sen

Karma: 169

sen 22 Feb 2016 7:40 UTC
0 points
in reply to: Luke_A_Somers’s comment on: Goal completion: noise, errors, bias, prejudice, preference and complexity
How is inverse temperature a penalty on models? If you’re referring to the inverse temperature in the Maxwell-Boltzmann distribution, the temperature is considered a constant, and it gives the likelihood of a particle having a particular configuration, not the likelihood of a distribution.

Also, I’m not sure it’s clear what you mean by “information to specify [a model]”. Does a high inverse temperature mean a model requires more information, because it’s more sensitive to small changes and therefore derives more information from them, or does it mean that the model requires less information, because it derives less information from inputs?

The entropy of the Maxwell-Boltzmann distribution I think is proportional to log-temperature, so high temperature (low sensitivity to inputs) is preferred if you go strictly by that. People that train neural networks generally do this as well to prevent overtraining, and they call it regularization.

If you are referring to the entropy of a model, you penalize a distribution for requiring more information by selecting the distribution that maximizes entropy subject to whatever invariants your model must abide by. This is typically done through the method of Lagrange multipliers.

sen 23 Feb 2016 4:23 UTC
0 points
in reply to: Luke_A_Somers’s comment on: Goal completion: noise, errors, bias, prejudice, preference and complexity
It’s true that the probability of a microstate is determined by energy and temperature, but the Maxwell-Boltzmann equation assumes that temperature is constant for all particles. Temperature is a distinguishing feature of two distributions, not of two particles within a distribution, and least-temperature is not a state that systems tend towards.

As an aside, the canonical ensemble that the Maxwell-Boltzmann distribution assumes is only applicable when a given state is exceedingly unlikely to be occupied by multiple particles. The strange behavior of condensed matter that I think you’re referring to (Bose-Einstein condensates) is a consequence of this assumption being incorrect for bosons, where a stars-and-bars model is more appropriate.

It is not true that information theory requires the conservation of information. The Ising Model, for example, allows for particle systems with cycles of non-unity gain. This effectively means that it allows particles to act as amplifiers (or dampeners) of information, which is a clear violation of information conservation. This is the basis of critical phenomena, which is a widely accepted area of study within statistical mechanics.

I think you misunderstand how models are fit in practice. It is not standard practice to determine the absolute information content of input, then to relay that information to various explanators. The information content of input is determined relative to explanators. However, there are training methods that attempt to reduce the relative information transferred to explanators, and this practice is called regularization. The penalty-per-relative-bit approach is taken by a method called “dropout”, where a random “cold” model is trained on each training sample, and the final model is a “heated” aggregate of the cold models. “Heating” here just means cutting the amount of information transferred from input to explanator by some fraction.

sen 24 Feb 2016 2:05 UTC
0 points
in reply to: Luke_A_Somers’s comment on: Goal completion: noise, errors, bias, prejudice, preference and complexity
I thought you were referring to degenerate gases when you mentioned nontrivial behavior in solid state systems since that is the most obvious case where you get behavior that cannot be easily explained by the “obvious” model (the canonical ensemble). If you were thinking of something else, I’m curious to know what it was.

I’m having a hard time parsing your suggestion. The “dropout” method introduces entropy to “the model itself” (the conditional probabilities in the model), but it seems that’s not what you’re suggesting. You can also introduce entropy to the inputs, which is another common thing to do during training to make the model more robust. There’s no way to introduce 1 bit of entropy per “1 bit of information” contained in the input though since there’s no way to measure the amount of information contained in the input without already having a model of the input. I think systematically injecting noise into the input based on a given model is not functionally different from injecting noise into the model itself, at least not in the ideal case where the noise is injected evenly.

You said that “if you add 1 bit of information, you have added 1 bit of entropy”. I can’t tell if you’re equating the two phrases or if you’re suggesting adding 1 bit of entropy for every 1 bit of information. In either case, I don’t know what it means. Information and entropy are negations of one another, and the two have opposing effects on certainty-of-an-outcome. If you’re equating the two, then I suspect you’re referring to something specific that I’m not seeing. If you’re suggesting adding entropy for a given amount of information, it may help if you explain which probabilities are impacted. To which probabilities would you suggest adding entropy, and which probabilities have information added to them?

sen 25 Feb 2016 6:21 UTC
0 points
in reply to: Luke_A_Somers’s comment on: Goal completion: noise, errors, bias, prejudice, preference and complexity
2) I think this is the distinction you are trying to make between the lattice model and the smoker model: in the lattice model, the equations and parameters are defined, whereas in the smoker model, the equations and parameters have to be deduced. Is that right? If so, my previous posts were referring to the smoker-type model.

