This book review was written by Eugene Kernes
“Meanwhile, exposure to so many new ideas was producing mass confusion. The amount of information was increasing much more rapidly than our understanding of what to do with it, or our ability to differentiate the useful information from the mistruths. Paradoxically, the result of having so much more shared knowledge was increasing isolation along national and religious lines. The instinctual shortcut that we take when we have “too much information” is to engage with it selectively, picking out the parts we like and ignoring the remainder, making allies with those who have made the same choices and enemies of the rest.” – Nate Silver, Introduction, Pages 3-4
“This book advises you to be wary of forecasters who say that the science is not very important to their jobs, or scientists who say that forecasting is not very important to their jobs! These activities are essentially and intimately related. A forecaster who says he doesn’t care about the science is like the cook who says he doesn’t care about food. What distinguishes science, and what makes a forecast scientific, is that it is concerned with the objective world. What makes forecasts fail is when our concern only extends as far as the method, maxim, or model.” – Nate Silver, Chapter 12: A Climate Of Healthy Skepticism, Page 403
Is This An Overview?
Having a lot of information does not mean there is a lot of
validity in the information. There is
difficulty in understanding large quantities of information, and difficult to
differentiate between useful information from misinformation. While people want useful information, want
the Signal, much of the information is not useful, information that is
noise. Noise distracts people from the
Signal. The quality of predictions, or
forecasts, depends on filtering the Signal from the Noise.
The data, the evidence, the numbers do not represent
themselves. The evidence is represented
by people, who tend to favor the evidence they want to hear. Confirming their views which limits their
decisions, and causes them to miss evidence that can affect the decisions being
made. People are biased, and therefore
develop biased predictions. To improve
data-driven predictions, people need to improve their ability to sort the
information.
Prediction failures tend to have features in common such as
focusing on what is wanted rather than what is, ignoring difficult to measure
risks, making inappropriate approximations and assumptions, and
misunderstanding uncertainty. Forecasts
tend to improve when people think of various alternative views, and update
their views to new information.
What Is Forecasting?
Models are a tool to represent the complexities of reality,
they do not substitute for reality. A
prediction is a definite and specific statement of what might happen, while a
forecast is a probabilistic statement of what might happen. Risk is knowing what the options are, while
uncertainty is not knowing the options or information that can affect the
options.
Systems which are dynamic and nonlinear (chaos theory), made
predictions difficult. People can change
their behavior to a prediction, therefore changing the prediction itself into a
self-fulfilling prediction as people support the claims or a failed prediction
by avoiding the claims.
Good forecasts are those which over time make more correct
predictions. A Bayesian analysis is a
method of updating beliefs, as the method gets the person closer and closer to
the truth.
Caveats?
Advice for how to improve decisions are described using
examples, the advice is hidden within the examples. The examples are noise that the reader needs
to engage with to find the signal. The
value of the examples depends on the interests of the reader. There is not much of a systematic analysis, a
lack of a summary for the advice.
