Publications

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Wargames are a common tool for investigating complex conflict scenarios and have a long history of informing military and strategic study. Historically, these games have often been one offs, may not rigorously collect data, and have been built primarily for exploration rather than developing data-driven analytical conclusions. Experimental wargaming, a new wargaming approach that employs the basic principles of experimental design to facilitate an objective basis for exploring fundamental research questions around human behavior (such as understanding conflict escalation), is a potential tool that can be used in combination with existing wargaming approaches. The Project on Nuclear Gaming, a consortium involving the University of California, Berkeley, Sandia National Laboratories, and Lawrence Livermore National Laboratory, developed an experimental wargame, SIGNAL, to explore questions surrounding conflict escalation and strategic stabil-ity in the nuclear context. To date, the SIGNAL experimental wargame has been played hundreds of times by thousands of players from around the world, creating the largest data-base of wargame data for academic purposes known to the authors. This paper discusses the design of SIGNAL, focusing on how the principles of experimental design influenced this design.

We describe the opportunities and challenges we faced when developing SIGNAL, an experimental wargame that was deployed as a distributed wargame.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Abstract not provided.

SIGNAL is a first of its kind experimental wargame developed as part of the Project on Nuclear Gaming (PoNG). In this document we describe the rules and game mechanics associated with the online version of SIGNAL created by team members from the University of California, Berkeley, Sandia National Laboratories, and Lawrence Livermore National Laboratory and sponsored by the Carnegie Corporation of New York. The game was developed as part of a larger research project to develop the experimental wargaming methodology and explore its use on a model scenario: the impact of various military capabilities on conflict escalation dynamics. We discuss the results of this research in a forthcoming paper that will include this manual as an appendix. It is our hope that this manual will both contribute to our players' understanding of the game prior to play and that it will allow for replication of the SIGNAL game environment for future research purposes. The manual begins by introducing the terminology used throughout the document. It then outlines the technical requirements required to run SIGNAL. The following section provides a description of the map, resources, infrastructure, tokens, and action cards used in the game environment. The manual then describes the user interface including the chat functions, trade mechanism, currency and population counts necessary for players to plan their actions. It then turns to the sequence of player actions in the game describing the signaling, action, and upkeep phases that comprise each round of play. It then outlines the use of diplomacy including alliances with minor states and trade between players. The manual also describes the process for scoring the game and determining the winner. The manual concludes with tips for players to remember as they embark upon playing the game.

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.

Abstract not provided.

Abstract not provided.

Abstract not provided.

Cross Reality (XR) immersive environments offer challenges and opportunities in designing for cognitive aspects (e.g. learning, memory, attention, etc.) of information design and interactions. Information design is a multidisciplinary endeavor involving data science, communication science, cognitive science, media, and technology. In the present paper the holodeck metaphor is extended to illustrate how information design practices and some of the qualities of this imaginary computationally augmented environment (a.k.a. the holodeck) may be achieved in XR environments to support information-rich storytelling and real life, face-to-face, and virtual collaborative interactions. The Simulation Experience Design Framework & Method is introduced to organize challenges and opportunities in the design of information for XR. The notion of carefully blending both real and virtual spaces to achieve total immersion is discussed as the reader moves through the elements of the cyclical framework. A solution space leveraging cognitive science, information design, and transmedia learning highlights key challenges facing contemporary XR designers. Challenges include but are not limited to interleaving information, technology, and media into the human storytelling process, and supporting narratives in a way that is memorable, robust, and extendable.

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.

Abstract not provided.

Abstract not provided.

This report pulls together the documentation produced for the IMPACT tool, a software-based decision support tool that provides situational awareness, incident characterization, and guidance on public health and environmental response strategies for an unfolding bio-terrorism incident.

Abstract not provided.

Abstract not provided.

Abstract not provided.

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.