From Cognitive Theory to Operational Transition: One Program’s Path Across the Valley of Death

2016 ◽  
Vol 60 (1) ◽  
pp. 163-167
Author(s):  
Wayne W Zachary ◽  
Stephen M. Fiore ◽  
Jeffery Morrison ◽  
Josey Wales ◽  
Christopher Wickens
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Cognitive Theory ◽  
Cognitive Engineering ◽  
Practical Application ◽  
End User ◽  
Program Manager ◽  
Human Decision ◽  
Applied Fields ◽  
Valley Of Death

Cognitive engineering and decision support are applied fields that build on cognitive theory and empirical data but ultimately seek to design and build interactive artifacts that improve human decision-making, performance, and use-experiences. However, the path from theory to operational transition has often been very difficult, often ending in failure in a “valley of death” between successful research and successful practical application. This panel presents different perspectives on the trek through this valley – researcher, end-user/operator, program manager, and what-can-we-learn-from-past-failures– set in the issues of current research program making that trip.

Perspectives on Decision Analysis for Decision Support System Design

1993 ◽  
Vol 37 (16) ◽  
pp. 1101-1101 ◽  
Author(s):  
Barbara J. Barnett
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
System Design ◽  
Support System ◽  
Information Organization ◽  
Human Decision ◽  
Tactical Decision ◽  
Decision Making Behavior

This symposium addresses the characterization of human decision making within a complex environment for the purpose of developing improved decision support systems. All of the work presented in this symposium was conducted under a Navy research program entitled “Tactical Decision Making Under Stress” (TADMUS). The overall objective of the TADMUS program is to improve tactical decision making of anti-air warfare (AAW) crew members within the Aegis cruiser's combat information center (CIC) under conditions of stress and uncertainty. The unique aspect of this effort is that each presentation addresses decision making behavior, within a single domain, from a different perspective. The goal of each effort is to characterize some aspect of expert decision making performance within the AAW task environment, and to make recommendations for the resulting decision support system design based upon these characterizations. The result is a multi-faceted, human-centered approach to information organization and interface display design for a decision support system.

OBJECTIFICATION OF FUZZY INFERENCE IN DECISION-MAKING PROBLEMS

2021 ◽  
Author(s):  
L. P. Vershinina ◽  
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support Systems ◽  
Statistical Models ◽  
Fuzzy Inference ◽  
Support Systems ◽  
Practical Application ◽  
Inference Process ◽  
Quality Of Information

The basis of modern decision support systems is not so much analytical and statistical models as the practical application of specialists ‘ knowledge. Such systems are based on fuzzy technologies. The quality of decisions made depends on how accurately the quality of information is reflected in the fuzzy inference process. Ways to improve the objectivity of fuzzy inference at the stages of fuzzification, aggregation, activation, and accumulation are proposed.

scholarly journals The Influence of Cognitive Biases in Production Planning and Control: Considering the Human Factor for the Design of Decision Support Systems

2021 ◽  
Author(s):  
Julia Bendul ◽  
Melanie Zahner
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Production Planning ◽  
Cognitive Biases ◽  
Production Planning And Control ◽  
Due Date ◽  
Planning And Control ◽  
Human Decision ◽  
And Control ◽  
The Impact

Production planning and control (PPC) requires human decision-making in several process steps like production program planning, production data management, and performance measurement. Thereby, human decisions are often biased leading to an aggravation of logistic performance. Exemplary, the lead time syndrome (LTS) shows this connection. While production planners aim to improve due date reliability by updating planned lead times, the result is actually a decreasing due date reliability. In current research in the field of production logistics, the impact of cognitive biases on the decision-making process in production planning and control remains at a silent place. We aim to close this research gap by combining a systematic literature review on behavioral operation management and cognitive biases with a case study from the steel industry to show the influence of cognitive biases on human decision-making in production planning and the impact on logistic performance. The result is the definition of guidelines considering human behavior for the design of decision support systems to improve logistic performance.

scholarly journals Explainable Artificial Intelligence for Human Decision Support System in the Medical Domain

