The first task faced by many teams endeavoring to solve complex scientific problems is to seek funding for their research venture. Often, this necessitates forming new, geographically dispersed teams of researchers from multiple disciplines. While the team science and organizational management fields have studied project teams extensively, nascent teams are underrepresented in the literature. Nonetheless, understanding proposal team dynamics is important because if left unaddressed, obstacles may persist beyond the funding decision and undermine the possibility of team successes adjunctive to funding. Participant observation of more than 100 multi-investigator proposal teams and semi-structured interviews with six leaders of multidisciplinary proposal teams identified investigator motivations for collaboration, obstacles to collaboration, and indicators of proposal team success. The motivations ranged from technical interests in the research question to a desire to have impact beyond oneself. The obstacles included inconsistent or non-existent communication protocols, unclear processes for producing and reviewing documents, ad hoc file and citation management systems, short and stressful time horizons, ambiguous decision-making procedures, and uncertainty in establishing a shared vision. While funding outcome was the most objective indicator of a proposal team’s success, other success indicators emerged, including whether the needs of the team member(s) had been met and the willingness of team members to continue collaborating. This multi-dimensional definition of success makes it possible for teams to simultaneously be considered successes and failures. As a framework to analyze and overcome obstacles, this work turned to the United States military’s command and control (C2) approach, which relies on specifying the following elements to increase an organization’s agility: patterns of interaction, distribution of information, and allocation of decision rights. To address disciplinary differences and varied motivations for collaboration, this work added a fourth element: shared meaning-making. The broader impact of this work is that by implementing a C2 framework to uncover and address obstacles, the proposal experience—from team creation, to idea generation, to document creation, to final submittal—becomes more rewarding for faculty, leading to greater job satisfaction. This in turn will change how university research enterprises create, organize, and share knowledge to solve complex problems in the post-industrial information age.

A mathematical approach was developed to evaluate the resilience of coupled power-water networks using a variant of contingency analysis adapted from electric transmission studies. In particular, the “what if” scenarios explored in power systems research were extended and applied for coupled power-water network research by evaluating how stressors and failures in the water network can propagate across system boundaries and into the electric network. Reduction in power system contingency reserves was the metric for determining violation of N-1 contingency reliability. Geospatial considerations were included using high-resolution, publicly available Geographic Information System data on infrastructure in the Phoenix Metropolitan Area that was used to generate a power network with 599 transmission lines and total generation capacity of 18.98 GW and a water network with 2,624 water network lines and capacity to serve up to 1.72M GPM of surface water. The steady-state model incorporated operating requirements for the power network—e.g., contingency reserves—and the water network—e.g., pressure ranges—while seeking to meet electric load and water demand. Interconnections developed between the infrastructures demonstrated how alternations to the system state and/or configuration of one network affect the other network, with results demonstrated through co-simulation of the power network and water network using OpenDSS and EPANET, respectively. Results indicate four key findings that help operators understand the interdependent behavior of the coupled power-water network: (i) two water failure scenarios (water flowing out of Waddell dam and CAP canal flowing west of Waddell dam) are critical to power-water network N-1 contingency reliability above 60% power system loading and at 100% water system demand, (ii) fast-starting natural gas generating units are necessary to maintain N-1 contingency reliability below 60% power system loading, (iii) Coolidge Station was the power plant to most frequently undergo a reduction in reserves amongst the water failure scenarios that cause a violation of N-1 reliability, (iv) power network vulnerability to water network failures was non-linear because it depends on the generating units that are dispatched, which can vary as line thermal limits or unit generation capacities are reached.

