The novelty of all of us working from home is wearing off
The Aleph Extraction by Dan Moren is a fun read and worthy sequel to the great Bayern Agenda. The mix of cold-war thriller and sci-fi setting is definitely in my sweet spot 📚
A scary, but important, conversation on the Making Sense podcast about the threat of nuclear weapons
Different watch faces for work and home
watchOS 7 has some interesting new features for enhancing and sharing watch faces. After an initial explosion of developing many special purpose watch faces, I’ve settled on two: one for work and another for home.
Both watch faces use the Modular design with the date on the top left, time on the top right, and Messages on the bottom right. I like keeping the faces mostly the same for consistency and muscle memory.
My work watch face than adds the Fantastical complication right in the centre, since I often need to know which meeting I’m about to be late for. Reminders is on the bottom left and Mail in the bottom centre. I have this face set to white to not cause too much distraction.
My home watch face swaps in Now Playing in the centre, since I’m often listening to music or podcasts. And I have Activity in the bottom centre. This face is in orange, mostly to distinguish it from the work watch face.
Surprisingly, I’ve found this distinction between a work and home watch face even more important in quarantine. Switching from one face to another really helps enforce the transition between work and non-work when everything is all done at home.
The watch face that I’d really like to use is the Siri watch face. This one is supposed to intelligently expose information based on my habits. Sounds great, but almost never actually works.
I added a HomePod to the newly renovated house. The sound quality is definitely as good as advertised and I appreciate the Apple Music integration 🎶. Siri has worked just fine for me so far, though I only really use it for reminders and calendar events.
Our Home Renovation Is Complete!
We’re very excited to move back home
Owen had his virtual grade 6 graduation with PJs on the bottom half
Decision-Making in a Time of Crisis is a good article on the danger of confusing bad outcomes with bad decisions. As advocated in the article, scenario planning is a useful tool for making decisions with uncertainty
I’m really enjoying these unapologetically nerdy posts on the logistics and tactics of Middle Earth armies.
The Last Emperox by John Scalzi is great fun and a fitting end to the trilogy 📚
We’ve compared our predictions of the 2019 🇨🇦 election to the actual votes. Overall, we were within 5% with no obvious geographical biases, though we did slightly overestimate support for the NDP at the expense of the Liberals. I think we’re on to something good here!
Comparing predicted to actual votes for the 2019 Federal election
Our predictions for the 2019 Federal race in Toronto were generated by our agent-based model that uses demographic characteristics and results from previous elections. Now that the final results are available, we can see how our predictions performed at the Electoral District level.
For this analysis, we restrict the comparison to just the major parties, as they were the only parties for which we estimated vote share. We also only compare the actual results to the predictions of our base scenario. In the future, our work will focus much more on scenario planning to explain political campaigns.
We start by plotting the difference between the actual votes and the predicted votes at the party and district level.
Distribution of the difference between the predicted and actual proportion of votes for all parties
The mean absolute value of differences from the actual results is 5.3%. In addition, the median value of the differences is 1.28%, which means that we slightly overestimated support for parties. However, as the histogram shows, there is significant variation in this difference across districts. Our highest overestimation was 15.6% and lowest underestimation was -18.5%.
To better understand this variation, we can look at a plot of the geographical distribution of the differences. In this figure, we show each party separately to illuminate the geographical structure of the differences.
Geographical distribution of the difference between the predicted and actual proportion of votes by Electoral District and party
The overall distribution of differences doesn’t have a clear geographical bias. In some sense, this is good, as it shows our agent-based model isn’t systematically biased to any particular Electoral District.
However, our model does appear to generally overestimate NDP support while underestimating Liberal support. These slight biases are important indicators for us in recalibrating the model.
Overall, we’re very happy with an error distribution of around 5%. As described earlier, our primary objective is to explain political campaigns. Having accurate predictions is useful to this objective, but isn’t the primary concern. Rather, we’re much more interested in using the model that we’ve built for exploring different scenarios and helping to design political campaigns.
I don’t remember being so organized on Wednesday 😀✅
Happy birthday to my favourite son!
Photosynthesis is a pretty elaborate game with more strategy than you might expect from trees ☀️🌲
There’s sure to be a good caper behind this busted safe in the middle of the woods
Happy birthday to my favourite daughter!
Lucy couldn’t quite manage the last burpee challenge in today’s One Academy Collision class
My favourite gym has a charity event on Saturday supporting Nellie’s Shelter for women & children. If you’re new to One Academy, your first class if free! So, a great chance to get a good workout & support an important cause
I’m really enjoying these Biggest Ideas in the Universe videos from Sean Carroll. Sufficiently nerdy to be interesting without getting too detailed.