Debiasing by rationalizing your own motives
Some time back, I saw somebody express an opinion that I disagreed with. Next, my mind quickly came up with emotional motives the other person might have for holding such an opinion, that would let me safely justify dismissing that opinion.
Now, it’s certainly conceivable that they did have such a reason for holding the opinion. People do often have all kinds of psychological, non-truth-tracking reasons for believing in something. So I don’t know whether this guess was correct or not.
But then I recalled something that has stayed with me: a slide from a presentation that Stuart Armstrong held several years back, that showed the way that we tend to think of our own opinions as being based on evidence, reasoning, etc.. And at the same time, we don’t see any of the evidence that caused other people to form their opinion, so instead we think of the opinions of others as being only based on rationalizations and biases.
Yes, it was conceivable that this person I was disagreeing with, held their opinion because of some bias. But given how quickly I was tempted to dismiss their view, it was even more conceivable that I had some similar emotional bias making me want to hold on to my opinion.
And being able to imagine a plausible bias that could explain another person’s position, is a Fully General Counterargument. You can dismiss any position that way.
So I asked myself: okay, I have invented a plausible bias that would explain the person’s commitment to this view. Can I invent some plausible bias that would explain my own commitment to my view?
I could think of several, right there on the spot. And almost as soon as I could, I felt my dismissive attitude towards the other person’s view dissolve, letting me consider their arguments on their own merits.
So, I’ll have to remember this. New cognitive trigger-action plan: if I notice myself inventing a bias that would explain someone else’s view, spend a moment to invent a bias that would explain my opposing view, in order to consider both more objectively.
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Kaj Sotala:
> ... political science needs to come to grips with the fact that AI is going to radically change the way we not only do research, but how we even think about problems. If we are not careful, AI will literally take our jobs. Already we have substantial efforts underway in various state government labs, defense labs, and other fora, for causal modeling for complex social interactions, such as the onset of war, socio-economic-political dynamics, and how to manipulate all of them too boot. We political scientists value parsimony; we love clear models with few causal variables and lots of data. The reality is, however, a machine learning algorithm doesn’t need any of it. In some instances, it is model free, and it just learns from vast—and I mean vast—amounts of data. If one has a neural network with hundreds of layers, with millions of data points, the system has very little need for humans. Our best datasets are a drop in the bucket. We almost look akin to Amish farmers driving horses with buggies as these new AI gurus pull up to us in their self-driving Teslas. Moreover, the holders of this much data remain in the hands of the private sector in the big six: Amazon, Facebook, Google, Microsoft, Apple and Baidu.
> This is not to say that we have nothing to offer, and that we ought to hang up our hats and go home. No, that is not true. But long dead are the days of worrying about degree of freedom problems (yay qualitative folks!) We have an immense and rich knowledge of complex problems and are able to cut through the noise in meaningful and nuanced ways. But we must not eschew or deride the coming AI tide. We must embrace it. We must think seriously about what it will mean for deterrence, war, international political economy and, frankly, power.
> Thankfully, for many of us versed in methods and methodology this is not a hard lift. Much of the work in AI is merely math. And we’re good at math. We can understand what is happening in a partially observed Markov Decision Process. We can understand control and optimization. And we can understand the limitations of type of perspective. This is our greatest contribution going forward. Let us bring to bear our great knowledge of statistics, mathematics and social phenomenon to problems that are currently lacking in nuance, subtly, area knowledge and political savvy. I highly recommend that we all go to our respective libraries and begin to teach ourselves algorithms and computer science.
Swan Song – For Now | Duck of Minerva
Kaj Sotala:
I approve of any ethics paper that begins by discussing KITT.
Kaj Sotala:
> He once recalled a question from the Army Officer Candidate School test:
> Q. You are in charge of a detail of 11 men and a sergeant. There is a 25-foot flagpole lying on the sandy, brush-covered ground. You are to erect the pole. What is your first order?
> The right answer is:
> A. “Sergeant, erect that flagpole.”
> In other words, if the sergeant knows how to do it, then there’s no need for you to risk your dignity as an officer and a gentleman by issuing some potentially ludicrous order about how to erect the flagpole. And if the sergeant doesn’t know either, well, he’ll probably order a corporal to do it, and so forth down the chain of command. But by the time the problem comes back up to you, it will be well established that nobody else has any more idea than you do.
In Memoriam: Jerry Pournelle
Kaj Sotala:
The portrait of the father of subjective probability, is probably not of him:
> The photograph [of Thomas Bayes] is [...] from the page facing December of the Springer Statistics Calendar 1981 by Stephen M. Stigler (pub. Springer-Verlag, New York, 1980). It is noted there that “[...] This is the only known portrait of him; it is taken from the 1936 History of Life Insurance (by Terence O’Donnell, American Conservation Co., Chicago). As no source is given, the authenticity of even this portrait is open to question”. The original source of this photograph still remains unknown. [...]
> “While the style of dress as shown in the picture is not atypical of that of a nonconformist minister of the 18th century, there is no reason to suppose that it is in fact a portrait of the illustrious Bayes. O’Donnell’s book, as he states in the preface, is ``a plethora of rare and curious old illustrations”, and no reference to the source of the photograph is provided. Moreover, the reference to insurance sheds further doubt on the ascription. O’Donnell is elsewhere unreliable. For example, he refers to the author of ``An Essay on Probabilities and their Application to Life Contingencies and Insurance Offices” as William de Morgan (who was in fact Augustus’s son).
Bayes' portrait