This dissertation advances the capability of water infrastructure utilities to anticipate and adapt to vulnerabilities in their systems from temperature increase and interdependencies with other infrastructure systems. Impact assessment models of increased heat and interdependencies were developed which incorporate probability, spatial, temporal, and operational information. Key findings from the models are that with increased heat the increased likelihood of water quality non-compliances is particularly concerning, the anticipated increases in different hardware components generate different levels of concern starting with iron pipes, then pumps, and then PVC pipes, the effects of temperature increase on hardware components and on service losses are non-linear due to spatial criticality of components, and that modeling spatial and operational complexity helps to identify potential pathways of failure propagation between infrastructure systems. Exploring different parameters of the models allowed for comparison of institutional strategies. Key findings are that either preventative maintenance or repair strategies can completely offset additional outages from increased temperatures though-- improved repair times reduce overall duration of outages more than preventative maintenance, and that coordinated strategies across utilities could be effective for mitigating vulnerability.

Serious play—the notion of bringing the benefits of play to bear on work-related tasks—is receiving more attention as a remedy to many challenges of the modern knowledge economy. Exploring and defining the role of serious play approaches to facilitate collaborative problem-solving and value creation, this dissertation consists of four related research papers.

The first research paper (RP1) reconciles three different conceptualizations of knowledge into a new theory of knowledge. This pluralistic definition allows knowledge to change character across the span of the value creation process. The paper further introduces a model called the Wheel of Knowledge (WoK) for mobilizing knowledge throughout the different knowledge conversions of the value creation process. The second research paper (RP2) advocates that serious play can scaffold and accelerate these knowledge conversion processes, it disaggregates existing serious play approaches, and starts to operationalize the WoK by using it to match different types of serious play approaches to different types of knowledge conversion challenges. The third research paper (RP3) validates the WoK by sorting the serious play literature according to how it applies to the different knowledge conversion processes. The paper provides a framework for ascertaining the applicability of serious play methods to specific knowledge conversion challenges and identifies under-explored research areas of the serious play field. The fourth research paper (RP4) tests the recommendations of RP3 by applying the LEGO® Serious Play® (LSP) method to a knowledge conversion challenge focused on tacit knowledge sharing. It reports on a mixed-methods, multi-session case study in which LSP was used to facilitate cross-disciplinary dialogue and deliberation about a wicked problem. Results show that LSP is particularly useful in the beginning of a value creation process and that it facilitates socialization and tacit knowledge sharing. Taken together the papers demonstrate the necessity, potential, and application of serious play as a catalyst for the knowledge conversion processes presented in the WoK. It is now clear that different serious play approaches are suitable as respectively: an accelerator for trust-building and collective creativity, as a conduit for iterative innovation, and as a way of making rote tasks more engaging.

The resilience of infrastructure essential to public health, safety, and well-being remains a priority among Federal agencies and institutions. National policies and guidelines enacted by these entities call for a holistic approach to resilience and effectively acknowledge the complex, multi-organizational, and socio-technical integration of critical infrastructure. However, the concept of holism is seldom discussed in literature. As a result, resilience knowledge among disciplines resides in near isolation, inhibiting opportunities for collaboration and offering partial solutions to complex problems. Furthermore, there is limited knowledge about how human resilience and the capacity to develop and comprehend increasing levels of complexity can influence, or be influenced by, the resilience of complex systems like infrastructure. The above gaps are addressed in this thesis by 1) applying an Integral map as a holistic framework for organizing resilience knowledge across disciplines and applications, 2) examining the relationships between human and technical system resilience capacities via four socio-technical processes: sensing, anticipating, adapting, and learning (SAAL), and 3) identifying an ontological framework for anticipating human resilience and adaptive capacity by applying a developmental perspective to the dynamic relationships between humans interacting with infrastructure. The results of applying an Integral heuristic suggest the importance of factors representing the social interior like organizational values and group intentionality may be under appreciated in the resilience literature from a holistic perspective. The analysis indicates that many of the human and technical resilience capacities reviewed are interconnected, interrelated, and interdependent in relation to the SAAL socio-technical processes. This work contributes a socio-technical perspective that incorporates the affective dimension of human resilience. This work presents an ontological approach to critical infrastructure resilience that draws upon the human resilience, human psychological development, and resilience engineering literatures with an integrated model to guide future research. Human mean-making offers a dimensional perspective of resilient socio-technical systems by identifying how and why the SAAL processes change across stages of development. This research suggest that knowledge of resilient human development can improve technical system resilience by aligning roles and responsibilities with the developmental capacities of individuals and groups responsible for the design, operation and management of critical infrastructures.

