach about the elements necessary to apply the big data concept in the decision making process. It identifies key components of the big data to define an integrated model of decision making using data mining, business intelligence, decision support systems, and organizational learning all working together to provide decision support with a reliable visualization of the decision-related opportunities. The concepts of data integration and semantic also was explored in order to demonstrate that, once mined, data must be integrated, ensuring conceptual connections and bequeathing meaning to use them appropriately for problem solving in decision.

6.5.2 Modeling and Analysis Framework for Risk-Informed Decision Making for FAA NextGen

2013 ◽  
Vol 23 (1) ◽  
pp. 399-411 ◽  
Author(s):  
Mark R. Blackburn ◽  
Art Pyster ◽  
Teresa Zigh ◽  
Richard Turner ◽  
Robin Dillon-Merrill
Keyword(s):  
Decision Making ◽  
Informed Decision ◽  
Informed Decision Making ◽  
Analysis Framework ◽  
Modeling And Analysis

The Development of Distinct Choice Process Segments over Time: A Stochastic Modeling Approach

1982 ◽  
Vol 46 (2) ◽  
pp. 48-59 ◽  
Author(s):  
Donald R. Lehmann ◽  
William L. Moore ◽  
Terry Elrod
Keyword(s):  
Decision Making ◽  
Problem Solving ◽  
Stochastic Modeling ◽  
Information Acquisition ◽  
Response Behavior ◽  
Model Framework ◽  
Individual Level ◽  
Amount Of Information ◽  
The Individual ◽  
Over Time

This paper examines Howard's (1963) typology dividing decision making into extensive problem solving (ESP), limited problem solving (LSP), and routinized response behavior (RRB). Specifically, the amount of information accessed in a longitudinal experiment is studied. Information acquisition is modeled stochastically at the individual level, and the existence of two segments (LSP and RRB) is tested in a nested-model framework.

The Full Knowledge of Big Data in the Integration of Inter-Organizational Information

2017 ◽  
Vol 9 (1) ◽  
pp. 16-31 ◽  
Author(s):  
Thiago Poleto ◽  
Victor Diogho Heuer de Carvalho ◽  
Ana Paula Cabral Seixas Costa
Keyword(s):  
Decision Making ◽  
Big Data ◽  
Decision Support ◽  
Decision Making Process ◽  
Data Repositories ◽  
Modeling And Analysis ◽  
Incremental Change ◽  
Working Together ◽  
Full Knowledge ◽  
Using Data

Big Data is a radical shift or an incremental change for the existing digital infrastructures, that include the toolset used to aid the decision making process such as information systems, data repositories, formal modeling, and analysis of decisions. This work aims to provide a theoretical approach about the elements necessary to apply the big data concept in the decision making process. It identifying key components of the big data to define an integrated model of decision making using data mining, business intelligence, decision support systems, and organizational learning all working together to provide decision support with a reliable visualization of the decision-related opportunities. The concepts of data integration and semantic also was explored in order to demonstrate that, once mined, data must be integrated, ensuring conceptual connections and bequeathing meaning to use them appropriately for problem solving in decision.

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