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Abstract 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. 

Abstract 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 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.