Modeling the Impact of Travel Information on Activity–Travel Rescheduling Decisions under Conditions of Travel Time Uncertainty

2005 ◽  
Vol 1926 (1) ◽  
pp. 79-87 ◽  
Author(s):  
Zhongwei Sun ◽  
Theo Arentze ◽  
Harry J. P. Timmermans
Keyword(s):  
Travel Time ◽  
Information Gain ◽  
Information Source ◽  
Perceived Value ◽  
Travel Information ◽  
Context Learning ◽  
Expected Information ◽  
Expected Information Gain ◽  
The Individual ◽  
The Impact

This study elaborates on a model system developed in earlier papers to predict the perceived value and use of travel information. The value of travel information is conceptualized as the extent to which the information allows the individual to make better activity–travel scheduling decisions at the beginning of the day and during execution of the schedule. By taking the broader schedule context into account, the model is sensitive to the impact of information and decisions on the full activity–travel pattern. Furthermore, the model includes Bayesian mechanisms to make sure that beliefs about travel times, and other uncertain events, and the credibility of the information source are updated each time information is received and the real travel time is experienced. This paper describes the results of numerical simulations conducted to illustrate the system and to derive theoretical implications from the model. The simulations show that the schedule context, learning, and expected information gain in combination determine the perceived value of information and that none of these factors can be ignored in the derivation of estimates of these values. A theoretical analysis further shows how decision trees can be pruned to reduce a potential problem of combinatorics.

scholarly journals Gaussian Process Based Expected Information Gain Computation for Bayesian Optimal Design

Entropy ◽  
2020 ◽  
Vol 22 (2) ◽  
pp. 258
Author(s):  
Zhihang Xu ◽  
Qifeng Liao
Keyword(s):  
Monte Carlo ◽  
Optimal Design ◽  
Information Gain ◽  
Computational Cost ◽  
Bayesian Optimization ◽  
Likelihood Functions ◽  
Expected Information ◽  
Bayesian Optimal Design ◽  
Bayesian Monte Carlo ◽  
Expected Information Gain

Optimal experimental design (OED) is of great significance in efficient Bayesian inversion. A popular choice of OED methods is based on maximizing the expected information gain (EIG), where expensive likelihood functions are typically involved. To reduce the computational cost, in this work, a novel double-loop Bayesian Monte Carlo (DLBMC) method is developed to efficiently compute the EIG, and a Bayesian optimization (BO) strategy is proposed to obtain its maximizer only using a small number of samples. For Bayesian Monte Carlo posed on uniform and normal distributions, our analysis provides explicit expressions for the mean estimates and the bounds of their variances. The accuracy and the efficiency of our DLBMC and BO based optimal design are validated and demonstrated with numerical experiments.

scholarly journals Visual attention maximizes expected information gain in goal inference

2021 ◽  
Vol 21 (9) ◽  
pp. 2187
Author(s):  
Bohao Shi ◽  
Zhen Li ◽  
Yazhen Peng ◽  
Zhuoxuan Liu ◽  
Jifan Zhou ◽  
...  
Keyword(s):  
Visual Attention ◽  
Information Gain ◽  
Expected Information ◽  
Expected Information Gain ◽  
Goal Inference

scholarly journals The Impact of the Perceived Value of Records on the Use of Electronic Recordkeeping Systems

2021 ◽  
Author(s):  
◽  
Matthew James Lewellen
Keyword(s):  
New Zealand ◽  
Technology Acceptance ◽  
Organizational Context ◽  
Rapid Development ◽  
Perceived Value ◽  
Digital Information ◽  
Research Model ◽  
Recordkeeping System ◽  
The Individual ◽  
The Impact