Photovoltaics (PV) is an environmentally promising technology to meet climate goals and transition away from greenhouse-gas (GHG) intensive sources of electricity. The dominant approach to improve the environmental gains from PV is increasing the module efficiency and, thereby, the renewable electricity generated during use. While increasing the use-phase environmental benefits, this approach doesn’t address environmentally intensive PV manufacturing and recycling processes.

Lifecycle assessment (LCA), the preferred framework to identify and address environmental hotspots in PV manufacturing and recycling, doesn’t account for time-sensitive climate impact of PV manufacturing GHG emissions and underestimates the climate benefit of manufacturing improvements. Furthermore, LCA is inherently retrospective by relying on inventory data collected from commercial-scale processes that have matured over time and this approach cannot evaluate environmentally promising pilot-scale alternatives based on lab-scale data. Also, prospective-LCAs that rely on hotspot analysis to guide future environmental improvements, (1) don’t account for stake-holder inputs to guide environmental choices in a specific decision context, and (2) may fail in a comparative context where the mutual differences in the environmental impacts of the alternatives and not the environmental hotspots of a particular alternative determine the environmentally preferable alternative

This thesis addresses the aforementioned problematic aspects by (1)using the time-sensitive radiative-forcing metric to identify PV manufacturing improvements with the highest climate benefit, (2)identifying the environmental hotspots in the incumbent CdTe-PV recycling process, and (3)applying the anticipatory-LCA framework to identify the most environmentally favorable alternative to address the recycling hotspot and significant stakeholder inputs that can impact the choice of the preferred recycling alternative.

The results show that using low-carbon electricity is the most significant PV manufacturing improvement and is equivalent to increasing the mono-Si and multi-Si module efficiency from a baseline of 17% to 21.7% and 16% to 18.7%, respectively. The elimination of the ethylene-vinyl acetate encapsulant through mechanical and chemical processes is the most significant environmental hotspot for CdTe PV recycling. Thermal delamination is the most promising environmental alternative to address this hotspot. The most significant stake-holder input to influence the choice of the environmentally preferable recycling alternative is the weight assigned to the different environmental impact categories.

Identity, or peoples’ situated sense of self, can be conceptualized and operationalized in a myriad of ways, including, among others, a person’s gender, socioeconomic status, degree of expertise, nationality, and disciplinary training. This study conceptualizes identity as fluid and constructed through social interaction with others, where individuals ask themselves “Who am I?” in relation to the people around them. Such a discursive conceptualization argues that we can observe peoples’ performance of identity through the close reading and examination of their talk and text. By discursively drawing boundaries around descriptions of “Who I am,” people inherently attribute value to preferred identities and devalue undesirable, “other” selves. This study analyzes ten workshops from the Toolbox Project conducted with graduate student scientists participating in the Integrative Graduate Education Research Traineeship (IGERT) program. The emotional tone, mood, and atmosphere of shared humor and laughter emerged as a context through which collaborators tested the limits of different identities and questioned taken for granted assumptions about their disciplinary identities and approaches to research. Through jokes, humorous comments, sarcasm, and laughter, students engaged in three primary forms of othering: 1) unifying the entire group against people outside the group, 2) differentiating group members against each other, and 3) differentiating oneself in comparison to the rest of the group. I use action-implicative discourse analysis to reconstruct these communicative practices at three levels—problem, technical, and philosophical—and explore the implications of group laughter and humor as sites of “othering” discursive strategies in graduate students’ efforts to negotiate and differentiate identity in the context of integrative collaboration.

