scholarly journals A Model based Realisation of Actor Model to Conceptualise an Aid for Complex Dynamic Decision-making

2017 ◽  
Author(s):  
Souvik Barat ◽  
Vinay Kulkarni ◽  
Tony Clark ◽  
Balbir Barn
Keyword(s):  
Decision Making ◽  
Dynamic Decision Making ◽  
Actor Model ◽  
Complex Dynamic ◽  
Model Based

Assessing Individual Differences in Decision Making Styles: Analytical vs. Intuitive

2002 ◽  
Vol 46 (12) ◽  
pp. 953-957 ◽  
Author(s):  
Thomas E. Nygren ◽  
Rebecca J. White
Keyword(s):  
Decision Making ◽  
Individual Differences ◽  
Human Performance ◽  
Self Report ◽  
Dynamic Decision Making ◽  
Complex Dynamic ◽  
Decision Style ◽  
Decision Strategies ◽  
Decision Making Styles ◽  
Report Measure

Decision strategies are often characterized as being intuition-based or analytically-based. The use of these strategies is proposed to be associated with individual differences in propensity toward using different decision making styles. A reliable self-report measure, the Decision Making Styles Inventory (DMI), consisting of 15 items on each of three scales was constructed. The items were found to differentiate among an “analytical”, an “intuitive”, and a “regret-based” emotional decision making style. The analytical and intuitive scales were found to predict differences in performance in a complex dynamic decision making task. on a decision making subtask, a greater general reliance on an analytical decision making style was found to lead to poorer performance. Greater reliance on a more intuitive approach had no effect on this subtask, but was found to predict better performance as workload levels increased. These findings suggest that human performance may be significantly influenced when either a more intuitive or analytical decision style is used. Implications for training the adaptive decision maker are discussed.

scholarly journals A problem shared is learning doubled: Deliberative processing in dyads improves learning in complex dynamic decision-making tasks

2015 ◽  
Vol 48 ◽  
pp. 654-662 ◽  
Author(s):  
Nadin Beckmann ◽  
Jens F. Beckmann ◽  
Damian P. Birney ◽  
Robert E. Wood
Keyword(s):  
Decision Making ◽  
Dynamic Decision Making ◽  
Complex Dynamic

scholarly journals The Benefits and the Costs of Using Auditory Warning Messages in Dynamic Decision-Making Settings

2017 ◽  
Vol 12 (2) ◽  
pp. 112-130 ◽  
Author(s):  
Cindy Chamberland ◽  
Helen M. Hodgetts ◽  
Benoît R. Vallières ◽  
François Vachon ◽  
Sébastien Tremblay
Keyword(s):  
Decision Making ◽  
Change Detection ◽  
Visual Cues ◽  
Dynamic Environments ◽  
Dynamic Decision Making ◽  
Complex Dynamic ◽  
Warning Messages ◽  
And Control ◽  
The Impact ◽  
Auditory Warning

The failure to notice critical changes in both visual and auditory scenes may have important consequences for performance in complex dynamic environments, especially those related to security, such as aviation, surveillance during major events, and command and control of emergency response. Previous work has shown that a significant number of situation changes remain undetected by operators in such environments. In the current study, we examined the impact of using auditory warning messages to support the detection of critical situation changes and to a broader extent the decision making required by the environment. Twenty-two participants performed a radar operator task involving multiple subtasks while detecting critical task-related events that were cued by a specific type of audio message. Results showed that about 22% of the critical changes remained undetected by participants, a percentage similar to that found in previous work using visual cues to support change detection. However, we found that audio messages tended to bias threat evaluation toward perceiving objects as more threatening than they were in reality. Such findings revealed both benefits and costs associated with using audio messages to support change detection in complex dynamic environments.

Dynamic information aggregation decision-making methods based on variable precision rough set and grey clustering

2014 ◽  
Vol 4 (2) ◽  
pp. 347-361 ◽  
Author(s):  
Yong Liu ◽  
Huan-huan Zhao
Keyword(s):  
Decision Making ◽  
Rough Set ◽  
Information Aggregation ◽  
Dynamic Decision Making ◽  
Dynamic Information ◽  
Content Type ◽  
Variable Precision ◽  
Model Based ◽  
Variable Precision Rough Set ◽  
Decision Making Model

Purpose – The purpose of this paper is to construct a dynamic information aggregation decision-making model based on variable precision rough set. Design/methodology/approach – To deal with the dynamic decision-making problems, the grey relational analysis method, grey fixed weight clustering based on the centre triangle whitening weight function and maximum entropy principle is used to establish the dynamic information aggregation decision-making model based on variable precision rough set. The method, to begin with, the grey relational analysis method is used to determine the attributes weights of each stage; taking the proximity of the attribute measurement value and positive and negative desired effect value and the uncertainty of time weight into account, a multi-objective optimisation model based on maximum entropy principle is established to solve the model with Lagrange multiplier method, so that time weights expression are acquired; what is more, the decision-making attribute is obtained by grey fixed weight clustering based on the centre triangle whitening weight function, so that multi-decision-making table with dynamic characteristics is established, and then probabilistic decision rules from multi-criteria decision table are derived by applying variable precision rough set. Finally, a decision-making model validates the feasibility and effectiveness of the model. Findings – The results show that it the proposed model can well aggregate the multi-stage dynamic decision-making information, realise the extraction of decision-making rules. Research limitations/implications – The method exposed in the paper can be used to deal with the decision-making problems with the multi-stage dynamic characteristics, and decision-making attributes contain noise data and the attribute values are interval grey numbers. Originality/value – The paper succeeds in realising both the aggregation of dynamic decision-making information and the extraction of decision-making rules.

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