A LAMSTAR Network-Based Human Judgment Analysis

2017 ◽  
Vol 47 (6) ◽  
pp. 951-957 ◽  
Author(s):  
Jae M. Yoon ◽  
David He ◽  
Matthew L. Bolton
Keyword(s):  
Human Judgment ◽  
Judgment Analysis

Measuring Judgment Interaction with Displays and Automation

2002 ◽  
Vol 46 (3) ◽  
pp. 511-515
Author(s):  
Amy R. Pritchett ◽  
Ann M. Bisantz
Keyword(s):  
Collision Detection ◽  
Model Analysis ◽  
Automated System ◽  
Lens Model ◽  
Automated Systems ◽  
Human Judgment ◽  
Judgment Analysis ◽  
Main Effects ◽  
The Impact

Methodologies for assessing human judgment in complex domains are important for design of both displays that inform judgments and automated systems that suggest judgments. This paper applies n-system Lens Model methods for evaluating human judgments, examining the impact of displays, and assessing the similarity between human judgments and the judgment policies used by automated systems. First, the need for and concepts underlying, judgment analysis are outlined. Then the n-system Lens Model and its parameters are formally described. This model is then used to examine a study of aircraft collision detection examined previously using standard ANOVA methods. Our analysis found the same main effects as the earlier analysis. However, the n-system Lens Model analysis provided greater resolution regarding the information relied upon for judgments, the impact of displays on judgment, and the attributes of human judgments that are - and are not - similar to judgments produced by automated system

Haunted by a Doppelgänger

2014 ◽  
Vol 61 (1) ◽  
pp. 12-22 ◽  
Author(s):  
Bettina von Helversen ◽  
Stefan M. Herzog ◽  
Jörg Rieskamp
Keyword(s):  
Relevant Information ◽  
Psychological Research ◽  
Decision Makers ◽  
Human Judgment ◽  
Rule Based ◽  
New Employees ◽  
High Performing ◽  
Facial Similarity ◽  
Similarity Effect ◽  
Judgment Strategy

Judging other people is a common and important task. Every day professionals make decisions that affect the lives of other people when they diagnose medical conditions, grant parole, or hire new employees. To prevent discrimination, professional standards require that decision makers render accurate and unbiased judgments solely based on relevant information. Facial similarity to previously encountered persons can be a potential source of bias. Psychological research suggests that people only rely on similarity-based judgment strategies if the provided information does not allow them to make accurate rule-based judgments. Our study shows, however, that facial similarity to previously encountered persons influences judgment even in situations in which relevant information is available for making accurate rule-based judgments and where similarity is irrelevant for the task and relying on similarity is detrimental. In two experiments in an employment context we show that applicants who looked similar to high-performing former employees were judged as more suitable than applicants who looked similar to low-performing former employees. This similarity effect was found despite the fact that the participants used the relevant résumé information about the applicants by following a rule-based judgment strategy. These findings suggest that similarity-based and rule-based processes simultaneously underlie human judgment.

Applications of computer technology to problems of human judgment and interpersonal conflict

1971 ◽  
Author(s):  
David A. Summers ◽  
Thomas R. Stewart ◽  
Peter J. R. Boyle ◽  
Monroe J. Miller ◽  
Kenneth R. Hammond
Keyword(s):  
Computer Technology ◽  
Interpersonal Conflict ◽  
Human Judgment

scholarly journals Humans rely more on algorithms than social influence as a task becomes more difficult

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Eric Bogert ◽  
Aaron Schecter ◽  
Richard T. Watson
Keyword(s):  
Decision Making ◽  
Social Media ◽  
Social Influence ◽  
Correct Answer ◽  
Online Reviews ◽  
Personal Networks ◽  
Human Judgment ◽  
Individual Decision ◽  
Individual Decision Making

AbstractAlgorithms have begun to encroach on tasks traditionally reserved for human judgment and are increasingly capable of performing well in novel, difficult tasks. At the same time, social influence, through social media, online reviews, or personal networks, is one of the most potent forces affecting individual decision-making. In three preregistered online experiments, we found that people rely more on algorithmic advice relative to social influence as tasks become more difficult. All three experiments focused on an intellective task with a correct answer and found that subjects relied more on algorithmic advice as difficulty increased. This effect persisted even after controlling for the quality of the advice, the numeracy and accuracy of the subjects, and whether subjects were exposed to only one source of advice, or both sources. Subjects also tended to more strongly disregard inaccurate advice labeled as algorithmic compared to equally inaccurate advice labeled as coming from a crowd of peers.

Dependent judgment analysis: A Markov chain based approach for aggregating crowdsourced opinions

2017 ◽  
Vol 396 ◽  
pp. 83-96 ◽  
Author(s):  
Sujoy Chatterjee ◽  
Anirban Mukhopadhyay ◽  
Malay Bhattacharyya
Keyword(s):  
Markov Chain ◽  
Judgment Analysis
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