scholarly journals EDITOR'S INTRODUCTION

2002 ◽  
Vol 6 (1) ◽  
pp. 1-4 ◽  
Author(s):  
Mark Salmon
Keyword(s):  
Transfer Function ◽  
Loss Function ◽  
Decision Rules ◽  
Common View ◽  
Nominal Model ◽  
Economic Decisions ◽  
Misspecified Model ◽  
The Face ◽  
Simple Bounds ◽  
Input Uncertainties

The papers collected in this issue are united in a common view that it is rational to recognize that we have a poor perception of the constraints we face when making economic decisions and hence we employ decision rules that are robust. Robustness can be interpreted in different ways but generally it implies that our decision rules should not depend critically on an exact description of these constraints but they should perform well over a prespecified range of potential variations in the assumed economic environment. So, we are interested in deriving optimal and hence rational decisions where our utility or loss function incorporates the need for robustness in the face of a misspecified model. This misspecification can involve placing simple bounds on deviations from the parameters we assume for a nominal model, or misspecified dynamics, neglected nonlinearities, time variation, or quite general arbitrary misspecification in the transfer function between the input uncertainties and the output variables in which we are ultimately interested.

scholarly journals Developing Scientific and Policy Methods That Support Precautionary Action in the Face of Uncertainty—The Institute of Medicine Committee on Agent Orange

2002 ◽  
Vol 117 (6) ◽  
pp. 534-545 ◽  
Author(s):  
Joel A. Tickner
Keyword(s):  
Vietnam Veterans ◽  
Scientific Information ◽  
Decision Rules ◽  
Agent Orange ◽  
Institute Of Medicine ◽  
Science And Policy ◽  
Institutional Structures ◽  
Precautionary Action ◽  
The Face ◽  
Tools And Methods

To be precautionary, decisions must be made to prevent the impacts of potentially harmful activities even though the nature and magnitude of harm have not been proven scientifically. The Institute of Medicine's Committee on the Health Effects in Vietnam Veterans of Exposures to Herbicides provides a novel example of science and policy structures that support precautionary action in the face of uncertainty. What makes this example unique is the clear set of precautionary decision rules that lowered the standard for evidence, which formed the basis for policy. These rules, established by Congress, strongly influenced the way scientific information was weighed and the subsequent compensation decisions. They encouraged committee members to think outside the confines of their disciplines and develop new tools and methods to fit their unique mandate. The result was a methodology, supported by strong institutional structures, that allowed scientists to discuss the evidence as a whole, reach decisions as a group, and clarify uncertainties.

scholarly journals Rough Set Approach toward Data Modelling and User Knowledge for Extracting Insights

Complexity ◽  
2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Xiaoqun Liao ◽  
Shah Nazir ◽  
Junxin Shen ◽  
Bingliang Shen ◽  
Sulaiman Khan
Keyword(s):  
Transfer Function ◽  
Rough Set ◽  
High Performance ◽  
Performance Metrics ◽  
Decision Rules ◽  
Experimental Setup ◽  
Data Modelling ◽  
Knn Classifier ◽  
User Knowledge ◽  
Local Transfer

Information is considered to be the major part of an organization. With the enhancement of technology, the knowledge level is increasing with the passage of time. This increase of information is in volume, velocity, and variety. Extracting meaningful insights is the dire need of an individual from such information and knowledge. Visualization is a key tool and has become one of the most significant platforms for interpreting, extracting, and communicating information. The current study is an endeavour toward data modelling and user knowledge by using a rough set approach for extracting meaningful insights. The technique has used different rough set algorithms such as K-nearest neighbours (KNN), decision rules (DR), decomposition tree (DT), and local transfer function classifier (LTF-C) for an experimental setup. The approach has found its accuracy for the optimal use of data modelling and user knowledge. The experimental setup of the proposed method is validated by using the dataset available in the UCI web repository. Results of the proposed study show that the model is effective and efficient with an accuracy of 96% for KNN, 87% for decision rules, 91% for decision trees, 85.04% for cross validation architecture, and 94.3% for local transfer function classifier. The validity of the proposed classification algorithms is tested using different performance metrics such as F-score, precision, accuracy, recall, specificity, and misclassification rates. For all these performance metrics, the KNN classifier outperformed, and this high performance shows the applicability of the KNN classifier in the proposed problem.

