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.

The Price of Predictability: Estimating Inconsistency Premiums in Social Interactions

2021 ◽  
pp. 014616722199853
Author(s):  
Judith Gerten ◽  
Michael K. Zürn ◽  
Sascha Topolinski
Keyword(s):  
Risk Premium ◽  
Psychological Research ◽  
Expected Value ◽  
Decision Makers ◽  
Expected Returns ◽  
Expected Return ◽  
Total N ◽  
Financial Decision ◽  
Variance Risk ◽  
Risky Option

For financial decision-making, people trade off the expected value (return) and the variance (risk) of an option, preferring higher returns to lower ones and lower risks to higher ones. To make decision-makers indifferent between a risky and risk-free option, the expected value of the risky option must exceed the value of the risk-free option by a certain amount—the risk premium. Previous psychological research suggests that similar to risk aversion, people dislike inconsistency in an interaction partner’s behavior. In eight experiments (total N = 2,412) we pitted this inconsistency aversion against the expected returns from interacting with an inconsistent partner. We identified the additional expected return of interacting with an inconsistent partner that must be granted to make decision-makers prefer a more profitable, but inconsistent partner to a consistent, but less profitable one. We locate this inconsistency premium at around 31% of the expected value of the risk-free option.

The Group Engagement Model: Procedural Justice, Social Identity, and Cooperative Behavior

2003 ◽  
Vol 7 (4) ◽  
pp. 349-361 ◽  
Author(s):  
Tom R. Tyler ◽  
Steven L. Blader
Keyword(s):  
Social Identity ◽  
Procedural Justice ◽  
Cooperative Behavior ◽  
Relevant Information ◽  
Psychological Research ◽  
Relational Model ◽  
Mediation Hypothesis ◽  
Value Model ◽  
Group Engagement ◽  
Group Engagement Model

The group engagement model expands the insights of the group-value model of procedural justice and the relational model of authority into an explanation for why procedural justice shapes cooperation in groups, organizations, and societies. It hypothesizes that procedures are important because they shape people's social identity within groups, and social identity in turn influences attitudes, values, and behaviors. The model further hypothesizes that resource judgments exercise their influence indirectly by shaping social identity. This social identity mediation hypothesis explains why people focus on procedural justice, and in particular on procedural elements related to the quality of their interpersonal treatment, because those elements carry the most social identity-relevant information. In this article, we review several key insights of the group engagement model, relate these insights to important trends in psychological research on justice, and discuss implications of the model for the future of procedural justice research.

CAN BAYESIAN METHODS MAKE DATA AND ANALYSES MORE RELEVANT TO DECISION MAKERS?

2001 ◽  
Vol 17 (1) ◽  
pp. 114-122 ◽  
Author(s):  
Steven H. Sheingold
Keyword(s):  
Decision Making ◽  
Bayesian Methods ◽  
Paradigm Shift ◽  
Relevant Information ◽  
Decision Makers ◽  
Evidence Based ◽  
Critical Importance ◽  
Bayesian Approaches ◽  
Bayesian Analyses ◽  
And Performance

Decision making in health care has become increasingly reliant on information technology, evidence-based processes, and performance measurement. It is therefore a time at which it is of critical importance to make data and analyses more relevant to decision makers. Those who support Bayesian approaches contend that their analyses provide more relevant information for decision making than do classical or “frequentist” methods, and that a paradigm shift to the former is long overdue. While formal Bayesian analyses may eventually play an important role in decision making, there are several obstacles to overcome if these methods are to gain acceptance in an environment dominated by frequentist approaches. Supporters of Bayesian statistics must find more accommodating approaches to making their case, especially in finding ways to make these methods more transparent and accessible. Moreover, they must better understand the decision-making environment they hope to influence. This paper discusses these issues and provides some suggestions for overcoming some of these barriers to greater acceptance.

A Methodology for Deliberating Prediction Criteria

2011 ◽  
Vol 130-134 ◽  
pp. 1758-1761 ◽  
Author(s):  
Chia Yean Lim ◽  
Vincent K.T. Khoo ◽  
Bahari Belaton
Keyword(s):  
Decision Makers ◽  
Predictive Analysis ◽  
Economic Downturn ◽  
Relative Importance ◽  
Rule Based ◽  
Customer Needs ◽  
Prediction Criteria ◽  
Unstructured Information ◽  
New Criteria ◽  
Technology Trends

The economic downturn has been forcing many companies to use predictive analysis for spotting emerging product and technology trends and also future customer needs. Since every company is unique, without the assistance of some methodologies and tools, decision makers encounter great difficulties in conducting predictive analysis, especially in the deliberation and prioritization of new prediction criteria derived from the publicly available unstructured information. This paper proposes a unique methodology which attempts to integrate the personalization and visualization of new prediction criteria. The challenging iterative tasks are achieved through a rule-based inconsistency detection triad-based comparison algorithm, supported by sophisticated visual displays of the relative importance among the prediction criteria. It is hoped that the proposed methodology will intuitively support the decision makers in exploring and deliberating new criteria for making better predictions.

Mixed qualitative–quantitative approach for bidding decisions in construction

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Emad Mohamed ◽  
Parinaz Jafari ◽  
Ahmed Hammad
Keyword(s):  
Expert System ◽  
Expert Knowledge ◽  
The Body ◽  
Decision Makers ◽  
Quantitative Approach ◽  
Rule Based ◽  
Content Type ◽  
Factors Affecting ◽  
Qualitative And Quantitative ◽  
Mixed Approach

PurposeThe bid/no-bid decision is critical to the success of construction contractors. The factors affecting the bid/no-bid decision are either qualitative or quantitative. Previous studies on modeling the bidding decision have not extensively focused on distinguishing qualitative and quantitative factors. Thus, the purpose of this paper is to improve the bidding decision in construction projects by developing tools that consider both qualitative and quantitative factors affecting the bidding decision.Design/methodology/approachThis study proposes a mixed qualitative-quantitative approach to deal with both qualitative and quantitative factors. The mixed qualitative-quantitative approach is developed by combining a rule-based expert system and fuzzy-based expert system. The rule-based expert system is used to evaluate the project based on qualitative factors and the fuzzy expert system is used to evaluate the project based on the quantitative factors in order to reach the comprehensive bid/no-bid decision.FindingsThree real bidding projects are used to investigate the applicability and functionality of the proposed mixed approach and are tested with experts of a construction company in Alberta, Canada. The results demonstrate that the mixed approach provides a more reliable, accurate and practical tool that can assist decision-makers involved in the bid/no-bid decision.Originality/valueThis study contributes theoretically to the body of knowledge by (1) proposing a novel approach capable of modeling all types of factors (either qualitative or quantitative) affecting the bidding decision, and (2) providing means to acquire, store and reuse expert knowledge. Practical contribution of this paper is to provide decision-makers with a comprehensive model that mimics the decision-making process and stores experts' knowledge in the form of rules. Therefore, the model reduces the administrative burden on the decision-makers, saves time and effort and reduces bias and human errors during the bidding process.

scholarly journals Interval Forecasting in Supply Chain with Small Sample

2018 ◽  
Vol 2018 ◽  
pp. 1-10
Author(s):  
Lifeng Wu ◽  
Yan Chen
Keyword(s):  
Supply Chain ◽  
Small Sample ◽  
Decision Makers ◽  
Small Samples ◽  
Forecasting Model ◽  
Future Trends ◽  
Human Judgment ◽  
Proposed Model ◽  
Grey Models ◽  
Short Time

To deal with the forecasting with small samples in the supply chain, three grey models with fractional order accumulation are presented. Human judgment of future trends is incorporated into the order number of accumulation. The output of the proposed model will provide decision-makers in the supply chain with more forecasting information for short time periods. The results of practical real examples demonstrate that the model provides remarkable prediction performances compared with the traditional forecasting model.

