Category Archives: PolarizationGame

Phil 11.7.18

Let the House Subcommittee investigations begin! Also, better redistricting?

7:00 – 5:00 ASRC PhD/BD

  • Rather than Deep Learning with Keras, I’m starting on Grokking Deep Learning. I need better grounding
    • Installed Jupyter
  • After lunch, send follow-up emails to the technical POCs. This will be the basis for the white paper: Tentative findings/implications for design. Modify it on the blog page first and then use to create the LaTex doc. Make that one project, with different mains that share overlapping content.
  • Characterizing Online Public Discussions through Patterns of Participant Interactions
    • Public discussions on social media platforms are an intrinsic part of online information consumption. Characterizing the diverse range of discussions that can arise is crucial for these platforms, as they may seek to organize and curate them. This paper introduces a computational framework to characterize public discussions, relying on a representation that captures a broad set of social patterns which emerge from the interactions between interlocutors, comments and audience reactions. We apply our framework to study public discussions on Facebook at two complementary scales. First, we use it to predict the eventual trajectory of individual discussions, anticipating future antisocial actions (such as participants blocking each other) and forecasting a discussion’s growth. Second, we systematically analyze the variation of discussions across thousands of Facebook sub-communities, revealing subtle differences (and unexpected similarities) in how people interact when discussing online content. We further show that this variation is driven more by participant tendencies than by the content triggering these discussions.
  • More latent space flocking from Innovation Hub
    • You Share Everything With Your Bestie. Even Brain Waves.
      •  Scientists have found that the brains of close friends respond in remarkably similar ways as they view a series of short videos: the same ebbs and swells of attention and distraction, the same peaking of reward processing here, boredom alerts there. The neural response patterns evoked by the videos — on subjects as diverse as the dangers of college football, the behavior of water in outer space, and Liam Neeson trying his hand at improv comedy — proved so congruent among friends, compared to patterns seen among people who were not friends, that the researchers could predict the strength of two people’s social bond based on their brain scans alone.

    • Similar neural responses predict friendship
      • Human social networks are overwhelmingly homophilous: individuals tend to befriend others who are similar to them in terms of a range of physical attributes (e.g., age, gender). Do similarities among friends reflect deeper similarities in how we perceive, interpret, and respond to the world? To test whether friendship, and more generally, social network proximity, is associated with increased similarity of real-time mental responding, we used functional magnetic resonance imaging to scan subjects’ brains during free viewing of naturalistic movies. Here we show evidence for neural homophily: neural responses when viewing audiovisual movies are exceptionally similar among friends, and that similarity decreases with increasing distance in a real-world social network. These results suggest that we are exceptionally similar to our friends in how we perceive and respond to the world around us, which has implications for interpersonal influence and attraction.
    • Brain-to-Brain coupling: A mechanism for creating and sharing a social world
      • Cognition materializes in an interpersonal space. The emergence of complex behaviors requires the coordination of actions among individuals according to a shared set of rules. Despite the central role of other individuals in shaping our minds, most cognitive studies focus on processes that occur within a single individual. We call for a shift from a single-brain to a multi-brain frame of reference. We argue that in many cases the neural processes in one brain are coupled to the neural processes in another brain via the transmission of a signal through the environment. Brain-to-brain coupling constrains and simplifies the actions of each individual in a social network, leading to complex joint behaviors that could not have emerged in isolation.
  • Started reading Similar neural responses predict friendship

