phil 7.12.18

Stampede thinking:

  • Lazy, not biased: Susceptibility to partisan fake news is better explained by lack of reasoning than by motivated reasoning
    • Gordon Pennycook
    • David Rand
    • Why do people believe blatantly inaccurate news headlines (“fake news”)? Do we use our reasoning abilities to convince ourselves that statements that align with our ideology are true, or does reasoning allow us to effectively differentiate fake from real regardless of political ideology? Here we test these competing accounts in two studies (total N = 3446 Mechanical Turk workers) by using the Cognitive Reflection Test (CRT) as a measure of the propensity to engage in analytical reasoning. We find that CRT performance is negatively correlated with the perceived accuracy of fake news, and positively correlated with the ability to discern fake news from real news – even for headlines that align with individuals’ political ideology. Moreover, overall discernment was actually better for ideologically aligned headlines than for misaligned headlines. Finally, a headline-level analysis finds that CRT is negatively correlated with perceived accuracy of relatively implausible (primarily fake) headlines, and positively correlated with perceived accuracy of relatively plausible (primarily real) headlines. In contrast, the correlation between CRT and perceived accuracy is unrelated to how closely the headline aligns with the participant’s ideology. Thus, we conclude that analytic thinking is used to assess the plausibility of headlines, regardless of whether the stories are consistent or inconsistent with one’s political ideology. Our findings therefore suggest that susceptibility to fake news is driven more by lazy thinking than it is by partisan bias per se – a finding that opens potential avenues for fighting fake news.

From Alessandro Bozzon (Scholar):

  • I am Assistant Professor with the Web Information Systemsgroup, at the Delft University of Technology. I am Research Fellow at the AMS Amsterdam Institute for Advanced Metropolitan Solutions, and a Faculty Fellow with the IBM Benelux Center of Advanced Studies.

    My research lies at the intersection of crowdsourcing, user modeling, and web information retrieval. I study and build novel Social Data science methods and tools that combine the cognitive and reasoning abilities of individuals and crowds, with the computational powers of machines, and the value of big amounts of heterogeneous data.

    I am currently active in three investigation lines related to Social Data Science: Intelligent Cities (SocialGlass; Crowdsourced Knowledge Creation in Online Social Communities (SEALINCMedia COMMIT/StackOverflow); and Enterprise Crowdsourcing (with IBM Benelux CAS).

  • Modeling CrowdSourcing Scenarios in Socially-Enabled Human Computation Applications
    • User models have been defined since the 1980s, mainly for the purpose of building context-based, user-adaptive applications. However, the advent of social networked media, serious games, and crowdsourcing/human computation platforms calls for a more pervasive notion of user model, capable of representing the multiple facets of social users and performers, including their social ties, interests, capabilities, activity history, and topical affinities. In this paper, we define a comprehensive model able to cater for all the aspects relevant for applications involving social networks and human computation; we capitalize on existing social user models and content description models, enhancing them with novel models for human computation and gaming activities representation. Finally, we report on our experiences in adopting the proposed model in the design and implementation of three socially enabled human computation platforms.
  • Sparrows and Owls: Characterisation of Expert Behaviour in StackOverflow
    • Question Answering platforms are becoming an important repository of crowd-generated knowledge. In these systems a relatively small subset of users is responsible for the majority of the contributions, and ultimately, for the success of the Q/A system itself. However, due to built-in incentivization mechanisms, standard expert identification methods often misclassify very active users for knowledgable ones, and misjudge activeness for expertise. This paper contributes a novel metric for expert identification, which provides a better characterisation of users’ expertise by focusing on the quality of their contributions. We identify two classes of relevant users, namely sparrows and owls, and we describe several behavioural properties in the context of the StackOverflow Q/A system. Our results contribute new insights to the study of expert behaviour in Q/A platforms, that are relevant to a variety of contexts and applications.

Phil 7.8.18

Scott Klemmer Keynote 2

  • What are interesting things that we can do with computers and teaching – 2011
  • Objective truth <-> Contextual truth
  • Design is in the middle, between objective and subjective truth
  • The act of assessing work is a good way to improve understanding
  • Problem finding as opposed to problem solving
  • “A negotiation around the valuation criteria” Jeff Nicholson
  • Negotiations also happen between the creators and the users, particularly in software design. The initial design is the starting point of that journey
  • What counts as preferred shifts over time
  • Talkabout – The subway model. Pick a time that you’re going to show up, and we’ll put you in a group. Small groups discuss topics.
  • Assigning to globally diverse discussion groups increase grades by greater amounts than more local, less diverse groups. Open-ended questions
  • DSCN0348DSCN0349DSCN0350DSCN0351DSCN0352

Participated in the panel on innovation in crowds (invited). There is a video, so I can figure out who to add:

  • Christopher Tucci,
  • Gianluigi Viscusi (GG)
  • Rosy Mondardini
  • Thomas Malone
  • Joel Chan
  • Philip Feldman

Eszter Hargitti – U of Zurich

  • Awareness of what is possible
  • The ability to create and share content
  • Wikigroan?
  • DSCN0353DSCN0354DSCN0355

When Ties Bind And When Ties Divide: The Effects Of Communication Networks On Group Processes And Performance DSCN0356.JPG_1DSCN0357.JPG_2

  • Network structural variance

Enhancing Collective Intelligence of Human-Machine Teams DSCN0358DSCN0359

  • Cognitive and ethnic diversity predict collective intelligence
  • Group structure, high level communication and equality of communication
  • It’s the quality of the individuals and the quality of the connections
  • Coordination technologies – connect humans

Implicit Coordination in Peer Production Networks DSCN0360DSCN0361DSCN0362DSCN0363

Collective Intelligence Systems for Analogical Search (must read! Joel Chan is at UMD)

  • Really interesting, worth reading. Purpose and mechanism may be related to belief spaces. Definitely trainable using NN to find purpose mechanism

Rational Collective Learning in the Laboratory

  • Groupthink. as a failure of design
  • Randomy constructed groups can make good design choices given failing parts with a history.

Phil 7.7.18

8:00 – 9:00 ASRC MKT

  • At CI 2018. Hell of a time setting up eduroam. Nice venue, though. Winston Churchill called for the unification of Europe from that podium. Probably without PowerPoint DSCN0310
  • Patrick Meier – keynote – Digital humanitarian efforts
    • Mission is to pioneer the next generation of humanitarian technology
    • DSCN0313
    • DSCN0315
  • Poster pitches
    • Multiple barriers to crowdsourcing, ranging from operational to strategic
    • Anita Wollie – trust in AI Embedded agency, Virtual agency, Physical Agency
    • Croudoscope – qualitative and quantitative surveys – open coments. Not lists, but graphs
    • Market volitility with High-Frequency trading an hmans
    • How many people constitutes a ‘crowd’
    • Is novelty an advantage in crowdfunding
    • QUEST – annotating questions on stackoverflow-style probles’
    • Cyber-physical systems – e.g. smart transportation systems
  • Papers
  • Keynote 2
    • Optimizing the Human-Machine Partnership with Zooniverse DSCN0321 DSCN0322
      • Lucy Fortson
      • Galaxy Zoo
      • Zooniverse is on its third iteration and now supports project building
      • Can also point to a project
  • Session 2
    • Collective Intelligence for Deep Reinforcement Learning (MIT, mostly)
      • Evolutionary strategies (Salimans 2017) DSCN0327
    • Social learning strategies for matters of taste (This is a must-read!)
      • DSCN0326DSCN0325DSCN0324
    • Photo Sleuth: Combining Collective Intelligence and Computer Vision to
      Identify Historical Portraits

      • Good discussion of how to blend human and ML person identification
    • Toward Safer Crowdsourced Content Moderation
    • How Intermittent Breaks in Interaction Improve Collective

Phil 7.1.18

On vacation, but oddly enough, I’m back on my morning schedule, so here I am in Bormio, Italy at 4:30 am.

I forgot my HDMI adaptor for the laptop. Need to order one and have it delivered to Zurich – Hmmm. Can’t seem to get it delivered from Amazon to a hotel. Will have to buy in Zurich

Need to add Gamerfate to the lit review timeline to show where I started to get interested in the problem – tried it but didn’t like it. I’d have to redo the timeline and I’m not sure I have the excel file

Add vacation pictures to slides – done!

Some random thoughts

  • When using the belief space example of the table, note that if we sum up all the discussions about tables, we would be able to build a pretty god map of what matters to people with regards to tables
  • Manifold learning is what intelligent systems do as a way of determining relationships between things (see curse of dimensionality). As groups of individuals, we need to coordinate our manifold learning activities so that we can us the power of group cognition. When looking at how manifold learning schemes like t-sne and particularly embedding systems such as word2vec create their own unique embeddings, it becomes clear that our machines are not yet engaged in group cognition, except in the simplest way of re-using trained networks and copied hyperparameters. This is very prone to stampedes
  • In conversation at dinner, Mike M mentioned that he’d like a language app that is able to indicate the centrality of a term an order that list so that it’s possible to learn a language in a “prioritized” way that can be context-dependent. I think that LMN with a few tweaks could do that.

