On Wednesday morning, I will be chairing our session “Discrimination-free Insurance Pricing” at the Insurance: Mathematics & Insurance Conference, in Chicago. With Olivier Côté, Lydia Gabric and Hong Beng Lim, we will be four speaker, just before lunch time. My talk will be a mix of recent work on quantifying and mitigating discrimination in scores (in insurance). Slides are available online.
Monday, I will be giving a short course at the workshop on Decentralized Insurance and Risk Sharing (SAC 161), in Chicago
My slides are available online.
In this course, we will get back to mathematical properties of risk sharing on networks, with reciprocal contrats. We will discuss conditions about stochastic dominance, proving that policyholers might have interest in sharing risks with “friends”.
Then, we will try to adress fairness issues, for such risk sharing mechanisms. If fairness has been recently intensively studied, either through group or individual fairness, there are yet not much litterature about fairness on networks. It is important to adress those issues, since perceived discrimination is usually associated with networks. We will see why the topology of the network is important, both to design peer-to-peer schemes to share risks, but also to see if perceived discrimination is associated with global disparate treatement.
I will be in Chicago next week, invited by 冯润桓 (Runhuan Feng). On Monday, with Jan Dhaene and Marco Mirabella, we will give some short courses “on decentralized insurance and risk sharing“.
Then, we will attend the IME Congress 2024 (that will be held between July 9th and 11th).
Next week, colleagues from the University of Illinois Urbana-Champaign and the Pennsylvania State University in the United States, Ulm University in Germany, and the University of New South Wales (UNSW Sydney) in Australia organize the 24th International Congress on Insurance: Mathematics and Economics, aka IME2021. I will present our joint work, with Michel Denuit and Julien Trufin, Autocalibration for Insurance Pricing with Machine Learning, Philipp Ratz will present our joint work on peer-to-peer insurance model. I will also chair some sessions, and participate to a round table on Thursday morning (Montréal time).
We have seen yesterday that finding an optimal strategy to publish is not that simple. And actually, it can be even more difficult in the case the journal rejects the paper (not because it is not correct, but because “it does not fit” with the standards, the quality of the journal, the audience, the editor’s mood, or whatever). The author has basically two choices,
But this last choice is not that easy, since sometimes the author think that this journal was indeed the one that should publish it (e.g. all the articles on the subject have been published in that journal).
So I was wondering if there were clusters of journals, i.e. journals that publish almost the same kind of articles (so that next time one of my paper is rejected by the editor, I just go to for some journal in the same cluster).
So what I did is extremely simple: I looked at articles titles and looked for correlations between words frequency (I could have done that in key words, but I am not a big fan of those key words). I looked at 35 journals (that are somehow related to my areas of interest) and looked at titles of all articles published over the last 20 years. Then I kept the top 1000 of words, and I removed standard short words (“a“, “the“, “is“, etc). Actually, my top words looks like
"models" "model" "data" "estimation" "analysis" "time" "processes" "risk" "random" "stochastic" "regression" "market" "approach" "optimal" "based" "information" "evidence" "linear" "games" "bayesian" "theory" "effects" "distribution" "multivariate" "tests" "markets" "markov" "equilibrium" "dynamic" "process" "distributions" "application" "stock" "likelihood"
Then, I ran a principal component analysis on my dataset (containing 960 variables – here words – and 35 observations – here journal names).
library("FactoMineR") res.pca = PCA(MATRICE, scale.unit=TRUE, ncp=5, graph=FALSE) plot.PCA(res.pca, axes=c(1, 2), choix="ind")
The projection of the journals on the first two axis looks like that
Here, we can clearly observe some clusters : on the up-left Journal of Finance and Journal of Banking and Finance (say financial journals) on the top-right Biometrika, Biometrics, Computational Statistics and Data Analysis and Journal of Econometrics (JASA is not far away, i.e. applied statistics journal). And below, on the right, Stochastic Processes and their Applications, Annals of Applied Probability, Journal of Applied Probability, Annals of Probability, Proceedings of AMS and Topology and Applications (ie more theoretical journal).
Note that the projection is rather robust: if I consider my first 200 words, the graph is the same
In order to go further in the interpretation, we can also plot variables, i.e. words from titles,
where we cannot distinguish anything. So if I just look at my top 30, here they are,
On top left we see market(s), risk or information; on top right analysis, effects, models or tests; while below we see Markov or process(es). And we can observe interesting facts: in finance in statistics, we talk about dynamics while in theoretical (mathematical) journal it is about processes.
But the goal was to find cluster, i.e. classes of journals that publish papers with similar titles.
Here we have
If some classes a rather natural (Journal of Applied Proba. and Advances in Applied Proba.or Economic Theory, Journal of Economic Theory and Journal of Mathematical Economics) some strong correlation are not simple to understand, (e.g. Insurance: Mathematics and Economics and Management Science or Annals of Statistics and the Journal of Multivariate Analysis).
Again, it might be possible to spend hours on the graphs, but if I want – someday – to submit something to one of those journals, I guess I have to stop here, and move to something else…