scholarly journals МОДЕЛЬ І МЕТОД НАВЧАННЯ КЛАСИФІКАТОРА КОНТЕКСТІВ СПОСТЕРЕЖЕННЯ НА ЗОБРАЖЕННЯХ ВІДЕОІНСПЕКЦІЇ СТІЧНИХ ТРУБ

2020 ◽  
pp. 59-66
Author(s):  
В’ячеслав Васильович Москаленко ◽  
Микола Олександрович Зарецький ◽  
Ярослав Юрійович Ковальський ◽  
Сергій Сергійович Мартиненко
Keyword(s):  
Machine Learning ◽  
Loss Function ◽  
Kansas City ◽  
Data Augmentation ◽  
Decision Rules ◽  
Main Idea ◽  
Test Sample ◽  
Sewer Pipe ◽  
Output Layer ◽  
And Training

Video inspection is often used to diagnose sewer pipe defects. To correctly encode founded defects according to existing standards, it is necessary to consider a lot of contextual information about the orientation and location of the camera from sewer pipe video inspection. A model for the classification of context on frames during observations in the video inspection of sewer pipes and a five-stage method of machine learning is proposed. The main idea of the proposed approach is to combine the methods of deep machine learning with the principles of information maximization and coding with self-correcting Hamming codes. The proposed model consists of a deep convolutional neural network with a sigmoid layer followed by the rounding output layer and information-extreme decision rules. The first stages of the method are data augmentation and training of the feature extractor in the Siamese model with softmax triplet loss function. The next steps involve calculating a binary code for each class of recognition that is used as a label in learning with a binary cross-entropy loss function to increase the compactness of the distribution of each class's observations in the Hamming binary space. At the last stage of the training method, it is supposed to optimize the parameters of radial-basis decision rules in the Hamming space for each class according to the existing information-extreme criterion. The information criterion, expressed as a logarithmic function of the accuracy characteristics of the decision rules, provides the maximum generalization and reliability of the model under the most difficult conditions in the statistical sense. The effectiveness of this approach was tested on data provided by Ace Pipe Cleaning (Kansas City, USA) and MPWiK (Wroclaw, Poland) by comparing learning results according to the proposed and traditional models and training schemes. The obtained model of the image frame classifier provides acceptable for practical use classification accuracy on the test sample, which is 96.8 % and exceeds the result of the traditional scheme of training with the softmax output layer by 6.8 %.

scholarly journals Uncertainty drives deviations in normative foraging decision strategies

2021 ◽  
Author(s):  
Zachary P Kilpatrick ◽  
Jacob Davidson ◽  
Ahmed El Hady
Keyword(s):  
Foraging Behavior ◽  
Decision Rules ◽  
Optimal Foraging Theory ◽  
Normative Theory ◽  
Behavioral Experiments ◽  
Patch Type ◽  
Marginal Value ◽  
High Resource ◽  
The Face ◽  
Resource Encounter

Nearly all animals forage, as it is essential to acquire energy for survival through efficient search and resource harvesting. Patch exploitation is a canonical foraging behavior, but a systematic treatment of how animals cope with uncertainty is lacking. To address these shortcomings, we develop a normative theory of patch foraging decisions, proposing mechanisms by which foraging behaviors emerge in the face of uncertainty. Our model foragers statistically and sequentially infer patch resource yields using Bayesian updating based on their resource encounter history. A decision to leave a patch is triggered when the certainty of the patch type or the estimated yield of the patch fall below a threshold. The timescale over which uncertainty in resource availability persists strongly impacts behavioral variables like patch residence times and decision rules determining patch departures. When patch depletion is slow, as in habitat selection, departures are characterized by a reduction of uncertainty, suggesting the forager resides in a low-yielding patch. Uncertainty leads patch-exploiting foragers to overharvest (underharvest) patches with initially low (high) resource yields in comparison to predictions of the marginal value theorem. These results extend optimal foraging theory and motivate a variety of behavioral experiments investigating patch foraging behavior.