Phil 10.30.18

7:00 – 3:30 ASRC PhD

  • Search as embodies in the “Ten Blue Links” meets the requirements of a Parrow “Normal Accident”
    • The search results are densely connected. That’s how PageRank works. Even latent connections matter.
    • The change in popularity of a page rapidly affects the rank. So the connections are stiff
    • The relationships of the returned links both to each other and to the broader information landscape in general is hidden.
    • An additional density and stiffness issue is that everyone uses Google, so there is a dense, stiff connection between the search engine and the population of users
  • Write up something about how
    • ML can make maps, which decrease the likelihood of IR contributing to normal accidents
    • AI can use these maps to understand the shape of human belief space, and where the positive regions and dangerous sinks are.
  • Two measures for maps are the concepts or Range and length. Range is the distance that a trajectory can be placed on the map and remain contiguous. Length is the total distance that a trajectory travels, independent of the map its placed on.
  • Write up the basic algorithm of ML to map production
    • Take a set of trajectories that are known to be in the same belief region (why JuryRoom is needed) as the input
    • Generate an N-dimensional coordinate frame that best preserves length over the greatest range.
    • What is used as the basis for the trajectory may matter. The range (at a minimum), can go from letters to high-level topics. I think any map reconstruction based on letters would be a tangle, with clumps around TH, ER, ON, and AN. At the other end, an all-encompassing meta-topic, like WORDS would be a single, accurate, but useless single point. So the map reconstruction will become possible somewhere between these two extremes.
  • The Nietzsche text is pretty good. In particular, check out the way the sentences form based on the seed  “s when one is being cursed.
    • the fact that the spirit of the spirit of the body and still the stands of the world
    • the fact that the last is a prostion of the conceal the investion, there is our grust
    • the fact them strongests! it is incoke when it is liuderan of human particiay
    • the fact that she could as eudop bkems to overcore and dogmofuld
    • In this case, the first 2-3 words are the same, and random, semi-structured text. That’s promising, since the compare would be on the seed plus the generated text.
  • Today, see how fast a “Shining” (All work and no play makes Jack a dull boy.) text can be learned and then try each keyword as a start. As we move through the sentence, the probability of the next words should change.
    • Generate the text set
    • Train the Nietzsche model on the new text. Done. Here are examples with one epoch and a batch size of 32, with a temperature of 1.0:
      ----- diversity: 0.2
      ----- Generating with seed: "es jack a 
      dull boy all work and no play"
      es jack a 
      dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
      ----- diversity: 0.5
      ----- Generating with seed: "es jack a 
      dull boy all work and no play"
      es jack a 
      dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
      ----- diversity: 1.0
      ----- Generating with seed: "es jack a 
      dull boy all work and no play"
      es jack a 
      dull boy all work and no play makes jack a dull boy anl wory and no play makes jand no play makes jack a dull boy all work and no play makes jack a 
      ----- diversity: 1.2
      ----- Generating with seed: "es jack a 
      dull boy all work and no play"
      es jack a 
      dull boy all work and no play makes jack a pull boy all work and no play makes jack andull boy all work and no play makes jack a dull work and no play makes jack andull

      Note that the errors start with a temperature of 1.0 or greater

    • Rewrite the last part of the code to generate text based on each word in the sentence.
      • So I tried that and got gobbledygook. The issues is that the prediction only works on waveform-sized chunks. To verify this, I created a seed from the input text, truncating it to maxlen (20 in this case):
        sentence = "all work and no play makes jack a dull boy"[:maxlen]

        That worked, but it means that the character-based approach isn’t going to work

        ----- temperature: 0.2
        ----- Generating with seed: [all work and no play]
        all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
        ----- temperature: 0.5
        ----- Generating with seed: [all work and no play]
        all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes 
        ----- temperature: 1.0
        ----- Generating with seed: [all work and no play]
        all work and no play makes jack a dull boy all work and no play makes jack a dull boy pllwwork wnd no play makes 
        ----- temperature: 1.2
        ----- Generating with seed: [all work and no play]
        all work and no play makes jack a dull boy all work and no play makes jack a dull boy all work and no play makes


    • Based on this result and the ensuing chat with Aaron, we’re going to revisit the whole LSTM with numbers and build out a process that will support words instead of characters.
  • Looking for CMAC models, I found Self Organizing Feature Maps at
  • Here’s How Much Bots Drive Conversation During News Events
    • Late last week, about 60 percent of the conversation was driven by likely bots. Over the weekend, even as the conversation about the caravan was overshadowed by more recent tragedies, bots were still driving nearly 40 percent of the caravan conversation on Twitter. That’s according to an assessment by Robhat Labs, a startup founded by two UC Berkeley students that builds tools to detect bots online. The team’s first product, a Chrome extension called, allows users to see which accounts in their Twitter timelines are most likely bots. Now it’s launching a new tool aimed at news organizations called, which allows journalists to see how much bot activity there is across an entire topic or hashtag

