Phil 2.27.18

7:00 – 5:00 ASRC MKT

  • More BIC
    • A mechanism is a general process. The idea (which I here leave only roughly stated) is of a causal process which determines (wholly or partly) what the agents do in any simple coordination context. It will be seen that all the examples I have mentioned are of this kind; contrast a mechanism that applies, say, only in two-person cases, or only to matching games, or only in business affairs. In particular, team reasoning is this kind of thing. It applies to any simple coordination context whatsoever. It is a mode of reasoning rather than an argument specific to a context. (pg 126)
    • In particular, [if U is Paretian] the correct theory of Hi-Lo says that all play A. In short, an intuition in favour of C’ supports A-playing in Hi-Lo if we believe that all players are rational and there is one rationality. (pg 130)
      • Another form of dimension reduction – “We are all the same”
  • Machine Theory of Mind
    • We design a Theory of Mind neural network – a ToMnet – which uses meta-learning to build models of the agents it encounters, from observations of their behaviour alone. Through this process, it acquires a strong prior model for agents’ behaviour, as well as the ability to bootstrap to richer predictions about agents’ characteristics and mental states using only a small number of behavioural observations. We apply the ToMnet to agents behaving in simple gridworld environments, showing that it learns to model random, algorithmic, and deep reinforcement learning agents from varied populations, and that it passes classic ToM tasks such as the “SallyAnne” test of recognising that others can hold false beliefs about the world
  • Classifier Technology and the Illusion of Progress (David Hand, 2006)
    • A great many tools have been developed for supervised classification, ranging from early methods such as linear discriminant analysis through to modern developments such as neural networks and support vector machines. A large number of comparative studies have been conducted in attempts to establish the relative superiority of these methods. This paper argues that these comparisons often fail to take into account important aspects of real problems, so that the apparent superiority of more sophisticated methods may be something of an illusion. In particular, simple methods typically yield performance almost as good as more sophisticated methods, to the extent that the difference in performance may be swamped by other sources of uncertainty that generally are not considered in the classical supervised classification paradigm.
  • Sensitivity and Generalization in Neural Networks: an Empirical Study
    • Neural nets generalize better when they’re larger and less sensitive to their inputs, are less sensitive near training data than away from it, and other results from massive experiments. (From @Jascha)
  • Graph-131941
    • The graph represents a network of 6,716 Twitter users whose recent tweets contained “#NIPS2017”, or who were replied to or mentioned in those tweets, taken from a data set limited to a maximum of 18,000 tweets. The network was obtained from Twitter on Friday, 08 December 2017 at 15:30 UTC.
  • Back to Basics: Benchmarking Canonical Evolution Strategies for Playing Atari
    • Evolution Strategies (ES) have recently been demonstrated to be a viable alternative to reinforcement learning (RL) algorithms on a set of challenging deep RL problems, including Atari games and MuJoCo humanoid locomotion benchmarks. While the ES algorithms in that work belonged to the specialized class of natural evolution strategies (which resemble approximate gradient RL algorithms, such as REINFORCE), we demonstrate that even a very basic canonical ES algorithm can achieve the same or even better performance. This success of a basic ES algorithm suggests that the state-of-the-art can be advanced further by integrating the many advances made in the field of ES in the last decades. 
      We also demonstrate qualitatively that ES algorithms have very different performance characteristics than traditional RL algorithms: on some games, they learn to exploit the environment and perform much better while on others they can get stuck in suboptimal local minima. Combining their strengths with those of traditional RL algorithms is therefore likely to lead to new advances in the state of the art.
  • Copied over SheetToMap to the Applications file on TOSHIBA
  • Created a Data folder, which has all the input and output files for the various applications
  • Need to add a curDir variable to LMN
  •  Presentation:
    • I need to put together a 2×2 payoff matrix that covers nomad/flock/stampede – done
    • Some more heat map views, showing nomad, flocking – done
    • De-uglify JuryRoom
    • Timeline of references – done
    • Collapse a few pages 22.5 minutes for presentation and questions – done
  • Start on white paper
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Phil 2.26.18