Developing a business model and internal reports as a way for advertising businesses to adapt to recession

2021 ◽  
Vol 20 (1) ◽  
pp. 107-123
Author(s):  
Mariya A. VAKHRUSHINA ◽  
Nadezhda E. GIRYA
Keyword(s):  
Business Model ◽  
Business Models ◽  
Advertising Agencies ◽  
Scientific Methods ◽  
Economic Decisions ◽  
The Face ◽  
General Scientific ◽  
Crisis Conditions ◽  
High Level ◽  
Management Reporting

Subject. The advertising business in Russia is characterized by high level of competition. Many areas of advertising activities showed a negative trend even before the start of the COVID-19 pandemic. In such unfavorable conditions, solutions to problems associated with the development of adequate business models of advertising agencies, become especially relevant. Objectives. Our aim is to underpin the need for transforming the business models of advertising agencies as a way of their survival in the face of future recession. Our working hypothesis is that under crisis conditions, organizations that continue to develop their business model tend to survive. Methods. The study employs general scientific methods, like comparison, deduction, analysis, synthesis. Results. We updated the definitions of ‘business model’, ‘optimal business model’, ‘internal reporting’, disclosed relationship between them. Based on foreign and domestic experience of advertising business functioning in conditions of the global financial and economic crisis of 2014–2015 and existing approaches to building business models, we offered an integrated business model. We also developed a form of management consolidated report, which reflects the specifics of advertising agencies. The recommended business model for an integrated group of companies and the adapted form of management consolidated report will provide the top management with integrated information about the total operating activities of agencies and enable adequate economic decisions. Conclusions. To address the threats of the looming crisis, companies should continue to develop their business model and improve in-house management reporting.

Synthetic Aids to Flying Training

1957 ◽  
Vol 61 (560) ◽  
pp. 509-528 ◽  
Author(s):  
W. Makinson ◽  
G. M. Hellings
Keyword(s):  
Transfer Function ◽  
Human Operator ◽  
Control Loop ◽  
Subsequent Performance ◽  
The Face ◽  
Integral Element

The piloted aircraft provides the supreme example of the highly complex machine performing a precise function in which the control loop is still completed through a human operator. Although both physically and mentally his task has been greatly assisted by the introduction of automatic pilots, auto-stabilisers, power controls, flight directors and the like, he still possesses the ultimate advantage over the machine of being able to use judgment in the face of arbitrarily changing circumstances. Thus, until it is possible in advance to define the required tasks exactly, and to measure the subsequent performance of the system in precise and unambiguous terms, the pilot will be saved from the encroachment of automation.The performance of the human operator is described by the aptness and speed of his reaction to the pertinent stimuli, in effect his transfer function as an integral element of the overall control loop.

scholarly journals Багатоетапний метод глибинного навчання з попереднім самонавчанням для класифікаційного аналізу дефектів стічних труб

2021 ◽  
pp. 71-81
Author(s):  
В’ячеслав Васильович Москаленко ◽  
Микола Олександрович Зарецький ◽  
Альона Сергіївна Москаленко ◽  
Артем Геннадійович Коробов ◽  
Ярослав Юрійович Ковальський
Keyword(s):  
Loss Function ◽  
Decision Rules ◽  
Information Criterion ◽  
Error Correcting Codes ◽  
Training Data ◽  
Classification Analysis ◽  
Second Stage ◽  
The Subject ◽  
Center Loss ◽  
Last Stage

A machine learningsemi-supervised method was developed for the classification analysis of defects on the surface of the sewer pipe based on CCTV video inspection images. The aim of the research is the process of defect detection on the surface of sewage pipes. The subject of the research is a machine learning method for the classification analysis of sewage pipe defects on video inspection images under conditions of a limited and unbalanced set of labeled training data. A five-stage algorithm for classifier training is proposed. In the first stage, contrast training occurs using the instance-prototype contrast loss function, where the normalized Euclidean distance is used to measure the similarity of the encoded samples. The second step considers two variants of regularized loss functions – a triplet NCA function and a contrast-center loss function. The regularizing component in the second stage of training is used to penalize the rounding error of the output feature vector to a discrete form and ensures that the principle of information bottlenecking is implemented. The next step is to calculate the binary code of each class to implement error-correcting codes, but considering the structure of the classes and the relationships between their features. The resulting prototype vector of each class is used as a label of image for training using the cross-entropy loss function.  The last stage of training conducts an optimization of the parameters of the decision rules using the information criterion to consider the variance of the class distribution in Hamming binary space. A micro-averaged metric F1, which is calculated on test data, is used to compare learning outcomes at different stages and within different approaches. The results obtained on the Sewer-ML open dataset confirm the suitability of the training method for practical use, with an F1 metric value of 0.977. The proposed method provides a 9 % increase in the value of the micro-averaged F1 metric compared to the results obtained using the traditional method.