Phil 9.27.18

7:00 – 6:00 ASRC MKT

  • Writing your own LaTex class
  • Multiple facets of biodiversity drive the diversity–stability relationship
    • A substantial body of evidence has demonstrated that biodiversity stabilizes ecosystem functioning over time in grassland ecosystems. However, the relative importance of different facets of biodiversity underlying the diversity–stability relationship remains unclear. Here we use data from 39 grassland biodiversity experiments and structural equation modelling to investigate the roles of species richness, phylogenetic diversity and both the diversity and community-weighted mean of functional traits representing the ‘fast–slow’ leaf economics spectrum in driving the diversity–stability relationship. We found that high species richness and phylogenetic diversity stabilize biomass production via enhanced asynchrony in the performance of co-occurring species. Contrary to expectations, low phylogenetic diversity enhances ecosystem stability directly, albeit weakly. While the diversity of fast–slow functional traits has a weak effect on ecosystem stability, communities dominated by slow species enhance ecosystem stability by increasing mean biomass production relative to the standard deviation of biomass over time. Our in-depth, integrative assessment of factors influencing the diversity–stability relationship demonstrates a more multicausal relationship than has been previously acknowledged.
  • Computer Algorithms, Market Manipulation and the Institutionalization of High Frequency Trading (adversarial herding?)
    • The article discusses the use of algorithmic models in finance (algo or high frequency trading). Algo trading is widespread but also somewhat controversial in modern financial markets. It is a form of automated trading technology, which critics claim can, among other things, lead to market manipulation. Drawing on three cases, this article shows that manipulation also can happen in the reverse way, meaning that human traders attempt to make algorithms ‘make mistakes’ by ‘misleading’ them. These attempts to manipulate are very simple and immediately transparent to humans. Nevertheless, financial regulators increasingly penalize such attempts to manipulate algos. The article explains this as an institutionalization of algo trading, a trading practice which is vulnerable enough to need regulatory protection.
  • Karin Knorr Cetina is interested in financial markets, knowledge and information, as well as in globalization, theory and culture. Her current projects include a book on global foreign exchange markets and on post-social knowledge societies. She continues to do research on the information architecture of financial markets, on their “global microstructures” (the global social and cultural form these markets take) and on trader markets in contrast to producer markets. She also studies globalization from a microsociological perspective, using an ethnographic approach, and she continues to be interested in “laboratory studies,” the study of science, technology and information at the site of knowledge production – particularly in the life sciences and in particle physics.
  • Reading A Sociology of Algorithms: High-Frequency Trading and the Shaping of Markets
    • Markets are politics,” (pg 8). I’d reverse that and say that politics are a market for power/influence, though that may be too glib.
    • three main types of algorithm discussed here (trading venues’ matching engines, which consummate trades; execution algorithms used by institutional investors to buy or sell large blocks of shares; and HFT algorithms), (pg 11)
    • a “lit” venue is one in which the electronic order book is visible to the humans and algorithms that trade on the venue; in a “dark” venue it is not visible.  (pg 11)
  • Meeting with USPTO folks. I went over their heads, but Aaron found the right level.

Phil 6.2.18


  • New internet accounts are Russian ops designed to sway U.S. voters, experts say
    • A website called appeared on the internet May 17 and called on Americans to rally in front of the White House June 14 to celebrate President Donald Trump’s birthday, which is also Flag Day.FireEye, a Milpitas, Calif., cybersecurity company, said Thursday that USA Really is a Russian-operated website that carries content designed to foment racial division, harden feelings over immigration, gun control and police brutality, and undermine social cohesion.The website’s operators once worked out of the same office building in St. Petersburg, Russia, where the Kremlin-linked Internet Research Agency had its headquarters, said Lee Foster, manager of information operations analysis for FireEye iSIGHT Intelligence.

CEPE 2019: 28–30 May, 2019, Norfolk, Virginia, USA

  • CEPE (Computer Ethics—Philosophical Enquiry) is a leading international conference and has played a significant role in defining the field since its first event in 1997. CEPE is held biennially, and is organized by INSEIT (the International Society for Ethics and Information Technology). For CEPE 2019, the conference theme will be Risk and Cybersecurity. We encourage submissions on this theme, but welcome submissions on any topic related to ethics and computers.

