Information Acquisition in Engineering Design: Descriptive Models and Behavioral Experiments

2020-07-29T20:49:55Z (GMT) by Ashish Mortiram Chaudhari
Engineering designers commonly make sequential information acquisition decisions such as selecting designs for performance evaluation, selecting information sources, deciding whom to communicate with in design teams, and deciding when to stop design exploration. There is significant literature on normative decision making for engineering design, however, there is a lack of descriptive modeling of how designers actually make information acquisition decisions. Such descriptive modeling is important for accurately modeling design decisions, identifying sources of inefficiencies, and improving the design process. To that end, the research objective of the dissertation is to understand how designers make sequential information acquisition decisions and identify models that provide the best description of a designer’s decisions strategies. For gaining this understanding, the research approach consists of a synthesis of descriptive theories from psychological and cognitive sciences, along with empirical evidence from behavioral experiments under different design situations. Statistical Bayesian inference is used to determine how well alternate descriptive decision models describe the experimental data. This approach quantifies a designer's decision strategies through posterior parameter estimation and Bayesian model comparison.

Two research studies, presented in this dissertation, focus on assessing the effects of monetary incentives, fixed budget, type of design space exploration, and the availability of system-wide information on information acquisition decisions. The first study presented in this dissertation investigates information acquisition by an individual designer when multiple information sources are available and the total budget is limited. The results suggest that the student subjects' decisions are better represented by the heuristic-based models than the expected utility(EU)-based models.
While the EU-based models result in better net payoff, the heuristic models used by the subjects generate better design performance. The results also indicate the potential for nudging designers' decisions towards maximizing the net payoff by setting the fixed budget at low values and providing monetary incentives proportional to the saved budget.

The second study investigates information acquisition through communication. The focus is on designers’ decisions about whom to communicate with, and how much to communicate when there is interdependence between subsystems being designed. This study analyzes team communication of NASA engineers at a mission design laboratory (MDL) as well as of engineering students designing a simplified automotive engine in an undergraduate classroom environment. The results indicate that the rate of interactions increases in response to the reduce in system-level design performance in both settings. Additionally, the following factors seem to positively influence communication decisions: the pairwise design interdependence, node-wise popularity (significant with NASA MDL engineers due to large team size), and pairwise reciprocity.

The dissertation work increases the knowledge about engineering design decision making in following aspects. First, individuals make information acquisition decisions using simple heuristics based on in-situ information such as available budget amount and present system performance.
The proposed multi-discipline approach proves helpful for describing heuristics analytically and inferring context-specific decision strategies using statistical Bayesian inference. This work has potential application in developing decision support tools for engineering design. Second, the comparison of communication patterns between student design teams and NASA MDL teams reveals that the engine experiment preserves some but not all of the communication patterns of interest. We find that the representativeness depends not on matching subjects, tasks, and context separately, but rather on the behavior that results from the interactions of these three dimensions. This work provides lessons for designing representative experiments in the future.