Bit of a Tangent
020 | Mental Models 3: Fantastic Biases and Where To Find Them

020 | Mental Models 3: Fantastic Biases and Where To Find Them

November 5, 2019

This is part 3 of the series on Mental Models, in which Gianluca and Jared discuss the cognitive biases that often impede aspiring rationalists. They discuss the idea of “System 1 and 2” thinking as a framing for the entire discussion, before exploring 12 of the most deadly thinking traps and how to avoid them. This includes everything from the Planning Fallacy and Anchoring, to more esoteric pitfalls that are yet to be named. Finally, they describe meta-biases—like the Bias Blindspot and the Fallacy Fallacy—equipping your mental toolkit with everything you need to upgrade your thinking. 




System 1 and 2 thinking in Kahneman’s “Thinking Fast and Slow”:

An awesome resource, the Cognitive Bias Codex:

A superb introduction to biases by Rob Bensinger:

Yudkowsky on scope insensitivity:

[Study] Scope insensitivity: The limits of intuitive valuation of human lives in public policy:

Bayes’ Theorem in medical testing:

Bayes’ Theorem examples visualised:

Hamburg’s philharmonic concert hall that ran 6 years and 1000% over budget:

Fascinating studies on the planning fallacy in students’ academic predictions:,_1994.pdf

Reference class forecasting:

How to overcome the planning fallacy with “fudge ratios”:

Spinning the wheel on the anchoring bias:

“Influence” by Robert Cialdini:

016 | Free Will, Compassion, and Reinforcement Learning:

Inferential distances:

Findings in Hedonic adaptation:

Dan Harris’ “10% Happier” podcast:

The Peak-End Rule:

019 | Mental Models 2: How To Think Like A Bayesian, Avoid Black Swans, & Know The Value of Your Decisions

019 | Mental Models 2: How To Think Like A Bayesian, Avoid Black Swans, & Know The Value of Your Decisions

October 28, 2019

This is part 2 of our series on Mental Models. In it, we’re bringing you the most useful and applicable models we’ve encountered in everything we’ve read, heard or seen over the years! In part 2 we unpack models from Bayesian reasoning, calibration, and expected value. We also cover fat-tailed distributions, counterfactual reasoning, inversion, and red-teaming. Throughout, we build up each model with examples and motivations, and often found ourselves making previously unseen (by us) links to other models which gave us several new insights. We hope this conversation will do the same for you!




Farnam Street Blog - 

Calibration training app by 80 000 Hours - 

Superforecasting by Philip Tetlock - 

0 And 1 Are Not Probabilities - 

Thinking In Bets by Annie Duke - 

The Black Swan by Nassim Taleb - 

CGP Grey on 7 Ways To Maximise Misery - 

Claude Shannon on Creative Thinking - 

The Bottom Line - 

018 | Mental Models 1: How To Have Better Ideas and Improve Your Thinking

018 | Mental Models 1: How To Have Better Ideas and Improve Your Thinking

October 22, 2019

This is part 1 of a new series on Mental Models - tips, tricks, and tools to add to your mental toolbox. In this episode we introduce the concept of a mental model but then quickly dive in to explanations of the most powerful models we’ve encountered. Join us and learn how to make better decisions (or know when a decision is not worth making), how to have more original and impactful ideas (and how to find the most promising ideas to work on out of the thousands you’ll soon have), and when tidying up your messy desk is just plain wrong (sorry, Marie Kondo!)




The Great Mental Models: General Thinking Concepts by Shane Parrish - 

Super Thinking: The Big Book of Mental Models by Gabriel Weinberg - 

Feynman technique - 

More Dakka by The Zvi - 

Least recently used idea: read the book Algorithms To Live By by Brian Christian - 

Eisenhower matrix - 

Josh Wolfe on Shane Parrish’s podcast - 

Eliezer Yudkowsky’s marvelous introduction to Bayes Theorem. Seriously, read this: 

Tim Urban’s WaitButWhy post on thinking from first principles like Elon Musk: 

Deep Work by Cal Newport - 

So Good They Can’t Ignore You by Cal Newport - 

Keep Your Identity Small by Paul Graham - 

Joscha Bach on the Singularity podcast:

017 | Bronwyn Williams: Universal Basic Income & Individual Sovereignty

017 | Bronwyn Williams: Universal Basic Income & Individual Sovereignty

October 14, 2019

This episode features the much-anticipated conversation with Bronwyn Williams on the topic of Universal Basic Income (UBI). Bronwyn is a futurist and trend analyst with a background in marketing and economics. Gianluca and Jared pick her brain about the economic viability of UBI, the influence of politics and big tech, and the trends in automation that will shape the future of humanity. Regardless of your fiscal intuitions or political leanings, this episode will equip you with the core arguments in the UBI debate and leave you with plenty of food for thought.





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Tweet at Bit of a Tangent:

Follow Bit of a Tangent (incl. behind the scenes) on Instagram:

Bronwyn Williams on Twitter:

Bronwyn’s website:

Flux Trends:

Apollo 42:

Negative income tax:

Alaskan oil dividend: n

Andrew Yang and the proposed “Freedom Dividend”:

Andrew Yang’s discussions about UBI with Sam Harris:

Social credit system in China:

Elon Musk on automation and UBI:

Neuralink’s human-brain interface:

Humanity’s increasing prosperity (Steven Pinker):

On having a “Venezuela moment”:

Single-payer healthcare:

Cost of bringing a drug to market:

Swiss referendum on UBI:

Bronwyn’s book reviews:

The Origins of Totalitarianism by Hannah Arendt:

Thinking Fast and Slow by Daniel Kahneman:

The Age of Surveillance Capitalism by Shoshana Zuboff:

Death of the Gods by Carl Miller:

Ben Hunt on Twitter:

David Pearce on Twitter:

Slate Star Codex breakdown of UBI:

Bayesian Conspiracy (with David Spearman) on the economics of UBI vs. NIT:

016 | Free Will, Compassion, and Reinforcement Learning

016 | Free Will, Compassion, and Reinforcement Learning

September 24, 2019

This is part 2 of our series on free will! In this episode we reconcile how it is that we can feel like we have free will (even when we don’t), give an evolutionary argument for why this might be the case and show how knowing this makes us more compassionate people who are (paradoxically) better at achieving our goals. Along the way, we explain what a Bayesian Network is (and why you should care about yours), and give an introduction to some of the key ideas and concepts in the field of Reinforcement Learning (a subfield of AI) and how we can use these concepts to clarify our view of ourselves and the world!




Dan Dennett essay on Sam Harris’s argument:

Sam Harris’s response to Dennett: 

Sam Harris’s “Free Will:

Good primer on the Libet experiments that preempted decision making:

The original Libet publication [paywalled]:

Details of more recent versions of the Libet experiments with 7 second preempting and some predictive capability:

A recent “debunking” of the Libet results:

A popular article on the Libet experiments in the light of the new model:

Radiolab Loops episode: 

Litany of Gendlin on LessWrong: 

Julia Galef on Bayes Nets: 

Learn Bayes Nets post on LessWrong: 


A good introductory courses on reinforcement learning for those interested: 

Video of RL agent walking on the back of its legs: 

Sean Carroll’s podcast: 

How to Win Friends and Influence Reality (episode 9 of Bit of a Tangent): 

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