Red Hat net income is growing pretty consistently though [0].
With this price for debt, IBM could be cash-flow positive on this deal within a couple of years even without any big synergy, just by the simple effect of cumulated growth on (net) income.
Basically Pearl argues that classical statistics completely ignored the concept of causality so far and introduces a complete framework to bring causal inference into statistical/data analysis. The framework is based on graphs and asks for causal hypotheses (like econometricians would do with instrumental variables) and allows to compute/quantify causal effects.
Anyone working with data should probably read this book. The fact that Pearl brought in a professional math/science writer as co-author is a huge boost to the main ideas accessibility and make for a nice albeit deep summer read.
I'm surprised to find no mention of Solaris by Stanislaw Lem among the fiction references. In this novel, a whole planet is somehow a living organism, truly alien to human conception of life.
On that topic Harari's Homo Deus is a pretty interesting read. He argues that Humanism is the de facto "new" (2-3 centuries old) religion. Soon to be replace by the celebration of something even more global -- data.
When you sign a legal contract, you are also likely to spend a non-negligible fraction of the contract to lawyers so that they review it thoroughly. Think of the legal costs of fund raising or the time spent negotiating and reviewing a contract for a large deal.
Some things that are at stake on the lawyers side are their competence (can they actually make sure the contract is "secure" ?), their reputation (track record of competence established over time) and some insurance mechanism (if things go wrong can you get something back from their insurance).
All of the above seem to be missing in the case of "smart-contracts".
My plan currently allows 5GB of roaming data per month, which under my current consumption pattern (I don't travel much more than a couple of times a month) is plenty.
I am wondering how much of this gets to be real-time. Are they computing the difficulty of finding a spot based on Maps/Waze users' live data or using daily/weekly patterns on past data?
There are actually two sides of what is referred to as causal inference. Either (a) inferring a causal graph from the data, or (b) given a graph and data, measuring the causal effect of variables among each other.
The broad idea in (a) is to start with a fully connected graph, and eliminate edges between nodes that can be tested as independent, or independent conditionally on other nodes. This gives you a non-directed graph which can be oriented by several methods (identifying V-structures, looking at residuals of regressions of X on Y vs Y on X).
The theory in (b) actually generalizes instrumental variables and lays out graphical configurations where you can measure the causal effect of a variable onto another variable, and how to compute that effect.
I would recommend the book Technological Revolutions and Financial Capital: The Dynamics of Bubbles and Golden Ages [0] by scholar Carlotta Perez.
She studies longer cycles though (~50 years) but the mechanism she describes seems pretty sound for the matter at hand.
It goes more or less like this:
- capital in search for long term returns goes to early moves of a big technological shift
- as successes from the new technology get more and more apparent, it attracts a much larger slice of capital available, and eventually gets over-funded (the real opportunity of this technology is limited)
- a bubble forms, most capital is in for a quick speculative return
- back to square one with a new technology (and former bubble bursts)
With this price for debt, IBM could be cash-flow positive on this deal within a couple of years even without any big synergy, just by the simple effect of cumulated growth on (net) income.
[0] https://www.macrotrends.net/stocks/charts/RHT/red-hat/net-in...