Semi-Markov Decision-Making Processes with Vector Gains

Decision-making processes are the utmost important to steer the organizational change whenever business process workarounds are attempted during operational times. However, to decide the non-compliant situations, e.g., bypasses, social resistance, or collusion; the business manager demands contextualized and correct interpretations of the existing business process redesign options to cope with workarounds. This article explores the need to aid the decision-making process with a full constructional perspective to optimize the business processes redesign. So, the Markov decision process is combined with the body of knowledge of business processes, in specific, the concepts of designing enterprise-wide business transactions. This methodology supports the management initiatives with more knowledge about the value of business processes redesign. A classical chain of Order-to-Cash business processes (the order, the production, the distribution and the selling of goods) illustrate the benefits of this quantitative approach. Results obtained for business processes redesign in reaction to workarounds are reported. The analysis results show that this approach can anticipate the sub-optimal solutions before taking actions and highlights the impact of discount factors in the final obtained value. The contribution of this novel conceptual integration to the business processes community is the forecast of value function of business transaction redesign options when facing non-compliant workarounds. From related literature, business processes compliance usually comprises offline computation and the redesign is only considered in the forthcoming business processes instances. This article is innovative in the sense that it anticipates the value impact of a redesign, allowing more effective decisions to be taken.

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Risk-Sensitive Multiagent Decision-Theoretic Planning Based on MDP and One-Switch Utility Functions

Mathematical Problems in Engineering ◽

10.1155/2014/697895 ◽

2014 ◽

Vol 2014 ◽

pp. 1-11

Author(s):

Wei Zeng ◽

Hongtao Zhou ◽

Mingshan You

Keyword(s):

Decision Making ◽

Attitude Change ◽

Optimal Policy ◽

Risk Attitude ◽

High Stakes ◽

Group Settings ◽

Risk Sensitive ◽

Markov Decision ◽

Decision Making Processes ◽

Initial Wealth

In high stakes situations decision-makers are often risk-averse and decision-making processes often take place in group settings. This paper studies multiagent decision-theoretic planning under Markov decision processes (MDPs) framework with considering the change of agent’s risk attitude as his wealth level varies. Based on one-switch utility function that describes agent’s risk attitude change with his wealth level, we give the additive and multiplicative aggregation models of group utility and adopt maximizing expected group utility as planning objective. When the wealth level approaches infinity, the characteristics of optimal policy are analyzed for the additive and multiplicative aggregation model, respectively. Then a backward-induction method is proposed to divide the wealth level interval from negative infinity to initial wealth level into subintervals and determine the optimal policy in states and subintervals. The proposed method is illustrated by numerical examples and the influences of agent’s risk aversion parameters and weights on group decision-making are also analyzed.

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The Machine-Learning Approach of Reinforcement Learning

How the Brain Makes Decisions ◽

10.1093/oso/9780198824367.003.0016 ◽

2020 ◽

pp. 105-109

Author(s):

Thomas Boraud

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Decision Making ◽

Reinforcement Learning ◽

Learning Algorithms ◽

Model Free ◽

Machine Learning Approach ◽

Markov Decision ◽

Decision Making Processes ◽

Alternative Approaches

This chapter assesses alternative approaches of reinforcement learning that are developed by machine learning. The initial goal of this branch of artificial intelligence, which appeared in the middle of the twentieth century, was to develop and implement algorithms that allow a machine to learn. Originally, they were computers or more or less autonomous robotic automata. As artificial intelligence has developed and cross-fertilized with neuroscience, it has begun to be used to model the learning and decision-making processes for biological agents, broadening the meaning of the word ‘machine’. Theoreticians of this discipline define several categories of learning, but this chapter only deals with those which are related to reinforcement learning. To understand how these algorithms work, it is necessary first of all to explain the Markov chain and the Markov decision-making process. The chapter then goes on to examine model-free reinforcement learning algorithms, the actor-critic model, and finally model-based reinforcement learning algorithms.

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An Active Inference Approach to Dissecting Reasons for Non-Adherence to Antidepressants

10.1101/743542 ◽

2019 ◽

Author(s):

Ryan Smith ◽

Sahib Khalsa ◽

Martin Paulus

Keyword(s):

Decision Making ◽

Process Model ◽

Time Course ◽

Antidepressant Medication ◽

Influential Factors ◽

Active Inference ◽

Individual Level ◽

Markov Decision ◽

Decision Making Processes ◽

Partially Observable

AbstractBackgroundAntidepressant medication adherence is among the most important problems in health care worldwide. Interventions designed to increase adherence have largely failed, pointing towards a critical need to better understand the underlying decision-making processes that contribute to adherence. A computational decision-making model that integrates empirical data with a fundamental action selection principle could be pragmatically useful in 1) making individual level predictions about adherence, and 2) providing an explanatory framework that improves our understanding of non-adherence.MethodsHere we formulate a partially observable Markov decision process model based on the active inference framework that can simulate several processes that plausibly influence adherence decisions.ResultsUsing model simulations of the day-to-day decisions to take a prescribed selective serotonin reuptake inhibitor (SSRI), we show that several distinct parameters in the model can influence adherence decisions in predictable ways. These parameters include differences in policy depth (i.e., how far into the future one considers when deciding), decision uncertainty, beliefs about the predictability (stochasticity) of symptoms, beliefs about the magnitude and time course of symptom reductions and side effects, and the strength of medication-taking habits that one has acquired.ConclusionsClarifying these influential factors will be an important first step toward empirically determining which are contributing to non-adherence to antidepressants in individual patients. The model can also be seamlessly extended to simulate adherence to other medications (by incorporating the known symptom reduction and side effect trajectories of those medications), with the potential promise of identifying which medications may be best suited for different patients.

