scholarly journals Decision-making competence predicts domain-specific risk attitudes

2015 ◽  
Vol 6 ◽  
Author(s):  
Joshua A. Weller ◽  
Andrea Ceschi ◽  
Caleb Randolph
Keyword(s):  
Decision Making ◽  
Risk Attitudes ◽  
Domain Specific ◽  
Specific Risk

Toward Considering Risk Attitudes in Engineering Organizations Using Utility Theory

2012 ◽  
Author(s):  
Douglas Van Bossuyt ◽  
Chris Hoyle ◽  
Irem Y. Tumer ◽  
Andy Dong ◽  
Toni Doolen ◽  
...  
Keyword(s):  
Decision Making ◽  
Risk Attitude ◽  
Expected Value ◽  
Utility Functions ◽  
Risk Attitudes ◽  
Risk Averse ◽  
Past Performance ◽  
Domain Specific ◽  
Specific Risk ◽  
Engineering Organizations

Design projects within large engineering organizations involve numerous uncertainties that can lead to unacceptably high levels of risk. Practicing designers recognize the existence of risk and commonly are aware of events that raise risk levels. However, a disconnect exists between past project performance and current project execution that limits decision-making. This disconnect is primarily due to a lack of quantitative models that can be used for rational decision-making. Methods and tools used to make decisions in risk-informed design generally use an expected value approach. Research in the psychology domain has shown that decision-makers and stakeholders have domain-specific risk attitudes that often have variations between individuals and between companies. Risk methods used in engineering such as Failure Modes and Effects Analysis (FMEA), Fault Tree Analysis (FTA), and others are often ill-equipped to help stakeholders make decisions based upon risk-tolerant or risk-averse decision-making conditions. This paper focuses on the specific issue of helping stakeholders make decisions under risk-tolerant or risk-averse decision-making conditions and presents a novel method of translating engineering risk data from the domain of expected value into a domain corrected for risk attitude. This is done by using risk utility functions derived from the Engineering-Domain-Specific Risk-Taking (E-DOSPERT) test. This method allows decisions to be made based upon data that is risk attitude corrected. Further, the method uses an aspirational measure of risk attitude as opposed to existing lottery methods of generating utility functions that are based upon past performance. An illustrative test case using a simplified space mission designed in a collaborative design center environment is included. The method is shown to change risk-informed decisions in certain situations where a risk-tolerant or risk-averse decision-maker would likely choose differently than the dictates of the expected value approach.

Risk attitudes in risk-based design: Considering risk attitude using utility theory in risk-based design

2012 ◽  
Vol 26 (4) ◽  
pp. 393-406 ◽  
Author(s):  
Douglas Van Bossuyt ◽  
Chris Hoyle ◽  
Irem Y. Tumer ◽  
Andy Dong
Keyword(s):  
Collaborative Design ◽  
Failure Modes ◽  
Expected Value ◽  
Utility Functions ◽  
Risk Attitudes ◽  
Risk Averse ◽  
Domain Specific ◽  
Industrial Sectors ◽  
Specific Risk ◽  
Risk Appetite

AbstractEngineering risk methods and tools account for and make decisions about risk using an expected-value approach. Psychological research has shown that stakeholders and decision makers hold domain-specific risk attitudes that often vary between individuals and between enterprises. Moreover, certain companies and industries (e.g., the nuclear power industry and aerospace corporations) are very risk-averse whereas other organizations and industrial sectors (e.g., IDEO, located in the innovation and design sector) are risk tolerant and actually thrive by making risky decisions. Engineering risk methods such as failure modes and effects analysis, fault tree analysis, and others are not equipped to help stakeholders make decisions under risk-tolerant or risk-averse decision-making conditions. This article presents a novel method for translating engineering risk data from the expected-value domain into a risk appetite corrected domain using utility functions derived from the psychometric Engineering Domain-Specific Risk-Taking test results under a single-criterion decision-based design approach. The method is aspirational rather than predictive in nature through the use of a psychometric test rather than lottery methods to generate utility functions. Using this method, decisions can be made based upon risk appetite corrected risk data. We discuss development and application of the method based upon a simplified space mission design in a collaborative design-center environment. The method is shown to change risk-based decisions in certain situations where a risk-averse or risk-tolerant decision maker would likely choose differently than the expected-value approach dictates.

