Flaws Lurking in Engineering Design-Decision Making: The Attribute Set Dissociation Problem

2016 ◽  
Author(s):  
Vijitashwa Pandey
Keyword(s):  
Decision Analysis ◽  
Engineering Design ◽  
Fundamental Problem ◽  
Design Decision ◽  
Design Problems ◽  
Design Decisions ◽  
Root Cause ◽  
Multiple Attributes ◽  
Lower Limits ◽  
Specific Definition

The applicability of theoretical decision analysis, while rationally sound, has eluded mainstream engineering design. A reason commonly overlooked is that basic concepts in decision analysis do not scale naturally to multiple attributes — which are encountered in, by far, most design problems. In this paper, we document a paradox when dealing with transactions involving multiple attributes. We show the possibility of a money pump where if we dissociate part of an attribute from a design, the rest of the design can be manipulated to get either a better design or create wealth out of nothing. To reconcile with paradox, it is argued that there is a fundamental problem dealing with multiple attributes where a frame of reference chosen (purposefully) ignores external inputs, assuming that design decisions happen in the vacuum of the frame chosen. For example, in a simple design valuation decision, the money amount committed does not necessarily come from a fixed range of negotiability (upper and lower limits) but is subject to change if significant changes in other attributes are possible. The root cause of this issue is that fungible attributes such as money can form a part of the attribute set or be trivially dissociated from it, if needed. We argue that this is rational behavior on a decision maker’s part. However, most utility formulations do not model it and lead to the paradox. We call this the attribute dissociation problem. A specific definition is provided as well as implications on design as well as preference elicitation methods are considered. Finally, formulations are presented that avoid this problem and recommendations are provided.

Taxonomy for classifying engineering decision problems and support systems

1995 ◽  
Vol 9 (5) ◽  
pp. 427-438 ◽  
Author(s):  
David G. Ullman ◽  
Bruce D'Ambrosio
Keyword(s):  
Decision Support ◽  
Engineering Design ◽  
Quantitative Data ◽  
Decision Problems ◽  
Design Decision ◽  
Decision Support Tools ◽  
Design Decisions ◽  
Wide Range ◽  
Support Tools ◽  
Need Support

AbstractThe design of even the simplest product requires thousands of decisions. Yet few of these decisions are supported with methods on paper or on computers. Is this because engineering design decisions do not need support or is it because techniques have yet to be developed that are usable on a wide basis? In considering this question a wide range of decision problem characteristics need to be addressed. In engineering design some decisions are made by individuals, others by teams – some are about the product and others about the processes that support the product – some are based on complete, consistent, quantitative data and others on sparse, conflicting, qualitative discussions. To address the reasons why so little support is used and the characteristics of potentially useful decision support tools, a taxonomy of decision characteristics is proposed. This taxonomy is used to classify current techniques and to define the requirements for an ideal engineering design decision support system.

Data-driven design paradigm in engineering problems

2016 ◽  
Vol 231 (8) ◽  
pp. 1522-1534 ◽  
Author(s):  
Changqing Liu ◽  
Xiaoqian Chen
Keyword(s):  
Engineering Design ◽  
Data Driven ◽  
Decision Strategy ◽  
Design Problems ◽  
Design Decisions ◽  
Uncertainty Factors ◽  
Satellite Design ◽  
Engineering Design Problems ◽  
Engineering Problems ◽  
Design Paradigm

Engineering design problems can, in general, be discussed under the framework of decision making, namely engineering design decisions. Inherently, accounting for uncertainty factors is an indispensable part in these decision processes. In a sense, the goal of design decisions is to control or reduce the variational effect in decision consequences induced by many uncertainty factors, by optimizing an expected utility objective or other preference functions. In this paper, the value of data in facilitating making engineering design decisions is highlighted, and a data-driven design paradigm for practical engineering problems is proposed. The definition of data in this paradigm is elaborated first. Then the data involvement in a whole stage-based design process is investigated. An overall decision strategy for design problems under the data-driven paradigm is proposed. By a concrete satellite design example, the key ideas of the proposed data-driven design paradigm are demonstrated. Future work is also advised.