7:00 – 6:00 ASRC MKT

  • Spread of information is dominated by search ranking f1-large
    • Twitter thread
      • The spreading process was linear because the background search rate is roughly constant day to day for discounts, and any viral element turned out to be quite small.
    • Paper
  •  BIC
    • There are many conceivable team mechanisms apart from simple direction and team reasoning; they differ in the way in which computation is distributed and the pattern of message sending. For example, one agent might compute o* and send instructions to the others. With the exception of team reasoning, these mechanisms involve the communication of information. If they do I shall call them modes of organization or protocols. (pg 125)
    • A mechanism is a general process. The idea (which I here leave only roughly stated) is of a causal process which determines (wholly or partly) what the agents do in any simple coordination context. It will be seen that all the examples I have mentioned are of this kind; contrast a mechanism that applies, say, only in two-person cases, or only to matching games, or only in business affairs. In particular, team reasoning is this kind of thing. It applies to any simple coordination context whatsoever. It is a mode of reasoning rather than an argument specific to a context. (pg 126)
  •  Presentation:
    • I need to put together a 2×2 payoff matrix that covers nomad/flock/stampede
    • Some more heat map views, showing nomad, flocking
    • De-uglify JuryRoom
    • Timeline of references
    • Collapse a few pages 22.5 minutes for presentation and questions
  • Work on getting SheetToMap in a swing app? Less figuring things out…
    • Slower going than I hoped, but mostly working now. As always, StackOverflow to the rescue: How to draw graph inside swing with GraphStream actually?
    • Adding load and save menu choices. Done! Had a few issues with getting the position of the nodes saved out. It seems like you should do this?
      GraphicNode gn = viewer.getGraphicGraph().getNode(name);
      row.createCell(cellIndex++).setCellValue(gn.getX());
      row.createCell(cellIndex++).setCellValue(gn.getY());
    • Anyway, pretty pix: 2018-02-26
  • Start on white paper
  • Fika

Phil 2.21.18

7:00 – 6:00 ASRC MKT

  • Wow – I’m going to the Tensorflow Summit! Need to get a hotel.
  • Dimension reduction + velocity in this thread
  • Global Pose Estimation with an Attention-based Recurrent Network
    • The ability for an agent to localize itself within an environment is crucial for many real-world applications. For unknown environments, Simultaneous Localization and Mapping (SLAM) enables incremental and concurrent building of and localizing within a map. We present a new, differentiable architecture, Neural Graph Optimizer, progressing towards a complete neural network solution for SLAM by designing a system composed of a local pose estimation model, a novel pose selection module, and a novel graph optimization process. The entire architecture is trained in an end-to-end fashion, enabling the network to automatically learn domain-specific features relevant to the visual odometry and avoid the involved process of feature engineering. We demonstrate the effectiveness of our system on a simulated 2D maze and the 3D ViZ-Doom environment.
  •  Slides
    • Location
    • Orientation
    • Velocity
    • IR context -> Sociocultural context
  • Writing Fika. Make a few printouts of the abstract
    • It kinda happened. W
  • Write up LMN4A2P thoughts. Took the following and put them in a LMN4A2P roadmap document in Google Docs
    • Storing a corpora (raw text, BoW, TF-IDF, Matrix)
      • Uploading from file
      • Uploading from link/crawl
      • Corpora labeling and exploring
    • Index with ElasticSearch
    • Production of word vectors or ‘effigy documents’
    • Effigy search using Google CSE for public documents that are similar
      • General
      • Site-specific
      • Semantic (Academic, etc)
    • Search page
      • Lists (reweightable) or terms and documents
      • Cluster-based map (pan/zoom/search)
  • I’m as enthusiastic about the future of AI as (almost) anyone, but I would estimate I’ve created 1000X more value from careful manual analysis of a few high quality data sets than I have from all the fancy ML models I’ve trained combined. (Thread by Sean Taylor on Twitter, 8:33 Feb 19, 2018)
  • Prophet is a procedure for forecasting time series data. It is based on an additive model where non-linear trends are fit with yearly and weekly seasonality, plus holidays. It works best with daily periodicity data with at least one year of historical data. Prophet is robust to missing data, shifts in the trend, and large outliers.
  • Done with Angular fundamentals. reDirectTo isn’t working though…
    • zone.js:405 Unhandled Promise rejection: Invalid configuration of route '': redirectTo and component cannot be used together ; Zone: <root> ; Task: Promise.then ; Value: Error: Invalid configuration of route '': redirectTo and component cannot be used together