2021 ◽  
Vol 3 (3) ◽  
pp. 740-770
Author(s):  
Samanta Knapič ◽  
Avleen Malhi ◽  
Rohit Saluja ◽  
Kary Främling
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Decision Support ◽  
Medical Image ◽  
Medical Image Analysis ◽  
Image Data ◽  
Data Set ◽  
Explainable Artificial Intelligence ◽  
Human Decision

In this paper, we present the potential of Explainable Artificial Intelligence methods for decision support in medical image analysis scenarios. Using three types of explainable methods applied to the same medical image data set, we aimed to improve the comprehensibility of the decisions provided by the Convolutional Neural Network (CNN). In vivo gastral images obtained by a video capsule endoscopy (VCE) were the subject of visual explanations, with the goal of increasing health professionals’ trust in black-box predictions. We implemented two post hoc interpretable machine learning methods, called Local Interpretable Model-Agnostic Explanations (LIME) and SHapley Additive exPlanations (SHAP), and an alternative explanation approach, the Contextual Importance and Utility (CIU) method. The produced explanations were assessed by human evaluation. We conducted three user studies based on explanations provided by LIME, SHAP and CIU. Users from different non-medical backgrounds carried out a series of tests in a web-based survey setting and stated their experience and understanding of the given explanations. Three user groups (n = 20, 20, 20) with three distinct forms of explanations were quantitatively analyzed. We found that, as hypothesized, the CIU-explainable method performed better than both LIME and SHAP methods in terms of improving support for human decision-making and being more transparent and thus understandable to users. Additionally, CIU outperformed LIME and SHAP by generating explanations more rapidly. Our findings suggest that there are notable differences in human decision-making between various explanation support settings. In line with that, we present three potential explainable methods that, with future improvements in implementation, can be generalized to different medical data sets and can provide effective decision support to medical experts.

The Effects of Pedigree and Source Type on Trust in a Dual Adviser Context

2017 ◽  
Vol 61 (1) ◽  
pp. 319-323 ◽  
Author(s):  
Carl J. Pearson ◽  
Christopher B. Mayhorn
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Aid ◽  
Decision Aids ◽  
Decision Makers ◽  
Influential Factor ◽  
Trust Development ◽  
Conflicting Information ◽  
Source Type ◽  
Human Decision

In a world with increasing ubiquity of automated decision aids, human decision makers often find themselves receiving input from automation and another human simultaneously. Previous research has shown how certain characteristics of a human or automated decision aid affect the development of trust. Little research has investigated how these factors of trust development are involved when more than one adviser is present. This study explored how pedigree (perceived expertise) and source type (human or automated) was related to trust and reliance in a decision-making task with conflicting information from two advisers. Results from this study indicate the pedigree is an influential factor across both human and automated decision aids. This study also found a relationship between trust attitudes and behavioral reliance. These findings are relevant for designing decision support systems that involve multiple advisers or for informing the effects of introducing decision aids in a manner with respect to decision aid pedigree.

The Mythical Decision Maker

2011 ◽  
pp. 653-660 ◽  
Author(s):  
Csaba Csáki
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Maker ◽  
Support Systems ◽  
Historical Context ◽  
Decision Makers ◽  
Organizational Decision Making ◽  
Theoretical Investigations ◽  
Human Decision ◽  
History Of

During the history of decision support systems (DSSs)— in fact, during the history of theoretical investigations of human decision-making situations—the decision maker (DM) has been the centre of attention who considers options and makes a choice. However, the notion and definitions of this decision maker, as well as the various roles surrounding his or her activity, have changed depending on both time and scientific areas. Reading the DSS literature, one might encounter references to such players as decision makers, problem owners, stakeholders, facilitators, developers, users, project champions, and supporters, and the list goes on. Who are these players, what is their role, and where do these terms come from? This article presents a review in historical context of some key interpretations aimed at identifying the various roles that actors may assume in an organizational decision-making situation.

scholarly journals Decision Aiding to Overcome Biases in Object Identification

2012 ◽  
Vol 2012 ◽  
pp. 1-7 ◽  
Author(s):  
Mary Fendley ◽  
S. Narayanan
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Decision Support System ◽  
Support System ◽  
Cognitive Biases ◽  
Target Identification ◽  
Object Identification ◽  
Identification Accuracy ◽  
Human Decision ◽  
The Impact

Human decision makers typically use heuristics under time-pressured situations. These heuristics can potentially degrade task performance through the impact of their associated biases. Using object identification in image analysis as the context, this paper identifies cognitive biases that play a role in decision making. We propose a decision support system to help overcome these biases in this context. Results show that the decision support system improved human decision making in object identification, including metrics such as time taken to identify targets in an image set, accuracy of target identification, accuracy of target classification, and quantity of false positive identification.

scholarly journals Risk-Based Decision Support Model for Offshore Installations

2018 ◽  
Vol 9 (2) ◽  
pp. 55-68 ◽  
Author(s):  
Gencer Erdogan ◽  
Atle Refsdal ◽  
Bjørn Nygård ◽  
Ole Petter Rosland ◽  
Bernt Kvam Randeberg
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Multi Criteria Decision Making ◽  
End User ◽  
Decision Support Model ◽  
Development Method ◽  
Domain Experts ◽  
Offshore Installations ◽  
Group Methods ◽  
Support Model

Abstract Background: During major maintenance projects on offshore installations, flotels are often used to accommodate the personnel. A gangway connects the flotel to the installation. If the offshore conditions are unfavorable, the responsible operatives need to decide whether to lift (disconnect) the gangway from the installation. If this is not done, there is a risk that an uncontrolled autolift (disconnection) occurs, causing harm to personnel and equipment. Objectives: We present a decision support model, developed using the DEXi tool for multi-criteria decision making, which produces advice on whether to disconnect/connect the gangway from/to the installation. Moreover, we report on our development method and experiences from the process, including the efforts invested. An evaluation of the resulting model is also offered, primarily based on feedback from a small group of offshore operatives and domain experts representing the end user target group. Methods/Approach: The decision support model was developed systematically in four steps: establish context, develop the model, tune the model, and collect feedback on the model. Results: The results indicate that the decision support model provides advice that corresponds with expert expectations, captures all aspects that are important for the assessment, is comprehensible to domain experts, and that the expected benefit justifies the effort for developing the model. Conclusions: We find the results promising, and believe that the approach can be fruitful in a wider range of risk-based decision support scenarios. Moreover, this paper can help other decision support developers decide whether a similar approach can suit them

Intelligent Management Information System

2010 ◽  
pp. 329-356
Author(s):  
Zude Zhou ◽  
Huaiqing Wang ◽  
Ping Lou
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Information System ◽  
Decision Support ◽  
Management Information System ◽  
Management Information ◽  
Intelligent Decision ◽  
Development Direction ◽  
Human Decision ◽  
Intelligent Management

The Intelligent Management Information System (IMIS) has the potential to transform human decision making by combining research in Artificial Intelligence, Information Technology, and Systems Engineering. The field of Intelligent Decision Making (IDM) is expanding rapidly, due in part to advances in artificial intelligence and networkcentric environments that can deliver the technology. Communication and coordination between dispersed systems can deliver just-in-time information, real-time processing, collaborative environments, and globally up-to-date information to the human decision maker. At the same time, artificial intelligence techniques have demonstrated that they have matured sufficiently to provide computational assistance to humans in practical applications. It is the development direction of modern management science and technology. In this chapter, firstly we introduce the introduction and background of IMIS, and briefly, the related design conception. Subsequently, the Intelligent Decision Support System (IDSS) is depicted, which is the most significant technology of IMIS and related activities in the manufacturing process. The applications of IDSS and two cases for industrial manufacturing are then presented, representing the future development direction of manufacturing management. Lastly, a summary of this chapter is given. IMIS researchers and technologists have built and investigated Decision Support Systems (DSS) for more than 35 years. The developments in DSS began with building model-oriented DSS in the late 1960s which were followed by theory developments in the 1970s, and the implementation of financial planning systems and Group DSS in the early and mid-1980s. During the mid-1980s, Intelligent DSS were implemented through combining knowledge systems with DSS. These developments are discussed below, as well as the origins of Executive Information Systems, On-line Analytical Processing (OLAP), Business Intelligence, and the implementation of Web-based DSS in the mid-1990s, which quickly became a topic for active discussion, and whose influence spread widely.

Sign in / Sign up

Export Citation Format

Share Document