Thinking in Pictures - Book Review
Contents
This blog post is a quick summary of the book “Thinking in Pictures”, written by Michael Blastland
![](images/thinking-in-pictures/book-cover.jpg)
This book has an interesting premise, i.e. all the books on smart-thinking out there are not that smart enough to aid us in our daily lives. When we read them, we intuitively like them, we seem to be acknowledging a few principles in it, but those books do to seem to be doing anything in our real lives. Most of the findings in these books suffer from replication bias. In the last few years or so, many books have been published that are supposedly there to make us smart
- Rationality by Steven Pinker
- Calling Bullshit by Jevon West and Carl Bergstrom
- How to Make the World Add Up by Tim Harford
- How to Read Numbers by David Chivers and Tom Chivers
- The End of Bias by Jessica Nordell
- How to Decide by Annie Duke
- The Great Mental Models, Volume 1 by Rhiannon Beaubien and Shane Parrish
- Noise by Daniel Kahneman, Olivier Sibony and Cass Sunstein
- Anthro-Vision by Gillian Tett
- Thinking Better by Marcus du Sautoy
- Super Thinking by Gabriel Weinberg and Lauren McCann
- Emotional: The New Thinking About Feelings by Leonard Mlodinow
- The Skeptics’ Guide to the Universe by Steven Novella
- How to Think by Tom Chatfield
- Think Again by Adam Grant
- The Scout Mindset by Julia Galef
- The Irrational Ape by David Grimes
The author picks up a theme in each of the chapters, uses a visual to show how messy our real world is, and how simple hacks mentioned in most of the books on “smart thinking” are woefully inadequate.
Unjoin your dots
![](images/thinking-in-pictures/chapter-2.png)
We always overestimate our knowledge and we think it is other who see patterns where there are one. We think we are in the wisdom box and will never move to the unicorn box. Key idea is to constantly question oneself and check the reliability of one’s knowledge about anything. Healthy skepticism about everything is desirable.
Un-count your sheep
![](images/thinking-in-pictures/chapter3.png)
sheep. How many?
Two. Obvs. Next question.
Or…
… maybe one?
Because one’s a lamb, not a sheep? Although saying that… aren’t lambs sheep?
OK, I see this depends on whether you want to be awkward or clever or whatever, or maybe you’d call it precise, or perhaps it depends on why you’re counting – say, because you’re a farmer who needs to know how many full-size fleeces you’ll have this year; or how many of the flock will be ready for slaughter and no one eats mutton these days (don’t think about it); or I don’t know why, because I know nothing about farming, or sheep…1
In which case… one and a half?
Although maybe the ewe is pregnant and about to give birth again in the next ten seconds (it’s not, but run with this), so how many sheep now? Three? One and two halves? Two and one half? Two? One plus a half plus a quarter? Still one? Though now we’re about it, that’s a pretty big lamb. Are we sure that’s a lamb?
Oh, come on! There are two white woolly things in this picture, can we not agree?
No, maybe we can’t. Counting just got tricky after one.
The point of the question - “how many sheep?” is that every data that you see and labeled as something, has a human angle to it. It is humans who are behind every piece of data or evidence you come across. As long as you keep this in mind and treat all data with healthy dose of skepticism, you will be better off than blindly trusting the data and categories.
But count in human
![](images/thinking-in-pictures/chapter-4.png)
One can look at data and say
-
They’re objective, revelatory, truth-seeking. The data don’t lie! Seize the power of data!
-
They’re lies and damned lies, they strut their pious claims to objectivity, but we know they’re flaky, selective, at it – like the shadowy forces who abuse them.
Truth lies somewhere in between and we should always develop a healthy skepticism over moving too close to either of the above paths
Beware nature's
fake news
Spirit of Nessie. The Loch Ness Monster seen on holiday over Greece.
![](images/thinking-in-pictures/chapter-5.png)
Beware of false positives, firstly in your own evidence repository and then among others evidence repository
But treasure the funnies
![](images/thinking-in-pictures/chapter-6.png)
Treasure your regularities because they’re knowledge, but don’t hold on too fast. Treasure your irregularities (funnies), too, but not if they’re meaningless scraps of chance.
Pay too much attention to every radical revisionist or claim of a shock finding and you’ll end up feeling dizzy. So don’t do that. But do be sure not to miss the surprising findings that matter.