Phil 2.20.18

7:00 – 5:00 ASRC MKT

  • Diversity injection: How to Inoculate the Public Against Fake News
    • Cambridge researchers developed a game to help people understand, broadly, how fake news works by having users play trolls and create misinformation. By “placing news consumers in the shoes of (fake) news producers, they are not merely exposed to small portions of misinformation,” the researchers write in their accompanying paper.
  • Physics of human cooperation: experimental evidence and theoretical models
    • Angel Sánchez (Scholar)
    • In recent years, many physicists have used evolutionary game theory combined with a complex systems perspective in an attempt to understand social phenomena and challenges. Prominent among such phenomena is the issue of the emergence and sustainability of cooperation in a networked world of selfish or self-focused individuals. The vast majority of research done by physicists on these questions is theoretical, and is almost always posed in terms of agent-based models. Unfortunately, more often than not such models ignore a number of facts that are well established experimentally, and are thus rendered irrelevant to actual social applications. I here summarize some of the facts that any realistic model should incorporate and take into account, discuss important aspects underlying the relation between theory and experiments, and discuss future directions for research based on the available experimental knowledge.
  • What We Read, What We Search: Media Attention and Public Attention Among 193 Countries
    • We investigate the alignment of international attention of news media organizations within 193 countries with the expressed international interests of the public within those same countries from March 7, 2016 to April 14, 2017. We collect fourteen months of longitudinal data of online news from Unfiltered News and web search volume data from Google Trends and build a multiplex network of media attention and public attention in order to study its structural and dynamic properties. Structurally, the media attention and the public attention are both similar and different depending on the resolution of the analysis. For example, we find that 63.2% of the country-specific media and the public pay attention to different countries, but local attention flow patterns, which are measured by network motifs, are very similar. We also show that there are strong regional similarities with both media and public attention that is only disrupted by significantly major worldwide incidents (e.g., Brexit). Using Granger causality, we show that there are a substantial number of countries where media attention and public attention are dissimilar by topical interest. Our findings show that the media and public attention toward specific countries are often at odds, indicating that the public within these countries may be ignoring their country-specific news outlets and seeking other online sources to address their media needs and desires.
  • Sent Jen a note about carpooling to CHIIR. Need to check out one day earlier
  • Add slides
    • Two phases – theoretical model building, then study
    • Implications for design based on Search Context
    • Something about velocity? Academic journal papers (slow production, slow consumption) at one end and twitter on the other (fast production, fast consumption)
  • Ingesting Documents (pdf, word, txt, etc) Into ElasticSearch
  • More Angular
  • Discussions with Aaron about getting some LMN capability into A2P.

Phil 2.16.18

7:00 – 3:00 ASRC MKT

  • Finished the first draft of the CI 2018 extended abstract!
  • And I also figured out how to run the sub projects in the Ultimate Angular src collection. You need to go to the root directory for the chapter, run yarn install, then yarn start. Everything works then.
  • Trolls on Twitter: How Mainstream and Local News Outlets Were Used to Drive a Polarized News Agenda
    • This is the kind of data that compels us to rethink how we understand Twitter — and what I feel are more influential platforms for reaching regular people that include Facebook, Instagram, Google, and Tumblr, as well as understand ad tech tracking and RSS feedharvesting as part of the greater propaganda ecosystem.
  • NELA News credibility classification toolkit
    • The News Landscape (NELA) Toolkit is an open source toolkit for the systematic exploration of the news landscape. The goal of NELA is to both speed up human fact-checking efforts and increase the understanding of online news as a whole. NELA is made up of multiple indepedent modules, that work at article level granularity: reliability prediction, political impartiality prediction, text objectivity prediction, and reddit community interest prediction. As well as, modules that work at source level granularity: reliability prediction, political impartiality prediction, content-based feature visualization. 
  • New benchmarks for approximate nearest neighbors
    • I built ANN-benchmarksto address this. It pits a bunch of implementations (including Annoy) against each other in a death match: which one can return the most accurate nearest neighbors in the fastest time possible. It’s not a new project, but I haven’t actively worked on it for a while.
  • Systems of Global Governance in the Era of Human-Machine Convergence
    • Technology is increasingly shaping our social structures and is becoming a driving force in altering human biology. Besides, human activities already proved to have a significant impact on the Earth system which in turn generates complex feedback loops between social and ecological systems. Furthermore, since our species evolved relatively fast from small groups of hunter-gatherers to large and technology-intensive urban agglomerations, it is not a surprise that the major institutions of human society are no longer fit to cope with the present complexity. In this note we draw foundational parallelisms between neurophysiological systems and ICT-enabled social systems, discussing how frameworks rooted in biology and physics could provide heuristic value in the design of evolutionary systems relevant to politics and economics. In this regard we highlight how the governance of emerging technology (i.e. nanotechnology, biotechnology, information technology, and cognitive science), and the one of climate change both presently confront us with a number of connected challenges. In particular: historically high level of inequality; the co-existence of growing multipolar cultural systems in an unprecedentedly connected world; the unlikely reaching of the institutional agreements required to deviate abnormal trajectories of development. We argue that wise general solutions to such interrelated issues should embed the deep understanding of how to elicit mutual incentives in the socio-economic subsystems of Earth system in order to jointly concur to a global utility function (e.g. avoiding the reach of planetary boundaries and widespread social unrest). We leave some open questions on how techno-social systems can effectively learn and adapt with respect to our understanding of geopolitical complexity.