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ALGORITHMS FOR FINDING OPTIMAL POLICY FOR INTELLIGENT AGENTS BASED ON MARKOV DECISION-MAKING PROCESSES

Issues of radio electronics ◽

10.21778/2218-5453-2018-11-29-32 ◽

2018 ◽

pp. 29-32

Author(s):

A. V. Lachikhin

Keyword(s):

Decision Making ◽

Dynamic Programming ◽

Optimal Policy ◽

Intelligent Agents ◽

Dynamic Programming Method ◽

Multi Agent Systems ◽

Markov Decision ◽

Decision Making Processes ◽

Multi Agent ◽

Method Of Dynamic Programming

Currently, the paradigm of intelligent agents and multi-agent systems is actively developing. The policy of agents ‘ actions can be represented as a Markov decision-making process. Such agents need methods to develop optimal policies. The purpose of this study is to review existing techniques, determine the possibility and conditions of their application. The main approaches based on linear and dynamic programming are considered. The specific algorithms used to find the extreme value of utility are given. The method of linear programming - simplex method, and the method of dynamic programming method-iteration of values are considered. The equations necessary to find the optimal policy of intelligent agent actions are given. Restrictions of application of various algorithms are considered. Conclusions the most suitable method for finding the optimal policy is the iteration of values.

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A Markov Decision Process Model for Traffic Prioritisation Provisioning

10.28945/2750 ◽

2004 ◽

Author(s):

Abdullah Gani ◽

Omar Zakaria ◽

Nor Badrul Anuar Jumaat

Keyword(s):

Decision Making ◽

Markov Decision Process ◽

Decision Process ◽

Process Model ◽

Decision Problems ◽

Sequential Decision ◽

Best Effort ◽

Sequential Decision Problems ◽

Markov Decision ◽

Decision Making Processes

This paper presents an application of Markov Decision Process (MDP) into the provision of traffic prioritisation in the best-effort networks. MDP was used because it is a standard, general formalism for modelling stochastic, sequential decision problems. The implementation of traffic prioritisation involves a series of decision making processes by which packets are marked and classified before being despatched to destinations. The application of MDP was driven by the objective of ensuring the higher priority packets are not delayed by the lower ones. The MDP is believed to be applicable in improving the traffic prioritisation arbitration.

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Cognitive Processing of Miscommunication in Interactive Listening: An Evaluation of Listener Indecision and Cognitive Effort

Journal of Speech Language and Hearing Research ◽

10.1044/2020_jslhr-20-00128 ◽

2021 ◽

pp. 1-17

Author(s):

Jennifer M. Roche ◽

Arkady Zgonnikov ◽

Laura M. Morett

Keyword(s):

Decision Making ◽

Cognitive Processing ◽

Language Processing ◽

Cognitive Effort ◽

Mouse Tracking ◽

Computer Mouse ◽

Decision Making Processes ◽

Computer Mouse Tracking ◽

Listening Task ◽

Processing Effort

Purpose The purpose of the current study was to evaluate the social and cognitive underpinnings of miscommunication during an interactive listening task. Method An eye and computer mouse–tracking visual-world paradigm was used to investigate how a listener's cognitive effort (local and global) and decision-making processes were affected by a speaker's use of ambiguity that led to a miscommunication. Results Experiments 1 and 2 found that an environmental cue that made a miscommunication more or less salient impacted listener language processing effort (eye-tracking). Experiment 2 also indicated that listeners may develop different processing heuristics dependent upon the speaker's use of ambiguity that led to a miscommunication, exerting a significant impact on cognition and decision making. We also found that perspective-taking effort and decision-making complexity metrics (computer mouse tracking) predict language processing effort, indicating that instances of miscommunication produced cognitive consequences of indecision, thinking, and cognitive pull. Conclusion Together, these results indicate that listeners behave both reciprocally and adaptively when miscommunications occur, but the way they respond is largely dependent upon the type of ambiguity and how often it is produced by the speaker.

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“It's Not Humanly Possible to Do Everything”: Perspectives on Intervention Decision-Making Processes of Parents of Children with Autism Spectrum Disorders

Perspectives on Language Learning and Education ◽

10.1044/lle22.1.13 ◽

2015 ◽

Vol 22 (1) ◽

pp. 13-21 ◽

Cited By ~ 5

Author(s):

Erinn Finke ◽

Kathryn Drager ◽

Elizabeth C. Serpentine

Keyword(s):

Decision Making ◽

Individualized Education Program ◽

Children With Autism ◽

Autism Spectrum ◽

Sources Of Information ◽

Individual Decision ◽

Decision Making Processes ◽

Spectrum Disorders ◽

Interview Methodology ◽

Communication Interventions

Purpose The purpose of this investigation was to understand the decision-making processes used by parents of children with autism spectrum disorder (ASD) related to communication-based interventions. Method Qualitative interview methodology was used. Data were gathered through interviews. Each parent had a child with ASD who was at least four-years-old; lived with their child with ASD; had a child with ASD without functional speech for communication; and used at least two different communication interventions. Results Parents considered several sources of information for learning about interventions and provided various reasons to initiate and discontinue a communication intervention. Parents also discussed challenges introduced once opinions of the school individualized education program (IEP) team had to be considered. Conclusions Parents of children with ASD primarily use individual decision-making processes to select interventions. This discrepancy speaks to the need for parents and professionals to share a common “language” about interventions and the decision-making process.

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