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Collaborative infrastructure systems are vital for managing scarce resources, particularly where user behaviors influence system sustainability. This study examines the relationship between design of constructed water infrastructure and strategic behaviors, focusing on flood irrigation systems as an example of collaborative infrastructure. The objectives are to investigate 1) whether shared water infrastructure can be effectively modeled using the stag hunt game framework and 2) how network topology impacts the strategic stability of user cooperation. Flood irrigation relies on collective action, where users balance risks of collaboration failure against benefits of successful cooperation. This situation closely aligns with stag hunt dynamics, in which users choose between a higher-value but riskier collaborative strategy or a lower-value, safer independent option. A key challenge arises when users opt out, increasing the burden on remaining collaborators. We apply a game-theoretic model using risk dominance criteria to analyze stability across four distinct infrastructure topologies: linear, tree, bus, and star. Results identify star and bus topologies as Pareto efficient, where a bus topology offers greater economic efficiency through reduced infrastructure costs and a star topology enhances stability due to equitable distribution of influence and reduced dependencies. An agent-based simulation validates analytical findings by dynamically captures user interactions under uncertainty and showing a strong correlation with game-theoretic results. Consequently, this study confirms the applicability of stag hunt frameworks for analyzing collaborative water infrastructure and provides practical insights into how topology design can influence cooperative resilience. These findings enhance knowledge for sustainable improvement of collaborative infrastructure. 

In collaborative systems, both technical and social factors influence decisions. While collaborative options may yield desired outcomes, a lack of understanding between parties can hinder collaboration. Effective communication facilitates information exchange and comprehension of partners' intentions, guiding designers toward collaborative decisions. This study examines the impact of a communication channel designed to share actors' collaboration intentions on the accuracy of information exchange and strategic decisions in a collaborative design process. The research uses secondary data from a human experiment involving a collaborative system design problem to assess the intervention's effects. The experimental procedure involves actors completing 30 paired tasks, earning or losing points based on joint decisions with their partners. Participants represent decision-makers from different car manufacturing companies. The experimental data includes 28 junior-year plus STEM undergraduate and graduate students completing paired decision-making collaborative tasks allowed to exchange verbal information and have an additional communication channel to share intentions. The usage of the communication channel is investigated using multiple statistical tests. Results indicate that actors share their intentions accurately and honestly via the communication channel. Even in inaccurate cases, actors’ decisions shift significantly due to their partner's reported strategic intentions. This research underscores the importance of communication for better management of collaborative systems. 

Abstract Understanding design processes and behaviors is important for building more effective design outcomes. During design tasks, teams exhibit sequences of actions that form strategies. This article investigates patterns of design actions in a paired parameter design experiment to discover design strategies that influence outcomes. The analysis 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 exploratory factor analysis to identify design strategies and regression analysis to evaluate their impacts on performance outcomes. The article finds that large actions and high action size variability significantly increase completion times, leading to poor performance outcomes. However, results show that frequently changing input controllers within and among designers significantly reduces completion times, leading to higher performance outcomes. Discussion states that larger actions can introduce unexpected errors, while smaller and consistent actions enhance designers’ understanding of the effects of each action, aiding in better planning for subsequent steps. Frequent controller switching reflects effective communication and understanding within design teams, which is crucial for cooperative tasks. 

Engineering systems, characterized by their high technical complexity and societal intricacies, require a strategic design approach to navigate multifaceted challenges. Understanding the circumstances that affect strategic action in these systems is crucial for managing complex real-world challenges. These challenges go beyond localized coordination issues and encompass intricate dynamics, requiring a deep understanding of the underlying structures impacting strategic behaviors, the interactions between subsystems, and the conflicting needs and expectations of diverse actors. Traditional optimization and game-theoretic approaches to guide individual and collective decisions need adaptation to capture the complexities of these design ecosystems, particularly in the face of increasing numbers of decision-makers and various interconnections between them. This paper presents a framework for studying strategic decision-making processes in collective systems. It tackles the combinatorial complexity and interdependencies inherent in large-scale systems by representing strategic decision-making processes as binary normal-form games, then dissects and reinterprets them in terms of multiple compact games characterized by two real-numbered structural factors and classifies them across four strategy dynamical domains associated with different stability conditions. We provide a mathematical characterization and visual representation of emergent strategy dynamics in games with three or more actors intended to facilitate its implementation by researchers and practitioners and elicit new perspectives on design and management for optimizing systems-of-systems performance. We conclude this paper with a discussion of the opportunities and challenges of adopting this framework within and beyond the context of engineering systems. 

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. 

The Arctic is warming faster than anywhere else on Earth, placing tundra ecosystems at the forefront of global climate change. Plant biomass is a fundamental ecosystem attribute that is sensitive to changes in climate, closely tied to ecological function, and crucial for constraining ecosystem carbon dynamics. However, the amount, functional composition, and distribution of plant biomass are only coarsely quantified across the Arctic. Therefore, we developed the first moderate resolution (30 m) maps of live aboveground plant biomass (g m− 2) and woody plant dominance (%) for the Arctic tundra biome, including the mountainous Oro Arctic. We modeled biomass for the year 2020 using a new synthesis dataset of field biomass harvest measurements, Landsat satellite seasonal synthetic composites, ancillary geospatial data, and machine learning models. Additionally, we quantified pixel-wise uncertainty in biomass predictions using Monte Carlo simulations and validated the models using a robust, spatially blocked and nested cross-validation procedure. Observed plant and woody plant biomass values ranged from 0 to ~6000 g m− 2 (mean ≈350 g m− 2), while predicted values ranged from 0 to ~4000 g m− 2 (mean ≈275 g m− 2), resulting in model validation root-mean-squared-error (RMSE) ≈400 g m− 2 and R2 ≈ 0.6. Our maps not only capture large-scale patterns of plant biomass and woody plant dominance across the Arctic that are linked to climatic variation (e.g., thawing degree days), but also illustrate how fine-scale patterns are shaped by local surface hydrology, topography, and past disturbance. By providing data on plant biomass across Arctic tundra ecosystems at the highest resolution to date, our maps can significantly advance research and inform decision-making on topics ranging from Arctic vegetation monitoring and wildlife conservation to carbon accounting and land surface modeling 

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. 

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. 

Abstract Plant biomass is a fundamental ecosystem attribute that is sensitive to rapid climatic changes occurring in the Arctic. Nevertheless, measuring plant biomass in the Arctic is logistically challenging and resource intensive. Lack of accessible field data hinders efforts to understand the amount, composition, distribution, and changes in plant biomass in these northern ecosystems. Here, we presentThe Arctic plant aboveground biomass synthesis dataset, which includes field measurements of lichen, bryophyte, herb, shrub, and/or tree aboveground biomass (g m⁻²) on 2,327 sample plots from 636 field sites in seven countries. We created the synthesis dataset by assembling and harmonizing 32 individual datasets. Aboveground biomass was primarily quantified by harvesting sample plots during mid- to late-summer, though tree and often tall shrub biomass were quantified using surveys and allometric models. Each biomass measurement is associated with metadata including sample date, location, method, data source, and other information. This unique dataset can be leveraged to monitor, map, and model plant biomass across the rapidly warming Arctic.