We have been talking about different models of opinion dynamics, but we have never mentioned that electoral system can distort the actual opinions of the populace. In some cases electoral system can be rigged to favor one of the competing parties. One of such example is found in the United States.

In the US boundaries of electoral districts play a major role, because during the elections there it does not matter how many votes are cast for each of the parties. Only who ``won'' that district matters. So you can actually noticeably influence the election outcome by drawing electoral boundaries in a smart way.

More details about gerrymandering in the following video by Above the Noise. We invite you to watch it.

One of the differences between how social scientists and sociophysicists view the modeling of social systems is the outlook towards independent behavior. Importance of individuality underlies many theories in social sciences, while physicists are used to explain various emergent properties through interactions. Obviously both approaches are oversimplifications of objective reality. This time we consider another generalization of the q-Voter model in which a group of physicists and social scientists attempted to bridge this gap.

Two years ago we have already explained that properties of individual agents in some cases do not matter, because they average out. This is obviously true if we build models from perspective of physics. But if we include intuitions from social sciences, then this "truth" might no longer be true. Thus in some cases minute details about individuality of agents can have interesting effects on the dynamics of the modeled social systems.

So here we discuss a generalization of the q-Voter model using so-called diamond model of social response [1]. An interesting result reported in [1] is that two different psycho-social approaches to non-conformative behavior generate somewhat different results when introduced into the q-Voter model.

Another interesting math video by Matt Parker, who this time talks to Hannah Fry about the historical origins of Bayesian statistics. Most of the videos time is devoted to replicating thought experiment proposed by Bayes, which highlights essential concepts underlying modern understanding of the Bayesian statistics. We invite you to be bayesian person!

q-Voter model is a generalization of the voter model [1]. The main difference is that in the q-voter model agent interacts with \( q \) randomly selected neighbors. While also external noise is also present in this model.

As we have seen few weeks ago there is no evidence for hot hands in sports. While there is skill in sports, but short term streaks do not exist. Their simply an illusion caused by the randomness involved.

There is obviously skill in science. Yet numerous studies (e.g. [1]) have concluded an interesting thing - the performance among scientists is mostly random. A scientist could produce his best work at any stage of his carrier. Yet the modern science financing schemes reward ones who produce their best works early. Such schemes kick off rich-gets-richer effects. Small number of groups start to get disproportionaly large share of finances in comparison to their as capable colleagues, only because they were lucky enough to produce their best works early.

This unfairness leads to suggestions on how to improve financing schemes. One of the interesting suggestion is based on a lottery [2]. Namely, the experts evaluate which research projects have sound plans and ideas. The ones who pass the threshold enter the lottery. Why this would be better than simply awarding the best? Because if the winners would always take all every time, this would kill off a reasonable competition and the winners would no longer need to do their best. While also because the experts are often quite subjective and this might play a major role if competition is large.

Is there skill in acting? Most likely yes. Yet unlike in the previously discussed cases the success in Hollywood is not random. It was shown that [3] we can build statistical models, which can reasonably predict the actors peek performance, because social network (not Facebook or Twitter, but the people the actor knows) effects matters quite a lot. Getting along well with other co-stars or director might find you the next job.

References

• R. Sinatra, D. Wang, P. Deville, C. Song, A-L Barabasi. Quantifying the evolution of individual scientific impact. Science 354: 3612 (2016).
• F. C. Fang, A. Casadevall. Research Funding: the Case for a Modified Lottery. mBio 7.2: e00422-16 (2016). doi: 10.1128/mBio.00422-16.
• O. E. Williams, L. Lacasa, V. Latora. Quantifying and predicting success in show business. arXiv:1901.01392 [physics.soc-ph].