Phil 5.31.18

7:00 – ASRC MKT

  • Via BBC Business Daily, found this interesting post on diversity injection through lunch table size:
  • KQED is playing America Abroad – today on russian disinfo ops:
    • Sowing Chaos: Russia’s Disinformation Wars 
      • Revelations of Russian meddling in the 2016 US presidential election were a shock to Americans. But it wasn’t quite as surprising to people in former Soviet states and the EU. For years they’ve been exposed to Russian disinformation and slanted state media; before that Soviet propaganda filtered into the mainstream. We don’t know how effective Russian information warfare was in swaying the US election. But we do know these tactics have roots going back decades and will most likely be used for years to come. This hour, we’ll hear stories of Russian disinformation and attempts to sow chaos in Europe and the United States. We’ll learn how Russia uses its state-run media to give a platform to conspiracy theorists and how it invites viewers to doubt the accuracy of other news outlets. And we’ll look at the evolution of internet trolling from individuals to large troll farms. And — finally — what can be done to counter all this?
  • Some interesting papers on the “Naming Game“, a form of coordination where individuals have to agree on a name for something. This means that there is some kind of dimension reduction involved from all the naming possibilities to the agreed-on name.
    • The Grounded Colour Naming Game
      • Colour naming games are idealised communicative interactions within a population of artificial agents in which a speaker uses a single colour term to draw the attention of a hearer to a particular object in a shared context. Through a series of such games, a colour lexicon can be developed that is sufficiently shared to allow for successful communication, even when the agents start out without any predefined categories. In previous models of colour naming games, the shared context was typically artificially generated from a set of colour stimuli and both agents in the interaction perceive this environment in an identical way. In this paper, we investigate the dynamics of the colour naming game in a robotic setup in which humanoid robots perceive a set of colourful objects from their own perspective. We compare the resulting colour ontologies to those found in human languages and show how these ontologies reflect the environment in which they were developed.
    • Group-size Regulation in Self-Organised Aggregation through the Naming Game
      • In this paper, we study the interaction effect between the naming game and one of the simplest, yet most important collective behaviour studied in swarm robotics: self-organised aggregation. This collective behaviour can be seen as the building blocks for many others, as it is required in order to gather robots, unable to sense their global position, at a single location. Achieving this collective behaviour is particularly challenging, especially in environments without landmarks. Here, we augment a classical aggregation algorithm with a naming game model. Experiments reveal that this combination extends the capabilities of the naming game as well as of aggregation: It allows the emergence of more than one word, and allows aggregation to form a controllable number of groups. These results are very promising in the context of collective exploration, as it allows robots to divide the environment in different portions and at the same time give a name to each portion, which can be used for more advanced subsequent collective behaviours.
  • More Bit by Bit. Could use some worked examples. Also a login so I’m not nagged to buy a book I own.
    • Descriptive and injunctive norms – The transsituational influence of social norms.
      • Three studies examined the behavioral implications of a conceptual distinction between 2 types of social norms: descriptive norms, which specify what is typically done in a given setting, and injunctive norms, which specify what is typically approved in society. Using the social norm against littering, injunctive norm salience procedures were more robust in their behavioral impact across situations than were descriptive norm salience procedures. Focusing Ss on the injunctive norm suppressed littering regardless of whether the environment was clean or littered (Study 1) and regardless of whether the environment in which Ss could litter was the same as or different from that in which the norm was evoked (Studies 2 and 3). The impact of focusing Ss on the descriptive norm was much less general. Conceptual implications for a focus theory of normative conduct are discussed along with practical implications for increasing socially desirable behavior. 
    • Construct validity centers around the match between the data and the theoretical constructs. As discussed in chapter 2, constructs are abstract concepts that social scientists reason about. Unfortunately, these abstract concepts don’t always have clear definitions and measurements.
      • Simulation is a way of implementing theoretical constructs that are measurable and testable.
  • Hyperparameter Optimization with Keras
  • Recognizing images from parts Kaggle winner
  • White paper
  • Storyboard meeting
  • The advanced analytics division(?) needs a modeling and simulation department that builds models that feed ML systems.
  • Meeting with Steve Specht – adding geospatial to white paper

Phil 5.30.18

7:15 – 6:00 ASRC MKT

  • More Bit by Bit
  • An interesting tweet about the dichotomy between individual and herd behaviors.
  • More white paper. Add something about awareness horizon, and how maps change that from a personal to a shared reality (cite understanding ignorance?)
  • Great discussion with Aaron about incorporating adversarial herding. I think that there will be three areas
    • Thunderdome – affords adversarial herding. Users have to state their intent before joining a discussion group. Bots and sock puppets allowed
    • Clubhouse – affords discussion with chosen individuals. THis is what I thought JuryRoom was
    • JuryRoom – fully randomized members and topics, based on activity in the Clubhouse and Thunderdome

Phil 5.1.18

7:00 – 4:30 ASRC MKT

  • Applications of big social media data analysis: An overview
    • Over the last few years, online communication has moved toward user-driven technologies, such as online social networks (OSNs), blogs, online virtual communities, and online sharing platforms. These social technologies have ushered in a revolution in user-generated data, online global communities, and rich human behavior-related content. Human-generated data and human mobility patterns have become important steps toward developing smart applications in many areas. Understanding human preferences is important to the development of smart applications and services to enable such applications to understand the thoughts and emotions of humans, and then act smartly based on learning from social media data. This paper discusses the role of social media data in comprehending online human data and in consequently different real applications of SM data for smart services are executed.
  • Explainable, Interactive Deep Learning
    • Recently, deep learning has been advancing the state of the art in artificial intelligence to yet another level, and humans are relying more and more on the outputs generated by artificial intelligence techniques than ever before. However, even with such unprecedented advancements, the lack of interpretability on the decisions made by deep learning models and no control over their internal processes act as a major drawback when utilizing them to critical decision-making processes such as precision medicine and law enforcement. In response, efforts are being made to make deep learning interpretable and controllable by humans. In this paper, we review recent studies relevant to this direction and discuss potential challenges and future research directions.
  • Building successful online communities: Evidence-based social design (book review)
    • In Building Successful Online Communities (2012), Robert Kraut, Paul Resnick, and their collaborators set out to draw links between the design of socio-technical systems with findings from social psychology and economics. Along the way, they set out a vision for the role of social sciences in the design of systems like mailing lists, discussion forums, wikis, and social networks, offering a way that behavior on those platforms might inform our understanding of human behavior.
  • Since I’ve forgotten my Angular stuff, reviewing UltimateAngular, Angular Fundamentals course. Finished the ‘Getting Started’ section
  • Strip out Guttenburg text from corpora – done!