Advancing sustainable food systems requires holistic understanding and solutions-oriented approaches that transcend disciplines, so expertise in a variety of subjects is necessary. Proposed solutions are usually technically or socially oriented, but disagreement over the best approach to the future of food dominates the dialogue. Technological optimists argue that scientific advances are necessary to feed the world, but environmental purists believe that reductions in consumption and waste are sufficient and less risky. Life cycle assessment (LCA) helps resolve debates through quantitative analysis of environmental impacts from products which serve the same function. LCA used to compare dietary choices reveals that simple plant-based diets are better for the environment than diets that include animal products. However, analysis of soy protein isolate (SPI) demonstrates that certain plant-based proteins may be less preferable for the environment than some unprocessed meats in several categories due to additional impacts that come from industrial processing. LCAs' focus on production risks ignoring consumers, but the food system exists to serve consumers, who can be major drivers of change. Therefore, the path to a sustainable food system requires addressing consumption issues as well. Existing methods for advancing sustainable food systems that equate more information with better behavior or performance are insufficient to create change. Addressing food system issues requires sufficient tacit knowledge to understand how arguments are framed, what the supporting content is, the findings of primary sources, and complex and controversial dialogue surrounding innovations and interventions for food system sustainability. This level of expertise is called interactional competence and it is necessary to drive and maintain holistic progress towards sustainability. Development strategies for interactional competence are informed by studying the motivations and strategies utilized by vegans. A new methodology helps advance understanding of expertise development by assessing levels of expertise and reveals insights into how vegans maintain commitment to a principle that influences their daily lives. The study of veganism and expertise reveals that while providing information to debunk fallacies is important, the development of tacit knowledge is fundamental to advance to a stage of competence.

Comparative life cycle assessment (LCA) evaluates the relative performance of multiple products, services, or technologies with the purpose of selecting the least impactful alternative. Nevertheless, characterized results are seldom conclusive. When one alternative performs best in some aspects, it may also performs worse in others. These tradeoffs among different impact categories make it difficult to identify environmentally preferable alternatives. To help reconcile this dilemma, LCA analysts have the option to apply normalization and weighting to generate comparisons based upon a single score. However, these approaches can be misleading because they suffer from problems of reference dataset incompletion, linear and fully compensatory aggregation, masking of salient tradeoffs, weight insensitivity and difficulties incorporating uncertainty in performance assessment and weights. Consequently, most LCA studies truncate impacts assessment at characterization, which leaves decision-makers to confront highly uncertain multi-criteria problems without the aid of analytic guideposts. This study introduces Stochastic Multi attribute Analysis (SMAA), a novel approach to normalization and weighting of characterized life-cycle inventory data for use in comparative Life Cycle Assessment (LCA). The proposed method avoids the bias introduced by external normalization references, and is capable of exploring high uncertainty in both the input parameters and weights.

Contrary to many previous travel demand forecasts there is increasing evidence that vehicle travel in developed countries may be peaking. The underlying causes of this peaking are still under much debate and there has been a mobilization of research, largely focused at the national scale, to study the explanatory drivers but research focused at the metropolitan scale, where transportation policy and planning are frequently decided, is relatively thin. Additionally, a majority of this research has focused on changes within the activity system without considering the impact transportation infrastructure has on overall travel demand. Using Los Angeles County California, we investigate Peak Car and whether the saturation of automobile infrastructure, in addition to societal and economic factors, may be a suppressing factor. After peaking in 2002, vehicle travel in Los Angeles County in 2010 was estimated at 78 billion and was 20.3 billion shy of projections made in 2002. The extent to which infrastructure saturation may contribute to Peak Car is evaluated by analyzing social and economic factors that may have impacted personal automobile usage over the last decade. This includes changing fuel prices, fuel economy, population growth, increased utilization of alternate transportation modes, changes in driver demographics , travel time and income levels. Summation of all assessed factors reveals there is at least some portion of the 20 billion VMT that is unexplained in all but the worst case scenario. We hypothesize that the unexplained remaining VMT may be explained by infrastructure supply constraints that result in suppression of travel. This finding has impacts on how we see the role of hard infrastructure systems in urban growth and we explore these impacts in the research.