scholarly journals Training Intuitive Decision Making in a Simulated Real-World Environment

2012 ◽  
Vol 55 (2) ◽  
pp. 333-345 ◽  
Author(s):  
Robert Earl Patterson ◽  
Byron J. Pierce ◽  
Alan S. Boydstun ◽  
Lisa M. Ramsey ◽  
Jodi Shannan ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Intuitive Decision Making ◽  
World Environment

The Human-IoT Ecosystem

2020 ◽  
pp. 1132-1156
Author(s):  
Vaughan Michell ◽  
James Olweny
Keyword(s):  
Decision Making ◽  
Real World ◽  
Human Perception ◽  
Smart Device ◽  
Context Information ◽  
Situation Theory ◽  
Powerful Approach ◽  
Iot Devices ◽  
World Environment ◽  
Support Decision Making

IoT devices offer a cheap and powerful approach to identifying real world states and situations and acting on this real world environment to change these states and the environment. Augmenting real world things with IoT technology enables the capture of real world context to support decision making and actions in the real world via powerful smart objects in a human- IoT ecosystem. Increasingly we will have to understand the Human-IoT or smart device ecosystem interaction in order to optimise and integrate the design of human and IoT systems. This chapter explores the design and categorisation of IoT devices in terms of their functionality and capability to support context to add to human perception. It then proposes how we can model the context information of both IoT devices and humans in a way that may help progress Human-IoT Ecosystem design using situation theory.

The Human-IoT Ecosystem

2017 ◽  
pp. 223-248 ◽  
Author(s):  
Vaughan Michell ◽  
James Olweny
Keyword(s):  
Decision Making ◽  
Real World ◽  
Human Perception ◽  
Smart Device ◽  
Context Information ◽  
Situation Theory ◽  
Powerful Approach ◽  
Iot Devices ◽  
World Environment ◽  
Support Decision Making

IoT devices offer a cheap and powerful approach to identifying real world states and situations and acting on this real world environment to change these states and the environment. Augmenting real world things with IoT technology enables the capture of real world context to support decision making and actions in the real world via powerful smart objects in a human- IoT ecosystem. Increasingly we will have to understand the Human-IoT or smart device ecosystem interaction in order to optimise and integrate the design of human and IoT systems. This chapter explores the design and categorisation of IoT devices in terms of their functionality and capability to support context to add to human perception. It then proposes how we can model the context information of both IoT devices and humans in a way that may help progress Human-IoT Ecosystem design using situation theory.

Decision-Making

2021 ◽  
pp. 104-117
Author(s):  
Itay Basevitch ◽  
Gershon Tenenbaum
Keyword(s):  
Decision Making ◽  
Coping Strategies ◽  
Real World ◽  
Self Regulation ◽  
Real Life ◽  
Neurophysiological Mechanisms ◽  
Underlying Mechanisms ◽  
And Coping ◽  
World Environment

Decision-making (DM) has been studied from two main perspectives: cognitive and ecological. Findings indicate that experts have advanced DM skills that enhance performance. The underlying mechanisms of DM skills relate to the attention and anticipation capacities to function without interruption under pressure of time and to counter various sources of stress (e.g., self-regulation and coping strategies). There are still many questions that must be addressed to fully account for the DM process and apply the findings in a real-world environment. The most urgent questions relate to the neurophysiological mechanisms underlying DM, team DM processes, training and measuring DM, making creative decisions, and comprehending the process of coaches’ DM during competitive conditions and other real-life situations.