‘But how do I know which is which?’ you scream. ‘How do I know when the funnies matter and when they don’t?’ Order is knowledge, but too much order is false knowledge. Disorder could be knowledge, too, except when it isn’t anything at all. Arrghhh!
All I can offer is the tough love that this is unsurprising, and we better get used to a world of ‘buts’ and ‘maybes’ and ‘sometimes’ and ‘ifs’ and ‘watch out!’ and borderline cases without simple rules. At least then we’re better prepared to live with life’s many surprises. And how is that? Sometimes (not always… arrghh!), by holding our truths lightly. Avoid nailing ourselves to them. Watch for exceptions and at least be willing to rethink, even if you decide later that this funny was a joke.
Focus, but don't
![](images/thinking-in-pictures/chapter-7.png)
We might be focusing too much on the door and missing the context in which the door is located in the first place. If we are forever analyzing how something could escape from the door, despite its build, hinges, lock etc., we miss the point. Is there a way to identify our blindspots ? By approaching others and collating diverse views
Draw the tiger
![](images/thinking-in-pictures/chapter-8.png)
The above image is to force you to own up your ignorance. You overestimate what you think you know.
Mind your pictures
![](images/thinking-in-pictures/chapter-9.png)
The key with all models is not that they’re wrong – they’re all wrong in some way – but whether they help. The answer for the London Tube map is: usually.
There’s a line in the philosophy of science that says what we need is not a map, but maps: ‘a collection of maps, all of them incomplete, which together gradually shape our understanding of a new piece of country. By bringing those maps together and constantly improving them… in time we build up a composite picture which brings us closer and closer to what the outside world is telling us.’15 Which sounds wise enough, until you think about how many maps you’re willing to carry and consult.
In the smart-thinking struggle between essence and detail, some rave about the power of essence, the abstract ideas and principles that help us cut through; some fret over the loss of detail, the particulars that make all the difference. They all have stories: of success for their own vision, failure for the other. Tension between them is inevitable.
Think bad thoughts
![](images/thinking-in-pictures/chapter-10.png)
chicanes on footpaths make cyclists slow down, reducing accidents. Someone’s modelled it, run trials of the real thing, has data – bet they do. On the strength of that, people make policies about chicanes and then off we pop to build them, because they work.
So, uh… yeah. And in this case, uh… no.
Improvised routes, like the bypassing of this chicane, are called desire lines: the popular will scrawled into the earth. The result can be worse than failure of the original plan – because the question in my mind is whether it’s only cyclists who are scrawling their desires here. If so, at least we could say we’d safely separated bikes and pedestrians. But bike, buggy, wheelchair, pedestrian, scooter, mobility scooter, horse… Does anyone take the chicane any more? Skaters, maybe, or if it’s wet or muddy. Or did we ruin the point of the whole footpath?
The planned, primary effect was to slow down certain people. The secondary effect was maybe that they didn’t slow down because they didn’t use the path, and now who does?
Desire lines are a neat metaphor for the human factor, one variety of the unplanned bolshiness of life. Before planning almost anything, beware the desire line.
Think twice upon a time
![](images/thinking-in-pictures/chapter-11.png)
The author takes a dig at our natural ability to tell stories and urges us to create multiple stories based on varying hypothesis
Think in bets
![](images/thinking-in-pictures/chapter-12.png)
It is far more productive to think about our decisions as bets and then adjust our behavior accordingly. Refining decision making process is a key skill to hone and one of the ways to do is via Bayesian thinking
Don't
trust
![](images/thinking-in-pictures/chapter-13.png)
Trustworthiness is not about who they are, their tribe or their answers; it’s about how they show, by their actions, that they deserve to be trusted.
Get a new attitude
![](images/thinking-in-pictures/chapter-14.png)
The chapter espouses scout mindset and says it is one of the ways towards smart thinking
Scout Mindset: curious and exploratory, the scout sets out to survey the territory, not to defend or conquer, and above all to bring back an accurate map. Julia defines this mindset as: ‘the motivation to see things as they are, not as you wish they were’. Thinking is discovery.
Takeaway
The book leaves the reader with an uneasy and uncertain feeling of just about everything. That I guess is the intent of the author. Being uncertain about things, Being skeptic about the ways of the world is probably one of the best ways to discover what is truly going on.