An Evolutionary Domain-Specific Risk Scale

2013 ◽  
Author(s):  
Andreas Wilke ◽  
Amanda Sherman ◽  
Bonnie Curdt ◽  
Sumona Mondal ◽  
Carey Fitzgerald ◽  
...  
Keyword(s):  
Domain Specific ◽  
Specific Risk ◽  
Risk Scale

An evolutionary domain-specific risk scale.

2014 ◽  
Vol 8 (3) ◽  
pp. 123-141 ◽  
Author(s):  
Andreas Wilke ◽  
Amanda Sherman ◽  
Bonnie Curdt ◽  
Sumona Mondal ◽  
Carey Fitzgerald ◽  
...  
Keyword(s):  
Domain Specific ◽  
Specific Risk ◽  
Risk Scale

scholarly journals Neural Responses to Ambiguity Involve Domain-general and Domain-specific Emotion Processing Systems

2013 ◽  
Vol 25 (4) ◽  
pp. 547-557 ◽  
Author(s):  
Maital Neta ◽  
William M. Kelley ◽  
Paul J. Whalen
Keyword(s):  
Decision Making ◽  
Facial Expressions ◽  
Visual Processing ◽  
Neural Responses ◽  
Social Signals ◽  
Emotional Facial Expressions ◽  
Biologically Relevant ◽  
Domain Specific ◽  
Specific Emotion ◽  
On Line

Extant research has examined the process of decision making under uncertainty, specifically in situations of ambiguity. However, much of this work has been conducted in the context of semantic and low-level visual processing. An open question is whether ambiguity in social signals (e.g., emotional facial expressions) is processed similarly or whether a unique set of processors come on-line to resolve ambiguity in a social context. Our work has examined ambiguity using surprised facial expressions, as they have predicted both positive and negative outcomes in the past. Specifically, whereas some people tended to interpret surprise as negatively valenced, others tended toward a more positive interpretation. Here, we examined neural responses to social ambiguity using faces (surprise) and nonface emotional scenes (International Affective Picture System). Moreover, we examined whether these effects are specific to ambiguity resolution (i.e., judgments about the ambiguity) or whether similar effects would be demonstrated for incidental judgments (e.g., nonvalence judgments about ambiguously valenced stimuli). We found that a distinct task control (i.e., cingulo-opercular) network was more active when resolving ambiguity. We also found that activity in the ventral amygdala was greater to faces and scenes that were rated explicitly along the dimension of valence, consistent with findings that the ventral amygdala tracks valence. Taken together, there is a complex neural architecture that supports decision making in the presence of ambiguity: (a) a core set of cortical structures engaged for explicit ambiguity processing across stimulus boundaries and (b) other dedicated circuits for biologically relevant learning situations involving faces.

An Investigation Into the Driving Factors of Creativity in Design for Additive Manufacturing

2017 ◽  
Author(s):  
Michael Barclift ◽  
Timothy W. Simpson ◽  
Maria Alessandra Nusiner ◽  
Scarlett Miller
Keyword(s):  
Additive Manufacturing ◽  
Self Report ◽  
Risk Attitudes ◽  
Design For Additive Manufacturing ◽  
Domain Specific ◽  
Common Framework ◽  
And Performance ◽  
Student Risk ◽  
Prior Experiences ◽  
Design Freedom

Additive manufacturing (AM) provides engineers with nearly unlimited design freedom, but how much do they take advantage of that freedom? The objective is to understand what factors influence a designer’s creativity and performance in Design for Additive Manufacturing (DFAM). Inspired by the popular Marshmallow Challenge, this exploratory study proposes a framework in which participants apply their DFAM skills in sketching, CAD modeling, 3D-Printing, and a part testing task. Risk attitudes are assessed through the Engineering Domain-Specific Risk-Taking (E-DOSPERT) scale, and prior experiences are captured by a self-report skills survey. Multiple regression analysis found that the average novelty of the participant’s ideas, engineering degree program, and risk seeking preference were statistically significant when predicting the performance of their ideas in AM. This study provides a common framework for AM educators to assess students’ understanding and creativity in DFAM, while also identifying student risk attitudes when conducting an engineering design task.

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