Phil 4.24.18

7:00 – 5:00 ASRC MKT

  • Aaron’s ot BoP today
  • Working on JuryRoom, particularly hooking up PHP to Angular
  • Here’s the hello world php app that’s working:
    header('Access-Control-Allow-Origin: *');
    echo '{"message": "hello"}';
  • And here’s the Angular side:
    uploadFile(event) {
      const elem =;
      if (elem.files.length > 0) {
        const f0 = elem.files[0];
        const formData = new FormData();
        formData.append('file', f0);
   'http://localhost/uploadImages/script.php', formData)
          .subscribe((data) => {
            const jsonResponse = data.json();
            console.log('Got some data from backend ', data);
          }, (error) => {
            console.log('Error! ', error);
  • Here’s how to connect to the deployment server for debugging (I hope!). From Importing settings from a server access (deployment) configurationDebugPhpServer
  • Can’t see the post info coming back, so I really need to get the debugger set up to talk to the server. Following these directions: Web Server Debug Validation Dialog. Here’s the dialog with some warnings to be corrected: EnablePhpDebug
  • Note that you HAVE TO RESTART APACHE for any php.ini changes to take
  • Had to Add XDebug Helper Chrome Extension. That helped with the php running in the browser, but not in the call to PHP from angular XDebugHelper
  • Works in Postman, but it doesn’t fire the debugger. Still, at least I know that the data can get to the php. Not sure if angular is sending it. Here’s the postman results: Postman
  • Here’s the debugger view. The data appears to be going up (formData), but it’s not coming back in the echo like it does in postman. I’ve played around with Content-type, and that doesn’t seem to help: Debugger
  • In the network view, we can see that the payload is there: Payload
  • So it must not be getting accepted in the PHP….

Phil 4.20.18

7:00 – ASRC MKT

  • Executing gradient descent on the earth
    • But the important question is: how well does gradient descent perform on the actual earth?
    • This is nice, because it suggests that we can compare GD algorithms on recognizable and visualizable terrains. Terrain locations can have multiple visualizable factors, height and luminance could be additional dimensions
  • Minds is the anti-facebook that pays you for your time
    • In a refreshing change from Facebook, Twitter, Instagram, and the rest of the major platforms, Minds has also retained a strictly reverse-chronological timeline. The core of the Minds experience, though, is that users receive “tokens” when others interact with their posts, or simply by spending time on the platform.
  • Continuing along with the Angular/PHP tutorial here. Nicely, there is also a Git repo
    • Had to add some styling to get the upload button to show
    • The HttpModule is deprecated, but sticking with it for now
    • Will need to connect/verify PHP server within IntelliJ, described here.
    • How to connect Apache, to IntelliJ
  • Installing and Configuring XAMPP with PhpStorm IDE. Don’t forget about deployment path: deploy

