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Gettiers in software engineering (2019)

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Re: Gettiers in software engineering (2019)

#141

Post by mannykannot »

orbisvicis wrote: post #83
mannykannot wrote: post #67

Earlier quoted context omitted.

Then, to be consistent, you should not trust either your deductions or even your choice of axioms. In other words, it looks like a form of solipsism.

You can not know what you are doing and still trust in logic. But what world it would be if you could flip a coin on any choice and still survive! If the world didn't follow any self-consistent logic, like a Roger Zelazny novel, that would be fantastic. Not sure that qualifies as solipsism, but still. Would society even be possible? Or even life? Here, as long as you follow cultural norms, every choice has pretty goo…

Logic will only tell you what follows from your choice of axioms, not how to choose them, and only if you can trust your ability to apply it correctly. Absent that, your only option appears to be to put your trust in other people - which is, I suppose, what you are saying in your final paragraph.

Re: Gettiers in software engineering (2019)

#142

Post by rjrodger »

Good to have a name for this!

My favourite debugging technique is "introduce a known error".

This validates that your set of "facts" about the file you think you're editing are actually facts about the actual file you are editing.

For example: is the damn thing even compiling?

Re: Gettiers in software engineering (2019)

#143

Post by merryocha »

I was a philosophy major in college and semantic quibbling over Gettier problems was popular while I was there. I have always believed that Gettier's popularity was due to the fact that the paper was only three pages, and therefore it was the only paper that the academics actually read to the end. I never thought there was anything particularly deep or noteworthy about the problem at all - it is fundamentally a debate over the definition of knowledge which you could debate forever, and that's exactly what they were doing - arguing about the definition of knowledge, one 30-page paper at a time.

Re: Gettiers in software engineering (2019)

#144

Post by lo_zamoyski »

jstrieb wrote: post #33

Relevant (deleted, as far as I can tell) tweet: > When I talk to Philosophers on zoom my screen background is an exact replica of my actual background just so I can trick them into having a justified true belief that is not actually knowledge. https://old.reddit.com/r/PhilosophyMemes/comments/gggqkv/get...

I would dispute your definition of "justification". I would also draw a distinction between "definition of knowledge" and "knowing that you know". I.e., that you know that the viewer doesn't know, even though he thinks he knows, is itself grounded in justification (here, the knowledge that you have put up a fake background).

I could just as easily construct a problem in which I quietly turn off your background, which would mean your Zoom partner does possess knowledge while you do not, even though now it is you who thinks he does.

Re: Gettiers in software engineering (2019)

#145

Post by bumby »

mjburgess wrote: post #136
dsr_ wrote: post #134

[Among the problems with] Justifed True Beliefs being "knowledge" is that humans are very bad at accurately stating their beliefs, and when they state those beliefs, they often adopt the inaccurate statement as the actual belief. Let's take the obscured cow example. Nobody outside the confines of a philosophy experiment believes that there is a cow in the field. They believe that they see something which looks like a…

Bayes theorem isnt a reasonable approximation, because it isnt answering the question -- it describes what you do when you have the answer. With bayes, you're computing P(Model|Evidence) -- but this doesnt explain where Model comes from or why Evidence is relevant to model. If you compute P(AllPossibleModels|AllSensoryInput) you end up never learning anything. What's happening with animals is that we have a certain,…

I'm not sure I'm understanding your stance fully, so please forgive any poor interpretation.

>certain, deterministic, non-bayesian primitive model of our bodies

What makes you certain the model of our body is non-Bayesian? Does this imply we have an innate model of our body and how it operates in space? I could be convinced that babies don't inherently have a model of their bodies (or that they control their bodies) and it is a learned skill. Possibly learned through some pseudo Bayesian process. Heck, the unathletic among us adults may still be updating our Bayesian priors with our body model, given how often it betrays our intentions :-)

Re: Gettiers in software engineering (2019)

#146

Post by bonoboTP »

CamperBob2 wrote: post #53
jstrieb wrote: post #33

Relevant (deleted, as far as I can tell) tweet: > When I talk to Philosophers on zoom my screen background is an exact replica of my actual background just so I can trick them into having a justified true belief that is not actually knowledge. https://old.reddit.com/r/PhilosophyMemes/comments/gggqkv/get...