Effective productivity management of mixed flower farms

2019 ◽  
Vol 68 (7) ◽  
pp. 1194-1207
Author(s):  
Avital Bechar ◽  
Gad Vitner
Keyword(s):  
Decision Rules ◽  
High Rate ◽  
Process Time ◽  
Cut Flowers ◽  
Work Processes ◽  
Content Type ◽  
Economic Decisions ◽  
Threshold Rate ◽  
Total Process ◽  
Waste Rate

Purpose The purpose of this paper is to investigate the issue of low yields in the packinghouses of green ornamentals and cut flowers due to the high rate of crops waste. Waste may be caused by pests, diseases and extreme weather or environmental conditions that are not under the farmer’s control. Other causes may relate to work processes as follows: irrigation, spraying, harvesting, handling, transportation, sorting, bundling and packaging. Design/methodology/approach The farm under study is a private owned business managed by the owner’s family members with growing area of 22 ha and eight daily workers. The farm produces about 2.5m units (flower stems) per year. The farm represents a typical flower farm in Israel. A costing model and decision rules were developed to identify the critical waste rate that will consider being economic to ship to the market. The model takes into account the production process, the production yield, the operational costs and sales price and calculates the breakeven point. A simulation model was developed to verify the relationships between waste rate to the total process time per stem and flow time. Findings Results show that the critical waste rate for Ruscus, Antirrhinum, Aralia and Aspidistra crops is 16, 74, 22 and 39 percent, respectively. The total process time per harvested stem decreases as the waste rate increases. Originality/value A working model was developed to determine the waste threshold rate and support the farmer in day-to-day economic decisions regarding shipment to the market and effective management of his workers.

TOLERANCE DESIGN BY BREAK-EVEN ANALYSIS FOR REDUCING VARIATION AND COST

1994 ◽  
Vol 01 (04) ◽  
pp. 445-457
Author(s):  
GUANGMING CHEN ◽  
KAILASH C. KAPUR
Keyword(s):  
Transfer Function ◽  
Loss Function ◽  
System Performance ◽  
Total Loss ◽  
Tolerance Design ◽  
Quality Loss ◽  
Design Technique ◽  
Quality Loss Function ◽  
Cost Increase ◽  
The Cost

Tolerance design technique balances the expected quality loss due to variations of the system performance and the cost due to controlling these variations. Measures of quality are discussed and quality loss function is used for tolerance design. The goal is to minimize the total loss that consists of the quality loss to the customer and the cost increase to the producer. The design methodologies are presented for the tolerances of products before shipping to the customer and the tolerances of lower-level characteristics. The approaches to tolerance design for components and subsystems are also demonstrated using the variation transfer function. Examples are given as illustrations of the methodology.

scholarly journals Collective decision-making appears more egalitarian in populations where group fission costs are higher

2019 ◽  
Vol 15 (12) ◽  
pp. 20190556 ◽  
Author(s):  
J. E. Herbert-Read ◽  
A. S. I. Wade ◽  
I. W. Ramnarine ◽  
C. C. Ioannou
Keyword(s):  
Decision Making ◽  
Poecilia Reticulata ◽  
Decision Rules ◽  
Collective Decision ◽  
Collective Decision Making ◽  
Group Fission ◽  
Theoretical Predictions ◽  
The Face ◽  
High Predation ◽  
Moving Groups

Collective decision-making is predicted to be more egalitarian in conditions where the costs of group fission are higher. Here, we ask whether Trinidadian guppies ( Poecilia reticulata ) living in high or low predation environments, and thereby facing differential group fission costs, make collective decisions in line with this prediction. Using a classic decision-making scenario, we found that fish from high predation environments switched their positions within groups more frequently than fish from low predation environments. Because the relative positions individuals adopt in moving groups can influence their contribution towards group decisions, increased positional switching appears to support the prediction of more evenly distributed decision-making in populations where group fission costs are higher. In an agent-based model, we further identified that more frequent, asynchronous updating of individuals' positions could explain increased positional switching, as was observed in fish from high predation environments. Our results are consistent with theoretical predictions about the structure of collective decision-making and the adaptability of social decision-rules in the face of different environmental contexts.

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