Phil 4.19.18

8:00 – ASRC MKT/BD

    • Good discussion with Aaron about the agents navigating embedding space. This would be a great example of creating “more realistic” data from simulation that bridges the gap between simulation and human data. This becomes the basis for work producing text for inputs such as DHS input streams.
      • Get the embedding space from the Jack London corpora (crawl here)
      • Train a classifier that recognizes JL using the embedding vectors instead of the words. This allows for contextual closeness. Additionally, it might allow a corpus to be trained “at once” as a pattern in the embedding space using CNNs.
      • Train an NN(what type?) to produce sentences that contain words sent by agents that fool the classifier
      • Record the sentences as the trajectories
      • Reconstruct trajectories from the sentences and compare to the input
      • Some thoughts WRT generating Twitter data
        • Closely aligned agents can retweet (alignment measure?)
        • Less closely aligned agents can mention/respond, and also add their tweet
    • Handed off the proposal to Red Team. Still need to rework the Exec Summary. Nope. Doesn’t matter that the current exec summary does not comply with the requirements.
    • A dog with high social influence creates an adorable stampede:
    • Using Machine Learning to Replicate Chaotic Attractors and Calculate Lyapunov Exponents from Data
      • This is a paper that describes how ML can be used to predict the behavior of chaotic systems. An implication is that this technique could be used for early classification of nomadic/flocking/stampede behavior
    • Visualizing a Thinker’s Life
      • This paper presents a visualization framework that aids readers in understanding and analyzing the contents of medium-sized text collections that are typical for the opus of a single or few authors.We contribute several document-based visualization techniques to facilitate the exploration of the work of the German author Bazon Brock by depicting various aspects of its texts, such as the TextGenetics that shows the structure of the collection along with its chronology. The ConceptCircuit augments the TextGenetics with entities – persons and locations that were crucial to his work. All visualizations are sensitive to a wildcard-based phrase search that allows complex requests towards the author’s work. Further development, as well as expert reviews and discussions with the author Bazon Brock, focused on the assessment and comparison of visualizations based on automatic topic extraction against ones that are based on expert knowledge.


Phil 4.18.18

7:00 – 6:30 ASRC MKT/BD

  • Meeting with James Foulds. We talked about building an embedding space for a literature body (The works of Jack London, for example) that agents can then navigate across. At the same time, train an LSTM on the same corpora so that the ML system, when given the vector of terms from the embedding (with probabilities/similarities?), produce a line that could be from the work that incorporates those terms. This provides a much more realistic model of the agent output that could be used for mapping. Nice paper to continue the current work while JuryRoom comes up to speed.
  • Recurrent Neural Networks for Multivariate Time Series with Missing Values
    • Multivariate time series data in practical applications, such as health care, geoscience, and biology, are characterized by a variety of missing values. In time series prediction and other related tasks, it has been noted that missing values and their missing patterns are often correlated with the target labels, a.k.a., informative missingness. There is very limited work on exploiting the missing patterns for effective imputation and improving prediction performance. In this paper, we develop novel deep learning models, namely GRUD, as one of the early attempts. GRU-D is based on Gated Recurrent Unit (GRU), a state-of-the-art recurrent neural network. It takes two representations of missing patterns, i.e., masking and time interval, and effectively incorporates them into a deep model architecture so that it not only captures the long-term temporal dependencies in time series, but also utilizes the missing patterns to achieve better prediction results. Experiments of time series classification tasks on real-world clinical datasets (MIMIC-III, PhysioNet) and synthetic datasets demonstrate that our models achieve state-of-the-art performance and provide useful insights for better understanding and utilization of missing values in time series analysis.
  •  The fall of RNN / LSTM
    • We fell for Recurrent neural networks (RNN), Long-short term memory (LSTM), and all their variants. Now it is time to drop them!
  • JuryRoom
  • Back to proposal writing
  • Done with section 5! LaTex FTW!
  • Clean up Abstract, Exec Summary and Transformative Impact tomorrow

