Sick echo chambers

Over the past year, I’ve been building a model that lets me look at how opinions evolve in belief space, much in the manner that flocks, herds and schools emerge in the wild.

CI_GP_Poster4a

Recently, I was Listening to BBC Business Daily this morning on Facebook vs Democracy:

  • Presenter Ed Butler hears a range of voices raising concern about the existential threat that social media could pose to democracy, including Ukrainian government official Dmytro Shymkiv, journalist Berit Anderson, tech investor Roger McNamee and internet pioneer Larry Smarr.

Roger McNamee and Larry Smarr in particular note how social media can be used to increase polarization based on emergent poles. In other words, “normal” opposing views can be amplified by attentive bad actors [page 24] with an eye towards causing generalized societal disruption.

My model explores emergent group interactions and I wondered if this adversarial herding in information space as it might work in my model.

These are the rough rules I started with:

  • Herders can teleport, since they are not emotionally invested in their belief space position and orientation
  • Herders appear like multiple individuals that may seem close and trustworthy, but they are actually a distant monolithic entity that is aware of a much larger belief space.
  • Herders amplify arbitrary pre-existing positions. The insight is that they are not herding in a direction, but to increase polarization
  • To add this to the model, I needed to do the following:
    • Make the size of the agent a function of the weight so we can see what’s going on
    • When in ‘herding mode’ the overall heading of the population is calculated, and the agent that is closest to that heading is selected to be amplified by our trolls/bot army.
    • The weight is increased to X, and the radius is increased to Y.
      • X represents AMPLIFICATION BY trolls, bots, etc.
      • A large Y means that the bots can swamp other, normally closer signals. This models the effect of a monolithic entity controlling thousands of bots across the belief space

Here’s a screenshot of the running simulation. There is an additional set of controls at the upper left that allow herding to be enables, and the weight of the influence to be set. In this case, the herding weight is 10. Though the screenshot shows one large agent shape, the amplified shape flits from agent to agent, always keeping closest to the average heading.

2017-10-28

The results are kind of scary. If I set the weight of the herder to 15, I can change the change the flocking behavior of the default to echo chamber.

  • Normal: No Herding
  • Herding weight set to 15, other options the same: HerdingWeight15

I did some additional tweaking to see if having highly-weighted herders ignore each other (they would be coordinated through C&C) would have any effect. It doesn’t. There is enough interaction through the regular populations to keep the alignment space reduced.

It looks like there is a ‘sick echo chamber’ pattern. If the borders are reflective, and the herding weight + influence radius is great enough, then a wall-hugging pattern will emerge.

The influence weight is sort of a credibility score. An agent that has a lot of followers, or says a lot of the things that I agree with has a lot of influence weight The range weight is reach.

Since a troll farm or botnet can be regarded as a single organization,  interacting with any one of the agents is really interacting with the root entity.  So a herding agent has high influence and high reach. The high reach explains the border hugging behavior.

It’s like there’s someone at the back of the stampede yelling YOUR’E GOING THE RIGHT WAY! KEEP AT IT! And they never go off the cliff because they are a swarm Or, it never goes of the cliff, because it manifests as a swarm.

A loud, distributed voice pointing in a bad direction means wall hugging. Note that there is some kind of floating point error that lets wall huggers creep off the edge.Edgecrawling

With a respawn border, we get the situation where the overall heading of the flock doesn’t change even as it gets destroyed as it goes over the border. Again, since the herding algorithm is looking at the overall population, it never crosses the border but influences all the respawned agents to head towards the same edge: DirectionPreserving

Who’d have thought that there could be something worse than runaway polarization?

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Trustworthy News Model Assumptions

Modifications

  • 12.13.16: Initial post
  • 12:16:16: Added reference to proposal and explicitly discussed explorer and exploiter types.

A web version of my Google Docs dissertation proposal is here. Blame them for the formatting issues. The section this is building on is Section 5.3.1. A standalone description of this task is here.

The first part of my dissertation work is to develop an agent-based simulation that exhibits information bubble/antibubble behavior. Using Sen and Chakrabarti’s Sociophysics as my guide, I’m working up the specifics of the model. My framework is an application (JavaFX, because that’s what I’m using at work these days). It’s basically an empty framework with a trivial model that allows clustering based on similar attributes such as color: strawmanapp

Going forward, I need to clarify and defend the model, so I’m going to be listing the components here.