Real-world environment affects the color appearance of virtual stimuli produced by augmented reality

2019 ◽  
Vol 2019 (1) ◽  
pp. 237-242
Author(s):  
Siyuan Chen ◽  
Minchen Wei
Keyword(s):  
Augmented Reality ◽  
Real World ◽  
Mixed Reality ◽  
Rapid Development ◽  
Light Level ◽  
Color Appearance ◽  
Chromatic Adaptation ◽  
Lighting Condition ◽  
The Real ◽  
World Environment

Color appearance models have been extensively studied for characterizing and predicting the perceived color appearance of physical color stimuli under different viewing conditions. These stimuli are either surface colors reflecting illumination or self-luminous emitting radiations. With the rapid development of augmented reality (AR) and mixed reality (MR), it is critically important to understand how the color appearance of the objects that are produced by AR and MR are perceived, especially when these objects are overlaid on the real world. In this study, nine lighting conditions, with different correlated color temperature (CCT) levels and light levels, were created in a real-world environment. Under each lighting condition, human observers adjusted the color appearance of a virtual stimulus, which was overlaid on a real-world luminous environment, until it appeared the whitest. It was found that the CCT and light level of the real-world environment significantly affected the color appearance of the white stimulus, especially when the light level was high. Moreover, a lower degree of chromatic adaptation was found for viewing the virtual stimulus that was overlaid on the real world.

scholarly journals Movie Recommendation through Multiple Bias Analysis

2021 ◽  
Vol 11 (6) ◽  
pp. 2817
Author(s):  
Tae-Gyu Hwang ◽  
Sung Kwon Kim
Keyword(s):  
Decision Making ◽  
Recommender System ◽  
Real World ◽  
Matrix Factorization ◽  
User Preferences ◽  
Improve Performance ◽  
Bias Analysis ◽  
Useful Knowledge ◽  
Existing Problems ◽  
Movie Recommendation

A recommender system (RS) refers to an agent that recommends items that are suitable for users, and it is implemented through collaborative filtering (CF). CF has a limitation in improving the accuracy of recommendations based on matrix factorization (MF). Therefore, a new method is required for analyzing preference patterns, which could not be derived by existing studies. This study aimed at solving the existing problems through bias analysis. By analyzing users’ and items’ biases of user preferences, the bias-based predictor (BBP) was developed and shown to outperform memory-based CF. In this paper, in order to enhance BBP, multiple bias analysis (MBA) was proposed to efficiently reflect the decision-making in real world. The experimental results using movie data revealed that MBA enhanced BBP accuracy, and that the hybrid models outperformed MF and SVD++. Based on this result, MBA is expected to improve performance when used as a system in related studies and provide useful knowledge in any areas that need features that can represent users.

Real‐world evidence to support regulatory decision making: New or expanded medical product indications

2021 ◽  
Author(s):  
Jessica M. Franklin ◽  
Kai‐Li Liaw ◽  
Solomon Iyasu ◽  
Cathy Critchlow ◽  
Nancy Dreyer
Keyword(s):  
Decision Making ◽  
Real World ◽  
Medical Product ◽  
Regulatory Decision ◽  
Real World Evidence

scholarly journals Systematic literature review of machine learning methods used in the analysis of real-world data for patient-provider decision making

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Alan Brnabic ◽  
Lisa M. Hess
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Literature Review ◽  
Systematic Literature Review ◽  
Real World ◽  
Learning Algorithms ◽  
External Validation ◽  
Machine Learning Algorithms ◽  
Learning Methods ◽  
Machine Learning Methods

Abstract Background Machine learning is a broad term encompassing a number of methods that allow the investigator to learn from the data. These methods may permit large real-world databases to be more rapidly translated to applications to inform patient-provider decision making. Methods This systematic literature review was conducted to identify published observational research of employed machine learning to inform decision making at the patient-provider level. The search strategy was implemented and studies meeting eligibility criteria were evaluated by two independent reviewers. Relevant data related to study design, statistical methods and strengths and limitations were identified; study quality was assessed using a modified version of the Luo checklist. Results A total of 34 publications from January 2014 to September 2020 were identified and evaluated for this review. There were diverse methods, statistical packages and approaches used across identified studies. The most common methods included decision tree and random forest approaches. Most studies applied internal validation but only two conducted external validation. Most studies utilized one algorithm, and only eight studies applied multiple machine learning algorithms to the data. Seven items on the Luo checklist failed to be met by more than 50% of published studies. Conclusions A wide variety of approaches, algorithms, statistical software, and validation strategies were employed in the application of machine learning methods to inform patient-provider decision making. There is a need to ensure that multiple machine learning approaches are used, the model selection strategy is clearly defined, and both internal and external validation are necessary to be sure that decisions for patient care are being made with the highest quality evidence. Future work should routinely employ ensemble methods incorporating multiple machine learning algorithms.

