Decision-Making Bias in Instance Matching Model Selection

2015 ◽  
pp. 392-407
Author(s):  
Mayank Kejriwal ◽  
Daniel P. Miranker
Keyword(s):  
Decision Making ◽  
Model Selection ◽  
Matching Model ◽  
Instance Matching

Investigating the Use of Reinforcement Learning for Multi-Fidelity Model Selection in the Context of Design Decision Making

2018 ◽  
Author(s):  
Jaskanwal P. S. Chhabra ◽  
Gordon P. Warn
Keyword(s):  
Decision Making ◽  
Reinforcement Learning ◽  
Model Selection ◽  
Design Process ◽  
Decision Process ◽  
Computational Cost ◽  
Discriminatory Power ◽  
Design Decision ◽  
Formal Framework ◽  
Optimal Modeling

Engineers often employ, formally or informally, multi-fidelity computational models to aid design decision making. For example, recently the idea of viewing design as a Sequential Decision Process (SDP) provides a formal framework of sequencing multi-fidelity models to realize computational gains in the design process. Efficiency is achieved in the SDP because dominated designs are removed using less expensive (low-fidelity) models before using higher-fidelity models with the guarantee the antecedent model only removes design solutions that are dominated when analyzed using more detailed, higher-fidelity models. The set of multi-fidelity models and discrete decision states result in a combinatorial combination of modeling sequences, some of which require significantly fewer model evaluations than others. It is desirable to optimally sequence models; however, the optimal modeling policy can not be determined at the onset of SDP because the computational cost and discriminatory power of executing all models on all designs is unknown. In this study, the model selection problem is formulated as a Markov Decision Process and a classical reinforcement learning, namely Qlearning, is investigated to obtain and follow an approximately optimal modeling policy. The outcome is a methodology able to learn efficient sequencing of models by estimating their computational cost and discriminatory power while analyzing designs in the tradespace throughout the design process. Through application to a design example, the methodology is shown to: 1) effectively identify the approximate optimal modeling policy, and 2) efficiently converge upon a choice set.

scholarly journals When Do Psychopathic Traits Affect Cooperative Behavior?

2019 ◽  
Vol 40 (4) ◽  
pp. 227-233
Author(s):  
Martina Testori ◽  
Maximillian Kempf ◽  
Rebecca B. Hoyle ◽  
Hedwig Eisenbarth
Keyword(s):  
Decision Making ◽  
Model Selection ◽  
Cooperative Behavior ◽  
Selection Procedure ◽  
Psychopathic Traits ◽  
Strong Impact ◽  
Online Game ◽  
Crucial Importance ◽  
Human Decision ◽  
The Impact

Abstract. Personality traits have been long recognized to have a strong impact on human decision-making. In this study, a sample of 314 participants took part in an online game to investigate the impact of psychopathic traits on cooperative behavior in an iterated Prisoner’s dilemma game. We found that disinhibition decreased the maintenance of cooperation in successive plays, but had no effect on moving toward cooperation after a previous defection or on the overall level of cooperation over rounds. Furthermore, our results underline the crucial importance of a good model selection procedure, showing how a poor choice of statistical model can provide misleading results.

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