Publications

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Sandia National Laboratories is investing in projects that aim to develop computational modeling and simulation applications that explore human cognitive and social phenomena. While some of these modeling and simulation projects are explicitly research oriented, others are intended to support or provide insight for people involved in high consequence decision-making. This raises the issue of how to evaluate computational modeling and simulation applications in both research and applied settings where human behavior is the focus of the model: when is a simulation 'good enough' for the goals its designers want to achieve? In this report, we discuss two years' worth of review and assessment of the ASC program's approach to computational model verification and validation, uncertainty quantification, and decision making. We present a framework that extends the principles of the ASC approach into the area of computational social and cognitive modeling and simulation. In doing so, we argue that the potential for evaluation is a function of how the modeling and simulation software will be used in a particular setting. In making this argument, we move from strict, engineering and physics oriented approaches to V&V to a broader project of model evaluation, which asserts that the systematic, rigorous, and transparent accumulation of evidence about a model's performance under conditions of uncertainty is a reasonable and necessary goal for model evaluation, regardless of discipline. How to achieve the accumulation of evidence in areas outside physics and engineering is a significant research challenge, but one that requires addressing as modeling and simulation tools move out of research laboratories and into the hands of decision makers. This report provides an assessment of our thinking on ASC Verification and Validation, and argues for further extending V&V research in the physical and engineering sciences toward a broader program of model evaluation in situations of high consequence decision-making.

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Since 1998, the Department of Energy/NNSA National Laboratories have invested millions in strategies for assessing the credibility of computational science and engineering (CSE) models used in high consequence decision making. The answer? There is no answer. There's a process--and a lot of politics. The importance of model evaluation (verification, validation, uncertainty quantification, and assessment) increases in direct proportion to the significance of the model as input to a decision. Other fields, including computational social science, can learn from the experience of the national laboratories. Some implications for evaluating 'low cognition agents'. Epistemology considers the question, How do we know what we [think we] know? What makes Western science special in producing reliable, predictive knowledge about the world? V&V takes epistemology out of the realm of thought and puts it into practice. What is the role of modeling and simulation in the production of reliable, credible scientific knowledge about the world? What steps, investments, practices do I pursue to convince myself that the model I have developed is producing credible knowledge?

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This white paper represents a summary of work intended to lay the foundation for development of a climatological/agent model of climate-induced conflict. The paper combines several loosely-coupled efforts and is the final report for a four-month late-start Laboratory Directed Research and Development (LDRD) project funded by the Advanced Concepts Group (ACG). The project involved contributions by many participants having diverse areas of expertise, with the common goal of learning how to tie together the physical and human causes and consequences of climate change. We performed a review of relevant literature on conflict arising from environmental scarcity. Rather than simply reviewing the previous work, we actively collected data from the referenced sources, reproduced some of the work, and explored alternative models. We used the unfolding crisis in Darfur (western Sudan) as a case study of conflict related to or triggered by climate change, and as an exercise for developing a preliminary concept map. We also outlined a plan for implementing agents in a climate model and defined a logical progression toward the ultimate goal of running both types of models simultaneously in a two-way feedback mode, where the behavior of agents influences the climate and climate change affects the agents. Finally, we offer some ''lessons learned'' in attempting to keep a diverse and geographically dispersed group working together by using Web-based collaborative tools.

Social and ecological scientists emphasize that effective natural resource management depends in part on understanding the dynamic relationship between the physical and non-physical process associated with resource consumption. In this case, the physical processes include hydrological, climatological and ecological dynamics, and the non-physical process include social, economic and cultural dynamics among humans who do the resource consumption. This project represents a case study aimed at modeling coupled social and physical processes in a single decision support system. In central New Mexico, individual land use decisions over the past five decades have resulted in the gradual transformation of the Middle Rio Grande Valley from a primarily rural agricultural landscape to a largely urban one. In the arid southwestern U.S., the aggregate impact of individual decisions about land use is uniquely important to understand, because scarce hydrological resources will likely limit the viability of resulting growth and development trajectories. This decision support tool is intended to help planners in the area look forward in their efforts to create a collectively defined 'desired' social landscape in the Middle Rio Grande. Our research question explored the ways in which socio-cultural values impact decisions regarding that landscape and associated land use. Because of the constraints hydrological resources place on land use, we first assumed that water use, as embodied in water rights, was a reasonable surrogate for land use. We thought that modeling the movement of water rights over time and across water source types (surface and ground) would provide planners with insight into the possibilities for certain types of decisions regarding social landscapes, and the impact those same decisions would have on those landscapes. We found that water rights transfer data in New Mexico is too incomplete and inaccurate to use as the basis for the model. Furthermore, because of its lack of accuracy and completeness, water rights ownership was a poor indicator of water and land usage habits and patterns. We also found that commitment among users in the Middle Rio Grande Valley is to an agricultural lifestyle, not to a community or place. This commitment is conditioned primarily by generational cohort and past experience. If conditions warrant, many would be willing to practice the lifestyle elsewhere. A related finding was that sometimes the pressure to sell was not the putative price of the land, but the taxes on the land. These taxes were, in turn, a function of the level of urbanization of the neighborhood. This urbanization impacted the quality of the agricultural lifestyle. The project also yielded some valuable lessons regarding the model development process. A facilitative and collaborative style (rather than a top-down, directive style) was most productive with the inter-disciplinary , inter-institutional team that worked on the project. This allowed for the emergence of a process model which combined small, discipline- and/or task-specific subgroups with larger, integrating team meetings. The project objective was to develop a model that could be used to run test scenarios in which we explored the potential impact of different policy options. We achieved that objective, although not with the level of success or modeling fidelity which we had hoped for. This report only describes very superficially the results of test scenarios, since more complete analysis of scenarios would require more time and effort. Our greatest obstacle in the successful completion of the project was that required data were sparse, of poor quality, or completely nonexistent. Moreover, we found no similar modeling or research efforts taking place at either the state or local level. This leads to a key finding of this project: that state and local policy decisions regarding land use, development, urbanization, and water resource allocation are being made with minimal data and without the benefit of economic or social policy analysis.

This study investigates the factors that lead countries into conflict. Specifically, political, social and economic factors may offer insight as to how prone a country (or set of countries) may be for inter-country or intra-country conflict. Largely methodological in scope, this study examines the literature for quantitative models that address or attempt to model conflict both in the past, and for future insight. The analysis concentrates specifically on the system dynamics paradigm, not the political science mainstream approaches of econometrics and game theory. The application of this paradigm builds upon the most sophisticated attempt at modeling conflict as a result of system level interactions. This study presents the modeling efforts built on limited data and working literature paradigms, and recommendations for future attempts at modeling conflict.

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