Phil 2.14.18

7:00 – 4:00 ASRC

  • Stampede? Herding? Twitter deleted 200,000 Russian troll tweets. Read them here.
    • Twitter doesn’t make it easy to track Russian propaganda efforts — this database can help
  • Add a “show all trajectories” checkbox.
    • That’s a nice visualization that shows the idea of the terrain uncovered by the trajectories: 2018-02-14
  • Continue with paper – down to 3 pages!
  • Continue with slides. Initial walkthrough with Aaron
  • 3:00 – 4:00 A2P meeting

Phil 2.12.18

7:00 – 4:00 ASRC MKT

  • The social structural foundations of adaptation and transformation in social–ecological systems
    • Social networks are frequently cited as vital for facilitating successful adaptation and transformation in linked social–ecological systems to overcome pressing resource management challenges. Yet confusion remains over the precise nature of adaptation vs. transformation and the specific social network structures that facilitate these processes. Here, we adopt a network perspective to theorize a continuum of structural capacities in social–ecological systems that set the stage for effective adaptation and transformation. We begin by drawing on the resilience literature and the multilayered action situation to link processes of change in social–ecological systems to decision making across multiple layers of rules underpinning societal organization. We then present a framework that hypothesizes seven specific social–ecological network configurations that lay the structural foundation necessary for facilitating adaptation and transformation, given the type and magnitude of human action required. A key contribution of the framework is explicit consideration of how social networks relate to ecological structures and the particular environmental problem at hand. Of the seven configurations identified, three are linked to capacities conducive to adaptation and three to transformation, and one is hypothesized to be important for facilitating both processes.
  • Starting to trim paper down to three pages
  • Starting on CHIIR slide stack – Still need to add future work
  • Springt Review
  • Rwanda radio transcripts
    • From October 1993 to late 1994, RTLM was used by Hutu leaders to advance an extremist Hutu message and anti-Tutsi disinformation, spreading fear of a Tutsi genocide against Hutu, identifying specific Tutsi targets or areas where they could be found, and encouraging the progress of the genocide. In April 1994, Radio Rwanda began to advance a similar message, speaking for the national authorities, issuing directives on how and where to kill Tutsis, and congratulating those who had already taken part.
  • Fika
    • Set up Fika Writing group that will meet Wednesdays at 4:00. We’ll see how that goes.

2.9.18

7:00 – 5:00 ASRC MKT

  • Add something about a population of ants – done
  • Add loaders for the three populations, and then one for trajectories
    • Promoted WeightWidget to JavaUtils
    • Moving 3d and UI building out of start
    • Ugh, new IntelliJ
    • Made the graph pieces selectable
    • Got drawmode (LINE) working
    • Reading in trajectories
    • Need to load each as a child and then draw all of them first, then make that selectable. Done!
  • Go over draft with Aaron. Hand off for rewrite 1? Nope – family emergency
  • 2:00 meeting with Aaron and IC team? Nope
  • Intro to deep learning course from MIT: introtodeeplearning.com
    • An introductory course on deep learning methods with applications to machine translation, image recognition, game playing, image generation and more. A collaborative course incorporating labs in TensorFlow and peer brainstorming along with lectures. Course concludes with project proposals with feedback from staff and panel of industry sponsors.
  • Topics, Events, Stories in Social Media
    • This thesis focuses on developing methods for social media analysis. Specifically, five directions are proposed here: 1) semi-supervised detection for targeted-domain events, 2) topical interaction study among multiple datasets, 3) discriminative learning about the identifications for common and distinctive topics, 4) epidemics modeling for flu forecasting with simulation via signals from social media data, 5) storyline generation for massive unorganized documents.
  • Communication by virus
    • The standard way to think about neurons is somewhat passive. Yes, they can exciteor inhibit the neurons they communicate with but, at the end of the day, they are passively relaying whatever information they contain. This is true not only in biologicalneurons but also in artificial neural networks. 

