Publications

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National security decisions are driven by complex, interconnected contextual, individual, and strategic variables. Modeling and simulation tools are often used to identify relevant patterns, which can then be shaped through policy remedies. In the paper to follow, however, we argue that models of these scenarios may be prone to the complexity-scarcity gap, in which relevant scenarios are too complex to model from first principles and data from historical scenarios are too sparse - making it difficult to draw representative conclusions. The result are models that are either too simple or are unduly biased by the assumptions of the analyst. We outline a new method of quantitative inquiry - experimental wargaming - as a means to bridge the complexity-scarcity gap that offers human-generated, empirical data to inform a variety of model and simulation tasks (model building, calibration, testing, and validation). Below, we briefly describe SIGNAL - our first-of-a-kind experimental wargame designed to study strategic stability in conflict settings with nuclear weapons. We then highlight the potential utility of this data for modeling and simulation efforts in the future using this data.

There is a wealth of psychological theory regarding the drive for individuals to congregate and form social groups, positing that people may organize out of fear, social pressure, or even to manage their self-esteem. We evaluate three such theories for multi-scale validity by studying them not only at the individual scale for which they were originally developed, but also for applicability to group interactions and behavior. We implement this multi-scale analysis using a dataset of communications and group membership derived from a long-running online game, matching the intent behind the theories to quantitative measures that describe players’ behavior. Once we establish that the theories hold for the dataset, we increase the scope to test the theories at the higher scale of group interactions. Despite being formulated to describe individual cognition and motivation, we show that some group dynamics theories hold at the higher level of group cognition and can effectively describe the behavior of joint decision making and higher-level interactions.

We develop a methodology for comparing two or more agent-based models that are developed for the same domain, but may differ in the particular data sets (e.g., geographical regions) to which they are applied, and in the structure of the model. Our approach is to learn a response surface in the common parameter space of the models and compare the regions corresponding to qualitatively different behaviors in the models. As an example, we develop an active learning algorithm to learn phase transition boundaries in contagion processes in order to compare two agent-based models of rooftop solar panel adoption.

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We report that over the past century, and particularly since the outset of the Cold War, wargames (interactive simulations used to evaluate aspects of tactics, operations, and strategy) have become an integral means for militaries and policy-makers to evaluate how strategic decisions are made related to nuclear weapons strategy and international security. Furthermore, these methods have also been applied beyond the military realm, to examine phenomena as varied as elections, government policy, international trade, and supply-chain mechanics. Today, a renewed focus on wargaming combined with access to sophisticated and inexpensive drag-and-drop digital game development frameworks and new cloud computing architectures have democratized the ability to enable massive multiplayer gaming experiences. With the integration of simulation tools and experimental methods from a variety of social science disciplines, a science-based experimental gaming approach has the potential to transform the insights generated from gaming by creating human-derived, large-n datasets for replicable, quantitative analysis. In the following, we outline challenges associated with contemporary simulation and wargaming tools, investigate where scholars have searched for game data, and explore the utility of new experimental gaming and data analysis methods in both policy-making and academic settings.

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In order to understand the effect of economic interdependence on conflict and on deterrents to conflict, and to assess the viability of online games as experiments to perform research, an online serious game was used to gather data on economic, political, and military factors in the game setting. These data were operationalized in forms analogous to variables from the real-world Militarized Interstate Disputes (MIDs) dataset. A set of economic predictor variables was analyzed using linear mixed effects regression models in an attempt to discover relationships between the predictor variables and conflict outcomes. Differences between the online game results and results from the real world are discussed.

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The doctrine of nuclear deterrence and a belief in its importance underpins many aspects of United States policy; it informs strategic force structures within the military, incentivizes multi-billion-dollar weapon-modernization programs within the Department of Energy, and impacts international alliances with the 29 member states of the North Atlantic Treaty Organization (NATO). The doctrine originally evolved under the stewardship of some of the most impressive minds of the twentieth century, including the physicist and H-bomb designer Herman Kahn, the Nobel Prize-winning economist Thomas Schelling, and the preeminent political scientist and diplomat Henry Kissinger.