Review Summary Generation in Online Systems: Frameworks for Supervised and Unsupervised Scenarios

2021 ◽  
Vol 15 (3) ◽  
pp. 1-33
Author(s):  
Wenjun Jiang ◽  
Jing Chen ◽  
Xiaofei Ding ◽  
Jie Wu ◽  
Jiawei He ◽  
...  
Keyword(s):  
Decision Making ◽  
Real World ◽  
Text Summarization ◽  
Experimental Results ◽  
Product Review ◽  
Comprehensive Review ◽  
Online Systems ◽  
Real World Datasets ◽  
Different Characteristics

In online systems, including e-commerce platforms, many users resort to the reviews or comments generated by previous consumers for decision making, while their time is limited to deal with many reviews. Therefore, a review summary, which contains all important features in user-generated reviews, is expected. In this article, we study “how to generate a comprehensive review summary from a large number of user-generated reviews.” This can be implemented by text summarization, which mainly has two types of extractive and abstractive approaches. Both of these approaches can deal with both supervised and unsupervised scenarios, but the former may generate redundant and incoherent summaries, while the latter can avoid redundancy but usually can only deal with short sequences. Moreover, both approaches may neglect the sentiment information. To address the above issues, we propose comprehensive Review Summary Generation frameworks to deal with the supervised and unsupervised scenarios. We design two different preprocess models of re-ranking and selecting to identify the important sentences while keeping users’ sentiment in the original reviews. These sentences can be further used to generate review summaries with text summarization methods. Experimental results in seven real-world datasets (Idebate, Rotten Tomatoes Amazon, Yelp, and three unlabelled product review datasets in Amazon) demonstrate that our work performs well in review summary generation. Moreover, the re-ranking and selecting models show different characteristics.

scholarly journals Postlaunch evidence-generation studies for medical devices in Spain: the RedETS approach to integrate real-world evidence into decision making

2021 ◽  
Vol 37 (1) ◽  
Author(s):  
Pedro Serrano-Aguilar ◽  
Iñaki Gutierrez-Ibarluzea ◽  
Pilar Díaz ◽  
Iñaki Imaz-Iglesia ◽  
Jesús González-Enríquez ◽  
...  
Keyword(s):  
Decision Making ◽  
Medical Devices ◽  
Real World ◽  
Policy Decision ◽  
Assessment Process ◽  
The European Union ◽  
Political Commitment ◽  
Real World Evidence ◽  
Health Policy Decision ◽  
Regional Health Authorities

Abstract The Monitoring Studies (MS) program, the approach developed by RedETS to generate postlaunch real-world evidence (RWE), is intended to complement and enhance the conventional health technology assessment process to support health policy decision making in Spain, besides informing other interested stakeholders, including clinicians and patients. The MS program is focused on specific uncertainties about the real effect, safety, costs, and routine use of new and insufficiently assessed relevant medical devices carefully selected to ensure the value of the additional research needed, by means of structured, controlled, participative, and transparent procedures. However, despite a clear political commitment and economic support from national and regional health authorities, several difficulties were identified along the development and implementation of the first wave of MS, delaying its execution and final reporting. Resolution of these difficulties at the regional and national levels and a greater collaborative impulse in the European Union, given the availability of an appropriate methodological framework already provided by EUnetHTA, might provide a faster and more efficient comparative RWE of improved quality and reliability at the national and international levels.

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