scholarly journals Additively Combining Utilities and Beliefs: Research Gaps and Algorithmic Developments

2021 ◽  
Vol 15 ◽  
Author(s):  
Anush Ghambaryan ◽  
Boris Gutkin ◽  
Vasily Klucharev ◽  
Etienne Koechlin
Keyword(s):  
Decision Making ◽  
Behavioral Flexibility ◽  
Sequential Decision ◽  
Complex Environments ◽  
Research Gaps ◽  
Future Studies ◽  
Effective Learning ◽  
Choice Making ◽  
Computational Resources ◽  
Further Development

Value-based decision making in complex environments, such as those with uncertain and volatile mapping of reward probabilities onto options, may engender computational strategies that are not necessarily optimal in terms of normative frameworks but may ensure effective learning and behavioral flexibility in conditions of limited neural computational resources. In this article, we review a suboptimal strategy – additively combining reward magnitude and reward probability attributes of options for value-based decision making. In addition, we present computational intricacies of a recently developed model (named MIX model) representing an algorithmic implementation of the additive strategy in sequential decision-making with two options. We also discuss its opportunities; and conceptual, inferential, and generalization issues. Furthermore, we suggest future studies that will reveal the potential and serve the further development of the MIX model as a general model of value-based choice making.

scholarly journals RESEARCH GAPS IN MULTI-CRITERIA DECSION MAKING METHODS FOR RESEARCHERS AS AN AREAS OF INTEREST

2020 ◽  
Vol 4 (2) ◽  
pp. 1-6
Author(s):  
Omar Ibrahim Ayasrah ◽  
Faiz Mohd Turan
Keyword(s):  
Decision Making ◽  
Sensitivity Analysis ◽  
Mobile Technologies ◽  
Decision Making Process ◽  
Multi Criteria Decision Making ◽  
Research Gaps ◽  
Future Studies ◽  
New Methods ◽  
Areas Of Interest ◽  
The Web

The increase in Multi criteria decision-making studies is reflecting its importance as an interested area for research. Even though a high number of revealed studies mainly in last decade, still this field have challenges that require attentions from researchers in future. Most of MCDM related studies did not consider determining DM weights or using subjective methods, and there is an absence of implementing sensitivity analysis to DM weights as well. In addition to the need to develop new methods that utilizing the web or mobile technologies to deal with complexity and uncertainty adhered to MCDM problems. This paper represents a trial to list current challenges in this field as areas of interest for researchers in future studies that aims to enhance the quality and effectiveness of decision-making process.

scholarly journals A top-level model of case-based argumentation for explanation: Formalisation and experiments

2021 ◽  
pp. 1-36
Author(s):  
Henry Prakken ◽  
Rosa Ratsma
Keyword(s):  
Machine Learning ◽  
Decision Making ◽  
Linear Models ◽  
Evaluation Studies ◽  
Data Sets ◽  
Machine Learning Applications ◽  
Level Model ◽  
Similarities And Differences ◽  
Further Development ◽  
Case Based

This paper proposes a formal top-level model of explaining the outputs of machine-learning-based decision-making applications and evaluates it experimentally with three data sets. The model draws on AI & law research on argumentation with cases, which models how lawyers draw analogies to past cases and discuss their relevant similarities and differences in terms of relevant factors and dimensions in the problem domain. A case-based approach is natural since the input data of machine-learning applications can be seen as cases. While the approach is motivated by legal decision making, it also applies to other kinds of decision making, such as commercial decisions about loan applications or employee hiring, as long as the outcome is binary and the input conforms to this paper’s factor- or dimension format. The model is top-level in that it can be extended with more refined accounts of similarities and differences between cases. It is shown to overcome several limitations of similar argumentation-based explanation models, which only have binary features and do not represent the tendency of features towards particular outcomes. The results of the experimental evaluation studies indicate that the model may be feasible in practice, but that further development and experimentation is needed to confirm its usefulness as an explanation model. Main challenges here are selecting from a large number of possible explanations, reducing the number of features in the explanations and adding more meaningful information to them. It also remains to be investigated how suitable our approach is for explaining non-linear models.

scholarly journals Optimal Policies for Quantum Markov Decision Processes

2021 ◽  
Author(s):  
Ming-Sheng Ying ◽  
Yuan Feng ◽  
Sheng-Gang Ying
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Quantum Systems ◽  
Sequential Decision Making ◽  
Mathematical Framework ◽  
Sequential Decision ◽  
Learning Techniques ◽  
Optimal Policies ◽  
Markov Decision ◽  
Programming Algorithms

AbstractMarkov decision process (MDP) offers a general framework for modelling sequential decision making where outcomes are random. In particular, it serves as a mathematical framework for reinforcement learning. This paper introduces an extension of MDP, namely quantum MDP (qMDP), that can serve as a mathematical model of decision making about quantum systems. We develop dynamic programming algorithms for policy evaluation and finding optimal policies for qMDPs in the case of finite-horizon. The results obtained in this paper provide some useful mathematical tools for reinforcement learning techniques applied to the quantum world.

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