Phil 3.7.18

7:00 – 5:00 ASRC MKT

  • Some surprising snow
  • Meeting with Sy at 1:30 slides
  • Meeting with Dr. DesJardins at 4:00
  • Nice chat with Wajanat about the presentation of the Saudi Female self in physical and virtual environments
  • Sprint planning
    • Finish ONR Proposal VP-331
    • CHIIR VP-332
    • Prep for TF dev conf VP-334
    • TF dev conf VP-334
  • Working on the ONR proposal
  • Oxford Internet Institute – Computational Propaganda Research Project
    • The Computational Propaganda Research Project (COMPROP) investigates the interaction of algorithms, automation and politics. This work includes analysis of how tools like social media bots are used to manipulate public opinion by amplifying or repressing political content, disinformation, hate speech, and junk news. We use perspectives from organizational sociology, human computer interaction, communication, information science, and political science to interpret and analyze the evidence we are gathering. Our project is based at the Oxford Internet Institute, University of Oxford.
    • Polarization, Partisanship and Junk News Consumption over Social Media in the US
      • What kinds of social media users read junk news? We examine the distribution of the most significant sources of junk news in the three months before President Donald Trump’s first State of the Union Address. Drawing on a list of sources that consistently publish political news and information that is extremist, sensationalist, conspiratorial, masked commentary, fake news and other forms of junk news, we find that the distribution of such content is unevenly spread across the ideological spectrum. We demonstrate that (1) on Twitter, a network of Trump supporters shares the widest range of known junk news sources and circulates more junk news than all the other groups put together; (2) on Facebook, extreme hard right pages—distinct from Republican pages—share the widest range of known junk news sources and circulate more junk news than all the other audiences put together; (3) on average, the audiences for junk news on Twitter share a wider range of known junk news sources than audiences on Facebook’s public pages
      • Need to look at the variance in the articles. Are these topical stampedes? Or is this source-oriented?
  • Understanding and Addressing the Disinformation Ecosystem
    • This workshop brings together academics, journalists, fact-checkers, technologists, and funders to better understand the challenges produced by the current disinformation ecosystem. The facilitated discussions will highlight relevant research, share best-practices, identify key questions of scholarly and practical concern regarding the nature and implications of the disinformation ecosystem, and outline a potential research agenda designed to answer these questions.
  • More BIC
    • The psychology of group identity allows us to understand that group identification can be due to factors that have nothing to do with the individual preferences. Strong interdependence and other forms of common individual interest are one sort of favouring condition, but there are many others, such as comembership of some existing social group, sharing a birthday, and the artificial categories of the minimal group paradigm. (pg 150)
    • Wherever we may expect group identity we may also expect team reasoning. The effect of team reasoning on behavior is different from that of individualistic reasoning. We have already seen this for Hi-Lo. This has wide implications. It makes the theory of team reasoning a much more powerful explanatory and predictive theory than it would be if it came on line only in games with th3e right kind of common interest. To take just one example, if management brings it about so that the firm’s employees identify with the firm, we may expect for them to team-reason and so to make choices that are not predicted by the standard theories of rational choice. (pg 150)
    • As we have seen, the same person passes through many group identities in the flux of life, and even on a single occasion more than one of these identities may be stimulated. So we will need a model of identity in which the probability of a person’s identification is distributed over not just two alternatives-personal self-identity or identity with a fixed group-but, in principle, arbitrarily many. (pg 151)

Phil 3.2.18

7:00 – 5:00 ASRC MKT

  • Got Wayne’s comments. Will integrate and see if EasyChair will take it
  • Work on ONR WhitePaper
  • Twitter proposal?
  • Society for Personality and Social Psychology
    • The mission of SPSP is to advance the scienceteaching, and application of social and personality psychology. SPSP members aspire to understand individuals in their social contexts for the benefit of all people.
    • Social psychology is the scientific study of how people’s thoughts, feelings, and behaviors are influenced by the actual, imagined, or implied presence of others.
  • Rebecca Hofstein Grady
    • I am interested in the ways that bias and motivation can affect our reasoning and memory to influence the judgments and decisions that we make.  In particular, I am currently studying how these biases apply to real-world situations, such as political conflicts, hiring decisions, and legal decision-making.  I explore not only how biases affect decision-making but what people think about their own biases and the best ways to help correct them.
    • Data from a pre-publication independent replication initiative examining ten moral judgement effects