Your toy meta-model is consistent with what I was thinking when I used the word “model” in my previous comments.

3) I see what you’re saying. If you add complexity to the model, you want to make sure that its improvement in ability is greater than the amount of complexity added. You want to make sure that the model isn’t just “memorizing” the correct results, and that all model complexity comes with some benefit of generalizability.

I don’t think temperature is the right analogy. What you want is to penalize a model that is too generally applicable. Here is a simple case:

simple case A one-hidden-layer feed-forward binary stochastic neural network the goal of which is to find binary-vector representations of its binary-vector inputs. It translates its input to an internal representation of length n, then translates that internal representation into some binary-vector output that is the same length as its input. The error function is the reconstruction error, measured as the KL-divergence from input to output.

The “complexity” you want is the length of its internal representation in unit bits since each element of the internal representation can retain at most one bit of information, and that bit can be arbitrarily reflected by the input. The information loss is the same as the reconstruction error in unit bits since that describes the probability of the model guessing correctly on a given input stream (assuming each bit is independent). Your criterion translates to “minimize reconstruction error + internal representation size”, and this can be done by repeatedly increasing the size of the internal representation until adding one more element reduces reconstruction error by less than one bit.

sen 28 Feb 2016 11:48 UTC
0 points
in reply to: Luke_A_Somers’s comment on: Goal completion: noise, errors, bias, prejudice, preference and complexity
I see. You’re treating “energy” as the information required to specify a model. Your analogy and your earlier posts make sense now.

sen 29 Nov 2016 8:58 UTC
0 points
on: Mismatched Vocabularies
I don’t think there’s anything wrong with comparing 1 and 3. Yes, Reaction 1 is defined by an ideal, Reaction 2 is defined by a goal, and Reaction 3 is defined by an evolutionary impulse (whatever that means), but that does not make these things incomparable. If you have a goal in mind, you can determine the relationship between these three Reactions to the goal, and you can hope to come up with some ordering of Reactions with respect to that goal. For example, if you want to judge these reactions in terms of how well they indicate an individual’s strive towards a goal that the reactor would consider worthwhile, I’d argue that Reaction 3 is, with high likelihood, a stupid option.

Regarding your sudden doubt in your own perspective, the problem here is that you didn’t define a goal. By not defining a goal, your implicit goal was allowed to become something you didn’t understand, and the reasoning behind your judgement was allowed to become highly subjective and non-conveyable. You can fix this particular issue by making sure to always think of “goodness” and “badness” in relation to explicitly specified goals. The more general issue is that you don’t have a basis for recognizing when you believe things are reasonably comparable. You can fix this more general issue by studying more math, specifically category theory.

Regarding your actual question, try redefining your three reactions in terms of each of the three properties you used to define them: reaction from ideal, reaction from goal, reaction from evolutionary impulse (whatever that means). Under what ideal is it correct to ignore the mismatch and carry on the conversation? (I think korin43 answered most of this question.) Under what ideal is it correct to display anger? Towards what ends is it good to Google the unknown reference before responding? Towards what ends is it good to get angry?

As a side note, I’ve never seen evolutionary anything used as a concrete justification for a phenomenon where it couldn’t equally well be used to justify the lack of the same phenomenon. More often than not, I see it as an attempt to hand-wave away a complex behavior because thinking is hard.

sen 3 Dec 2016 7:50 UTC
13 points
on: Which areas of rationality are underexplored? - Discussion Thread
Non-bayesian reasoning. Seriously, pretty much everything here is about experimentation, conditional probabilities, and logical fallacies, and all of the above are derived from bayesian reasoning. Yes, these things are important, but there’s more to science and modeling than learning to deal with uncertainty.

