Weighing the #Brexit vote by age

The Brexit result showed an inverse relationship between the percentage of ‘remain’ voters and age group; the lower a voter’s age, the more likely they were to vote ‘remain’. The following image from the BBC provides a graphical breakdown:



One issue made of this outcome is that the winning decision to leave has been supported more by older people who will be less affected by the decision over time whilst the losing decision to remain has been supported more by younger people who will be more affected by the decision given their greater remaining lifespan.

This got me thinking of a voting system whereby the impact of an individual’s vote is adjusted by a weighting; the younger the voter the greater the weighting. I was going to write up an example of this idea applied to the Brexit vote, but just found the following article which espouses the same idea: Here’s what would have happened if Brexit vote was weighted by age.

Such a voting system would only apply to decisions with direct long term consequences. Some might claim it ageist, but I see it as a perfectly reasonable way to incorporate consideration of the impact a voter has on a decision and the impact the decision will have on them.

#Regrexit

Some theoretical mulling: given the reports that some #Brexit leave voters regretted their decision, I’m wondering about the possibility of having a voting system whereby (1) people vote first round (2) the results are made public (3) people can change their vote in the second round with knowledge of the first round result. I say this with a general interest in voting procedures, not because I have a particular position in this referendum.

Analyzing Donald Trump’s Speeches

I am currently doing some text analysis with IBM’s Alchemy. I thought that it would be amusing and somewhat interesting to run some transcripts of Donald Trump speeches through the online demo, particularly to see the results of Alchemy’s emotion analysis: https://alchemy-language-demo.mybluemix.net.

Sure enough, out of the 5 emotions of anger, disgust, fear, joy and sadness, the negative emotions ‘trump’ the positive emotions, with anger and fear being the most prominent. Here is an example speech and its emotion scores
http://www.p2016.org/photos15/summit/trump012415spt.html:

Anger 1
Disgust 0.086204
Fear 0.981015
Joy 0.067289
Sadness 0.086029

Upcoming Talk: Truth and integrity constraints in logical database updating/merging

I’ll be giving a talk later this month at the RMIT CSIT Seminar Series.

Date and Time: Friday 27th November, 2015. 11.30am – 12.30pm.

Venue: RMIT, Swanston St, Melbourne, Building 80 (Swanston Academic Building), Level 5, Room 12 (080.05.012)

Abstract: Methods for the updating/merging of logical databases have traditionally been mainly concerned with the relations between pieces of data and the logical coherence of operations without as much concern for whether the datasets resulting from such operations have epistemically valuable properties such as truth and relevance. Gardenfors for example, who developed the predominant AGM framework for belief revision, argues that the concepts of truth and falsity become irrelevant for the analysis of belief change as “many epistemological problems can be attacked without using the notions of truth and falsity”.

However this may be, given that agents process incoming data with the goal of using it, this lacuna between updating/merging and epistemic utilities such as truth and relevance merits attention. In this talk I address this issue by looking at some ways in which updating/merging methods can be supplemented and shaped when combined with formal measures of truthlikeness, including cases where integrity constraints are involved.