Publications

Military Operations Research

Letchford, Joshua L.; Epifanovskaya, Laura E.; Lakkaraju, Kiran L.; Armenta, Mika; Reddie, Andrew W.; Whetzel, Jonathan H.; Reinhardt, Jason C.; Chen, Andrew C.; Fabian, Nathan D.; Hingorani, Sheryl H.; Iyer, Roshani I.; Krishman, Roshan K.; Laderman, Sarah L.; Lee, Mansook L.; Mohan, Jahani M.; Nacht, Michael; Prakkamakul, Soravis P.; Sumner, Mathew S.; Tibbets, Jake T.; Valdez, Allie V.; Zhang, Charlie Z.

Abstract not provided.

Armenta, Mika; Hermina, Wahid L.; Hayden, Nancy K.; Passell, Howard D.; Garcia, Pablo G.; Jordan, Ramiro J.; Koechner, Donna K.; Amadei, Bernard A.

There are urgent calls to action by the NAE, the Nobel Prize Summit, the UN, and global scientists to address and solve, in this decade (2020 – 2030), crucial and widely recognized global challenges to peace and security before they become more complex and more environmentally, financially, and socially costly; before we reach the point of no return.

PLoS ONE

Sarkar, Soumajyoti; Shakarian, Paulo; Sanchez, Danielle; Armenta, Mika; Lakkaraju, Kiran L.

It is widely believed that one's peers influence product adoption behaviors. This relationship has been linked to the number of signals a decision-maker receives in a social network. But it is unclear if these same principles hold when the "pattern" by which it receives these signals vary and when peer influence is directed towards choices which are not optimal. To investigate that, we manipulate social signal exposure in an online controlled experiment using a game with human participants. Each participant in the game decides among choices with differing utilities. We observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the choices, decision-makers tend to deviate from the obvious optimal decision when their peers make a similar decision which we call the influence decision, (2) when the quantity of social signals vary over time, the forwarding probability of the influence decision and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals. To better understand how these rules of peer influence could be used in modeling applications of real world diffusion and in networked environments, we use our behavioral findings to simulate spreading dynamics in real world case studies. We specifically try to see how cumulative influence plays out in the presence of user uncertainty and measure its outcome on rumor diffusion, which we model as an example of sub-optimal choice diffusion. Together, our simulation results indicate that sequential peer effects from the influence decision overcomes individual uncertainty to guide faster rumor diffusion over time. However, when the rate of diffusion is slow in the beginning, user uncertainty can have a substantial role compared to peer influence in deciding the adoption trajectory of a piece of questionable information.

Lakkaraju, Kiran L.; Epifanovskaya, Laura W.; Letchford, Joshua L.; Whetzel, Jonathan H.; Armenta, Mika; Goldblum, Bethany L.; Tibbetts, Jake T.

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.

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

Sarkar, Soumajyoti; Aleali, Ashkan; Shakarian, Paulo; Armenta, Mika; Sanchez, Danielle; Lakkaraju, Kiran L.

It is widely believed that the adoption behavior of a decision-maker in a social network is related to the number of signals it receives from its peers in the social network. It is unclear if these same principles hold when the “pattern” by which they receive these signals vary and when potential decisions have different utilities. To investigate that, we manipulate social signal exposure in an online controlled experiment with human participants. Specifically, we change the number of signals and the pattern through which participants receive them over time. We analyze its effect through a controlled game where each participant makes a decision to select one option when presented with six choices with differing utilities, with one choice having the most utility. We avoided network effects by holding the neighborhood network of the users constant. Over multiple rounds of the game, we observe the following: (1) even in the presence of monetary risks and previously acquired knowledge of the six choices, decision-makers tend to deviate from the obvious optimal decision when their peers make similar choices, (2) when the quantity of social signals vary over time, the probability that a participant selects the decision similar to the one reflected by the social signals and therefore being responsive to social influence does not necessarily correlate proportionally to the absolute quantity of signals and (3) an early subjugation to higher quantity of peer social signals turned out to be a more effective strategy of social influence when aggregated over the rounds.

Proceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2019

Sarkar, Soumajyoti; Shakarian, Paulo; Armenta, Mika; Sanchez, Danielle; Lakkaraju, Kiran L.

While social media enables users and organizations to obtain useful information about technology like software and security feature usage, it can also allow an adversary to exploit users by obtaining information from them or influencing them towards injurious decisions. Prior research indicates that security technology choices are subject to social influence and that these decisions are often influenced by the peer decisions and number of peers in a user’s network. In this study we investigated whether peer influence dictates users’ decisions by manipulating social signals from peers in an online, controlled experiment. Human participants recruited from Amazon Mechanical Turk played a multi-round game in which they selected a security technology from among six of differing utilities. We observe that at the end of the game, a strategy to expose users to high quantity of peer signals reflecting suboptimal choices, in the later stages of the game successfully influences users to deviate from the optimal security technology. This strategy influences almost 1.5 times the number of users with respect to the strategy where users receive constant low quantity of similar peer signals in all rounds of the game.

Armenta, Mika; Armenta, Mika

The purpose of this document is to provide an initial overview of (new) information regarding the gaps in DOE's work on HPC, ML & algorithms, and human systems emerging from the Sep. 2018 Summit, and to briefly forecast the attendee community's next steps.

Armenta, Mika

Abstract not provided.

Trumbo, Michael C.; Armenta, Mika; Haass, Michael J.; Butler, Karin B.; Jones, Aaron P.; Robinson, Charles S.

Abstract not provided.

Haass, Michael J.; Matzen, Laura E.; Wilson, Andrew T.; Divis, Kristin; Armenta, Mika

Abstract not provided.

Eye Tracking Research and Applications Symposium (ETRA)

Haass, Michael J.; Matzen, Laura E.; Butler, Karin B.; Armenta, Mika

From the seminal work of Yarbus [1967] on the relationship of eye movements to vision, scanpath analysis has been recognized as a window into the mind. Computationally, characterizing the scanpath, the sequential and spatial dependencies between eye positions, has been demanding. We sought a method that could extract scanpath trajectory information from raw eye movement data without assumptions defining fixations and regions of interest. We adapted a set of libraries that perform multidimensional clustering on geometric features derived from large volumes of spatiotemporal data to eye movement data in an approach we call GazeAppraise. To validate the capabilities of GazeAppraise for scanpath analysis, we collected eye tracking data from 41 participants while they completed four smooth pursuit tracking tasks. Unsupervised cluster analysis on the features revealed that 162 of 164 recorded scanpaths were categorized into one of four clusters and the remaining two scanpaths were not categorized (recall/sensitivity=98.8%). All of the categorized scanpaths were grouped only with other scanpaths elicited by the same task (precision=100%). GazeAppraise offers a unique approach to the categorization of scanpaths that may be particularly useful in dynamic environments and in visual search tasks requiring systematic search strategies.

Haass, Michael J.; Matzen, Laura E.; Butler, Karin B.; Armenta, Mika

Abstract not provided.

Haass, Michael J.; Matzen, Laura E.; Butler, Karin B.; Armenta, Mika

Abstract not provided.

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

Trumbo, Michael C.; Armenta, Mika; Haass, Michael J.; Butler, Karin B.; Jones, Aaron P.; Robinson, Charles S.H.

Inferring the cognitive state of an individual in real time during task performance allows for implementation of corrective measures prior to the occurrence of an error. Current technology allows for real time cognitive state assessment based on objective physiological data though techniques such as neuroimaging and eye tracking. Although early results indicate effective construction of classifiers that distinguish between cognitive states in real time is a possibility in some settings, implementation of these classifiers into real world settings poses a number of challenges. Cognitive states of interest must be sufficiently distinct to allow for continuous discrimination in the operational environment using technology that is currently available as well as practical to implement.

Haass, Michael J.; Matzen, Laura E.; Wilson, Andrew T.; Divis, Kristin; Armenta, Mika; McNamara, Laura A.

Abstract not provided.