Continuing the Evolution of Cooperation. A thing that strikes me is that once a TIT FOR TAT successfully takes over, then it becomes computationally easier to ALWAYS COOPERATE. That could evolve to become dominant and be completely vulnerable to ALWAYS DEFECT

Phil 6.27.18

7:00 – 12:00 ASRC MKT

  • Print out documents! Done. Got passport drive too.
  • Need to write an extractor that lets the user navigate the xml file containing influences of selected agents. This could be a sample-by sample network. Maybe two modes?
    • Select an agent and see all the other agents come in and out of influcene
    • Select an number of agents and only watch the mutual influence.
    • There is an integrated JavaFX charts that I could use, or it could be an uploaded webapp? JavaFX would be easier in the short term, but a webapp would help more with JuryRoom…
    • Another option would be Python, since that’s where the LSTM code will live.
    • On the whole, two days before leaving on travel is probably the wrong time to start coding
  • Fixed a bug in the xml file generation
  • copied the new jar file onto the thumb drive
  • copied the xml file onto the thumb drive

12:00 – 4:00 ASRC A2P

  • Pomoting things to QA – done! Or at least, up to date with the excel files

Phil 6.26.18

7:00 – 5:00 ASRC MKT

  • Started back with the Evolution of Cooperation
  • Social loafing (Scholar results)
    • In social psychologysocial loafing is the phenomenon of a person exerting less effort to achieve a goal when they work in a group than when they work alone. This is seen as one of the main reasons groups are sometimes less productive than the combined performance of their members working as individuals, but should be distinguished from the accidental coordination problems that groups sometimes experience. Research on social loafing began with rope pulling experiments by Ringelmann, who found that members of a group tended to exert less effort in pulling a rope than did individuals alone. In more recent research, studies involving modern technology, such as online and distributed groups, have also shown clear evidence of social loafing. Many of the causes of social loafing stem from an individual feeling that his or her effort will not matter to the group.
  • NELA2017 contains almost every news article from 92 sources between April 2017 and October 2017, amounting to over 136K articles. This data set is the first release of NELA datasets. This version of the data set can be found on github and a full description and use cases can be found in our 2018 ICWSM paper.
  • Submitted “One Simple Trick” final to SASO
  • Updated ArXive
  • Fixed a bug that prevented population interactions in FlockingAgentManager.initializeAgents():
                // add to the global list
                allBoidsList.add(fs);
    
                // add a pointer to the global list to each shape
                fs.setFlockingShapeList(allBoidsList);
    
                // Add to the flock so that we can get flock headings
                List flock = flockListsMap.get(flockName);
                flock.add(fs);

    Seriously, what was I thinking?

  • Continued GUI tweaking. I think it looks pretty good, and it fits (mostly) on my laptop Version6.26.18
  • Verified that the influences record agents from different flocks and sources.
  • Copied all CI 2018 things I can think of onto the thumb drive

Phil 6.25.18

7:00 – 9:00 ASRC MKT

  • Update laptop – Intellij, Java, GroupPolarazation codebase
  • Add XML output for influence – done!
  • Refactored the GUI to work with smaller (laptop) screens)

9:00 – 2:30 ASRC A2P

  • Debug what’s going on with the excel reading. Try a new config file first?
  • Ground slowly through options
    • Replaced the config file
    • Stepped through the debugger, and noticed that the worksheet was null. Tried a different worksheet/config, and that was *not* null
    • Created a new workbook and copied everything over without formatting. That worked on the converter, but didn’t work with A2P
    • Reformatted the new workbook and wound up using the Funding Summary Details data with the formatting, which is *crazy*….
    • Had some issues getting connected to the server. Pageant forgot my key.

3:00 – 4:00 ASRC MKT

  • Fika. No, not really. Wound up chatting with Will

Phil 6.23.18

Registered for SASO

ArXive papers with Github repos

Mapping interest communities in Russian Facebook Ads. Preliminary visualisation reveals a number of broad interest groups around ethnicity; reveals a bit of Internet Research Agency’s strategy...

  • Dr Bharath Ganesh
    • Bharath is a political geographer focusing on data science and local government and the ethics and politics of researching violent online extremism.

More good stuff from Ian Couzin

  • Revealing the hidden networks of interaction in mobile animal groups allows prediction of complex behavioral contagion
    • We know little about the nature of the evolved interaction networks that give rise to the rapid coordinated collective response exhibited by many group-living organisms. Here, we study collective evasion in schooling fish using computational techniques to reconstruct the scene from the perspective of the organisms themselves. This method allows us to establish how the complex social scene is translated into behavioral response at the level of individuals and to visualize, and analyze, the resulting complex communication network as behavioral change spreads rapidly through groups. Thus, we can map, for any moment in time, the extent to which each individual is socially influential during collective evasion and predict the magnitude of such behavioral epidemics before they actually occur

This playlist contains tutorials to learn how to use Keras, a neural network API written in Python. Each video focuses on a specific concept and shows how the full implementation is done in code using Keras and Python.

