This is a the network of political attitudes in the US in 2021. (Thus, nodes - i.e. the circles - are not peopleπ§ but attitudesπ£οΈ).
In the top figure you see the colors based on response level to a survey (i.e. red=the most Republican response, blue=the most Democrat, grey=the neutral response, etc).
Interestingly, we find two very weird clusters. Indeed, while one cluster is mainly Democrat and the other mainly Republican, we see that the neutral attitudes are completely absorbed in the Republican cluster.
Even more surprising: three attitudes which should be mildly democrat (e.g. Abortion should be illegal: somewhat disagree) are still in the Republican cluster!
This is confirmed by looking at the self-identity associated to every attitude. Indeed, all but one of the attitudes in the Republican cluster were chosen by self-identified Republicans.
Why is that?
What these graphs are telling us is that Democrats had a very clear positioning on these items - i.e. if you are a Democrat you will strongly agree/disagree.
On the contrary, Republicans seemed to be way more scattered across different positions, ranging very widely from "Strongly Republican" to even include some "Mildly Democrat" positions.
Interestingly, this also tells us that expressions of neutral positions, will probably not be perceived as neutral, but as Republican attitudes...
Of course, this is a very simplified extract, and you can find all the detailed and rigorous information in the publication:π
https://t.co/GP66d3MCfx
An even deeper study on the dataset can be found here:
https://t.co/KLYFhekkmG
Thank you to @Quayle and @Adrian_Lueders for their wonderful work on these articles!
Today at 15:15 CET I'll be speaking at the Social Simulation Fest about Science Communication and Narratisation! π€
It's an online event, so you can still join by registering here: π
https://t.co/IhwWDwbBbx
Opinion dynamics models do _not_ need to be an abstraction of reality. You can also build them to reproduce measured human behaviour in experiments.
https://t.co/T3LgJwtlO1
It took me a while, but we finally have the second episode of the Computational Social Science podcast!
This time, we talk about collective intelligence π§
You can find it on YouTube...
https://t.co/C3JlVxVMsZ
...or on your favourite platform:
https://t.co/xvLsa65DJF
Just found out that the paper where Galton introduced correlation has fewer than 1,000 citations. It makes sense that people donβt cite it every time, but that still feels surprisingly low!
https://t.co/l8FuPMfZao
Year 3 of asking ChatGPT to write a joke about ABMs.
Main finding: Instead of producing better jokes, itβs just becoming aware that theyβre simply not funny.
Note: since this is very innovative, we still don't have "the whole system". Here we show that a completely "flat" system could work even with millions of people collaborating together. But future works should explore potential exploits, limitations, improvements, etc.
So, somebody wrote a comment on one of my articles. Their core message is rather sound (indeed it's the same as what we wrote in our article).
The problem is that they make so many mistakes I don't really know what to make of this...
https://t.co/cYCnDiZrA8
Today we have the first public event of the Special Interest Group on Strongly Empirical Modelling, with a talk from Bruce Edmonds titled: Towards inferring clusters of βcognitive modelsβ from survey data. See you at 2pm CET!
https://t.co/HpQyNFZ3X3
Why do people prefer using statistical tests that nobody ever heard of instead of just bootstrapping? (I'm really asking, it's not a complaint or sarcastic or anything)
Wouldn't bootstrapping also make results more understandable?
Some years ago I applied ResIN to a Harry Potter dataset. The more two people are similar (in terms of how they relate to other characters), the tighter their connection. As you can see, the HP world is heavily polarized.
But now my question is: why did I do something like that??
How can we develop a better understanding of Complex Attitude-Identity Systems (CAIS)? Hereπwe introduce a theory of Belief Networks to explain how to accurately measure them. This will help us understand how beliefs connect, evolve and shape identity.
https://t.co/PIZioPIBcB
Today, both Samuel Moor-Smith and I will give a presentation at the Social Simulation Conference. If you are interested in the validation of ABMs, their classes of equivalence and how psychometrics plays an important role, join us!