Phil 2.7/18

7:30 – 5:30 ASRC MKT

  • Freezing rain and general ick, so I’m working from home. Thus leading to the inevitable updating of IntelliJ
  • Working on the 3D mapping app.
    • Reading in single spreadsheet with nomad graph info
    • Building a NodeInfo inner class to keep the nomad positions for the other populations
    • Working! 2018-02-07
    • Better: 2018-02-07 (2)
    • Resisting the urge to code more and getting back to the extended abstract. I also need to add a legend to the above pix.
  • Back to extended abstract
    • Added results and future work section
    • got all the pictures in
    • Currently at 3 pages plus. Not horrible.
  • Demographics and Dynamics of Mechanical Turk Workers
    • There are about 100K-200K unique workers on Amazon. On average, there are 2K-5K workers active on Amazon at any given time, which is equivalent to having 10K-25K full-time employees. On average, 50% of the worker population changes within 12-18 months. Workers exhibit widely different patterns of activity, with most workers being active only occasionally, and few workers being very active. Combining our results with the results from Hara et al, we see that MTurk has a yearly transaction volume of a few hundreds of millions of dollars.

Phil 2.6.18

7:30 – 5:00 ASRC MKT

  • Took four much needed days off on Sanibel island. Forgot to pack some things? Need to call the hotel at (239) 215-3401
  • Starting CI 2018 abstract. And oddly, the abstract isn’t showing??? Sent a note to the conference chair. IN the meantime, I have a subsection for the abstract. It appears to be acmlarge for the most part, so maybe use that????
  • Was going to get back to Angular, but stuck with 404s on CRUD operations: 404
  • Working on the 3D map application. Decided to go with JavaFX and their 3d implementation. It’s going quickly. MapApp1
  • I’ve also gotten the graph generator creating spreadsheets that the map app can read in. So the next job will be to wire everything together, where the position information is based off the nomad trajectories, with the size and visitor (height) data being overlayed with the different colors.

Phil 2.1.18

7:00 – 3:30 ASRC MKT

  • Communications Handbook for IPCC scientists
  • The Barnes-Hut Approximation
    • Efficient computation of N-body forces
      By: Jeffrey Heer
      Computers can serve as exciting tools for discovery, with which we can model and explore complex phenomena. For example, to test theories about the formation of the universe, we can perform simulations to predict how galaxies evolve. To do this, we could gather the estimated mass and location of stars and then model their gravitational interactions over time.
  • Need to get started on the extended abstract for Collective Intelligence 2018! One month! March 2, 2018!
    • Set up the LaTex template for the conference. Done
    • Think I want to call it Mapping Simon’s Anthill
  • Need to contact the CHIIR 2018 folks to see what is expected for the DC
  • More Angular, feeling my way through the Http code, which has been deprecated. Looked at the similar code in Tour of Heroes. We’ll see if the old stuff works and then try to update? Need to ask Jeremy.
  • Back to BIC. Evolutionary reasons for cooperation as group fitness, where group payoff is maximized. This makes the stag salient in stag hunt.
  • A thorough explanation of synchronization/phase locking. My mental model is this: Imaging a set of coaxial but randomly oscillating identical weights sliding back and forth in their section of lightweight tubing. From the outside, the tube would be stationary, as all the forces would be cancelling. If the weights can synchronize, then the lightweight tube will be doing most of the moving. Since the mass of the tube is lower than the mass of the combined weights,   The force required for the whole system will be lower, and as a result (I think?) the system will run more efficiently and longer. Need to work out the math.

