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Risk attitudes in risk-based design: Considering risk attitude using utility theory in risk-based design
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Engineering 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.
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... This does not imply that a system cannot be designed without expressly addressing these factors, but rather available information on the psychological disposition of the SOMs should be incorporated to improve the system design process [6]. Methods already exist to understand the psychology [31] and risk attitudes of engineers designing systems [25], and incorporate those attitudes into design decision-making processes [32]. However, we are unaware of any existing methods or processes that explicitly consider the risk attitudes of SOMs in the context of operational availability of systems in general and especially in the context of Naval systems. ...
... Recent research indicates that a general risk aversion-risk seeking inclination may be present in individuals that applies across domains [35]. Risk attitude data from psychometric survey techniques has been found to be aspirational in nature while choice lotteries are generally predictive [32,36]. In this research, we take the perspective of aspirational risk attitude measures (i.e., psychometric risk surveys) in line with existing research on applying risk attitudes to engineering analyses and trade-off studies [32,37]. ...
... Risk attitude data from psychometric survey techniques has been found to be aspirational in nature while choice lotteries are generally predictive [32,36]. In this research, we take the perspective of aspirational risk attitude measures (i.e., psychometric risk surveys) in line with existing research on applying risk attitudes to engineering analyses and trade-off studies [32,37]. ...
Systems engineering practices in the maritime industry and the Navy consider operational availability as a system attribute determined by system components and a maintenance concept. A better understanding of the risk attitudes of system operators and maintainers may be useful in understanding potential impacts the system operators and maintainers have on operational availability. This article contributes to the literature a method that synthesizes the concepts of system reliability, and operator and maintainer risk attitudes to provide insight into the effect that risk attitudes of systems operators and maintainers have on system operational availability. The method consists of four steps providing the engineer with a risk-attitude-adjusted insight into the system's potential operational availability. Systems engineers may use the method to iterate a system's design or maintenance concept to improve expected operational availability. If it is deemed necessary to redesign a system, systems engineers will likely choose new system components and/or alter their configuration; however, redesign is not limited to physical alteration of the system. Several other options may be more practical depending the system's stage in the life cycle to address low risk-adjusted operational availability such as changes to maintenance programs and system supportability rather than on component and system reliability. A simple representative example implementation is provided to demonstrate the method and discussion of the potential implications for Navy ship availability are discussed. Potential future work is also discussed.
... We have further extended the work on dimension reduction during simulation or uncertainty propagation of the system and quantification of uncertainty of the quantities of interest using stochastic collocation methods [39] with a sparse grid technique [40]. Utility theory [41] and inverse reliability method [42] are applied for evaluating robust objective and validating probabilistic constraints. We provide detail descriptions on the methodologies in Secs. ...
... Objective and Validation of Probabilistic Constraints. We have modified our objective function into utility function [41] to calculate the maximum/minimum expected utility. Applying the Truncated KL expansion and stochastic collocation method as described in Secs. ...
A common issue in energy allocation problems is managing the tradeoff between selling surplus energy to maximize short-term revenue, versus holding surplus energy to hedge against future shortfalls. For energy allocation problems, this surplus represents resource flexibility. The decision maker has an option to sell or hold the flexibility for future use. As a decision in the current period can affect future decisions significantly, future risk evaluation of uncertainties is recommended for the current decision in which a traditional robust optimization is not efficient. Therefore, an approach to flexible-robust optimization has been formulated by integrating a real options (RO) model with the robust optimization framework. In the energy problem, the real option model evaluates the future risk, and provides the value of holding flexibility, whereas the robust optimization quantifies uncertainty and provides a robust solution of net revenue by selling flexibility. This problem is solved using bilevel programming and a complete general mathematical formulation of bilevel flexible-robust optimization model is presented for multireservoir systems and results shown to provide an efficient decision making process in energy sectors. To reduce the computational expense, mathematical techniques have been used in the proposed model to reduce the dimension in the quantification and propagation of uncertainties.
... Function expresses the intention of the designer. The concept of function bridges the gap between human intention and physical reality [231] and represents the goal the designer has for the [ 108,109,110,111,112,113,114,115,116] Risk, Reliability, and Resiliency in System Design [117,118,119,120,121,122,123,124] [ 125,126,127,128,129,130,79,131] [ 132,133,134,135,136,137,138,139] [ 140,141,142,143,144,145,146] [ 147,148,149,150,151,152] system [232]. Erden et al. provide a broad survey of functional modeling approaches [233]. ...
In the context of model-based product and system design, the capability to assess the impact of potential component faults, undesired interactions, and fault propagation is important for design decision-making. Addressing these potential negative outcomes should occur as early in the design process as possible to enable designers to make impactful changes to the design. To this end, a set of tools and methods have been developed over the last 20 years that leverage a function-based approach assessing the potential faults and fault propagation and develop system health management strategies. These tools and methods must overcome challenges of high abstraction and satisfaction of safety or risk requirements with limited design specification. This paper provides a detailed survey of a particular function-based analysis tool as a lens to understanding the challenges for other tools in this domain. Specifically, development and evolution of the Function Failure Identification and Propagation Framework (FFIP) is used as a lens to survey the challenges of this field. The objective of this paper is to explore the specific challenges and advancements of the FFIP framework and related tools that address similar modeling and analysis challenges. We provide an overall categorization and summary of the research efforts to date and identify specific known limitations and unaddressed challenges in the area of design-stage system risk and safety analysis.
... Similar work has been conducted by McIntire in a mechanical design context [117]. This is also similar to work done on engineering risk attitudes [118,119]. ...
Increasingly tight coupling and heavy connectedness in systems of systems (SoS) presents new problems for systems designers and engineers. While the failure of one system within a SoS may produce little collateral damage beyond a loss in SoS capability, a highly interconnected SoS can experience significant damage when one member system fails in an unanticipated way. It is therefore important to develop systems that are “good neighbors” with the other systems in a SoS by failing in ways that do not further degrade a SoS’s ability to complete its mission.
This paper presents a method to (1) analyze a system for potential spurious emissions and (2) choose mitigation strategies that provide the best return on investment for the SoS. The method is suited for use during the system architecture phase of the system design process. A functional and flow approach to analyzing spurious emissions and developing mitigation strategies is used in the method. Use of the method may result in a system that causes less SoS damage during a failure event.
... Decision theory and utility theory have been used to help understand how people can appear to behave irrationally, [76][77][78][79][80][81] including how neural systems work 82,83 Through the application of utility and decision theory, it is now possible to develop system models that deviate from the expected value theorem and instead match a specific utility function of either an individual or an organization. 84 We contend that (much like humans) while a system may appear to be behaving irrationally to an outside observer, the system's utility function may be different from the observer's expectation. In other words, the system is behaving normally based on its own internal utility function but appears to an external observer to be behaving irrationally. ...
System of interest (SoI) failures can sometimes be traced to an unexpected behavior occurring within another system that is a member of the system of systems (SoS) with the SoI. This article presents a method for use when designing an SoI that helps to analyze an SoS for unexpected behaviors from existing SoS members during the SoI's conceptual functional modeling phase of system architecture. The concept of irrationality initiators—unanticipated or unexpected failure flows emitted from one system that adversely impact an SoI, which appear to be impossible or irrational to engineers developing the new system—is introduced and implemented in a quantitative risk analysis method. The method is implemented in the failure flow identification and propagation framework to yield a probability distribution of failure paths through an SoI in the SoS. An example of a network of autonomous vehicles operating in a partially denied environment is presented to demonstrate the method. The method presented in this paper allows practitioners to more easily identify potential failure paths and prioritize fixing vulnerabilities in an SoI during functional modeling when significant changes can still be made with minimal impact to cost and schedule.
Prognostics and Health Management (PHM) techniques have traditionally been used to analyze electrical and mechanical systems, but similar techniques can be adapted for less mechatronically-focused processes such as crewed space missions. By applying failure analysis techniques taken from PHM, the probability of success for missions can be calculated. Extensive work has been conducted to predict space mission failure, but many existing methods do not take full advantage of modern computing power and the potential for real-time calculation of mission failure probabilities. The Active Mission Success Estimation (AMSE) method is developed in this paper to track and calculate the probability of mission failure as the mission progresses, and is intentionally adaptable for shifting mission objectives and parameters. This form of mission modelling takes a broader view of the mission and objectives, and develops statistical probability models of success or failure for multiple possible choice combinations that is used to inform real-time decisions and maximize probability of mission success. A case study of a generalized crewed Mars mission that has turned into a survival scenario is considered where an astronaut has been left behind on the surface and must survive for an extended period of time before undertaking a long-distance journey to a new launch site for rescue and return to Earth. The AMSE method presented here aims to establish real-time probabilistic modeling of decision outcomes during an active mission and can be used to inform mission decisions.