Augmenting Medical Diagnosis Decisions? An Investigation into Physicians’ Decision-Making Process with Artificial Intelligence

2021 ◽  
Author(s):  
Ekaterina Jussupow ◽  
Kai Spohrer ◽  
Armin Heinzl ◽  
Joshua Gawlitza
Keyword(s):  
Artificial Intelligence ◽  
Decision Making ◽  
Medical Errors ◽  
Decision Makers ◽  
Medical Decision ◽  
Decision Making Process ◽  
Self Monitoring ◽  
Rule Based ◽  
Human Actors

Systems based on artificial intelligence (AI) increasingly support physicians in diagnostic decisions, but they are not without errors and biases. Failure to detect those may result in wrong diagnoses and medical errors. Compared with rule-based systems, however, these systems are less transparent and their errors less predictable. Thus, it is difficult, yet critical, for physicians to carefully evaluate AI advice. This study uncovers the cognitive challenges that medical decision makers face when they receive potentially incorrect advice from AI-based diagnosis systems and must decide whether to follow or reject it. In experiments with 68 novice and 12 experienced physicians, novice physicians with and without clinical experience as well as experienced radiologists made more inaccurate diagnosis decisions when provided with incorrect AI advice than without advice at all. We elicit five decision-making patterns and show that wrong diagnostic decisions often result from shortcomings in utilizing metacognitions related to decision makers’ own reasoning (self-monitoring) and metacognitions related to the AI-based system (system monitoring). As a result, physicians fall for decisions based on beliefs rather than actual data or engage in unsuitably superficial evaluation of the AI advice. Our study has implications for the training of physicians and spotlights the crucial role of human actors in compensating for AI errors.

Conceptualizing Emergency Department Scenarios

2015 ◽  
pp. 1351-1368 ◽  
Author(s):  
Maya Kaner ◽  
Tamar Gadrich ◽  
Shuki Dror ◽  
Yariv N. Marmor
Keyword(s):  
Emergency Department ◽  
Real Life ◽  
Relevant Information ◽  
Literature Survey ◽  
Decision Makers ◽  
Simulation Experiments ◽  
Management Staff ◽  
Ed Operations ◽  
Relevant Factors

To handle problems and trends in emergency department (ED) operations, designers and decision makers often simulate and evaluate various case-specific scenarios before testing them in a real-life environment. However, conceptualizing broad possible scenarios for ED operations prior to simulation operationalization is usually neglected. The authors developed a methodology that integrates design of simulation experiments (DSE) as follows: 1) From a literature survey, they culled generic factors whose varying levels determine possible scenarios; 2) the authors drew up a set of generic interactions among these generic factors; 3) a questionnaire was constructed to serve as an instrument to gather the relevant information from management staff about relevant factors, their levels and interactions for a specific ED. Questionnaire responses support a schematic conceptualization of scenarios that should be simulated for a specific ED. They illustrate the application of the authors' methodology for conceptualization of ED simulation scenarios in two different EDs.

Data Warehousing and OLAP

2008 ◽  
pp. 169-178
Author(s):  
Jose Hernandez-Orallo
Keyword(s):  
Information Systems ◽  
Data Warehousing ◽  
Relevant Information ◽  
Decision Makers

Information systems provide organizations with the necessary information to achieve their goals. Relevant information is gathered and stored to allow decision makers to obtain quick and elaborated reports from the data.

Conceptualizing Emergency Department Scenarios

2013 ◽  
Vol 4 (1) ◽  
pp. 30-46
Author(s):  
Maya Kaner ◽  
Tamar Gadrich ◽  
Shuki Dror ◽  
Yariv Marmor
Keyword(s):  
Emergency Department ◽  
Real Life ◽  
Relevant Information ◽  
Literature Survey ◽  
Decision Makers ◽  
Simulation Experiments ◽  
Management Staff ◽  
Ed Operations ◽  
Relevant Factors

To handle problems and trends in emergency department (ED) operations, designers and decision makers often simulate and evaluate various case-specific scenarios before testing them in a real-life environment. However, conceptualizing broad possible scenarios for ED operations prior to simulation operationalization is usually neglected. The authors developed a methodology that integrates design of simulation experiments (DSE) as follows: 1) From a literature survey, they culled generic factors whose varying levels determine possible scenarios; 2) the authors drew up a set of generic interactions among these generic factors; 3) a questionnaire was constructed to serve as an instrument to gather the relevant information from management staff about relevant factors, their levels and interactions for a specific ED. Questionnaire responses support a schematic conceptualization of scenarios that should be simulated for a specific ED. They illustrate the application of the authors’ methodology for conceptualization of ED simulation scenarios in two different EDs.

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