Deterministic thinking: a problem in how we think, not just in how we act
statmodeling.stat.columbia.edu
statmodeling.stat.columbia.edu
Dissolving the fermi paradox[0], convolves distributions instead of multiplying point estimates of life/no life.
How to NOT measure latency, looks at CDFs instead of slow/fast thresholds.
[0] https://arxiv.org/pdf/1806.02404.pdf [1] https://www.youtube.com/watch?v=lJ8ydIuPFeU
As far as I can see the phenomenon they're talking about has nothing to do with the quality of being deterministic, and I didn't notice any explanation of why they've chosen that particular word. Is there some other notion of deterministic aside from the usual one?
One example might be "I had a bad experience using $SOFTWARE, so if you use $SOFTWARE then our project is going to fail and it's going to suck". You're thinking "If X then Y", not "If X then P(Y) > n". I.e. you're thinking in a purely deterministic way, when the evidence really only supports a probability.
This way of thinking is ultimately due to a deterministic model of how the world works, rather than a probabilistic one.
Are you implying after one occurrence a person thinking "in a deterministic way" is going to take the outcome and think it will reoccur again?
I don't think that is deterministic thinking because a determinists accounts all the variables that made the outcome. Such as how the software was used and understands that will be different for the outcome of some other user.
The scenario is something like a situation in which individuals are categorized into two discrete groups, and then inference proceeds as if the fate of each individual has been determined by which group they fell into (rather than determined probabilistically). The fact that a discrete categorization is assumed to 'determine' the outcome (the possibility wave function 'collapses' at the moment of categorization) could certainly be referred to as deterministic. I agree that it's not an application of the word that we are familiar with.
I found it funny he spends the entire article eliciting a dichotomy between thinking “deterministically” (dichotomized) and thinking non-deterministically.. yet chiefly to form the suggestion that one should not make such dichotomies to better understand things.
How could we model the co-existence of the discrete and dichotomized with the continuous and probabilistic... without involving at least one dichotomy?
He uses the phrase "inappropriate discretization", which fits better with what he's describing as a problem in how we think.
To me it seems to boil down to "duality" being a fundamental property/strategy of thinking. Buddhist philosophy points this out in numerous ways, and the paradox/contradiction inherent in it.
From the article's conclusion:
> ..When we’re talking about the problems of deterministic thinking, or premature collapse of the “wave function” of inferential uncertainty, we really are talking about a failure to incorporate enough of a continuous view of the world in our mental model.
I think the point is that we should be more willing to accept uncertainty and probabilities in thinking about the world, rather than build mental models on "false certainties" and "dichotomania" that reduces the world to what we think we know and understand.
Lets say you have an internal value function F which you use to judge peoples worth. If F is deterministic it means that for any observation X it produces a value F(X) = V, where V is some number.
With this function a rational person would be racist, as statistically whites are more educated than blacks, so maybe F(WHITE) = 0.7 and F(BLACK) = 0.4. People from this category say things like "Of course he failed the maths test, he is black!".
But lets say that your F output a probability distribution instead, then you'd recognize that the information is incomplete, so even though E(F(WHITE)) = 0.7 it could both be lower and higher. With this way to see the world the statement "Of course he failed the maths test, he is black!" doesn't make sense, as there are lots of black people who do very well in maths so your value function would include those possibilities as well.
Note that the talk about discrete categorization doesn't make sense, having real valued prejudice function based on loads of factors is still deterministic.
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In my experience most people go with the deterministic value function when not pressured. But when you say something like "Do you really mean that no black person could have done well on that test?" they typically acknowledge that there are some black people who would have done well, so they can still think probabilistic but it takes more effort. Maybe said effort is too much for the average person to apply it everyday so we are doomed to experience the current political strife forever.
If you wonder how we solved racism, we did it with simple and blunt rules everyone can follow such as "don¨t say the n-word" or "don't say that whites are better than blacks". People are still racist, but simple rules like that makes it harder to rile up a mob enough to burn down a black neighborhood (I know it happened in Tulsa, that is why I used it as an example since it probably wouldn't happen today).
Your theory explains why people misunderstand each other and are "doomed" to states of conflict due to how their minds work. It needs to go further to explain how good outcomes are produced despite conflict and misunderstanding.
Why do they accept "simple and blunt rules"? Where are these rules coming from? Why aren't the rules pushing people in the opposite direction? Are there other mechanisms beyond imposing simple and blunt rules on everyone? etc etc etc Don't be satisfied with your theory. It's a good start but keep digging.
For example, given that event A correlates well with B one is likely to ask:
1) Does A cause B?
2) Does B cause A?
3) Does some unknown cause A and B?
Framing the possibilities in this way neglects interactions in which all possibilities are true to some degree. The author argues that all possibilities being true to some non-zero degree (A causes B and B causes A and an unknown causes both A and B) should be the more common expectation in natural systems. Therefor, framing problems in a way that suggests there are clear exclusions i.e. determinations, leads to cognitive bias and premature conclusions.
*As apposed the the common usage of deterministic in computer science where a process is said to be deterministic if every execution with identical inputs produces the same result.
Nonetheless, it's possible to acknowledge uncertainty more than we usually do.
In psycholinguistics it's called categorical perception
To apply this principle here, I'm not sure I know much about what Hacker News readers do. It's easier to know what they comment about, since this is something I actually see.
It seems to be that as we learn more about what it means to think less objectively-- our ability to see and understand more dimensionality to information quality expand.
Deterministic thinking is fine as a methodology for understanding aspects of object X, as long as it is does not resolve as absolute. This is why the practice of history is imperative, but a also slippery slope.
Also the "near-miss" analysis reminds me of seeing smart companies requiring reporting of near-misses, not only of accidents -- that kind of more nuanced analysis is more likely to prevent accidents even before the first one happens, rather than losing at least one finger/hand/person to a problem type.