Publications

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Much has been written on the potential for games to enhance our ability to study complex systems. In this chapter we focus on how we can use games to study national security issues. We reflect on the benefits of using games and the inherent difficulties that we must address. As a means of grounding the discussion, we will present a case study of a retrospective analysis of gaming data.

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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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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. 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.

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

Alisa M. Rogers University of Georgia The US faces persistent, distributed threats from malevolent individuals, groups and or- ganizations around the world. Computational Social Models (CSMs) help anticipate the dynamics and behaviors of these actors by modeling the behavior and interactions of indi- viduals, groups and organizations. For strategic planners to trust the results of CSMs, they must have confidence in the validity of the models. Establishing validity before model use will enhance confidence and reduce the risk of error. One problem with validation is design- ing an appropriate controlled test of the model, similar to the testing of physical models. Lab experiments can do this, but are often limited to small numbers of subjects, with low subject diversity and are often in a contrived environment. Natural studies attempt to test models by gathering large-scale observational data (e.g., social media) however this loses the controlled aspect. We propose a new approach to run large-scale, controlled online ex- periments on diverse populations. Using Amazon Mechanical Turk, a crowdsourcing tool, we will draw large populations into controlled experiments in a manner that was not possible just a few years ago. In this report we describe the "Controlled, Large Online Social Experimentation (CLOSE)" platform -- a prototype platform develop to conduct online social experiments. Through an extensive survey we find that online subject pools can be recruited to participate in longitudinal online social experiments. We describe the characteristics of these subject pools and their suitability for longitudinal online experiments.

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.

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Agent-based modeling is commonly used for studying complex system properties emergent from interactions among many agents. We present a novel data-driven agent-based modeling framework applied to forecasting individual and aggregate residential rooftop solar adoption in San Diego county. Our first step is to learn a model of individual agent behavior from combined data of individual adoption characteristics and property assessment. We then construct an agent-based simulation with the learned model embedded in artificial agents, and proceed to validate it using a holdout sequence of collective adoption decisions. We demonstrate that the resulting agent-based model successfully forecasts solar adoption trends and provides a meaningful quantification of uncertainty about its predictions. We utilize our model to optimize two classes of policies aimed at spurring solar adoption: one that subsidizes the cost of adoption, and another that gives away free systems to low-income house-holds. We find that the optimal policies derived for the latter class are significantly more efficacious, whereas the policies similar to the current California Solar Initiative incentive scheme appear to have a limited impact on overall adoption trends.

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Massively-multiplayer online games (MMOGs) can serve asa unique laboratory for studying large-scale human behaviors. However,one question that often arises is whether the observed behavior is specificto the game world and its winning conditions. This paper studiesthe nature of conflict and communication across two game worlds thathave different game objectives. We compare and contrast the structureof attack networks with trade and communication networks. Similar toreal-life, social structures play a significant role in the likelihood of interplayerconflict.

Attitude diffusion is when "attitudes" (general, relatively enduring evaluative responses to a topic) spread through a population. Attitudes play an incredibly important role in human decision making and are a critical part of social psychology. However, existing models of diffusion do not account for key differentiating aspects of attitudes. We develop the "Multi-Agent, Multi-Attitude" (MAMA) model which incorporates several of these key factors: (1) multiple, interacting attitudes; (2) social influence between individuals; and (3) media influence. All three components have strong support from the social science community. Using the MAMA model, we study influence maximization in a attitude diffusion setting where media influence is possible - we show that strategic manipulation of the media can lead to statistically significant decreases in diffusion of attitudes.

Amazon Mechanical Turk (AMT) has become a powerful tool for social scientists due to its inexpensiveness, ease of use, and ability to attract large numbers of workers. While the subject pool is diverse, there are numerous questions regarding the composition of the workers as a function of when the “Human Intelligence Task”(HIT) is posted. Given the “queue” nature of HITs and the disparity in geography of participants, it is natural to wonder whether HIT posting time/day can have an impact on the population that is sampled.We address this question using a panel survey on AMT and show (surprisingly) that except for gender, there is no statistically significant difference in terms of demographics characteristics as a function of HIT posting time.

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