George A. Backus

Foundations to the unified psycho-cognitive engine.

This document outlines the key features of the SNL psychological engine. The engine is designed to be a generic presentation of cognitive entities interacting among themselves and with the external world. The engine combines the most accepted theories of behavioral psychology with those of behavioral economics to produce a unified simulation of human response from stimuli through executed behavior. The engine explicitly recognizes emotive and reasoned contributions to behavior and simulates the dynamics associated with cue processing, learning, and choice selection. Most importantly, the model parameterization can come from available media or survey information, as well subject-matter-expert information. The framework design allows the use of uncertainty quantification and sensitivity analysis to manage confidence in using the analysis results for intervention decisions.

Modeling Populations of Interest in Order to Simulate Cultural Response to Influence Activities

This paper describes an effort by Sandia National Laboratories to model and simulate populations of specific countries of interest as well as the population’s primary influencers, such as government and military leaders. To accomplish this, high definition cognition models are being coupled with an aggregate model of a population to produce a prototype, dynamic cultural representation of a specific country of interest. The objective is to develop a systems-level, intrinsic security capability that will allow analysts to better assess the potential actions, counteractions, and influence of powerful individuals within a country of interest before, during, and after an US initiated event. 1 Societal Assessment Capability The United States is finding itself increasingly engaged in the development of unconventional partnerships that require a variety of non-traditional activities to better support political and economic stability in regions of interest. Unfortunately, there is no effective means to adequately forecast and assess how both individual leaders, and the people they influence, will behave with regard to possible US policies and actions. It is asserted here that an accurate characterization of a society must represent this interaction between people under control, those influencing power, and external variables, such as US actions or oil revenue variation (in counties dependent on oil). While assessment tools have modeled and simulated societies, they have, thus far, been limited to gross behavioral models. Furthermore, no existing macroeconomic or societal model addresses security dynamics or coordinated multiple kinetic and non-kinetic interventions. We believe that the phenomena that maintain or transition dictatorship and democracy have recently become understandable enough to pose testable hypotheses amenable to simulation. As such, the ability to address intervention dynamics and unintended, higher order consequences is a key goal of this work. In pursuit of this goal, Sandia National Laboratories (Sandia) has developed a prototype societal assessment capability that assists in the behavioral influence analysis of foreign targets of interest. The objective of the described work is to develop a systems-level capability that will allow analysts to better assess potential actions and counter-actions of individuals interacting within a foreign country of interest before, during, and after an US initiated event. The assessment is designed to address the dynamics that drive stability and instability. Specifically, it is designed to: (1) assess adversarial choice options that allow analysts to pose “what-if” queries concerning hypothetical policy and/or military initiatives to help determine how and why a population may react to a specific event, leader, or operation across time, (2) assess potential blind spots by providing analysts with the ability to better understand higher order interaction effects between leaders and local societies and how allegiances are formed and changed over time, (3) perform risk analysis by determining the limiting assumptions and unknowns for the successful outcome, and (4) perform risk management by establishing whether there are delayed consequences that will require mitigation or adjustments to planning. Collectively, this type of simulation is designed to permit assessment of shaping activities and US tactics in an operational environment by creating a system that can help an analyst better understand the interaction between leaders and local societies and how allegiances are formed and changed over time. To accomplish this Sandia is utilizing its extensive technical expertise in Modeling & Simulation (M&S) to create a social simulation platform that couples HighDefinition Cognitive Models (HDCM) with a cultural, economic, and policy-based simulation. The HDCMs are purposely designed to computationally represent the mindset of specific individuals, including their cognitive perceptions, goals, emotion states, and action intentions. The actions of one HDCM can affect the mindset and actions of others, as well as the general mindset of the society in which they are situated. The society, computationally represented in this initial effort by Sandia’s Systems Dynamics-based Aggregate Societal Model (SDASM) can, in turn, affect the actions of the HDCMs (see Figure 1). The HDCM is focused on individual or smallgroup level of analysis, whereas the SDASM is focused at an aggregate level social, economic, and cultural level of analysis. These models are joined to provide a highfidelity, scaleable assessment tool of individuals, small groups, and society to produce outcome distributions investigating attitudinal and behavioral reactions to US policies for a given country, group, or ethnic region. Figure 1. A conceptual view of Sandia’s High Definition Aggregate Societal Model-

Complexity Science: A Mechanism for Strategic Foresight and Resiliency in National Security Decision-Making.

Most national policy decisions are complex with a variety of stakeholders, disparate interests and the potential for unintended consequences. While a number of analytical tools exist to help decision makers sort through the mountains of data and myriad of options, decision support teams are increasingly turning to complexity science for improved analysis and better insight into the potential impact of policy decisions. While complexity science has great potential, it has only proven useful in limited cases and when properly applied. In advance of more widespread use, a national-level effort to refine complexity science and more rigorously establish its technical underpinnings is recommended.