Phil 1.31.18

7:00 – 7:00 ASRC MKT

  • The Matrix Calculus You Need For Deep Learning
    • Most of us last saw calculus in school, but derivatives are a critical part of machine learning, particularly deep neural networks, which are trained by optimizing a loss function. Pick up a machine learning paper or the documentation of a library such as PyTorch and calculus comes screeching back into your life like distant relatives around the holidays. And it’s not just any old scalar calculus that pops up—you need differential matrix calculus, the shotgun wedding of linear algebra and multivariate calculus.
  • Continuing BIC
    • Explaining the evolution of any human behavior trait (say, a tendency to play C in Prisoner’s Dilemmas) raises three questions. The first is the behavior selection question: why did this trait, rather than some other, get selected by natural selection? Answering this involves giving details of the selection process, and saying what made the disposition confer fitness in the ecology in which selection took place. But now note that ‘When a behavior evolves, a proximate mechanism also must evolve that allows the organism to produce the target behavior. Ivy plants grow toward the light. This is a behavior, broadly construed. For phototropism to evolve, there must be some mechanism inside of ivy plants that causes them to grow in one direction rather than in another’ (Sober and Wilson 1998, pp. 199-200). This raises the second question, the production question: how is the behavior produced within the individual-what is the ‘proximate mechanism’? In the human case, the interest is often in a psychological mechanism: we ask what perceptual, affective and cognitive processes issue in the behavior. Finally, note that these processes must also have evolved, so an answer to the second question brings a third: why did this proximate mechanism evolve rather than some other that could have produced the same behavior? This is the mechanism selection question. (pg 95)
      • These are good questions to answer, or at least address. Roughly, I thing my answers are
        • Selection Question: The three phases are a very efficient way to exploit an environment
        • Production Question: Neural coupling, as developed in physical swarms and moving on to cognitive clustering
        • Mechanism Question: Oscillator frequency locking provides a natural foundation for  collective behavior. Dimension reduction is how axis are selected for matching.
  • Value Orientations, Expectations and Voluntary Contributions in Public Goods
    • ValueOrientation
  • Discussion with Aaron about JuryRoom design
  • Observable is a better way to code.
    • Discover insights faster and communicate more effectively with interactive notebooks for data analysis, visualization, and exploration.
  • More Angular. Finished with module communication, starting with services
  • Meeting with Wayne
    • Submit to JASS
    • Abstract to CI 2018 July 7-8, 2018 at the University of Zurich, Switzerland

Phil 1.26.18

7:00 – 4:00 ASRC MKT

  • Tweaked my hypotheses from this post. I need to promote to a Phlog page.
  • Using Self-Organizing Maps to solve the Traveling Salesman Problem
    • The Traveling Salesman Problem is a well known challenge in Computer Science: it consists on finding the shortest route possible that traverses all cities in a given map only once. To solve it, we can try to apply a modification of the Self-Organizing Map (SOM) technique. Let us take a look at what this technique consists, and then apply it to the TSP once we understand it better.
  • Starting JuryRoom project with Jeremy.
    • Angular material  design
    • VerdictBox (Scenario and verdict)
    • Chat message
    • Live discussion cards (right gutter)
    • Topics (alphabetic, ranking, trending) with sparklines
    • Progress!!!!!! JuryRoom

Phil 1.23.18

7:00 – 5:00 ASRC MKT

  • Lesser-known trolley problem variations
  • News presented as a list: The 270 people connected to the Russia probes
  • continuing BIC
    • Group as Frame
    • Categorizatino and bias
  • Groups are defined by a common location, orientation, and velocity through a physical or virtual space. They influence each other dependent on awareness and trust. The lower the number of dimensions, the easier it is to produce a group.
  • Russia’s Full Spectrum Propaganda
    • This post examines one full spectrum case to illustrate the method. @DFRLab examined this case in an earlier post; since then, further evidence emerged, which changed and improved our understanding of the technique.
  • More Angular. Nice progress. I had some issues where I wanted to keep an old version of the app directory and did a refactor. This (of course) refactored the calling program, so I broke quite a few things figuring it out. That being said, Angular 1.5 is really, really nice.
  • Long chat about handling Trolls in the discussion app

Phil 1.19.18

7:00 – 5:00 ASRC

  • Look! Adversarial Herding: https://twitter.com/katestarbird/status/954802718018686976
  • Reconnected with Wayne. Arranging a time to meet the week of the 29th. Sent him a copy of the winter sim conference paper
  • Continuing with Beyond Individual Choice. Actually, wound up adding a section on how attention and awareness interplay, and how high social trust makes for much more efficient way to approach games such as the prisoner’s dilemma on my thoughts about trust and awareness
  • Starting Angular course
    • Architecture overview
  • Meeting with Jeremy, Heath and Aaron on Project structure/setup
  • More Angular. Yarn requires Python 2.x, which I hope doesn’t break my Python 3.x
  • Could not get the project to serve once built
  • Adversarial herding via The Opposition
    • Clint WattsClint is a consultant and researcher modeling and forecasting threat actor behavior and developing countermeasures for disrupting and defeating state and non-state actors. As a consultant, Clint designs and implements customized training and research programs for military, intelligence and law enforcement organizations at the federal, state and local level. In the private sector, he helps financial institutions develop best practices in cybersecurity intelligence operations. His research predominately focuses on terrorism forecasting and trends seeking to anticipate emerging extremist hotspots and anticipate appropriate counterterrorism responses. More recently, Clint used modeling to outline Russian influence operations via social media and the Kremlin’s return to Active Measures.