scholarly journals Achieving party unity in the Netherlands: Representatives’ sequential decision-making mechanisms at three levels of Dutch government

2019 ◽  
Vol 25 (5) ◽  
pp. 664-678
Author(s):  
Cynthia MC van Vonno
Keyword(s):  
Decision Making ◽  
Sequential Decision Making ◽  
Relative Importance ◽  
Sequential Decision ◽  
Dutch Version ◽  
Party Discipline ◽  
Party Loyalty ◽  
Party Line ◽  
Member Of Parliament ◽  
Different Levels

How do parties at different levels of government get their representatives to vote according to the party line? Employing the sequential decision-making approach to party unity, we explore the relative importance of cue-taking, party agreement, party loyalty, and party discipline as individual representative decision-making mechanisms. On the basis of the Dutch version of the PartiRep comparative Member of Parliament survey, we find few differences between national and subnational representatives when it comes to the first two mechanisms, but party loyalty and party discipline seem to play a less important role in determining representatives’ decision whether to vote with the party group line. This is, in part, in line with our theoretical expectation that subnational representatives are less likely to be motivated by office-seeking and vote-seeking than their national counterparts.

Automation Reliability and Decision Strategy: A Sequential Decision Making Model for Automation Interaction

2018 ◽  
Vol 62 (1) ◽  
pp. 144-148 ◽  
Author(s):  
Aditya Acharya ◽  
Andrew Howes ◽  
Chris Baber ◽  
Tom Marshall
Keyword(s):  
Decision Making ◽  
Information Search ◽  
Sequential Decision Making ◽  
Decision Strategy ◽  
Sequential Decision ◽  
Decision Strategies ◽  
Information Reliability ◽  
Decision Making Model ◽  
Different Levels

The question of how people make use of automation to support their decision making is becoming increasingly important. As computers provide ever greater input to the collection, analysis and interpretation of data, so they are more likely to be partners in decision making. However, when automation makes recommendations that the human disagrees with or that might be based on erroneous analysis, then this could result in a change in decision strategy. It is not simply a matter of ignoring or rejecting the recommendation but rather a matter of deciding how best to make use of the automation’s output. By modeling information search and decision strategies under different levels of information reliability, we demonstrate that it makes sense to adapt decision strategy to the information context.

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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