 

Phil 6.22.18

7:00 – 5:30 ASRC MKT

  • Twitter experiment on a fake Gary Indiana secession. IFTTT retweeting leads to interesting behavior.
  • Fixed FlockingShape casting by adding a customDrawStep(GraphicsContext gc) to the SmartShape base class that’s called from draw().
  • Add records to each agent that store a list of source and agent influences at each time sample. It should include the name of the item and the amount of influence. Probably save as an XML file, since it has too many dimensions. The file could then be used to create terms or spreadsheets.
    • Started on CAInfluence class which will be added to CA classes in an arrayList in BaseCA;
  • More file conversion with Bob – and everything worked great until I try one after Bob leaves. Ka-BOOM!
    • Installed all the packages to get everything to run in the debugger. Found what appears to be a perfectly good line “range” that causes the problem? Will start debugging on Wednesday.
  • Project MERCATOR proposal
  • Meeting with Sy

Phil 6.21.18

7:00 – 4:00 ASRC MKT

  • Add an attractor scalar for agents that’s normally zero. A vector to each agent within the SIH is calculated and scaled by the attractor scalar. That vector is then added to the direction vector to the agent – done
  • Remove the heading influence based on site – done
  • Add a white circle to the center of the agent that is the size of the attraction scalar. Done
  • Add attraction radius slider that is independent of the SIH. -done
  • Add a ‘site trajectory’ to the spreadsheet that will have the site lists (and their percentage?)
  • There is now an opportunity for a poster and a demo at SASO
  • Add stories, lists and maps to implication slides – done
  • Got all my connections set up
  • Successfully converted and deployed cosmos-2
  • Voted!

Phil 6.20.18

7:00 – 9:00 2:00 – 5:00 ASRC MKT

  • Redo doodle for all of August – done
  • Schooling Fish May Offer Insights Into Networked Neurons
    • Iain Couzin is deciphering the rules that govern group behavior. The results might provide a fresh perspective on how networks of neurons work together.
  • City arts and lectures: The New Science Of Psychedelics With Michael Pollan
    • Psychedelics reduce the section of the brain that have to do with the sense of self. Pollan thinks that this also happens with certain types of rhythmic music and in crowd situations. This could be related to stampedes and flocking.
    • LSD May Chip Away at the Brain’s “Sense of Self” Network
      • Brain imaging suggests LSD’s consciousness-altering traits may work by hindering some brain networks and boosting overall connectivity
  • Add an attractor scalar for agents that’s normally zero. A vector to each agent within the SIH is calculated and scaled by the attractor scalar. That vector is then added to the direction vector to the agent – done?
  • Remove the heading influence based on site – done
  • Add a white circle to the center of the agent that is the size of the attraction scalar. Done
  • Add a ‘site trajectory’ to the spreadsheet that will have the site lists (and their percentage?)
  • Worked on A2P white paper with Aaron.
  • Worked on a response to Dr. Li’s response

ASRC IRAD 9:00 – 2:00

  • Mind meld with Bob
    • Revisit Yarn
    • Excel stuff?
    • Connect to AWS using bastion. Look in FoxyProxy how to. I need certs
    • Drop on rabbit to deploy to CI and QA and NESDIS  ONE (production)
    • Don’t want sensitive information in Git. We use sharepoint instead
    • Notes and screenshots in document.

Phil 6.19.18

7:00 – 9:00, 4:00 – 5:00 ASRC MKT

  • Here’s a list of organizations that are mobilizing to help immigrant children separated from their families
  • SASO trip
  • Rebuilt all the binaries, now I need to put them on the thumb drive – done
  • Added knobs to the implications slide. They sit next to the dimension and SIH lines. I realize that my slide deck is becoming a physical version of a memory palace.
  • Continuing Irrational Exuberance, though feeling like I should be reading Axelrod. Bring Evolution of Cooperation on the flight?
  • Naive Diversification Strategies in Defined Contribution Saving Plans
    • There is a worldwide trend toward defined contribution saving plans and growing interest in privatized social security plans. In both environments, individuals are given some responsibility to make their own asset allocation decisions, raising concerns about how well they do at this task. This paper investigates one aspect of the task, namely diversification. We show that many investors have very naive notions about diversification. For example, some investors follow what we call the 1/n strategy: they divide their contributions evenly across the funds offered in the plan. When this strategy (or others only slightly more sophisticated) is used, the assets chosen depend greatly on the make-up of the funds offered in the plan. We find evidence of naive diversification strategies both in experiments using employees at the University of California and the actual behavior of participants in a wide range of savings plans. In particular, we find the proportion of the assets the participants invest in stocks depends strongly on the proportion of stock funds in the plan. The results raise very serious questions about how privatized social security systems should be designed, questions that would be ignored in most economic analyses.
    • This is very much a dimension reduction exercise.
  • A2P maintenance proposal

9:00 – 4:00 ASRC A2P

  • Coming up to speed on the Angular interface
    • Logging into CI and QA
    • Dashboard configurations