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Understanding design processes and behaviors are important for building more effective design outcomes. During design tasks, teams exhibit sequences of actions that form strategies. This paper investigates patterns of design actions to identify successful design strategies in paired parameter design tasks. The paper uses secondary data from a design experiment in which each pair completes a series of simplified cooperative parameter design tasks to minimize completion time. Analysis of 192 task observations uses principal component analysis to identify design strategies and regression analysis to evaluate their impacts on performance outcomes. Results show that the design strategy of short average action time, small average action size, and low action variation significantly decreases completion time. Discussion of results suggests smaller and more frequent actions provide more rapid feedback about each action to improve communication and understanding between pairs, leading to more efficient design processes. Results show that task order and the number of variables also significantly contribute to performance outcomes, which aligns with past literature. Results also show a negative relationship between lower English ability, experience level, and team performance outcomes. The discussion suggests that lower English ability can be a barrier to communication between pairs, and a lower experience level can decrease the ability to create effective strategies. 

Collaboration enables multiple actors with different objectives to work together and achieve a goal beyond individual capabilities. However, strategic uncertainty from partners' actions introduces a potential for losses under failed collaboration relative to pursuing an independent system. The fundamental tradeoff between high‐value but uncertain outcomes from collaborative systems and lower‐value but more certain outcomes for independent systems induces a bistability strategic dynamic. Actors exhibit different risk attitudes that impact decisions under uncertainty which complicate shared understanding of collaborative dynamics. This paper investigates how risk attitudes affect design and strategy decisions in collaborative systems through the lens of game theory. First, an analytical model studies the effect of differential risk attitudes in a two‐actor problem with stag‐hunting strategic dynamics formulated as single‐ and bi‐level games. Next, a simulation model pairs actors with different risk attitudes in a 29‐game tournament based on a prior behavioral experiment. Results show that outcomes collaborative design problems change based on the risk attitudes of both actors. Results also emphasize that considering conservative lower‐level design options facilitates collaboration by providing risk‐averse actors with a safer solution. By accepting that decision‐making actors are not all risk‐neutral, future work seeks to develop new design methods to strengthen the adoption of efficient collaborative solutions.

 

This paper evaluates a questionnaire-based risk attitude assessment method to quantify individual risk attitudes for strategic, multi-actor design decisions. A lottery-equivalence questionnaire elicits a utility curve for risky payoffs which is fit to a Constant Absolute Risk Aversion (CARA) model. Secondary data from a multi-actor design experiment provides observations of strategic decisions in two-actor design games for validation. 124 participants complete the risk attitude questionnaire and a series of 29 experimental tasks. Assuming participants follow the risk dominance equilibrium selection criterion, a risk-neutral utility function accurately predicts 62.2% of decisions. Incorporating risk attitudes elicited from the questionnaire only increases the accuracy to 63.3% while incorporating risk attitudes inferred from observations increases the accuracy to 77.5%. While participants exhibit differential risk attitudes in design tasks, results show the lottery-equivalent questionnaire does not provide risk attitudes consistent with strategic design decisions. Results support findings that risk in the engineering domain is contextual. This paper concludes that risk attitude is an important factor in understanding strategic decisions in interactive engineering design settings and understanding risk attitudes can help create more efficient design processes. 

Built infrastructure for water and energy supply, transportation, and other such services underpins human well‐being and socioeconomic development. A fundamental understanding of how infrastructure design and user strategies interact can guide important design decisions as well as policy formulation for ensuring long‐term infrastructure viability in conjunction with improved individual user benefits. In this work, an agent based model (ABM) is developed to study this issue for the specific case of irrigation canals. Cooperatively maintained irrigation canals serve essential roles in sustaining agriculture‐based economies in many regions. Canal system design can strongly affect benefits derived by distributed users, regional agricultural output, and the long‐term viability of the shared infrastructure itself. Here, an ABM is used to investigate how an option to use an independent water source interacts with canal design to affect canal maintenance cooperation and farmer income. The independent water source is stylized as a well that provides access to groundwater and represents astrategically robustdesign option; a design option that reduces the implementer's utility vulnerability to unfavorable actions by other actors. Research in other systems has demonstrated that strategically robust designs can improve both implementer utility and the probability of collaboration. The results of this research, in contrast, demonstrate that the option of individual resource access, the strategically robust design option, as represented by a well, reduces cooperative maintenance in most cases. However, wells also improve farmer income, especially for downstream farmers that are most affected by water theft.

