Month: January 2019

The Book of Where

I have some exciting news to share — my co-author, Tony Schwartz and I, just signed a contract to write what surely will become a best seller: The Book of Where.

The book is a culmination of years of research into a revolutionary new science that is concerned with figuring out, you know, where things are.

For generations geographers, cartographers, topographers, sailors, and other location scientists have been trying in vain to pin down the idea of location and missing it by a mile. Sure they have their Mercator projections, triangulations, GPS, and other round-about contraptions, but what they don’t have is a language of location that is capable of precisely identifying this elusive entity. Until now.

We have come up with an operator that makes it possible, finally, to uncover, you know, where shit is. Yes, you guessed it, it is the find() operator and the corresponding find-calculus.

And it’s not all theory! If you order the book, you will be able to answer such age-old questions as:

  • Where the f*ck are my keys?
  • Where is the Bermuda triangle and how to get there?
  • What is a map anyway?

Tony and I are thrilled to get this in front of popular audiences and we are looking forward to a productive public discussion about this important topic.

Now, go out there and find something!

2019 Predictions

Prediction is very difficult, especially if it’s about the future.

— Niels Bohr

2018 had turned the page and we are already completed approximately 0.27% of 2019. I don’t know about you but I feel like I am behind. So to procrastinate some more, here are my (silly) predictions for 2019.

  • Trump will remain president with P = 0.60. 2019 will no doubt be a tough year for Trump as the Mueller report will likely become public, but I am betting that Republicans will continue to support him and even though the impeachment in the house is quite likely, the removal from office is not so certain.
  • The market (SP500) will continue to be volatile with the VIX staying well above its historic average (~11) for most of the year with P = 0.70. If we are to believe the model, there is about 90% chance that SPX will be between 3,200 and 2,000 by the end of April or about 45% chance that it will be below its current level and above 2,000. I am more pessimistic and I will give it P = 0.60 that it will be below the current level of 2,500 by April.
SP500 Model Based Price Distribution
  • The UK will not exit the EU (no Brexit) with P = 0.60. This is purely based on my conversation with someone who lives in the EU and spends a lot of time analyzing European economies.
  • I recently bought some cryptocurrency (a tiny amount of BTC and ETH) so I can keep myself informed and also because everyone was aggressively selling. I am pretty bullish on crypto longer term, but less certain about the current crop of offerings, although BTC proved to be very resilient. My prediction for 2019 is that BTC will not recover and will stay under its highs with P = 0.90.
  • We will not find a cure for any cancers with P = 0.80, which is a reversal from my last year’s prediction, and the one I am hoping to lose. I like where the cancer therapies are going, but our understanding of the mechanism is still quite weak, the methods we use to evaluate their effectiveness are quite poor (but getting better), and I am not holding my breath for data mining technologies (also known as AI) making any breakthroughs in this space.
  • I selfishly hope that 2019 will be the year of Bayes. I would like to see more universities offering Bayesian courses at undergraduate and graduate levels (this one from Aki @ Aalto looks amazing, for example), more companies getting started using sound probabilistic approaches, and FDA and EMA moving closer to embracing the Bayesian paradigm (we are rooting for you, Frank). I have no idea how to measure this, so no specific predictions here.

How did I do on my 2018 predictions

On 1 Jan 2018, I made the following entry into my journal

  • Will Trump still be president? Yes. (P = 80%)
  • Will Mueller team link Russia to Trump: a) To Trump campaign yes (P = 60%); b) to Trump No (P = 70%)
  • Will Crypto continue to rise? Yes. (P = 60%)
  • Will the stock market end its rise? No. (P = 55%)
  • Will Republicans lose control of the house in November? Yes. (P = 75%)
  • Will there be a war with North Korea? No. (P = 95%)
  • Will the New York Times go out of business? No. (P = 85%)
  • Will we cure one specific type of cancer? Yes. (P = 60%)
  • Will there be at least one Bayesian-based company that will raise Series B? (P = 70%)

I also said that I would compute my gain/loss using a hypothetical payoff function: \(100*\text{log}(2p) \) if I am right and \(100*\text{log}(2 * (1-p)) \) if I am wrong, where p is the probability I assign to the event occurring. We could use any base for a log but base 2 is natural as it compensates at the notional value ($100) if the bet is made with probability 1. I will describe why this particular payoff function makes sense in another post. (The tacit assumption here is that I would have been able to find a counterparty for each one of these bets, which is debatable.)

  • Trump is still president: \(100*\text{log2}(2*0.80) = 68\)
  • Mueller linked Trump campaign to Russia. The word link was not defined. I think it is reasonable to assume that the link had been established, but I could see how if my counterparty was a strong Trump supported, they could dispute this claim. Anyway: \(100*\text{log2}(2*0.60) = 26\)
  • Mueller linked Trump to Russia. Same as above in terms of the likelihood of it being contested, but think I lost this bet: \(100*\text{log2}(2*0.30) = -74\)
  • Crypto did not continue to rise: \(100*\text{log2}(2*0.40) = -32\)
  • Stock market ended its rise: \(100*\text{log2}(2*0.45) = -15\)
  • Republicans lost control of the house in November: \(100*\text{log2}(2*0.75) = 58\)
  • Thankfully, there is no war with North Korea: \(100*\text{log2}(2*0.95) = 93\)
  • New York Times is still in business: \(100*\text{log2}(2*0.85) = 76\)
  • I am not sure what made me so optimimistic regarding the cure for one type of cancer. Currently, the most promising cancer therapied are PD-1/PD-L1 immune checkpoint inhibitors and there have been documented cases for people who become cancer-free after being treated with one of these drugs, but I think it would be too generous to say that we have cured one type of cancer. Perhaps more impressively, Luxturna will cure your blindness with one shot to each eye if a) you have a rare form of blindness that this drug targets and b) you have $850,000 to spend. \(100*\text{log2}(2*0.40) = -32\)
  • There were a few startups based on the Bayesian paradigm and Gamalon came close with a $20M Series A round, but none raised Series B to my knowledge: \(100*\text{log2}(2*0.30) = -74\)

To summarize, I am up $94. Is this good or bad? It depends. A good forecaster is well-calibrated and we do not enough here to compute my calibration. The second condition is that for the same level of calibration we prefer a forecaster that predicts with higher certainty, a concept known as sharpness. Check out this paper if you are curious.