Agent assumptions

  • Agents get their information from global sources (news media). They have equal access, but visibility is restricted
    • Agents are Explorers or Exploiters (Which may be made up of Confirmers and Avoiders)
    • Agents have ‘budgets’ that they can allocate
    • Finding sources has a cost. Sources from the social network has a lower cost to access
    • Keeping a source is cheaper than getting a new one
    • For explorers, the cost of getting a new source is lower than an exploiter.
    • The ‘belief’ as a set of ‘statements’ appears to be valid
    • The collection of statements and the associated values create a position in an n-dimensional hilbert space of information. Position and velocity should be calculable.
    • Start at one dimension to reproduce prior opinion models

Network assumptions

  • There are two items that we are looking for.
    • The first is the network configuration over time. What nodes do agents connect to for their information.
    • The second is the content of that information. For that, we’ll probably need some dimensionality reduction, such as NMF (look for a post on implementing this later). This is where we look for echo chambers of information, as opposed to the agents participating in them
  • Adjustable to include scale-free, small world, and null configurations
  • What about loops? Feedback could be interesting, since a small group that is semi-isolated could form into a very loud bubble that could lower the cost of finding information. So a notion of volume might be needed that emerges from a set of agreeing agents. This could be attraction, though I think I like an economic approach more?
  • There is also a ‘freezing’ issue, where a stable state is reached where two cliques containing different states are lightly connected, but not enough that the neighbors in one clique can be convinced to change their opinion [Fig. 6.2, pg 135]

Measures

  • Residual Energy: The difference between the actual energy and the known energy of the perfectly-ordered ground state (full consensus).
  • Deviation from null network.
  • Clustering as per community detection (Girard et. al)

Implementation details

  • Able to be run multiple times with the same configuration but different seed
  • Outputs to… something. MySql or Excel probably
  • Visualization using t-SNE? Description plus Java implementation is here: https://lvdmaaten.github.io/tsne/

More to come as the model fleshes out.

 

Standard programming mysteries.

I hate it when I’m starting off with an API and the tutorials don’t work.

I was working with the LWJGL OpenAl examples described in earlier posts, and could not get the audio files in the examples to open. The file was there, it could be converted to an InputStream, but when I tried creating a LWJGL WaveData object, the underlying java audio code would throw an error and I’d get a null object.

So since I’m now trying to figure out what gives with the javax.sound.sampled.* system, I might as well see if I can do what I want to do here. So off we go to the Internet once more, looking for examples that work.

The first thing I found was the Oracle Java Sound API documents, which are huge, loaded with theory and very little code. I’m not complaining really, because they look like they will be really useful when I have a little bit of code working. But there needs to be a “Hello, World”.

Which showed up after a bit of searching. There is a website JSResources, put together by two German (?) developers; Florian Bomers and Matthias Pfisterer. It is loaded with examples that seem to work. I’ve just gotten one of their simple examples running with a minimum of fuss. (Here’s the (slightly) modified code).  Since this is straight sound library, we know that we can run all the channels. Now we just need to get from here to there.

Wireless. But in a bad way.

Image

I got my amp (2 channels) and USB Dolby 7.1 adapter from Amazon last night and wired up the transducers this morning, hoping to make some vibrations this evening. However, I forgot that I needed to get the sound *from* the Dolby unit *to* the amplifier. Oops. Looks like a trip to radio shack tonight. And no work on this tomorrow, since I’ve got class.

Sigh. With luck, Friday.

Sorting out 3D Sound Libraries

Being a creature of habit, my thought was to go to J3D and use their api, which is quite nice, though essentially unchanged since 2000. It was split off of the main development line when Oracle came in and was then moved off to Java.net – more specifically, java3d.java.net.

Since I have the “The Java 3D API Specification 2nd ed”, I downloaded the latest version (1.5.2) and installed it, pulled out the audio examples from the book’s CD (I know, how quaint), loaded everything into eclipse and built the three examples and their support classes.

Things were not happy when I tried to run though. I got an error saying that I shouldn’t use the 32 bit libraries on a 64 bit machine. Problem is, I have an Intel chip and the dll is for AMD chips. So I uninstalled the 32 bit code and tried out the 64-bit. By golly it compiles and runs. The only problem is the following:

java.lang.UnsupportedOperationException: No AudioDevice specified
	at com.sun.j3d.utils.universe.Viewer.createAudioDevice(Viewer.java:986)
	at SimpleSounds.init(SimpleSounds.java:232)
	at com.sun.j3d.utils.applet.MainFrame.run(MainFrame.java:267)
	at java.lang.Thread.run(Thread.java:722)
Java 3D: audio is disabled

Now, I know I have audio on my gaming-level development box, so that’s disturbing. This forum post looks promising. I’ll give that a try tomorrow. Failing that, I can go to the LWJGL, which has hooks to OpenAL. That appears to have more activity, and I like the LWJGL folks, they write good code. They even have tutorials!

In addition, I’ve ordered a Vantec USB External 7.1 Channel Audio Adapter, and an Audio Mini Amplifier to try hooking up various sound sources to my collection of tactile transducers from Parts Express.