A Taxonomy for Classifying Engineering Decision Problems and Support Systems

1995 ◽  
Author(s):  
David G. Ullman ◽  
Bruce D’Ambrosio
Keyword(s):  
Decision Support ◽  
Engineering Design ◽  
Quantitative Data ◽  
Decision Problems ◽  
Design Decision ◽  
Decision Support Tools ◽  
Design Decisions ◽  
Wide Range ◽  
Support Tools ◽  
Need Support

Abstract The design of even the simplest product requires thousands of decisions. Yet very few of these decisions are supported with methods on paper or on computers. Is this because engineering design decisions don’t need support or is it because techniques have yet to be developed that are usable on a wide basis? In considering this question a wide range of decision problem characteristics need to be addressed. In engineering design some decisions are made by individuals, others by teams — some are about the product and others about the processes that support the product — some are based on complete, consistent, quantitative data and others on sparse, conflicting, qualitative discussions. In order to address the reasons why so little support is used and the characteristics of potentially useful decision support tools, a taxonomy of decision characteristics is proposed.1 This taxonomy is used to classify current techniques and to define the requirements for an ideal engineering design decision support system.

The Value of Using Imprecise Probabilities in Engineering Design

2005 ◽  
Author(s):  
Jason Matthew Aughenbaugh ◽  
Christiaan J. J. Paredis
Keyword(s):  
Decision Making ◽  
Engineering Design ◽  
Pressure Vessel ◽  
Computational Experiment ◽  
Probabilistic Approach ◽  
Imprecise Probabilities ◽  
Design Problems ◽  
Design Decisions ◽  
Available Information ◽  
Best Fit

Engineering design decisions inherently are made under uncertainty. In this paper, we consider imprecise probabilities (i.e. intervals of probabilities) to express explicitly the precision with which something is known. Imprecision can arise from fundamental indeterminacy in the available evidence or from incomplete characterizations of the available evidence and designer’s beliefs. Our hypothesis is that, in engineering design decisions, it is valuable to explicitly represent this imprecision by using imprecise probabilities. We support this hypothesis with a computational experiment in which a pressure vessel is designed using two approaches, both variations of utility-based decision making. In the first approach, the designer uses a purely probabilistic, precise best-fit normal distribution to represent uncertainty. In the second approach, the designer explicitly expresses the imprecision in the available information using a probability box, or p-box. When the imprecision is large, this p-box approach on average results in designs with expected utilities that are greater than those for designs created with the purely probabilistic approach. In the context of decision theory, this suggests that there are design problems for which it is valuable to use imprecise probabilities.

Decision Topology Assessment in Engineering Design Under Uncertainty

2014 ◽  
Author(s):  
Vijitashwa Pandey ◽  
Zissimos P. Mourelatos ◽  
Matthew P. Castanier
Keyword(s):  
Decision Analysis ◽  
Engineering Design ◽  
Decision Maker ◽  
Mathematical Optimization ◽  
Linear Functions ◽  
Design Decision ◽  
Utility Analysis ◽  
Decision Making Problem ◽  
Single Attribute ◽  
Incremental Construction

The implications of decision analysis (DA) on engineering design are well known. Recently, the authors proposed decision topologies (DT) as a visual method for making design decisions and proved that they are consistent with normative decision analysis. This paper addresses the practical issue of assessing DTs for a decision maker (DM) using their responses, particularly under uncertainty. This is critical to encoding decision maker preferences so that further analysis and mathematical optimization can be performed using the correct set of preferences. We show how multiattribute DTs can be directly assessed from DM responses. Four methods are shown to evolutionarily assess DTs among which one that requires the DM to rank alternatives and another where a utility function is first assessed. It is also shown that preferences under uncertainty can be easily incorporated. In addition, we show that topologies can be constructed using single attribute topologies similarly to multi-linear functions in utility analysis. This incremental construction simplifies the process of topology construction. The reverse problem of inferring single attribute DTs is also presented. The proposed assessment methods are used on a design decision making problem of a welded beam.

