distracting redistricting?

“We at FiveThirtyEight will be tracking the whole redistricting process, from proposed maps to final maps, so watch this space for updates!”

FiveThirtyEight is keeping a tracker on the “redistricting” of U.S. states, namely the decennial redrawing of electoral districts. This is still an early stage when no map has been validated by the state legislature and hence I cannot tell whether or not FiveThirtyEight will be analysing gerrymandering in a statistical manner, to figure out how extreme the map is within the collection of all electoral maps. The States being the States, the rules vary widely between them, from the legislators themselves setting the boundaries (while sometimes being very open on their intentions to favour their own side) to independent commissions being in charge. I did not spot any clear involvement of statisticians in the process.

“The application of differential privacy will bring significant harm to Alabama (…) The Census Bureau has not shown that other disclosure avoidance methods
would not satisfy the privacy requirements” Case No. 3:21-cv-00211

While looking at this highly informative webpage maintained by University of Colorado Law School Doug Spencer, I came across this federal court challenge by the State of Alabama again the Census Bureau for using differential privacy! A statistical version of “shoot the messenger”?! The legal argument of the State is “the Fifth Amendment, alleging that differential privacy is a violation of the one-person, one-vote principle and will result in the dilution of their votes.” I however wonder what is the genuine (political) reason for this challenge!

non-reversible gerrymandering

Gregory Herschlag, Jonathan C. Mattingly [whom I met in Oaxaca and who acknowledges helpful conversations with Manon Michel while at CIRM two years ago], Matthias Sachs, and Evan Wyse just posted an arXiv paper using non-reversible MCMC methods to improve sampling of voting district plans towards fighting (partisan) Gerrymandering. In doing so we extend thecurrent framework for construction of non-reversible Markov chains on discrete samplingspaces by considering a generalization of skew detailed balance. Since this means sampling in a discrete space, the method using lifting. Meaning adding a dichotomous dummy variable, “based on a notion of flowing the center of mass of districts along a defined vector field”. The paper is quite detailed about the validation and the implementation of the method. With this interesting illustration for the mixing properties of the different versions:

 

voting inequalities in the US

“We’re the only advanced democracy that deliberately discourages people from voting.” Barack Obama

Following a poorly attended local election in France last weekend, over-interpreted by media and political analysts as usual, with poorer categories more likely to abstain, I reflected on the supplementary degree of voting inequality in the US, where active voter suppression and voting discrimination run uncontested by legislative and constitutional bodies. As it happens, even for federal elections, the election laws are state-based, voted by partisan state lawmakers and implemented by equally partisan officials.This means discriminating practices can become part of these laws, including different restrictions on acceptable forms of identification that poorer voters may be unable to purchase, restrictions on voter registration and in particular on active drives for minority registrations, discriminatory closures of voting (poll) places,  as e.g. a single voting place for 600,000 voters, meaning unreachable stations for those without transportation means and those housebound, abusive voter purges by local administrations, e.g., the Interstate Voter Registration Crosscheck System having 99% more chances to remove legitimate than illegitimate voters, lifelong felon disenfranchisement, including for citizens having completed their sentence, some places asking for on-the-spot proof of US citizenship, involving document poorer voters cannot access, mail-in voting discrimination, no worker protection for participating in the vote, which takes place during the week, grossly underfunded poll budgets, leading for instance to hour long polling queues and various mismanagement of the votes, the possibility for National Guard staffing poll stations, and the century long absurdity of gerrymandering, where something like 60 million Americans live in a place where the ruling party has received the minority of the votes in a state election. Not to mention the election by an electoral college of the president where the winner may lag by 3 million votes behind his contender… And running uncontested grossly misleading political adds

 

computational statistics and molecular simulation [18w5023]

 I truly missed the gist of the first talk of the Wednesday morning of our X fertilisation workshop by Jianfeng Lu partly due to notations, although the topic very much correlated to my interests like path sampling, with an augmented version of HMC using an auxiliary indicator. And mentions made of BAOAB. Next, Marcello Pereyra spoke about Bayesian image analysis, with the difficulty of setting a prior on an image. In case of astronomical images there are motivations for an L¹ penalisation sparse prior. Sampling is an issue. Moreau-Yoshida proximal optimisation is used instead, in connection with our MCMC survey published in Stats & Computing two years ago. Transferability was a new concept for me, as introduced by Kerrie Mengersen (QUT), to extrapolate an estimated model to another system without using the posterior as a prior. With a great interlude about the crown of thorns starfish killer robot! Rather a prior determination based on historical data, in connection with recent (2018) Technometrics and Bayesian Analysis papers towards rejecting non-plausible priors. Without reading the papers (!), and before discussing the matter with Kerrie, here or in Marseille, I wonder at which level of precision this can be conducted. The use of summary statistics for prior calibration gave the approach an ABC flavour.

The hand-on session was Jonathan Mattingly’s discussion of gerrymandering reflecting on his experience at court! Hard to beat for an engaging talk reaching between communities. As it happens I discussed the original paper last year. Of course it was much more exciting to listen to Jonathan explaining his vision of the problem! Too bad I “had” to leave before the end for a [most enjoyable] rock climbing afternoon… To be continued at the dinner table! (Plus we got the complete explanation of the term gerrymandering, including this salamander rendering of the first identified as gerrymandered district!)

graph of the day & AI4good versus AI4bad

Apart from the above graph from Nature, rendering in a most appalling and meaningless way the uncertainty about the number of active genes in the human genome, I read a couple of articles in this issue of Nature relating to the biases and dangers of societal algorithms. One of which sounded very close to the editorial in the New York Times on which Kristian Lum commented on this blog. With the attached snippet on what is fair and unfair (or not).

The second article was more surprising as it defended the use of algorithms for more democracy. Nothing less. Written by Wendy Tam Cho, professor of political sciences, law, statistics, and mathematics at UIUC, it argued that the software that she develops to construct electoral maps produces fair maps. Which sounds over-rosy imho, as aiming to account for all social, ethnic, income, &tc., groups, i.e., most of the axes that define a human, is meaningless, if only because the structure of these groups is not frozen in time. To state that “computers are impervious to the lure of power” is borderline ridiculous, as computers and algorithms are [so far] driven by humans. This is not to say that gerrymandering should not be fought by technological means, especially and obviously by open source algorithms, as existing proposals (discussed here) demonstrate, but to entertain the notion of a perfectly representative redistricting is not only illusory, but also far from democratic as it shies away from the one person one vote  at the basis of democracy. And the paper leaves us on the dark as to whom will decide on which group or which characteristic need be represented in the votes. Of course, this is the impression obtained by reading a one page editorial in Nature [in an overcrowded and sweltering commuter train] rather than the relevant literature. Nonetheless, I remain puzzled at why this editorial was ever published. (Speaking of democracy, the issue contains also warning reports about Hungary’s ultra-right government taking over the Hungarian Academy of Sciences.)