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Do design decisions depend on “dictators”?

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Abstract

Design decisions often require input from multiple stakeholders or require balancing multiple design requirements. However, leading axiomatic approaches to decision-based design suggest that combining preferences across these elements is virtually guaranteed to result in irrational outcomes. This has led some to conclude that a single “dictator” is required to make design decisions. In contrast, proponents of heuristic approaches observe that aggregate decisions are frequently made in practice, and argue that this widespread usage justifies the value of these heuristics to the engineering design community. This paper demonstrates that these approaches need not be mutually exclusive. Axiomatic approaches can be informed by empirically motivated restrictions on the way that individuals can order their preferences. These restrictions are represented using “anigrafs”—structured relationships between alternatives that are represented using a graph–theoretic formalism. This formalism allows for a computational assessment of the likelihood of irrational outcomes. Simulation results show that even minimal amounts of structure can vastly reduce the likelihood of irrational outcomes at the level of the group, and that slightly stronger restrictions yield probabilities of irrational preferences that never exceed 5%. Next, an empirical case study demonstrates how anigrafs may be extracted from survey data, and a model selection technique is introduced to examine the goodness-of-fit of these anigrafs to preference data. Taken together, these results show how axiomatic consistency can be combined with empirical correspondence to determine the circumstances under which “dictators” are necessary in design decisions.

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Notes

  1. Here, “optimal” means that an aggregate preference order can be defined, that a top choice can be defined for this preference order, and that the method used to generate this preference order is most likely to result in a preference ordering that is consistent with the group’s common design goal, as defined by Condorcet’s formulation discussed in Young (1995). Importantly, Condorcet notes that the existence of such an optimum is not guaranteed.

  2. If so, then Scott and Antonsson’s (1999) argument, that designers tend to avoid unrestricted domain problems by restricting their attention to local optima, seems to somewhat refute Franssen’s (2005) argument for many real designs. However, one could certainly conceive of designs where there are several local optima – the situation to which Franssen presumably refers.

  3. One might object to the assumption of a symmetric distribution over these two options. However, if large and consistent preferences for one option over the other exist, the anigraf itself would possess a different structure. Thus, in such a case, we are no longer dealing with “micro”-preferences at all.

  4. The pairwise plurality rule generates a partial order over options, and not always a total order. In this case, the partial order is E > {(D ~ F) > B > C > (D ~ F)} > G > A, where E is the top choice, A is the last choice, G is second-to-last, and the remaining options are all ranked worse than E but better than G and A. In addition, the group is indifferent between D and F. However, this is only a partial order, rather than a total order, because the relationships between F, B, C, and D lead to cycles, precluding an aggregate ranking of these alternatives relative to one another.

  5. Richards et al. (2002) distinguished between “knowledge depth”, as defined here, and “knowledge depth with indifference”, meaning that only the top k options are ranked, and all remaining options are given rank k + 1. The simulations in this manuscript all assume knowledge depth without indifference. Nevertheless, the results presented here do not differ qualitatively from those presented by Richards et al. (2002) who assumed knowledge depth with indifference.

  6. Experts may be able to hold many more alternatives in memory because their expertise allows them to “chunk” or abstract sequences of alternatives more efficiently (e.g., Mathy and Feldman 2012). In such a case, knowledge depth would be much greater than 3, which would impose even more structure on outcomes. As will be shown below, this means that experts are even more likely to avoid cyclic outcomes compared to novices.

  7. A “feature” can, but need not, be a continuous design attribute. Indeed, taking continuous design attributes as given, and using them as features, can be misleading. For example, when trying to characterize the relationship between alternatives in a color space defined by hue, saturation, and lightness axes, Richards and Koenderink (1995) found that “transformations used by the subjects to move along a route in the constant lightness plane must vary in both saturation and hue simultaneously”. Similarly, a feature may correspond to a transformation that takes alternative A into alternative B, as in Richards and Koenderink’s (1995) texture space example, where they found that “the proper representation for textures is not simply a space of material types, but rather a space of types of transformations”. In general, features capture the subject’s perceptions of how various options are related or how one may be transformed into the other holding all else equal.

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Authors and Affiliations

  1. Department of Engineering Management and Systems Engineering, School of Engineering and Applied Science, The George Washington University, 800 22nd St. NW #2700, Washington, DC, 20052, USA

    David A. Broniatowski

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  1. David A. Broniatowski
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Correspondence to David A. Broniatowski.

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Preparation of this manuscript was supported in part by the National Institute of General Medical Sciences under award number R01GM114771.

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Broniatowski, D.A. Do design decisions depend on “dictators”?. Res Eng Design 29, 67–85 (2018). https://doi.org/10.1007/s00163-017-0259-2

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