William Poundstone – Fortune’s formula
Despite all mathematical considerations, the prerequisite for a truly successful betting strategy inside knowledge.
William Poundstone – Fortune’s formula
Despite all mathematical considerations, the prerequisite for a truly successful betting strategy inside knowledge.
Kris Verburgh – The longevity code
Healthy food seems to be your safest bet for living longer, because technology that reverses aging is not (yet) available
Peter Thiel’s war on Gawker Media shows that money is a decisive factor in the US legal system.
By far the more readable book on org structure that I have come across.
Elegant guide to putting contrarian thinking into action, which tries a bit too had to show it is scientific.
What seems to have started as entrepreneurial over-confidence ended in a web of fraud and lies.
Mario Livio – The golden ratio
Comfortingly conscientious in his evaluation of claims about ao pyramids and the parthenon.
Daniel Pink – To sell is human
A charming plea for a compassionate approach to influencing.
Kate Raworth – the doughnut economy
The idea of challenging the implicit assumptions of traditional economics is not new, yet the emphasis on framing the debate is valuable.
At first the polemic style is charming, but over-all the writer’s objective to crush the system by his brain power is poorly executed and overlooks too many credible alternative lines of argument.
Jamie Bartlett – The people vs Tech
Summary of how tech firms form a risk for democracy, but without a thorough assessment of how technology itself can be applied to improve the democratic process.
Rich repository of one-liners for those who seek to make bold moves.
Turn data driven decision making into continuous learning
Most humans dislike change. And continuously ongoing change is even worse.
What does this mean for data driven decision making?
First of all, you can count on a lot of resistance when you roll-out AI-driven solutions: Can the models be trusted? Is my professionalism still valued? Will I lose my autonomy? However important to address, managing such concerns is not my topic here.
Suppose that you have made it to a full roll-out unscratched. Analytics drive key decisions. Benefits are measurable. Most likely, your decisions will become more structured. While predictive and prescriptive analytics can unlock great value, these come with a risk.
Stability.
Everyone in your organization will love it when little changes. That is, unless your company is truly digital. Stability will create the suggestion that everything is under control. That targets will be met and nothing can go wrong.
By contrast, statistical models live by change. They need to observe change to predict change. And that means that you should be consciously creating the variance you need to continue learning.
Luckily, there is a lot of change that occurs naturally. Customers change their ways. Stores do not execute recommendations. Suppliers’ price hikes are charged-on to customers. You name it. Although it is a good start, this type of variance may be mightily skewed. Or not representative for what you want to learn. In other words, you most likely will need a different kind of variance. To turn data driven decision making into continuous learning, you need to have a strategy for conscious, targeted, and ongoing experimentation and testing.
Remember, remember: learn to love the unexpected.
A surprisingly ‘zen’ view on creating a high performing team.
Steven Pinker – Enlightnent now
Considering his plea for scientific thinking, Pinker is remarkably confident on (1) hard to assess long term risks and (2) strong realism (in the epistomological sense).
Chris Bailey – The productivity project
A bunch of unstructured and badly documented tests by a frat boy, who presents his efforts as “experiments”.
Most illustrative are the descriptions of failed competitors, which show importance of both luck and ruthlessness.
John Brockman – This idea is brilliant
A rollercoaster ride through a laundry list of hot topics in science today.
Brinkmann’s many nuances and exceptions kill his argument and concept.
N.B. Read in Dutch translation
Emanuel Derman – Models.Behaving.Badly.
Derman’s discussion of models in life, physics, and finance is not a juicy as the title suggests, but it offers some good one-liners nontheless.