 

Abstract Robust designs protect system utility in the presence of uncertainty in technical and operational outcomes. Systems-of-systems, which lack centralized managerial control, are vulnerable to strategic uncertainty from coordination failures between partially or completely independent system actors. This work assesses the suitability of a game-theoretic equilibrium selection criterion to measure system robustness to strategic uncertainty and investigates the effect of strategically robust designs on collaborative behavior. The work models interactions between agents in a thematic representation of a mobile computing technology transition using an evolutionary game theory framework. Strategic robustness and collaborative solutions are assessed over a range of conditions by varying agent payoffs. Models are constructed on small world, preferential attachment and random graph topologies and executed in batch simulations. Results demonstrate that systems designed to reduce the impacts of coordination failure stemming from strategic uncertainty also increase the stability of the collaborative strategy by increasing the probability of collaboration by partners; a form of robustness by environment shaping that has not been previously investigated in design literature. The work also demonstrates that strategy selection follows the risk dominance equilibrium selection criterion and that changes in robustness to coordination failure can be measured with this criterion. 

This paper evaluates perception of complexity in a novel explanatory model that relates product performance and engineering effort. Complexity is an intermediate factor with two facets: it enables desired product performance but also requires effort to achieve. Three causal mechanisms explain how exponential growth bias, excess complexity, and differential perception lead to effort overruns. Secondary data from a human subject experiment validates the existence of perception of complexity as a context‐dependent factor that influences required design effort. A two‐level mixed effects regression model quantifies differences in perception among 40 design groups. Results summarize how perception of complexity may contribute to effort overruns and outline future work to further validate the explanatory model and causal mechanisms.

 

Strategy dynamics are hypothesized to be a fundamental factor that influences interactive decision-making activities among autonomous design actors. The objective of this research is to understand how strategy dynamics in characteristic engineering design problems influence cooperative behaviors and collective efficiency for pairs of design actors. Using a bi-level model of collective decision processes based on design optimization and strategy selection, we formulate a series of two-actor parameter design tasks that exhibit four strategy dynamics (harmony, coexistence, bistability, and defection), associated with low and high levels of structural fear and greed. In these tasks, actors work collectively to maximize their individual values while managing the trade-offs between aligning with or deviating from a cooperative collective strategy. Results from a human-subject design experiment indicate cognizant actors generally follow normative predictions for some strategy dynamics (harmony and coexistence) but not strictly for others (bistability and defection). Cumulative link model regression analysis shows a greed factor contributing to strategy dynamics has a stronger effect on collective efficiency and equality of individual outcomes compared to a fear factor. Results of this study establish a foundation for future work to study strategic decision-making in engineering design problems and enable new methods and processes to mitigate potential unfavorable effects of their underlying strategy dynamics through social constructs or mechanism design. 

Abstract The engineering of complex systems involves large number of individuals within multiple organizations spanning multiple years. Since it is challenging to perform empirical studies directly on real organizations at scale, some researchers in systems engineering and design have begun relying on abstracted model worlds that aim to be representative of the reference socio-technical system, but only preserve some aspects of it. However, there is a lack of corresponding knowledge on how to design representative model worlds for socio-technical research. Our objective is to create such knowledge through a reflective case study of the development of a model world. This “inner” study examines how two factors influence interdisciplinary communication during a concurrent design process. The reference real world system is a mission design laboratory (MDL) at NASA, and the model world is a simplified engine design problem in an undergraduate classroom environment. Our analysis focuses on the thought process followed, the key model world design decisions made, and a critical assessment of the extent to which communication phenomena in the model world (engine experiment) are representative of the real world (NASA's MDL). We find that the engine experiment preserves some but not all of the communication patterns of interest, and we present case-specific lessons learned for achieving and increasing representativeness in this type of study. More generally, we find that representativeness depends not on matching subjects, tasks, and context separately, but rather on the behavior that results from the interactions of these three dimensions.