Renaud Di Francesco

The Institution of Engineering and Technology | IET · Electrical Engineering

In public life, democratic mechanisms can be misused. Fitting electoral rules to desired results is called Gerrymandering to remember a prominent politician who was an expertin such Machiavellian moves.

We have explored the case, motivated by recent demands in Associations to reshuffle the voting power from the dominance of one category of members to the dominance of another category of members. Our investigation has two parts: no-math, and with-math. We establish the necessary and sufficient conditions for reconfigurability. Any views, observations on similar needs, or cases of detrimental changes of electoral rules?

Covid19 caught us by surprise. The previous comparable event had happened 100 years earlier and it had been named the Spanish flu, and it probably killed more people than World War I. The 1919 lessons learnt by health policy makers such as the US cities and European governments had been long forgotten.

This time, it's 2023, and the Covid years, which are not over, have left a clear memory. We have no excuse, not to go prepared.

What about behavioural economists, looking at the exchange of value, time, tasks, anything, in human groups?

What can they tell us, of practical and explanatory interest for the next wave of Covid, or ahead of a similar event?

Behavioural economics scenarios and the pandemic

Can I suggest to look back at the micro-problem replicated a large number of times worldwide, where each household had to self-manage meals, schooling the children, etc. Trade-offs happened at high frequency between the members of the household, seen as agents in a behavioral nano-economy of the house...

What do you consider worthwhile for planning the next wave each household likely to have to isolate for while, at least now and then?

Let me share the assessment and model developed for the case of Covid19 "household lockdown":

REF

[1] Agent Based Model for Covid 19 Transmission: -field approach based on context of interaction, July 2020,R. Di Francesco, DOI: 10.13140/RG.2.2.24583.83364

[2] "Nanoeconomics of Households in Lockdown Using Agent Models during COVID-19," Sustainability, by Javier Cifuentes-Faura & Renaud Di Francesco, 2022, vol. 14(4), pages 1, February.

[3] Microeconomics of intertemporal choice in zero-space during Covid-19: a behavioral economics perspective. by Cifuentes-Faura, J., Di Francesco, R., J Health Econ 23, 559–563 (2022). https://doi.org/10.1007/s10198-021-01403-z

We all have lived through Covid19, across the world. Prior to the availability of vaccines, Non-Pharmaceutic Interventions by health-aware governments were implemented, with significant success, well into a stage of lockdown, where residents of a country were asked and then required to stay at home, with stringent conditions to get out of their homes. The logistics of food supply was usually well managed, even if there were cases of people remaining isolated from food supply at times.

The key to not letting Covid-19 take its toll, and it actually did take its toll, especially among elderly residents of care homes, in Italy, France, the UK, etc, was to model in an anticipatory manner the spread of the disease and assess its risk realistically.

Modelling was mostly at macro-level: cities, regions, countries. However the different context of human interaction in daily life received much less attention, although large data sets and use cases build on a number of elementary interactions, and smaller numbers of humans involved in each.

Our endeavour, which could not afford the ambition of health statisticians in larger teams, to model the spread of the disease at country level, focused instead then, in the years 2019-2020, onto elementary use cases of interaction, with few humans involved (few starting from 2). Such use cases covered elderly patients of care homes, and their interactions during joint meals in the care home meal area, with tables shared, it also covered households in close (and closed) interaction during lockdown. It also tried to make sense of large events, where many humans interact during a limited time (football game, women's day celebration, etc).

The typology of likely propagation in such use cases was modeled, and parameters of a simple but robust model were tuned to known data, and in turn simulations could be run, and such simulation could be assessed on other known outcomes (such as the observation of virus propagation among the citizen team running a polling center during elections in France).

Can we ask the researchgate community if anyone is interested to undertake similar micro-level models of elementary human interaction leading to a likely spread of the virus?

Could we consider building a federated collaborative project, with data fed by anyone having access to these (literature, publications, etc)?

What approach do you recommend? Have you published on the topic?

[1] Agent Based Model for Covid 19 Transmission: -field approach based on context of interaction, July 2020, R. Di Francesco, DOI: 10.13140/RG.2.2.24583.83364

I have looked at data base management and applications, data-sets and their use in different contexts. I have looked at digital in general, and I have noticed that there seems to be a single split:

-binary computers, performing number crunching (basically), and behind this you find the Machine Learning, ML, DL, RL, etc at the root fo the current AI

-quantum computing, still with numbers as key objects, with added probability distributions, randomisation, etc. This deviates from deterministic binary computing but only to a certain extent.

Then, WHAT ABOUT computing "DIRECTLY ON SETS", instead of "speaking of sets" and actually only "extracting vectors of numbers from them"? We can program and operate with non-numerical objects, old languages like LISP and LELISP, where the basic objects are lists of characters of any length and shape have done just that decades ago.

So, to every desktop user of spreadsheets (the degree-zero of data-set analytics) I am saying: you work with matrices, the mathematical name of tables of numbers, you know about data-sets, and about analytics. Why would not YOU put the two together: sets are flexible. Sets are sometimes are incorrectly named "bags" because it sounds fashionable (but bags have holes, they may be of plastic, not reusable, sets are more sustainable, math is clean -joking). It's cool to speak of "bags of words", I don't do that. Sets, why? Sets handle heterogeineity, and they can be formed with anything you need them to contain, in the same way a vehicle can carry people, dogs, potatoes, water, diamonds, paper, sand, computers. Matrices? Matrices nicely "vector-multiply", and are efficient in any area of work, from engineering to accounting to any science or humanities domain. They can be simplified in many cases (eigenvector, eigenvalue, along some geometric directions operations get simple, sometimes the change of reference vectors gives a diagonal matrix with zeros everywhere except on the diagonal, by a simple change of coordinates (geometric transformation).

HOW DO WE DO THAT IN PRACTICE? Compute on SETS NOT ON NUMBERS? One can imagine the huge efficiencies gained in some domains, potentially (new: yet to be explored, maybe BY YOU? IN YOUR AREA). Here is the math, simple, it combines knowledge of 11 years old (basic set theory) and knowledge of 15 years old (basic matrix theory). SEE FOR YOURSELF ,and please POST YOUR VIEW on where and how to apply...

Is Leg-tec providing a solution?

How do you train legal workers and civil servants to using digital technology and remove errors by accessing the right text/version of the Law of interest, applicable at a certain date?

Any experience of multiple versions of the same law pulled out by different people on the same case?

How do you explain Management inertia to recognise legal errors in Legal Processes (three versions, at least two of them are false/fake law)?

See attached real story of official documents quoting incompatible text for the same article of Law. Unexpected but TRUE!

I have put together a geometric method to compute the sum of the power p of the first n integers. Do you know of previous articles? A more general framework? Suggestions of applications?

I carve an hypercube into shells of thickness 1, and I repeat until nothing remains. See here https://www.researchgate.net/publication/363484666_A_Geometric_approach_to_Sums_of_Powers_of_Integers_-Faulhaber's_formula_from_Partitions_of_Squares_Cubes_Hypercubes#fullTextFileContent

The world context has been facing over the last few month a new energy challenge. Therefore it is advisable to revisit how we address data-based problem solving, encompassing what Internet Platform call data science.

Data gathering, data search and processing ("machine learning", deep or not), using AI in many cases, should not be repeated blindly again and again, instead of keeping records of first results, modeling structural relations from within the datasets, and sharing preliminary analysis results.

The Framework of Matrices of Set, enhancing and generalising the matrices of numbers halps keep data until final computation on numbers is required.

An example of application is Collaborative filtering, observing users facing products, and formulating comments.

Below some references with details: