This report describes the features of Aspen-EE (Electricity Enhancement), a new model for simulating the interdependent effects of market decisions and disruptions in the electric power system on other critical infrastructures in the U.S. economy. Aspen-EE extends and modifies the capabilities of Aspen, an agent-based model previously developed by Sandia National Laboratories. Aspen-EE was tested on a series of scenarios in which the rules governing electric power trades were changed. Analysis of the scenario results indicates that the power generation company agents will adjust the quantity of power bid into each market as a function of the market rules. Results indicate that when two power markets are faced with identical economic circumstances, the traditionally higher-priced market sees its market clearing price decline, while the traditionally lower-priced market sees a relative increase in market clearing price. These results indicate that Aspen-EE is predicting power market trends that are consistent with expected economic behavior.4

To model the telecommunications infrastructure and its role and robustness to shocks, we must characterize the business and engineering of telecommunications systems in the year 2003 and beyond. By analogy to environmental systems modeling, we seek to develop a "conceptual model" for telecommunications.Here, the conceptual model is a list of high-level assumptions consistent with the economic and engineering architectures of telecommunications suppliers and customers, both today and in the near future. We describe the present engineering architectures of the most popular service offerings, and describe the supplier markets in some detail. We also develop a characterization of the customer base for telecommunications services and project its likely response to disruptions in service, base-lining such conjectures against observed behaviors during 9/11. 4 CaveatTelecommunications over the last 20 years has experienced more technology and market change than, say, aviation has in 40 years or the railroads in 100 years. Nuclear technologies over the last 30 years seem positively moribund compared to the rate of change in information technologies. Water resources and many other technologies have never experienced the same absolute amount or rate of change. Only computer technologies have experienced an equivalent rate of change in the last 50 years. Noting that, it is easy to find predictions from computer technology leaders in the 1960s and 1970s that seem quaint or laughable today. This should warn any modeler away from an uncritical reading of this or any other document that purports to accurately characterize future (and even present) states of such a fast-changing technology, much less customer response to these technologies. There are future equilibrium states, e.g., open-spectrum proposals, that may eventually lead to some maturation and relative stability in telecommunications, but how and when we will arrive at such states is more speculation than science. The seersucker hypothesis has not been disproved by this study, that hypothesis being that for every seer, there is a sucker.5

This white paper represents a summary of work intended to lay the foundation for development of a climatologicaVagent 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.

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Policy makers will most likely need to make decisions about climate policy before climate scientists have resolved all relevant uncertainties about the impacts of climate change. This study demonstrates a risk-assessment methodology for evaluating uncertain future climatic conditions. We estimate the impacts of climate change on U.S. state-and national-level economic activity from 2010 to 2050. To understand the implications of uncertainty on risk and to provide a near-term rationale for policy interventions to mitigate the course of climate change, we focus on precipitation, one of the most uncertain aspects of future climate change. We use results of the climate-model ensemble from the Intergovernmental Panel on Climate Change's (IPCC) Fourth Assessment Report 4 (AR4) as a proxy for representing climate uncertainty over the next 40 years, map the simulated weather from the climate models hydrologically to the county level to determine the physical consequences on economic activity at the state level, and perform a detailed 70-industry analysis of economic impacts among the interacting lower-48 states. We determine the industry-level contribution to the gross domestic product and employment impacts at the state level, as well as interstate population migration, effects on personal income, and consequences for the U.S. trade balance. We show that the mean or average risk of damage to the U.S. economy from climate change, at the national level, is on the order of $1 trillion over the next 40 years, with losses in employment equivalent to nearly 7 million full-time jobs.4