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DTSTART;TZID=Europe/Paris:20210415T130000
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DTSTAMP:20260415T070437
CREATED:20210106T205942Z
LAST-MODIFIED:20221114T100847Z
UID:877-1618491600-1618495200@www.naxys.be
SUMMARY:Luca Gamberi (King's College London\, UK)
DESCRIPTION:Title: Maximal modularity and the optimal size of parliaments: how big should your parliament be? \nAn important question in representative democracies is how to determine the optimal parliament size of a given country. According to an old conjecture\, known as the cubic root law\, there is a fairly universal power-law relation\, with a 1/3 exponent\, between the size of an elected parliament and the country’s population. Empirical data in modern European countries support such universality but are consistent with a larger exponent. In this seminar\, I will introduce the audience to this intriguing regularity and discuss the current state of social science research on the topic. I will then move on to introducing our model\, which is derived from fundamental complex network theory. We will see how modelling the population of a democratic country as a random network\, drawn from a “clustered” growth model\, enable us to determine an optimal number of constituencies – and thus of representatives – for a given population. In particular\, I will briefly show how the modularity of the population can be calculated analytically and that its functional relation with the number of constituencies is strongly non-monotonic\, exhibiting a maximum that depends on the population size. following a criterion of maximal modularity allows we are able to predict that the number of representatives and show that it naturally scales as a power-law in the size of the population – a finding that is qualitatively confirmed by the empirical analysis of real-world data.  \n\nLink to the seminar here \nPeople outside Université de Namur with a Microsoft Teams account may join. For any questions\, write to riccardo.muolo@unamur.be
URL:https://www.naxys.be/event/luca-gamberi-kings-college-london/
CATEGORIES:NAXYS Seminar
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