Reasoning Strategies

2020 ◽  
pp. 101-114
Author(s):  
Stephen K. Reed
Keyword(s):  
Decision Making ◽  
Situation Awareness ◽  
Correct Response ◽  
Applied Research ◽  
Incorrect Response ◽  
Reasoning Strategies ◽  
Reasoning System ◽  
Military Engagement

A dichotomy that has influenced much theoretical and applied research on reasoning is the distinction between System I and System II reasoning. System I is intuitive, fast, based on associations, and subject to biases. System II is analytic, slow, based on rules, and more competent. It should be kept in mind, however, that these distinctions do not always apply. A fast, correct response occurs when an expert automatically responds quickly, and a slow, incorrect response occurs when the answer is unknown. One tactic to improve reasoning is the use of nudges to steer people’s choices in a direction to improve their lives. Another tactic is the use of boosts to educate people to make better decisions. Action-based decision-making, such as firefighting and military engagement, requires making a series of decisions as the situation changes. Situation awareness is critical for making good decisions.

DECISION-MAKING ON INVESTMENT OF IT-INNOVATION BASED ON FUZZY EXPERT INFORMATION

2018 ◽  
pp. 103-111
Author(s):  
Olga Olegovna Eremenko ◽  
Lyubov Borisovna Aminul ◽  
Elena Vitalievna Chertina
Keyword(s):  
Decision Making ◽  
Individual Characteristics ◽  
Production Model ◽  
Investment Management ◽  
It Projects ◽  
Product Model ◽  
Project Cost ◽  
Reasoning System ◽  
Managerial Decisions ◽  
The Individual

The subject of the research is the process of making managerial decisions for innovative IT projects investing. The paper focuses on the new approach to decision making on investing innovative IT projects using expert survey in a fuzzy reasoning system. As input information, expert estimates of projects have been aggregated into six indicators having a linguistic description of the individual characteristics of the project type "high", "medium", and "low". The task of decision making investing has been formalized and the term-set of the output variable Des has been defined: to invest 50-75% of the project cost; to invest 20-50% of the project cost; to invest 10-20% of the project cost; to send the project for revision; to turn down investing project. The fuzzy product model of making investment management decisions has been developed; it adequately describes the process of investment management. The expediency of using constructed production model on a practical example is shown.

Models of decision support in socio-cyber-physical systems

2019 ◽  
pp. 55-70 ◽  
Author(s):  
A. V. Smirnov ◽  
T. V. Levashova
Keyword(s):  
Decision Making ◽  
Decision Support ◽  
Situation Awareness ◽  
Cyber Physical Systems ◽  
Emergent Behavior ◽  
Emergent Properties ◽  
Decision Making Process ◽  
Physical Systems ◽  
Decision Support Models ◽  
Common Goals

Introduction: Socio-cyber-physical systems are complex non-linear systems. Such systems display emergent properties. Involvement of humans, as a part of these systems, in the decision-making process contributes to overcoming the consequences of the emergent system behavior, since people can use their experience and intuition, not just the programmed rules and procedures.Purpose: Development of models for decision support in socio-cyber-physical systems.Results: A scheme of decision making in socio-cyber-physical systems, a conceptual framework of decision support in these systems, and stepwise decision support models have been developed. The decision-making scheme is that cybernetic components make their decisions first, and if they cannot do this, they ask humans for help. The stepwise models support the decisions made by components of socio-cyber-physical systems at the conventional stages of the decision-making process: situation awareness, problem identification, development of alternatives, choice of a preferred alternative, and decision implementation. The application of the developed models is illustrated through a scenario for planning the execution of a common task for robots.Practical relevance: The developed models enable you to design plans on solving tasks common for system components or on achievement of common goals, and to implement these plans. The models contribute to overcoming the consequences of the emergent behavior of socio-cyber-physical systems, and to the research on machine learning and mobile robot control.