Phil 2.19.18

7:30 – 4:30 ASRC MKT

  • Back to BIC.
    • BIC_102 (page 102)
    • BIC107 (pg 107)
    • BIC107b (pg 107)
    • Sociality: Coordinating bodies, minds and groups
      • Human interaction, as opposed to aggregation, occurs in face-to-face groups. “Sociality theory” proposes that such groups have a nested, hierarchical structure, consisting of a few basic variations, or “core configurations.” These function in the coordination of human behavior, and are repeatedly assembled, generation to generation, in human ontogeny, and in daily life. If face-to-face groups are “the mind’s natural environment,” then we should expect human mental systems to correlate with core configurations. Features of groups that recur across generations could provide a descriptive paradigm for testable and non-intuitive evolutionary hypotheses about social and cognitive processes. This target article sketches three major topics in sociality theory, roughly corresponding to the interests of biologists, psychologists, and social scientists. These are (1) a multiple levels-of-selection view of Darwinism, part group selectionism, part developmental systems theory; (2) structural and psychological features of repeatedly assembled, concretely situated face-to-face coordination; and (3) superordinate, “unsituated” coordination at the level of large-scale societies. Sociality theory predicts a tension, perhaps unresolvable, between the social construction of knowledge, which facilitates coordination within groups, and the negotiation of the habitat, which requires some correspondence with contingencies in specific situations. This tension is relevant to ongoing debates about scientific realism, constructivism, and relativism in the philosophy and sociology of knowledge.
        • These definitions seem to span atomic (mother/child, etc), small group (situated, environmental), and societal (unsituated, normative)
      • Coordination occurs to the extent that knowledge and practice domains overlap or are complementary. I suggest that values serve as a medium. Humans live in a value-saturated environment; values are known from interactions with people, natural objects, and artifacts
        • Dimension reduction
  •  I’m starting to think that agents as gradient descent machines within networks is something to look for:
    • Individual Strategy Update and Emergence of Cooperation in Social Networks
      • In this article, we critically study whether social networks can explain the emergence of cooperative behavior. We carry out an extensive simulation program in which we study the most representative social dilemmas. For the Prisoner’s Dilemma, it turns out that the emergence of cooperation is dependent on the microdynamics. On the other hand, network clustering mostly facilitates global cooperation in the Stag Hunt game, whereas degree heterogeneity promotes cooperation in Snowdrift dilemmas. Thus, social networks do not promote cooperation in general, because the macro-outcome is not robust under change of dynamics. Therefore, having specific applications of interest in mind is crucial to include the appropriate microdetails in a good model.
    • Alex Peysakhovich and Adam Lerer
      • Prosocial learning agents solve generalized Stag Hunts better than selfish ones
        • Deep reinforcement learning has become an important paradigm for constructing agents that can enter complex multi-agent situations and improve their policies through experience. One commonly used technique is reactive training – applying standard RL methods while treating other agents as a part of the learner’s environment. It is known that in general-sum games reactive training can lead groups of agents to converge to inefficient outcomes. We focus on one such class of environments: Stag Hunt games. Here agents either choose a risky cooperative policy (which leads to high payoffs if both choose it but low payoffs to an agent who attempts it alone) or a safe one (which leads to a safe payoff no matter what). We ask how we can change the learning rule of a single agent to improve its outcomes in Stag Hunts that include other reactive learners. We extend existing work on reward-shaping in multi-agent reinforcement learning and show that that making a single agent prosocial, that is, making them care about the rewards of their partners can increase the probability that groups converge to good outcomes. Thus, even if we control a single agent in a group making that agent prosocial can increase our agent’s long-run payoff. We show experimentally that this result carries over to a variety of more complex environments with Stag Hunt-like dynamics including ones where agents must learn from raw input pixels.
      • The Good, the Bad, and the Unflinchingly Selfish: Cooperative Decision-Making Can Be Predicted with High Accuracy Using Only Three Behavioral Types
        • The human willingness to pay costs to benefit anonymous others is often explained by social preferences: rather than only valuing their own material payoff, people also care in some fashion about the outcomes of others. But how successful is this concept of outcome-based social preferences for actually predicting out-of-sample behavior? We investigate this question by having 1067 human subjects each make 20 cooperation decisions, and using machine learning to predict their last 5 choices based on their first 15. We find that decisions can be predicted with high accuracy by models that include outcome-based features and allow for heterogeneity across individuals in baseline cooperativeness and the weights placed on the outcome-based features (AUC=0.89). It is not necessary, however, to have a fully heterogeneous model — excellent predictive power (AUC=0.88) is achieved by a model that allows three different sets of baseline cooperativeness and feature weights (i.e. three behavioral types), defined based on the participant’s cooperation frequency in the 15 training trials: those who cooperated at least half the time, those who cooperated less than half the time, and those who never cooperated. Finally, we provide evidence that this inclination to cooperate cannot be well proxied by other personality/morality survey measures or demographics, and thus is a natural kind (or “cooperative phenotype”)
        • “least”, “intermediate” and “most” cooperative. Doesn’t give percentages, though it says that 17.8% were cooperative?


  • Talk Susan Gregurick (
    • All of Us research program
    • Opiod epidemic – trajectory modeling?
    • PZM21 computational drug
    • Develop advanced software and tools. Specialized generalizable and accessible tools for biomedicing (finding stream). Includes mobile, data indexing, etc.
    • NIH Data Fellows? Postdocs to senior industry
    • T32 funding? Mike Summers at UMBC
    • (look for data?
    • Primary supporter for machine learning is NIMH (imaging), then NIGNS, and NCI Team science (Multi-PI) is a developing thing
    • $400m in computing enabled interactions (human in the loop decision tools. Research Browser?
    • Big Data to Knowledge Initiative (BD2K)
    • Interagency Modeling and Analysis Group (IMAG) imagewiki,
    • funding:
    • NIH RePorter Check out matchmaker. What’s the ranking algorithm?
    • NIDDK predictive analytics for budgeting <- A2P-ish?
    • Most of thi srequires preliminary data and papers to be considered for funding. There is one opportunity for getting funding to get preliminary data. Need to get more specific infor here.
    • Each SRO normalizes grade as a percentile, not the score, since some places inflate, and others are hard.
    • Richard Aargon at NIGMS
    • Office of behavioral and social science – NIH center Francis Collins. Also agent-based simulation
    • Really wants a Research Browser to go through proposals
  • Fika – study design
    • IRB – you can email and chat with the board if you have a tricky study

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.