Preference Construction, Sequential Decision Making, and Trade Space Exploration

Author(s):  
Simon W. Miller ◽  
Timothy W. Simpson ◽  
Michael A. Yukish ◽  
Lorri A. Bennett ◽  
Sara E. Lego ◽  
...  

This paper develops and explores the interface between two related concepts in design decision making. First, design decision making is a process of simultaneously constructing one’s preferences while satisfying them. Second, design using computational models (e.g., simulation-based design and model-based design) is a sequential process that starts with low fidelity models for initial trades and progresses through models of increasing detail. Thus, decision making during design should be treated as a sequential decision process rather than as a single decision problem. This premise is supported by research from the domains of behavioral economics, psychology, judgment and decision making, neuroeconomics, marketing, and engineering design as reviewed herein. The premise is also substantiated by our own experience in conducting trade studies for numerous customers across engineering domains. The paper surveys the pertinent literature, presents supporting case studies and identifies use cases from our experiences, synthesizes a preliminary model of the sequential process, presents ongoing research in this area, and provides suggestions for future efforts.

Author(s):  
David Wolf ◽  
Timothy W. Simpson ◽  
Xiaolong Luke Zhang

Thanks to recent advances in computing power and speed, designers can now generate a wealth of data on demand to support engineering design decision-making. Unfortunately, while the ability to generate and store new data continues to grow, methods and tools to support multi-dimensional data exploration have evolved at a much slower pace. Moreover, current methods and tools are often ill-equipped at accommodating evolving knowledge sources and expert-driven exploration that is being enabled by computational thinking. In this paper, we discuss ongoing research that seeks to transform decades-old decision-making paradigms rooted in operations research by considering how to effectively convert data into knowledge that enhances decision-making and leads to better designs. Specifically, we address decision-making within the area of trade space exploration by conducting human-computer interaction studies using multi-dimensional data visualization software that we have been developing. We first discuss a Pilot Study that was conducted to gain insight into expected differences between novice and expert decision-makers using a small test group. We then present the results of two Preliminary Experiments designed to gain insight into procedural differences in how novices and experts use multi-dimensional data visualization and exploration tools and to measure their ability to use these tools effectively when solving an engineering design problem. This work supports our goal of developing training protocols that support efficient and effective trade space exploration.


2004 ◽  
Vol 98 (3) ◽  
pp. 495-513 ◽  
Author(s):  
CHRISTIAN LIST

I model sequential decisions over multiple interconnected propositions and investigate path-dependence in such decisions. The propositions and their interconnections are represented in propositional logic. A sequential decision process is path-dependent if its outcome depends on the order in which the propositions are considered. Assuming that earlier decisions constrain later ones, I prove three main results: First, certain rationality violations by the decision-making agent—individual or group—are necessary and sufficient for path-dependence. Second, under some conditions, path-dependence is unavoidable in decisions made by groups. Third, path-dependence makes decisions vulnerable to strategic agenda setting and strategic voting. I also discuss escape routes from path-dependence. My results are relevant to discussions on collective consistency and reason-based decision-making, focusing not only on outcomes, but also on underlying reasons, beliefs, and constraints.


1997 ◽  
Vol 119 (4) ◽  
pp. 485-493 ◽  
Author(s):  
V. Krishnan ◽  
S. D. Eppinger ◽  
D. E. Whitney

In this paper, we consider the cross-functional design decision making process and discuss how sequential decision making leads to a degradation in design quality even when downstream design tasks are not rendered infeasible by preceding upstream decisions. We focus on the problem of simplifying the design iterations required to address this quality loss. Two properties, called sequence invariance and task invariance, are introduced to help reduce the complexity of subsequent design iterations. We also discuss how these properties may be used by designers in situations where mathematical descriptions of the design performance characteristics are unavailable. We illustrate the utility of these properties by showing their applicability to the design of catalytic converter diagnostic systems at a major U.S. automotive firm.


AI Magazine ◽  
2010 ◽  
Vol 31 (4) ◽  
pp. 79 ◽  
Author(s):  
David C. Parkes ◽  
Ruggiero Cavallo ◽  
Florin Constantin ◽  
Satinder Singh

Much of AI is concerned with the design of intelligent agents. A complementary challenge is to understand how to design “rules of encounter” by which to promote simple, robust and beneficial interactions between multiple intelligent agents. This is a natural development, as AI is increasingly used for automated decision making in real-world settings. As we extend the ideas of mechanism design from economic theory, the mechanisms (or rules) become algorithmic and many new challenges surface. Starting with a short background on mechanism design theory, the aim of this paper is to provide a nontechnical exposition of recent results on dynamic incentive mechanisms, which provide rules for the coordination of agents in sequential decision problems. The framework of dynamic mechanism design embraces coordinated decision-making both in the context of uncertainty about the world external to an agent and also in regard to the dynamics of agent preferences. In addition to tracing some recent developments, we point to ongoing research challenges.


Author(s):  
Erin MacDonald ◽  
Richard Gonzalez ◽  
Panos Papalambros

Research from behavioral psychology and experimental economics asserts that individuals construct preferences on a case-by-case basis when called to make a decision. A common, implicit assumption in engineering design is that user preferences exist a priori. Thus, preference elicitation methods used in design decision making can lead to preference inconsistencies across elicitation scenarios. This paper offers a framework for understanding preference inconsistencies, within and across individual users. We give examples of three components of this new framework: comparative, internal, and external inconsistencies across users. The examples demonstrate the impact of inconsistent preference construction on common engineering and marketing design methods, including discrete choice analysis, modeling stated vs. revealed preferences, and the Kano method and thus QFD. Exploring and explaining preference inconsistencies produces new understandings of the relationship between user and product.


2015 ◽  
Vol 44 ◽  
pp. 174-183 ◽  
Author(s):  
Michael A. Yukish ◽  
Simon W. Miller ◽  
Timothy W. Simpson

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