Sequential Decision Making in a Conflict Environment

1972 ◽  
Vol 14 (6) ◽  
pp. 561-571
Author(s):  
Herbert C. Puscheck ◽  
James H. Greene
Keyword(s):  
Decision Making ◽  
Decision Time ◽  
Sequential Decision Making ◽  
Decision Making Process ◽  
Sequential Decision ◽  
Military Academy ◽  
Simple Strategy ◽  
Lower Ranking ◽  
Decision Making Models ◽  
The U.S

A two-sided wargame simulation and four decision making models to play one side of the game were developed. The game and models were used to study the decision making process exhibited by 64 students at the U.S. Military Academy. It was concluded that these students utilized a simple strategy; decisions were unaffected, within the range indicated by opponent decision delays; students displayed a learning effect during the game; there existed a positive correlation between mean decision time and score; academically lower ranking students received higher scores than higher ranking players; and players received higher scores when opposing certain more sophisticated opponents than when opposing selected simpler models. The results are discussed. The wargame and associated decision making models were run on a GE-225 computer from remote Teletype terminals. The investigation suggests a number of additional applications for the wargame and decision making models.

Simplifying Iterations in Cross-Functional Design Decision Making

1997 ◽  
Vol 119 (4) ◽  
pp. 485-493 ◽  
Author(s):  
V. Krishnan ◽  
S. D. Eppinger ◽  
D. E. Whitney
Keyword(s):  
Decision Making ◽  
Catalytic Converter ◽  
Sequential Decision Making ◽  
Decision Making Process ◽  
Design Decision ◽  
Functional Design ◽  
Sequential Decision ◽  
Diagnostic Systems ◽  
Design Quality ◽  
And Task

In this paper, we consider the cross-functional design decision making process and discuss how sequential decision making leads to a degradation in design quality even when downstream design tasks are not rendered infeasible by preceding upstream decisions. We focus on the problem of simplifying the design iterations required to address this quality loss. Two properties, called sequence invariance and task invariance, are introduced to help reduce the complexity of subsequent design iterations. We also discuss how these properties may be used by designers in situations where mathematical descriptions of the design performance characteristics are unavailable. We illustrate the utility of these properties by showing their applicability to the design of catalytic converter diagnostic systems at a major U.S. automotive firm.

EEG Classification with a Sequential Decision-Making Method in Motor Imagery BCI

2017 ◽  
Vol 27 (08) ◽  
pp. 1750046 ◽  
Author(s):  
Rong Liu ◽  
Yongxuan Wang ◽  
Geoffrey I. Newman ◽  
Nitish V. Thakor ◽  
Sarah Ying
Keyword(s):  
Decision Making ◽  
Stopping Time ◽  
Decision Time ◽  
Sequential Decision Making ◽  
Ratio Test ◽  
Sequential Decision ◽  
Eeg Classification ◽  
Average Maximum ◽  
Subject Specific ◽  
Speed Accuracy

To develop subject-specific classifier to recognize mental states fast and reliably is an important issue in brain–computer interfaces (BCI), particularly in practical real-time applications such as wheelchair or neuroprosthetic control. In this paper, a sequential decision-making strategy is explored in conjunction with an optimal wavelet analysis for EEG classification. The subject-specific wavelet parameters based on a grid-search method were first developed to determine evidence accumulative curve for the sequential classifier. Then we proposed a new method to set the two constrained thresholds in the sequential probability ratio test (SPRT) based on the cumulative curve and a desired expected stopping time. As a result, it balanced the decision time of each class, and we term it balanced threshold SPRT (BTSPRT). The properties of the method were illustrated on 14 subjects’ recordings from offline and online tests. Results showed the average maximum accuracy of the proposed method to be 83.4% and the average decision time of 2.77[Formula: see text]s, when compared with 79.2% accuracy and a decision time of 3.01[Formula: see text]s for the sequential Bayesian (SB) method. The BTSPRT method not only improves the classification accuracy and decision speed comparing with the other nonsequential or SB methods, but also provides an explicit relationship between stopping time, thresholds and error, which is important for balancing the speed-accuracy tradeoff. These results suggest that BTSPRT would be useful in explicitly adjusting the tradeoff between rapid decision-making and error-free device control.

Organizational Decision-Making Structures in the Age of Artificial Intelligence

2019 ◽  
Vol 61 (4) ◽  
pp. 66-83 ◽  
Author(s):  
Yash Raj Shrestha ◽  
Shiko M. Ben-Menahem ◽  
Georg von Krogh
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Search Space ◽  
Sequential Decision Making ◽  
Decision Making Process ◽  
Sequential Decision ◽  
Organizational Decision Making ◽  
Process And Outcome ◽  
Alternative Set

How does organizational decision-making change with the advent of artificial intelligence (AI)-based decision-making algorithms? This article identifies the idiosyncrasies of human and AI-based decision making along five key contingency factors: specificity of the decision search space, interpretability of the decision-making process and outcome, size of the alternative set, decision-making speed, and replicability. Based on a comparison of human and AI-based decision making along these dimensions, the article builds a novel framework outlining how both modes of decision making may be combined to optimally benefit the quality of organizational decision making. The framework presents three structural categories in which decisions of organizational members can be combined with AI-based decisions: full human to AI delegation; hybrid—human-to-AI and AI-to-human—sequential decision making; and aggregated human–AI decision making.

Modeling Freeway Merging in a Weaving Section as a Sequential Decision-Making Process

2017 ◽  
Vol 143 (5) ◽  
pp. 05017002 ◽  
Author(s):  
Xia Wan ◽  
Peter J. Jin ◽  
Haiyan Gu ◽  
Xiaoxuan Chen ◽  
Bin Ran
Keyword(s):  
Decision Making ◽  
Sequential Decision Making ◽  
Decision Making Process ◽  
Sequential Decision
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