An Information Economic Approach for Model Selection In Engineering Design

2006 ◽  
Author(s):  
Jay Ling ◽  
Christiaan J. J. Paredis
Keyword(s):  
Engineering Design ◽  
Economic Value ◽  
Information Economics ◽  
Cost Benefit ◽  
Design Decision ◽  
Support Design ◽  
Design Decisions ◽  
Expected Benefits ◽  
Two Phases ◽  
The Cost

An important element of successful engineering design is the effective management of resources to support design decisions. Design decisions can be thought of as having two phases—a formulation phase and a solution phase. As part of the formulation phase, engineers must decide which models to use in support of design decisions. Although more accurate models typically lead to better decisions, they also cost more. The question therefore is: Which model provides the best cost-benefit trade-off? In this paper, we focus in particular on the situation in which the systematic error in the models can be bounded by an interval. Based on principles of information economics, the interval-based model error results in bounds on the expected economic value of using a particular model in support of a certain design decision. The decision maker can then select the model that provides the best overall value, considering both the expected benefits resulting from the decision and the cost of the decision-making process. The approach is illustrated with the design of an I-beam structure.

Entropic Method for Sequencing Discrete Design Decisions

2009 ◽  
Author(s):  
Chiradeep Sen ◽  
Farhad Ameri ◽  
Joshua D. Summers
Keyword(s):  
Engineering Design ◽  
Design Process ◽  
Design Solution ◽  
Design Problems ◽  
Design Decisions ◽  
Additional Information ◽  
Task Sequencing ◽  
Design Variables ◽  
Engineering Design Problems ◽  
Measure Of Uncertainty

Early stages of engineering design processes are characterized by high levels of uncertainty due to incomplete knowledge. As the design progresses, additional information is externally added or internally generated within the design process. As a result, the design solution becomes increasingly well-defined and the uncertainty of the problem reduces, diminishing to zero at the end of the process when the design is fully defined. In this research a measure of uncertainty is proposed for a class of engineering design problems called discrete design problems. Previously, three components of complexity in engineering design, namely, size, coupling and solvability, were identified. In this research uncertainty is measured in terms of the number of design variables (size) and the dependency between the variables (coupling). The solvability of each variable is assumed to be uniform for the sake of simplicity. The dependency between two variables is measured as the effect of a decision made on one variable on the solution options available to the other variable. A measure of uncertainty is developed based on this premise, and applied to an example problem to monitor uncertainty reduction through the design process. Results are used to identify and compare three task-sequencing strategies in engineering design.

Standards, Codes of Practice and Expert Systems

1988 ◽  
Vol 21 (1) ◽  
pp. 5-9 ◽  
Author(s):  
E G McCluskey ◽  
S Thompson ◽  
D M G McSherry
Keyword(s):  
Expert System ◽  
Expert Systems ◽  
Engineering Design ◽  
Performance Criteria ◽  
Design Problems ◽  
Codes Of Practice ◽  
Engineering Design Problems ◽  
And Performance

Many engineering design problems require reference to standards or codes of practice to ensure that acceptable safety and performance criteria are met. Extracting relevant data from such documents can, however, be a problem for the unfamiliar user. The use of expert systems to guide the retrieval of information from standards and codes of practice is proposed as a means of alleviating this problem. Following a brief introduction to expert system techniques, a tool developed by the authors for building expert system guides to standards and codes of practice is described. The steps involved in encoding the knowledge contained in an arbitrarily chosen standard are illustrated. Finally, a typical consultation illustrates the use of the expert system guide to the standard.

Biologically Inspired Design: A Macrocognitive Account

2010 ◽  
Author(s):  
Swaroop S. Vattam ◽  
Michael Helms ◽  
Ashok K. Goel
Keyword(s):  
Biological Sciences ◽  
Information Processing ◽  
Engineering Design ◽  
Design Patterns ◽  
Information Processing Model ◽  
Biologically Inspired ◽  
Design Problems ◽  
Engineering Design Problems ◽  
Other Information ◽  
Biologically Inspired Design

Biologically inspired engineering design is an approach to design that espouses the adaptation of functions and mechanisms in biological sciences to solve engineering design problems. We have conducted an in situ study of designers engaged in biologically inspired design. Based on this study we develop here a macrocognitive information-processing model of biologically inspired design. We also compare and contrast the model with other information-processing models of analogical design such as TRIZ, case-based design, and design patterns.

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