Hmm. That seems like a better example of the problem than either of the examples at https://en.wikipedia.org/wiki/Gettier_problem . The cases cited in the article don't seem to raise any interesting issues at all, in fact. The observer who sees the dark cloud and 'knows' there is a fire is simply wrong, because the cloud can serve as evidence of either insects or a fire and he lacks the additional evidence needed to…

One should distinguish between one instance and a mechanism/process for producing them. We could take randomness and entropy as an analogy: Shannon entropy quantifies randomness of a sequence generator, not the randomness/complexity of individual instances (which would be more akin to Kolmogorov complexity).

Similarly, the real interesting stuff regards the reliability and predictive power of knowledge-producing mechanisms, not individual pieces produced by it.

Another analogy is confidence intervals, which are defined through a collective property, a confidence interval is an interval produced by a confidence process and the meat of the definition concerns the confidence process, not its output.

I always found the Gettier problems unimpressive and mainly a distraction and a language game. Watching out for smoke-like things to infer whether there is a fire is a good survival tool in the woods and advisable behavior. Neither it nor anything else is a 100% surefire way to obtain bulletproof capital-letter Truth. We are never 100% justified ("what if you're in a simulation?", "you might be a Boltzmann brain!"). Even stuff like math is uncertain and we may make a mistake when mentally adding 7454+8635, we may even have a brainfart when adding 2+2, it's just much less likely, but I'm quite certain that at least one human manages to mess up 2+2 in real life every day.

It's a dull and uninteresting question whether it's knowledge. What do you want to use the fact of it being knowledge or not for? Will you trust stuff that you determine to be knowledge and not other things? Or is it about deciding legal court cases? Because then it's better to cut the middle man and directly try to determine whether it's good to punish something or not, without reference to terms like "having knowledge".

Re: Gettiers in software engineering (2019)

#147

Post by artursapek »

merryocha wrote: post #143

I was a philosophy major in college and semantic quibbling over Gettier problems was popular while I was there. I have always believed that Gettier's popularity was due to the fact that the paper was only three pages, and therefore it was the only paper that the academics actually read to the end. I never thought there was anything particularly deep or noteworthy about the problem at all - it is fundamentally a debat…

I was going to say, I don’t even understand how the second example in this post is a gettier. He thought one event caused the issue to start, but a different event did instead. And they happened around the same time. Ok? This doesn’t seem very philosophical to me.

Re: Gettiers in software engineering (2019)

#148

Post by cfiggers »

mjburgess wrote: post #136
dsr_ wrote: post #134

[Among the problems with] Justifed True Beliefs being "knowledge" is that humans are very bad at accurately stating their beliefs, and when they state those beliefs, they often adopt the inaccurate statement as the actual belief. Let's take the obscured cow example. Nobody outside the confines of a philosophy experiment believes that there is a cow in the field. They believe that they see something which looks like a…

Bayes theorem isnt a reasonable approximation, because it isnt answering the question -- it describes what you do when you have the answer. With bayes, you're computing P(Model|Evidence) -- but this doesnt explain where Model comes from or why Evidence is relevant to model. If you compute P(AllPossibleModels|AllSensoryInput) you end up never learning anything. What's happening with animals is that we have a certain,…

> If you compute P(AllPossibleModels|AllSensoryInput) you end up never learning anything.

...whoa. That makes complete sense.

So you're saying that there must be some form of meta-rationality that gives cues to our attempts at Bayesian reasoning, directing those attempts how to make selections from each set (the set of all possible models and the set of all sensory inputs) in order to produce results that constitute actual learning.

And you're suggesting that in animals and humans at least, the feedback loop of our embodied experience is at least some part of that meta-rationality.

That's an incredible one-liner.

Re: Gettiers in software engineering (2019)

#150

Post by naasking »

jstrieb wrote: post #33

Relevant (deleted, as far as I can tell) tweet: > When I talk to Philosophers on zoom my screen background is an exact replica of my actual background just so I can trick them into having a justified true belief that is not actually knowledge. https://old.reddit.com/r/PhilosophyMemes/comments/gggqkv/get...

Funny comment, but it either fails JTB or is JTB: a) nobody thinks backgrounds on Zoom have to represent your actual background, eg. they don't think it's justified to assert this conclusion, and b) that the background corresponds 1:1 the real background means even if you had some other justification for thinking it was the background, the proposition is true so you would have a JTB.
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