Bibliographic record
Abstract
Motivation-biased design Cameron Shelley (cam_shelley@yahoo.ca) Centre for Society, Technology, and Values, 200 University Ave. W. Waterloo, ON, N2L 3G1 Canada Abstract Motivation-biased design concerns how positive attitudes of designers can inhibit critical evaluation of their designs. Good intentions, admiration for certain design elements, or even concern to make a good impression on others can inhibit designers from being sufficiently critical of their designs. The result may be designs that are not as good as they would be otherwise. This article presents examples of motivation- biased design, explores cognitive mechanisms that might explain it, and considers how knowledge of the phenomenon might be useful in improving design practice. Keywords: design; motivation-biased design, bounded rationality; good intentions; just a tool; social cognition; moral capital; positive attitudes; errors. Best laid plans The City of Los Angeles experiences chronic water shortages, making water conservation increasingly pressing. In the summer of 2009, the City instituted measures designed to conserve water usage by citizens. Included in the measures were items such as lawn-watering restrictions: Citizens in a given area were allowed to water their lawns, for example, only on Mondays or Thursdays. The measure proved to be highly successful: City officials claimed that water usage by citizens in 2009 was the lowest in 31 years (Zahniser and Garrison, 2009). Curiously, the summer of 2009 also saw a record number of water main leaks and bursts. A “blue ribbon” panel of scientists was convened in order to investigate the plague of breakages. The panel concluded that the water conservation measures themselves were partly to blame. On days when lawn-watering was allowed, water pressure dropped considerably as Angelenos took the opportunity to water the grass. On days when lawn-watering was not allowed, water pressure rose considerably. In the opinion of the expert panel, the unaccustomed swings in water pressure were too much for many of the City’s aging pipes. They accelerated the effects of corrosion and metal fatigue, resulting in the record number of breakages (Zahniser and Garrison, 2009). Good intentions How did the City engineers and counselors not anticipate this serious problem with their design for water conservation? The implications of the measure for pressure in the pipes, and the subsequent effect on the pipes themselves, seem straightforward enough. Also, these effects are clearly quite relevant for the effectiveness of the plan. Yet, the problem was not anticipated by its designers. Undoubtedly, the full explanation is a complicated one. Other cities, such as Long Beach, had instituted similar plans, without suffering similar consequences. In addition, however, the attitudes of the planners themselves may well have been a contributing factor. In fact, Councilman Paul Koretz summarized this issue succinctly (Zahniser and Garrison, 2010): ‘“It was such a well-intentioned program, he said. But I think intuitively, once somebody raised the idea, it made perfect sense; you have brittle pipes and you have dramatically increasing and decreasing water pressure.”’ Koretz identifies the good intentions as a contributory factor. Briefly put, the fact that the engineers and council members had a good feeling about the goal of the design helped to make them less critical in evaluating it. Water conservation is a good thing, and is a pressing priority for a huge city with low water supplies. Unfortunately, if Koretz is correct, this feeling itself helped to prevent the council members and the City engineers from identifying problems that were, certainly in retrospect, reasonably evident. Motivation-biased design The potential for good intentions to suppress critical appraisal of designs is an instance of what I will call motivation-biased design. Motivation-biased design occurs when a designer’s motivations tend to inhibit rational assessment of the design at issue. In the case of the Los Angeles water-conservation plan, the positive attitude that the designers held towards the goal of the plan, namely water conservation, inhibited their assessment of its relevant consequences. The purpose of this article is to provide a broad characterization of motivation-biased design and to sketch out an explanation for it. This sketch will then set the scene for further investigation. Naturally, all design is motivated. That is, the act of designing usually includes a set of goals that the designer attempts to satisfy. Those goals constitute the designer’s motivation. However, motivations can also arise in other ways. The example above suggests that some of the effects of motivations arise not because of their status as goals per se but because of an attitude of the designer towards those goals. A positive attitude towards a goal helps to motivate the designer to achieve it but may also inhibit the designer from evaluating promising candidates as carefully as possible. Social scientists have long recognized a similar phenomenon involving the effects of motivations on inference. Kunda (1987) explored the nature of motivated
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".