List of Cognitive Biases
en.wikipedia.org
en.wikipedia.org
I think the best practical material on the subject is Charlie Munger talks. Particularly his talk "On the psychology of human misjudgement" (https://buffettmungerwisdom.files.wordpress.com/2013/01/mung...) and "Poor Charlie's Almanack: The Wit and Wisdom of Charles T. Munger", which is an edited collectoin of his many talks.
My main conclusion from reading all the books/talks is that you could only be aware of the existence of the biases, but cannot realize which one(s) is(are) at play now in your brain and cannot "fix" a bias with any cognitive effort. So "sleeping on"/delaying an important decision is the best practical way I have found to mitigate the always present pervasive biases.
I don't mindmap sensitive data yet, if I would, then the private git repo would be on a Raspberry Pi.
Familiarity means you will notice some containers in the store before others. At that point bias has already occurred before conscious thought.
It's possible to feel that my current thinking is not perfectly rational and not emotionally detached. But even when having that feeling I cannot just pick e.g. "I'm having biases 1, 7, 14 and 19 from the list and I have to do that and that to overcome them...". Better to go to sleep, run 10K, go to a bar, etc. when there are hints that the brain in a state that is not perfect for decision making. Somehow in the background the rational analysis never ends, and when one reaches a clear, calm, and emotionally detached state of mind the best decision is usually already obvious.
For anyone who hasn't read this book, it's worthwhile if only to humble oneself into realizing we're rarely as rational as we'd like to think.
When you estimate something, never ever estimate a single value. Always estimate within a range. He showed in his training that, for me, I would come to more sane averages/point estimates. It helped me. Unfortunately, that's simply anecdata.
In any case, when you google on "debias training" or something similar, you will see that many efforts are underway.
The author also offers webinars, so maybe it was from him: https://www.howtomeasureanything.com
1) Make a point estimate 2) Imagine that you're wrong (what direction are you wrong?) 3) Make a second point estimate 4) Average the two
Excellent advice from an excellent book!
[1] https://www.amazon.com/Epistemology-Psychology-Judgment-Mich...
EDIT: I am genuinely interested in knowing, since it would be helpful to know which of these are reliable - in order to change my behavior accordingly.
https://www.theatlantic.com/science/archive/2018/11/psycholo...
Perhaps many! Maybe by trying to emulate a human brain we will end up recreating its flaws.
I am very excited in the progress of deep learning applied to symbolic, logical reasoning, like theorem proving. Theorem verification is easy and tractable, proving is not.
We can have heuristic algorithms come up with provably correct algorithms! That is vaguely analogous to a human writing a program then proving it correct. Now that will be useful.
I'd be tempted to down-vote myself for snarky trolling except that I work in the field of psychological research, and perhaps it is my bias, but many of the cognitive biases that came from social-psychology research do not stand up to scrutiny, too frequently resulting from bad statistical practice...at least two decades ago.
There is an interesting course of his on Coursera ( https://www.coursera.org/learn/mindware)
I believe many biases listed here can substantiate themselves.
All theories within Psychology and Economics are based on people being 'rational'. Any thing contrary to their theory is branded 'irrational' and given a name. The name usually sounds like a 'disease/ailment'.
Take Luce's coffee cup example as an illustration. You prefer black coffee to sweet coffee. Suppose you compare coffee with no sugar to coffee with one grain of sugar added. You're indifferent: a~b. Then add another grain, and so on. You will get comparisons a~b, b~c, c~d, e~f, ..., j~k, and then suddenly a>k, a violation of the supposed transitivity of equipreference (aka indifference, equally good). But that seems to be rational.
So people relaxed rationality requirements and now there is the problem what 'rationality' actually means.
Fast forward a few years and empirical studies found the following strange behavior: If you mention a high number before asking people for some fictional charity contribution, then people tend to be be willing to pay more than if you mention a low number before, and it does not matter in which way you mention the numbers. (Actual experiments were made by making people roll a rigged lottery wheel before doing some completely different task, for example.) You can even tell participants about the observed effect before, it will still be observed.
I see no way how this "anchoring effect" could be described as being rational.
But many people nowadays share your opinion, and there is a whole field called "ecological rationality" in which scholars try to re-interpret supposedly irrational biases as good and rational heuristics increasing e.g. evolutionary fitness. I don't think they're right in general, though. Some of the biases are just flaws. If I flash a number before your eyes and this affects your subsequent decision making, then that's not a useful heuristics, it's a flaw in your brain processing. My 2 cents, others disagree with me.
It's probably a trait that is (or was) advantageous in one context, that is disadvantageous in this new or less vital context.
It works for perception research: when science needs to explain an illusion and why people are subjects for it, to be able to explain why people see or hear "wrong" things, or why this "wrong" things are not wrong at all, is a good science. Often times science use a very artificial setup for an experiment, the very setup is tuned so that people start to make mistakes. Take Ames Room as an example[1], it is artificially created environment when participant have not enough information to be sure, and his mind make a mistake. But mistake of the mind is a great achievement, if you try to do better with an AI, I suspect you'll end with the same result. Mind take into a consideration a lot of details, for Ames Room to work reliably, experimenter needs to draw skewed windows on the back wall, that would look as rectangles after projection. So the setup is highly unlikely a priori and mind makes a good bayesian decision that the most probable explanation is two people of different sizes.
