Decision Making: A Computational Approach

1990 ◽  
pp. 185-197
Author(s):  
Abhijit Chaudhury ◽  
Andrew B. Whinston
Keyword(s):  
Decision Making ◽  
Computational Approach

scholarly journals Computational approach to synthesis of the multiversion structure of distributed information decision-making support system

2021 ◽  
Vol 2094 (3) ◽  
pp. 032068
Author(s):  
I V Kovalev ◽  
N A Testoyedov ◽  
A A Voroshilova ◽  
D I Kovalev ◽  
D V Borovinskii
Keyword(s):  
Decision Making ◽  
Distributed System ◽  
Support System ◽  
Formal Model ◽  
Technical Condition ◽  
Computational Approach ◽  
Local Information ◽  
Distributed Information ◽  
Complex Control System ◽  
Decision Making Support

Abstract The computational approach to synthesis of the multiversion structure of distributed information decision-making support system is presented. A formal model of the local information system is given. This system is intended to ensure the functioning of a complex control system based on the multiversion approach and consisting of a set of multiversion objects. The problem of distribution of objects by local information subsystems has been solved. For a set of valid queries in a distributed system, the answers for the decision maker are formed sequentially without repeating queries. To take into account certain requirements regarding the structure of a distributed system, it is necessary to formulate these requirements in formalized constraints and introduce them into the mathematical description of the problem. Note that the effectiveness of the targeted use of the system depends both on the results of synthesis (structure and parameters of the system) and on the correct organization of the subsystem for monitoring its technical condition during operation.

scholarly journals Examining learning coherence in group decision-making: triads vs. tetrads

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tsutomu Harada
Keyword(s):  
Decision Making ◽  
Group Decision Making ◽  
Small Groups ◽  
Majority Rule ◽  
Group Decision ◽  
Computational Approach ◽  
Approach To Learning ◽  
Q Learning ◽  
Learning Framework ◽  
Better Than

AbstractThis study examined whether three heads are better than four in terms of performance and learning properties in group decision-making. It was predicted that learning incoherence took place in tetrads because the majority rule could not be applied when two subgroups emerged. As a result, tetrads underperformed triads. To examine this hypothesis, we adopted a reinforcement learning framework using simple Q-learning and estimated learning parameters. Overall, the results were consistent with the hypothesis. Further, this study is one of a few attempts to apply a computational approach to learning behavior in small groups. This approach enables the identification of underlying learning parameters in group decision-making.

scholarly journals Effects of emotional responses on learning in decision-making: A computational approach

2013 ◽  
Vol 20 (3) ◽  
pp. 71-76
Author(s):  
Kentaro Katahira ◽  
Tomomi Fujimura ◽  
Yoshi-Taka Matsuda ◽  
Kazuo Okanoya ◽  
Masato Okada
Keyword(s):  
Decision Making ◽  
Emotional Responses ◽  
Computational Approach

scholarly journals Pupil responses as indicators of value-based decision-making

2018 ◽  
Author(s):  
Joanne C. Van Slooten ◽  
Sara Jahfari ◽  
Tomas Knapen ◽  
Jan Theeuwes
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Computational Model ◽  
Cognitive Processes ◽  
Learning Task ◽  
Brain Regions ◽  
Computational Approach ◽  
Prediction Errors ◽  
Reward Prediction ◽  
Exciting Possibility

AbstractPupil responses have been used to track cognitive processes during decision-making. Studies have shown that in these cases the pupil reflects the joint activation of many cortical and subcortical brain regions, also those traditionally implicated in value-based learning. However, how the pupil tracks value-based decisions and reinforcement learning is unknown. We combined a reinforcement learning task with a computational model to study pupil responses during value-based decisions, and decision evaluations. We found that the pupil closely tracks reinforcement learning both across trials and participants. Prior to choice, the pupil dilated as a function of trial-by-trial fluctuations in value beliefs. After feedback, early dilation scaled with value uncertainty, whereas later constriction scaled with reward prediction errors. Our computational approach systematically implicates the pupil in value-based decisions, and the subsequent processing of violated value beliefs, ttese dissociable influences provide an exciting possibility to non-invasively study ongoing reinforcement learning in the pupil.

scholarly journals Drift Diffusion Model of Animacy Categorization Task Can Detect Patients with Mild Cognitive Impairment and Mild Alzheimer's Disease

2021 ◽  
Author(s):  
Hamed Karimi ◽  
Haniye Marefat ◽  
Mahdiye Khanbagi ◽  
Alireza Karami ◽  
Zahra Vahabi
Keyword(s):  
Alzheimer’S Disease ◽  
Decision Making ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Diffusion Model ◽  
Computational Approach ◽  
Drift Diffusion Model ◽  
Drift Diffusion ◽  
Categorization Task ◽  
Early Stages

Purpose: The process of neurodegeneration in Alzheimer's Disease (AD) is irreversible using current therapeutics. An earlier diagnosis of the disease can lead to earlier interventions, which will help patients sustain their cognitive abilities for longer. Individuals within the early stages of AD, shown to have trouble making confident and sounds decisions. Here we proposed a computational approach to quantify the decision-making ability in patients with mild cognitive impairment and mild AD. Materials and Methods: To study the quantified decision-making abilities at the early stages of the disease, we took advantage of a 2-Alternative Forced-Choice (2AFC) task. We applied the Drift Diffusion Model to determine whether the information accumulation process in a categorization task is altered in patients with mild cognitive impairment and mild AD. We implemented a classification model to detect cognitive impairment based on the Drift Diffusion Model's estimated parameters. Results: The results show a significant correlation of the classification score with the standard pen-and-paper tests, suggesting that the quantified decision-making parameters are undergoing significant change in patients with cognitive impairment. Conclusion: We confirmed that the decision-making ability deteriorates at the early stages of AD. We introduced a computational approach for measuring the decline in decision-making and used that measurement to distinguish patients from healthy individuals.

scholarly journals A computational framework for the study of confidence in humans and animals

2012 ◽  
Vol 367 (1594) ◽  
pp. 1322-1337 ◽  
Author(s):  
Adam Kepecs ◽  
Zachary F. Mainen
Keyword(s):  
Decision Making ◽  
Prima Facie ◽  
Computational Approach ◽  
Self Report ◽  
Computational Framework ◽  
Decision Confidence ◽  
Wide Range ◽  
Neural Computations ◽  
Self Assessments ◽  
Relationship Of

Confidence judgements, self-assessments about the quality of a subject's knowledge, are considered a central example of metacognition. Prima facie, introspection and self-report appear the only way to access the subjective sense of confidence or uncertainty. Contrary to this notion, overt behavioural measures can be used to study confidence judgements by animals trained in decision-making tasks with perceptual or mnemonic uncertainty. Here, we suggest that a computational approach can clarify the issues involved in interpreting these tasks and provide a much needed springboard for advancing the scientific understanding of confidence. We first review relevant theories of probabilistic inference and decision-making. We then critically discuss behavioural tasks employed to measure confidence in animals and show how quantitative models can help to constrain the computational strategies underlying confidence-reporting behaviours. In our view, post-decision wagering tasks with continuous measures of confidence appear to offer the best available metrics of confidence. Since behavioural reports alone provide a limited window into mechanism, we argue that progress calls for measuring the neural representations and identifying the computations underlying confidence reports. We present a case study using such a computational approach to study the neural correlates of decision confidence in rats. This work shows that confidence assessments may be considered higher order, but can be generated using elementary neural computations that are available to a wide range of species. Finally, we discuss the relationship of confidence judgements to the wider behavioural uses of confidence and uncertainty.

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