<p>Today’s electronic documents and digital records are rapidly superseding traditional paper records and similarly need to be managed and stored for the future. This need is driving new theoretical recordkeeping models, international electronic recordkeeping standards, many instances of national recordkeeping legislation, and the rapid development of electronic recordkeeping systems for use in organizations. Given the legislative imperative, the exponential growth of electronic records, and the importance to the individual, organization, and society of trustworthy electronic recordkeeping, the question arises: why are electronic recordkeeping systems experiencing different rates of acceptance and utilization by end users? This research seeks to address that question through identifying the factors that influence a user’s intention to use an electronic recordkeeping system.  Although a significant body of research has been dedicated to studying system use in various situations, no research in the information systems discipline has yet focused specifically on electronic recordkeeping and its unique set of use-influencing factors.  This research creates a new conceptual research model by selecting constructs to represent the technology acceptance literature and adding additional constructs to represent organizational context and knowledge interpretation. It also introduces a new construct: the perceived value of records.  A survey instrument was developed and administered to a sample of public servants from the New Zealand government in order to evaluate the research model quantitatively and determine the relative importance of the factors.  By identifying the factors that impact the use of electronic recordkeeping systems, this research will inform future strategies to improve the capture and retention of our digital heritage. As Archives New Zealand states: “Do nothing, lose everything. If no action is taken, public sector digital information will be lost.”</p>

scholarly journals The Impact of the Perceived Value of Records on the Use of Electronic Recordkeeping Systems

2021 ◽  
Author(s):  
◽  
Matthew James Lewellen
Keyword(s):  
New Zealand ◽  
Technology Acceptance ◽  
Organizational Context ◽  
Rapid Development ◽  
Perceived Value ◽  
Digital Information ◽  
Research Model ◽  
Recordkeeping System ◽  
The Individual ◽  
The Impact

<p>Today’s electronic documents and digital records are rapidly superseding traditional paper records and similarly need to be managed and stored for the future. This need is driving new theoretical recordkeeping models, international electronic recordkeeping standards, many instances of national recordkeeping legislation, and the rapid development of electronic recordkeeping systems for use in organizations. Given the legislative imperative, the exponential growth of electronic records, and the importance to the individual, organization, and society of trustworthy electronic recordkeeping, the question arises: why are electronic recordkeeping systems experiencing different rates of acceptance and utilization by end users? This research seeks to address that question through identifying the factors that influence a user’s intention to use an electronic recordkeeping system.  Although a significant body of research has been dedicated to studying system use in various situations, no research in the information systems discipline has yet focused specifically on electronic recordkeeping and its unique set of use-influencing factors.  This research creates a new conceptual research model by selecting constructs to represent the technology acceptance literature and adding additional constructs to represent organizational context and knowledge interpretation. It also introduces a new construct: the perceived value of records.  A survey instrument was developed and administered to a sample of public servants from the New Zealand government in order to evaluate the research model quantitatively and determine the relative importance of the factors.  By identifying the factors that impact the use of electronic recordkeeping systems, this research will inform future strategies to improve the capture and retention of our digital heritage. As Archives New Zealand states: “Do nothing, lose everything. If no action is taken, public sector digital information will be lost.”</p>

Bayesian Optimal Design of Experiments for Inferring the Statistical Expectation of Expensive Black-Box Functions

2019 ◽  
Vol 141 (10) ◽  
Author(s):  
Piyush Pandita ◽  
Ilias Bilionis ◽  
Jitesh Panchal
Keyword(s):  
Optimal Design ◽  
Design Of Experiments ◽  
Information Gain ◽  
Black Box ◽  
Expected Information ◽  
Bayesian Optimal Design ◽  
Physical Response ◽  
Hypothetical Experiment ◽  
Statistical Expectation ◽  
Expected Information Gain

Abstract Bayesian optimal design of experiments (BODEs) have been successful in acquiring information about a quantity of interest (QoI) which depends on a black-box function. BODE is characterized by sequentially querying the function at specific designs selected by an infill-sampling criterion. However, most current BODE methods operate in specific contexts like optimization, or learning a universal representation of the black-box function. The objective of this paper is to design a BODE for estimating the statistical expectation of a physical response surface. This QoI is omnipresent in uncertainty propagation and design under uncertainty problems. Our hypothesis is that an optimal BODE should be maximizing the expected information gain in the QoI. We represent the information gain from a hypothetical experiment as the Kullback–Liebler (KL) divergence between the prior and the posterior probability distributions of the QoI. The prior distribution of the QoI is conditioned on the observed data, and the posterior distribution of the QoI is conditioned on the observed data and a hypothetical experiment. The main contribution of this paper is the derivation of a semi-analytic mathematical formula for the expected information gain about the statistical expectation of a physical response. The developed BODE is validated on synthetic functions with varying number of input-dimensions. We demonstrate the performance of the methodology on a steel wire manufacturing problem.

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