Training Situation Awareness and Adaptive Decision-Making Skills Using a Desktop Computer Simulation

2009 ◽  
Author(s):  
Robert J. Pleban ◽  
Jennifer S. Tucker ◽  
Vanessa Johnson Katie /Gunther ◽  
Thomas R. Graves
Keyword(s):  
Decision Making ◽  
Computer Simulation ◽  
Situation Awareness ◽  
Desktop Computer ◽  
Adaptive Decision Making

scholarly journals Trust me; I know what I am doing investigating the effect of choice list elicitation and domain-relevant training on preference reversals in decision making for others

2021 ◽  
Author(s):  
Sebastian Neumann-Böhme ◽  
Stefan A. Lipman ◽  
Werner B. F. Brouwer ◽  
Arthur E. Attema
Keyword(s):  
Decision Making ◽  
Economic Theory ◽  
Medical Students ◽  
Practical Problem ◽  
Applied Research ◽  
Significant Problem ◽  
Preference Reversals ◽  
Health Domain ◽  
Economics Students ◽  
Financial Domain

AbstractOne core assumption of standard economic theory is that an individual’s preferences are stable, irrespective of the method used to elicit them. This assumption may be violated if preference reversals are observed when comparing different methods to elicit people’s preferences. People may then prefer A over B using one method while preferring B over A using another. Such preference reversals pose a significant problem for theoretical and applied research. We used a sample of medical and economics students to investigate preference reversals in the health and financial domain when choosing patients/clients. We explored whether preference reversals are associated with domain-relevant training and tested whether using guided ‘choice list’ elicitation reduces reversals. Our findings suggest that preference reversals were more likely to occur for medical students, within the health domain, and for open-ended valuation questions. Familiarity with a domain reduced the likelihood of preference reversals in that domain. Although preference reversals occur less frequently within specialist domains, they remain a significant theoretical and practical problem. The use of clearer valuation procedures offers a promising approach to reduce preference reversals.

Effects of Decision Training on Decision Making and Performance in Young Tennis Players: An Applied Research

2014 ◽  
Vol 26 (4) ◽  
pp. 426-440 ◽  
Author(s):  
Luis García-González ◽  
Alberto Moreno ◽  
Alexander Gil ◽  
M. Perla Moreno ◽  
Fernando Del Villar
Keyword(s):  
Decision Making ◽  
Applied Research ◽  
Tennis Players ◽  
And Performance

Autonomous Ship Navigation Under Deep Learning and the Challenges in COLREGs

2018 ◽  
Author(s):  
Lokukaluge P. Perera
Keyword(s):  
Decision Making ◽  
Deep Learning ◽  
Collision Avoidance ◽  
Situation Awareness ◽  
Type System ◽  
Performance Standards ◽  
Transportation Systems ◽  
Agent Based ◽  
Distributed Intelligence ◽  
System Intelligence

A general framework to support the navigation side of autonomous ships is discussed in this study. That consists of various maritime technologies to achieve the required level of ocean autonomy. Decision-making processes in autonomous vessels will play an important role under such ocean autonomy, therefore the same technologies should consist of adequate system intelligence. Each onboard application in autonomous vessels may require localized decision-making modules, therefore that will introduce a distributed intelligence type strategy. Hence, future ships will be agent-based systems with distributed intelligence throughout vessels. The main core of this agent should consist of deep learning type technology that has presented promising results in other transportation systems, i.e. self-driving cars. Deep learning can capture helmsman behavior, therefore that type system intelligence can be used to navigate autonomous vessels. Furthermore, an additional decision support layer should also be developed to facilitate deep learning type technology including situation awareness and collision avoidance. Ship collision avoidance is regulated by the Convention on the International Regulations for Preventing Collisions at Sea, 1972 (COLREGs) under open sea areas. Hence, a general overview of the COLREGs and its implementation challenges, i.e. regulatory failures and violations, under autonomous ships are also discussed with the possible solutions as the main contribution of this study. Furthermore, additional considerations, i.e. performance standards with the applicable limits of liability, terms, expectations and conditions, towards evaluating ship behavior as an agent-based system on collision avoidance situations are also illustrated in this study.

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