Arctic Climate Systems Analysis

This study began with a challenge from program area managers at Sandia National Laboratories to technical staff in the energy, climate, and infrastructure security areas: apply a systems-level perspective to existing science and technology program areas in order to determine technology gaps, identify new technical capabilities at Sandia that could be applied to these areas, and identify opportunities for innovation. The Arctic was selected as one of these areas for systems level analyses, and this report documents the results. In this study, an emphasis was placed on the arctic atmosphere since Sandia has been active in atmospheric research in the Arctic since 1997. This study begins with a discussion of the challenges and benefits of analyzing the Arctic as a system. It goes on to discuss current and future needs of the defense, scientific, energy, and intelligence communities for more comprehensive data products related to the Arctic; assess the current state of atmospheric measurement resources available for the Arctic; and explain how the capabilities at Sandia National Laboratories can be used to address the identified technological, data, and modeling needs of the defense, scientific, energy, and intelligence communities for Arctic support.

Sensitivity of the Community Land Model (CLM4.0) to Key Modeling Parameters and Modeling of Key Physical Processes with Focus on the Arctic Environment

The purpose of this study was to identify major parameters and physical processes that have greatest impacts on the near surface energy balance in the Arctic environment. The historical data set for the period of 1948 to 2004 from National Center for Atmospheric Research (NCAR) was used to generate atmospheric forcing data for this analysis. The CLM 4.0 (Community Land Model) was used for land simulations of the point grid cell located near Fairbanks, Alaska. A range of hydrogeologic and thermal soil properties and vegetation characteristics were defined for the vegetation and soil data. The current approach used in CLM was modified to simulate soil moisture to allow for more realistic water table representation. Multiple CLM sensitivity runs were analyzed with regard to their effects on the feedbacks to the atmospheric model. This analysis allowed for identifying major parameters and important physical processes with the potential to impact the climate either in the short or long term. . Sensitivity of the Community Land Model (CLM4.0) to Key Modeling Parameters and Modeling of Key Physical Processes with Focus on the Arctic Environment

Risk assessment of climate systems for national security.

Climate change, through drought, flooding, storms, heat waves, and melting Arctic ice, affects the production and flow of resource within and among geographical regions. The interactions among governments, populations, and sectors of the economy require integrated assessment based on risk, through uncertainty quantification (UQ). This project evaluated the capabilities with Sandia National Laboratories to perform such integrated analyses, as they relate to (inter)national security. The combining of the UQ results from climate models with hydrological and economic/infrastructure impact modeling appears to offer the best capability for national security risk assessments.

Executive Summary for Assessing the Near-Term Risk of Climate Uncertainty: Interdependencies among the U.S. States

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 Changes (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

Anticipating the unintended consequences of security dynamics.

1 trillion over the next 40 years, with losses in employment equivalent to nearly 7 million full-time jobs.

Global situational awareness and early warning of high-consequence climate change.

In a globalized world, dramatic changes within any one nation causes ripple or even tsunamic effects within neighbor nations and nations geographically far removed. Multinational interventions to prevent or mitigate detrimental changes can easily cause secondary unintended consequences more detrimental and enduring than the feared change instigating the intervention. This LDRD research developed the foundations for a flexible geopolitical and socioeconomic simulation capability that focuses on the dynamic national security implications of natural and man-made trauma for a nation-state and the states linked to it through trade or treaty. The model developed contains a database for simulating all 229 recognized nation-states and sovereignties with the detail of 30 economic sectors including consumers and natural resources. The model explicitly simulates the interactions among the countries and their governments. Decisions among governments and populations is based on expectation formation. In the simulation model, failed expectations are used as a key metric for tension across states, among ethnic groups, and between population factions. This document provides the foundational documentation for the model.

Assessing the Near-Term Risk of Climate Uncertainty:Interdependencies among the U.S. States.

Global monitoring systems that have high spatial and temporal resolution, with long observational baselines, are needed to provide situational awareness of the Earths climate system. Continuous monitoring is required for early warning of high-consequence climate change and to help anticipate and minimize the threat. Global climate has changed abruptly in the past and will almost certainly do so again, even in the absence of anthropogenic interference. It is possible that the Earths climate could change dramatically and suddenly within a few years. An unexpected loss of climate stability would be equivalent to the failure of an engineered system on a grand scale, and would affect billions of people by causing agricultural, economic, and environmental collapses that would cascade throughout the world. The probability of such an abrupt change happening in the near future may be small, but it is nonzero. Because the consequences would be catastrophic, we argue that the problem should be treated with science-informed engineering conservatism, which focuses on various ways a system can fail and emphasizes inspection and early detection. Such an approach will require high-fidelity continuous global monitoring, informed by scientific modeling.