Complex decision making in medical training: key internal and external influences in developing practical wisdom

2018 ◽  
Vol 53 (2) ◽  
pp. 165-174 ◽  
Author(s):  
Paul Paes ◽  
David Leat ◽  
Jane Stewart
Keyword(s):  
Decision Making ◽  
Medical Training ◽  
Practical Wisdom ◽  
External Influences ◽  
Complex Decision Making ◽  
Complex Decision

scholarly journals Goal-driven active learning

2021 ◽  
Vol 35 (2) ◽  
Author(s):  
Nicolas Bougie ◽  
Ryutaro Ichise
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Learning Process ◽  
Real World ◽  
Imitation Learning ◽  
Learning Approaches ◽  
Wide Range ◽  
Fixed Set ◽  
Complex Decision Making ◽  
Complex Decision

AbstractDeep reinforcement learning methods have achieved significant successes in complex decision-making problems. In fact, they traditionally rely on well-designed extrinsic rewards, which limits their applicability to many real-world tasks where rewards are naturally sparse. While cloning behaviors provided by an expert is a promising approach to the exploration problem, learning from a fixed set of demonstrations may be impracticable due to lack of state coverage or distribution mismatch—when the learner’s goal deviates from the demonstrated behaviors. Besides, we are interested in learning how to reach a wide range of goals from the same set of demonstrations. In this work we propose a novel goal-conditioned method that leverages very small sets of goal-driven demonstrations to massively accelerate the learning process. Crucially, we introduce the concept of active goal-driven demonstrations to query the demonstrator only in hard-to-learn and uncertain regions of the state space. We further present a strategy for prioritizing sampling of goals where the disagreement between the expert and the policy is maximized. We evaluate our method on a variety of benchmark environments from the Mujoco domain. Experimental results show that our method outperforms prior imitation learning approaches in most of the tasks in terms of exploration efficiency and average scores.

The effect of the cognitive demands of the distraction task on unconscious thought

2014 ◽  
Vol 37 (1) ◽  
pp. 44-45 ◽  
Author(s):  
Laurent Waroquier ◽  
Marlène Abadie ◽  
Olivier Klein ◽  
Axel Cleeremans
Keyword(s):  
Decision Making ◽  
Unconscious Thought ◽  
Cognitive Demands ◽  
The Unconscious ◽  
Unconscious Processes ◽  
Distraction Task ◽  
Complex Decision Making ◽  
Complex Decision

AbstractThe unconscious-thought effect occurs when distraction improves complex decision making. Recent studies suggest that this effect is more likely to occur with low- than high-demanding distraction tasks. We discuss implications of these findings for Newell & Shanks' (N&S's) claim that evidence is lacking for the intervention of unconscious processes in complex decision making.

scholarly journals Integration of AHP and GTMA to Make a Reliable Decision in Complex Decision-Making Problems: Application of the Logistics Provider Selection Problem as a Case Study

Symmetry ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 766
Author(s):  
Danijela Tuljak-Suban ◽  
Patricija Bajec
Keyword(s):  
Decision Making ◽  
Analytic Network Process ◽  
Third Party ◽  
Selection Problem ◽  
New Method ◽  
Analytic Hierarchy ◽  
Decision Making Problem ◽  
Network Process ◽  
Complex Decision Making ◽  
Complex Decision

When solving a Multi-Criteria Decision-Making problem of any degree of complexity, many researchers rely on the analytic hierarchy process (AHP). To consider mutual connections between criteria and clusters at the same level and not only the hierarchical structure between criteria and subcriteria, researchers often upgrade from AHP to the Analytic Network Process (ANP), which also examines the interdependency of criteria. However, the ANP method requires a large number of pairwise comparisons. In the case of a complex decision-making problem, the authors of this paper suggest upgrading the AHP method with the graph theory and matrix approach (GTMA) for several reasons: (1) The new method is based on digraphs and permanent value computation, which does not require a hypothesis about interdependency; (2) in case of similar alternatives, the distinguishable coefficient of the new method is higher than those computed for AHP and ANP; (3) the new method allows decision makers to rank comparable alternatives and to combine structurally similar methods without increasing the number of comparisons and the understanding of the results. The developed method (AH-GTMA) is validated by a numerical example of a complex decision-making problem based on a symmetrical set of similar alternatives, a third party logistic provider (3PLP) selection problem.

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