For cognitive biases we also need to be wary, because the process of creating the right experimental setup could include a lot of tweaks to make people's decision process "to fail". Scientist needs an effect that could be shown with a statistics, so he/she tweaks setup until it works.
It leads to a conclusion, that if cognitive bias lacks explanation why it is a rational thing, we cannot say that this bias is a "disease/ailment".
If you're interested in rationality and cognitive biases, I'd highly recommend reading Eliezer Yudkowsky's "Rationality: A-Z" sequences: https://www.lesswrong.com/rationality
i grabbed a copy of that HBR article and will read it later. thanks!
I think all the more reason to meditate, be mindful and adopt philosophies that are not always rational, but good instead.
Also, the truth is often very complex or very dark, so thinking is only going to bring incorrect simplified (black/white) conclusions or negativity/resentment.
https://upload.wikimedia.org/wikipedia/commons/1/18/Cognitiv...
In some sense, many of these biases seem like specific instances of a more general phenomenon. For example, illusion of control and pareidolia both seem like they'd arise if you buy into the brain as doing [predictive processing](https://slatestarcodex.com/2017/09/05/book-review-surfing-un...). So it's not exactly that we have over 100 ways that our thinking goes wrong, but that the same types of mistakes occur in different ways.
In which case, for preventative reasons, knowing the core mechanism at play seems much more important. Similarly, I feel that lists of mental models might also be missing the point; no one can really go through a list of 100+ items to figure out which one is at play. You're going to need a smaller, more general toolkit.
Examples: - Following through the steps of a proof vs covering up the proof and doing it yourself - Asking yourself if something sounds familiar instead of trying to summarize it - Criticizing an idea instead of adding a new one or suggesting an improvement
It is an awful sign for a scientific community when they are working on a theory that includes 196 different exceptions and adjustments that have to be made in order to make a model fit the data. It means that your underlying model probably isn't right.
This reminds me of when Astronomers thought the universe revolves around the Earth, rather than the Sun. The earth-centered theory made sense until we got better data, and then sometimes planets appeared to go backwards. Sometimes they appeared to swirl around a line. Sometimes there were swirls within the swirls, and sometimes swirls within those: https://invisible.college/attention/dissertation/retrogrades...
Astronomers had to account for this data with a complex set of retrograde motions and epicycles layered upon epicycles. These complexities only increased as telescopes and charting techniques improved, uncovering more distortions from in the idealized orbital lens. Take, for instance, the numerous parameterized gears required for an early Galilean planetary model: https://invisible.college/attention/dissertation/galileo2.jp...
Only when Copernicus and Kepler put the sun in the center of the universe could the models be simplified. Suddenly, each planet's orbit fit a perfect elipse -- no epicycles, no retrograde motions.
We can do the same thing for Economic theory, by moving the center of the utility function from the future to the present. Right now, Economics models humans as optimizing future outcomes. The modeled humans are focused on the future: they allocate infinite attention to computing the optimal action for the future. But real humans have scarce attention for computing the future. When they run out of attention, these 194 heuristics and biases display themselves in full effect.
We solve this dilemma when we evaluate the utility function in the present, rather than the future. Instead of assuming humans have infinite attention, the utility function itself predicts how humans allocate their scarce attention. The new utility function evaluates the utility of attention itself.
And it turns out that we can empirically measure this value of this utility function, by running controlled experiments online with 1,000s of participants, and paying them different amounts of money to attend to different tasks. This lets us measure how much utility people ascribe to paying attention to television shows, sexy pictures, video games, advertisements, iPhone screens, or reddit posts. We can measure it in pennies per second.
This new model is a measurable Attention Economics: https://invisible.college/attention/dissertation.html
Or you just overestimate the validity of the biases as defined.
You probably have doubt yourself, otherwise the reference to astronomers wouldn't be here.
For instance, the planning fallacy is a correction to the idea that people will rationally predict how much time something will take. So we first estimate how long something might take, and then the planning fallacy teaches us to increase it to account for our bias.
> Any model of psychology or economics which purports to unify these phenomena must be able to explain/predict each bias individually
That's close to correct, but I'd like to distinguish explaining the bias vs. the data. The new theory should explain the data, not the biases in the old theory. Consider that Kepler's elliptical orbit theory didn't explain each individual planetary epicycle -- it didn't need to. Kepler's theory didn't need epicycles at all to explain the data.
Likewise, Attention Economics doesn't need a "Planning Fallacy", because it doesn't assume humans are good planners. It rather looks at how people actually allocate their attention while planning. Consider that if people allocate more attention to their plans, they are likely to make better estimates. So how are they allocating their attention when planning? In the "planning fallacy" [1], Kahneman and Tversky envisage "that planners focus on the most optimistic scenario for the task, rather than using their full experience of how much time similar tasks require." I haven't run the experiments myself, but one could certainly test for this in an Attention Economic experiment, by seeing how much more attracted people are to focus on the most optimistic scenario for their task, rather than the pessimistic scenarios. And then we can learn why they focus on the optimistic scenario, by manipulating other variables until we see which ones lead people to consider optimistic vs. pessimistic scenarios when planning.