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As concerns with cyber security and network protection increase, there is a greater need for organizations to deploy state-of-the-art technology to keep their cyber information safe. However, foolproof cyber security and network protection are a difficult feat since a security breach can be caused simply by a single employee who unknowingly succumbs to a cyber threat. It is critical for an organization’s workforce to holistically adopt cyber technologies that enable enhanced protection, help ward off cyber threats, and are efficient at encouraging human behavior towards safer cyber practices. It is also crucial for the workforce, once they have adopted cyber technologies, to remain consistent and thoughtful in their use of these technologies to keep resistance strong against cyber threats and vulnerabilities. Adoption of cyber technology can be difficult. Many organizations struggle with their workforce adopting newly-introduced cyber technologies, even when the technologies themselves have proven to be worthy solutions. Research, especially in the domain of cognitive science and the human dimension, has sought to understand how technology adoption works and can be leveraged. This paper reviews what empirical literature has found regarding cyber technology adoption, the current research gaps, and how non-research based efforts can influence adoption. Focusing on current efforts accomplished by a government-sponsored activity entitled “ACT” (Adoption of Cybersecurity Technologies), the aim of this paper is to empirically study cyber technology adoption to better understand how to influence operational adoption across the government-sector as well as how what can be done to develop a model that enables cyber technology adoption.

The wealth of information available on seemingly every topic creates a considerable challenge both for information providers trying to rise above the noise and discerning individuals trying to find relevant, trustworthy information. We approach this information problem by investigating how passive versus interactive information interventions can impact the antecedents of behavior change using the context of solar energy adoption, where persistent information gaps are known to reduce market potential. We use two experiments to investigate the impact of both passive and interactive approaches to information delivery on the antecedents (attitudes, subjective norms, and perceived behavioral control in the Theory of Planned Behavior) of intentions and behavior, as well as their effect on intentions and behavior directly. The passive information randomized control trial delivered via Amazon Mechanical Turk tests the effectiveness of delivering the same content in a single message versus multiple shorter messages. The interactive information delivery uses an online (mobile and PC) trivia-style gamification platform. Both experiments use the same content and are carried out over a two-week time period. Lastly, our findings suggest that interactive, gamified information has greater impact than passive information, and that shorter multiple messages of passive information are more effective than a single passive message.

Agent-based modeling is commonly used for studying complex system properties emergent from interactions among agents. However, agent-based models are often not developed explicitly for prediction, and are generally not validated as such. We therefore present a novel data-driven agent-based modeling framework, in which individual behavior model is learned by machine learning techniques, deployed in multi-agent systems and validated using a holdout sequence of collective adoption decisions. We apply the framework to forecasting individual and aggregate residential rooftop solar adoption in San Diego county and demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. Meanwhile, we construct a second agent-based model, with its parameters calibrated based on mean square error of its fitted aggregate adoption to the ground truth. Our result suggests that our data-driven agent-based approach based on maximum likelihood estimation substantially outperforms the calibrated agent-based model. Seeing advantage over the state-of-the-art modeling methodology, we utilize our agent-based model to aid search for potentially better incentive structures aimed at spurring more solar adoption. Although the impact of solar subsidies is rather limited in our case, our study still reveals that a simple heuristic search algorithm can lead to more effective incentive plans than the current solar subsidies in San Diego County and a previously explored structure. Finally, we examine an exclusive class of policies that gives away free systems to low-income households, which are shown significantly more efficacious than any incentive-based policies we have analyzed to date.

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People use social media resources like Twitter, Facebook, forums etc. to share and discuss various activities or topics. By aggregating topic trends across many individuals using these services, we seek to construct a richer profile of a person’s activities and interests as well as provide a broader context of those activities. This profile may then be used in a variety of ways to understand groups as a collection of interests and affinities and an individual’s participation in those groups. Our approach considers that much of these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping of individuals with shared interests based on shared conversation, and not on explicit social software linking them. In this paper, we discuss a proof-of-concept application called Grandmaster built to pull short sections of text, a person’s comments or Twitter posts, together by analysis and visualization to allow a gestalt understanding of the full collection of all individuals: how groups are similar and how they differ, based on their text inputs.

People use social media resources like Twitter, Facebook, forums etc. to share and discuss various activities or topics. By aggregating topic trends across many individuals using these services, we seek to construct a richer profile of a person’s activities and interests as well as provide a broader context of those activities. This profile may then be used in a variety of ways to understand groups as a collection of interests and affinities and an individual’s participation in those groups. Our approach considers that much of these data will be unstructured, free-form text. By analyzing free-form text directly, we may be able to gain an implicit grouping of individuals with shared interests based on shared conversation, and not on explicit social software linking them. In this paper, we discuss a proof-of-concept application called Grandmaster built to pull short sections of text, a person’s comments or Twitter posts, together by analysis and visualization to allow a gestalt understanding of the full collection of all individuals: how groups are similar and how they differ, based on their text inputs.