This paper proposes a semi-automatic methodology to assist the user in creating surveys about FMEA and Risk Analysis, based on a customized use of the tools for semantic analysis and in particular a home-developed syntactic parser called Kompat Cognitive. The core of this work has been the analysis of the specific FMEA-related jargon and its common modalities of description within scientific papers and patents in order to systematize the linguistic analysis of the reference documents within the proposed step-divided procedure. The main goals of the methodology are to assist not skilled in the art users about FMEA during the analysis of generic and specific features, by considering large moles of contributions in restricted amounts of time. The methodology has then been tested on the same pool of 286 documents, divided between 177 and 109 patents, manually analyzed in our previous survey, in order to replicate part of its classifications through the proposed new modality. In this way we evaluated the abilities of the methodology both to automatically suggesting the main features of interest and to classify the documents according to them.
Failure Modes and Effects Analysis (FMEA) has been applied in a large series of cases from different sectors, such as automotive, electronics, construction and services, and has become a standard procedure in many companies for quality control and for the design of new products. FMEA has also a great following in the scientific community as testified by the vast multitude of related documents from scientific and patent literature; to date, more than 3,600 papers in Scopus Database (DB) and 146 patents in Espacenet DB come up by just searching for FMEA without synonyms, with a trend of constant growth over the years. The chapter proposes a semi-automatic semantic analysis about documents related to FMEA modifications and the subsequent manual review for reassuming each of them through a simple sentence made by a causal chain including the declaration of the goals, the followed strategies, and integrations with methods/tools.
In graph-based function models, the function verbs and flow nouns are usually controlled by predefined vocabularies. The vocabulary class definitions, combined with function modeling grammars defined at various levels of formalism, enable function-based reasoning. However, the text written in plain English for the names of the functions and flows is presently not exploited for formal reasoning. This paper presents a formalism (representation and reasoning) to support semantic and physics-based reasoning on function models, esp. to automatically decompose black box models and to generate multiple design alternatives, using the information hidden in those plain-English texts. First, semantic reasoning infers the changes of flow types, flow attributes, and the direction of those changes between the input and output of the black box. Then, a representation of qualitative physics is used to determine the energy exchanges between the flows and the function features capable of achieving them. Finally, topological reasoning is used to infer multiple options of composing those function features into topologies and thus generate multiple alternative designs. The data representation formalizes flow phases, flow attributes, qualitative value scales, and qualitative physics laws. An eight-step algorithm manipulates this data for reasoning. The paper shows two validation case studies to demonstrate these workings of this formalism.
This work is motivated by a desire to put DfM on solid theoretical foundations. The paper evaluates measures of manufacturability and classes of DfM methods and frameworks independent of the specific manufacturing processes. Criteria used in evaluation include theoretical foundation, accuracy, flexibility in choosing utility/objective function, domain independence, ease of use, level and extent of information required, computational cost, ability to incorporate uncertainty and market factors. We introduce a DfM approach based on Benefit/Cost analysis. All design utilities are lumped into a single “Design Benefit (RD)”, all manufacturability factors into another parameter “Manufacturing Rating (RM)”, and then techniques of benefit-cost analysis and value engineering are used to make decisions about design improvements. Use of overall and marginal DfM ratings allows trade-offs to be made. Any set of desired objectives can be used for computing the ratings. It is also possible to incorporate design or manufacturing constraints.
This paper summarizes useful concepts for analyzing attitude toward risk taking in decision analysis practice. Particular attention is given to the exponential utility function which is widely used in applications. Conditions are reviewed under which this utility function form is appropriate. Tables are presented which aid in using the exponential utility function, including ± nding the value of the risk tolerance. The use of the exponential utility function is considered in analyzing portfolio decisions and determining the value of perfect information. The accuracy is considered of an approximate formula for determining certainty equivalents when the exponential utility function holds. Exercises on this material are also included.
Collaborative design centers often employ software tools to conduct trade studies. Commonly, this takes the form of a software program to aggregate and pass data between multiple computer workstations. This allows multiple people to concurrently create a conceptual design. Trade study software continues to evolve to meet the demands of modern collaborative design centers. However, the risks associated with moving from one trade study software tool to another are not well understood. Additionally, little is known about the software preferences of Collaborative Design Center (CDC) staff. This paper determines software preferences of two user groups consisting of graduate and undergraduate mechanical engineering students. This paper then explores the risks in deploying new trade study software in a collaborative design center. A method for estimating and mitigating risks with changing trade study software is presented. Recommendations for a smooth transition between software packages are given. The risk model developed in this paper offers a quick way of estimating and mitigating conversion risk for collaborative design centers.