Take a look at the Wikipedia page on the Standard Model of particle physics, and count the number of times uncertainty and bayesian reasoning are mentioned. If your number is greater than zero, then they must have changed the page recently. Bayesian reasoning tells you what to expect given an existing set of beliefs. It doesn’t tell you how to develop those underlying beliefs in the first place. For much of physics, that’s pretty much squarely in the domain of group theory / symmetry. It’s ironic that a group so heavily based on the sciences doesn’t mention this at all.

Rationality is about more than empirical studies. It’s about developing sensible models of the world. It’s about conveying sensible models to people in ways that they’ll understand them. It’s about convincing people that your model is better than theirs, sometimes without having to do an experiment.

It’s not like these things aren’t well-studied. It’s called math, and it’s been studied for thousands of years. Everything on this site focuses on one tiny branch, and there’s so much more out there.

Apologies for the rant. This has been bugging me for a while now. I tried to create a thread on this a little while ago and met with the karma limitation. I didn’t want to deal with it at the time, and now it’s all coming back to me, rage and all.

Also, this discussion topic is suboptimal if your aim is to explore new areas of rationality, as it presumes that all unexplored areas will arise from direct discussion. It should have been paired with the question “How do we discover underexplored areas of rationality?” My answer is to that is to encourage non-rational discussion where people believe, intuitively or otherwise, that it should be possible to make the discussion rational. You’re not going to discover the boundaries of rationality by always staying within them. You need to look both outside and inside to see where the boundary might lie, and you need to understand non-rationality if you ever want hope of expanding the boundaries of rationality.

End rant.

sen 3 Dec 2016 8:36 UTC
2 points
in reply to: ArisKatsaris’s comment on: December 2016 Media Thread
Some short papers worth reading.

A good introduction to topos theory, which in turn explains why the Yoneda embedding so so useful. I would not recommend this as an introduction to category theory or the Yoneda lemma.

A love letter to adjoint functors discussing their meaning and philosophical significance.

A paper on categorification, from which I will include a quote below in hopes of showing some of you heathens the light.

If one studies categorification one soon discovers an amazing fact: many deep-sounding results in mathematics are just categorifications of facts we learned in high school! There is a good reason for this. All along, we have been unwittingly ‘decategorifying’ mathematics by pretending that categories are just sets. We ‘decategorify’ a category by forgetting about the morphisms and pretending that isomorphic objects are equal. We are left with a mere set: the set of isomorphism classes of objects.

To understand this, the following parable may be useful. Long ago, when shepherds wanted to see if two herds of sheep were isomorphic, they would look for an explicit isomorphism. In other words, they would line up both herds and try to match each sheep in one herd with a sheep in the other. But one day, along came a shepherd who invented categorification. She realized one could take each herd and ‘count’ it, setting up an isomorphism between it and some set of ‘numbers’, which were nonsense words like ‘one, two, three, . . . ’ specially designed for this purpose. By comparing the resulting numbers, she could show that two herds were isomorphic without explicitly establishing an isomorphism! In short, by decategorifying the category of finite sets, the set of natural numbers was invented.

According to this parable, decategorification started out as a stroke of mathematical genius. Only later did it become a matter of dumb habit, which we are now struggling to overcome by means of categorification. While the historical reality is far more complicated, categorification really has led to tremendous progress in mathematics during the 20th century. For example, Noether revolutionized algebraic topology by emphasizing the importance of homology groups. Previous work had focused on Betti numbers, which are just the dimensions of the rational homology groups. As with taking the cardinality of a set, taking the dimension of a vector space is a process of decategorification, since two vector spaces are isomorphic if and only if they have the same dimension. Noether noted that if we work with homology groups rather than Betti numbers, we can solve more problems, because we obtain invariants not only of spaces, but also of maps. In modern parlance, the nth rational homology is a functor defined on the category of topological spaces, while the nth Betti number is a mere function defined on the set of isomorphism classes of topological spaces. Of course, this way of stating Noether’s insight is anachronistic, since it came before category theory. Indeed, it was in Eilenberg and Mac Lane’s subsequent work on homology that category theory was born!

sen 3 Dec 2016 9:09 UTC
3 points
in reply to: DataPacRat’s comment on: Open thread, Nov. 28 - Dec. 04, 2016
You sound insane desu.

Stop browsing reddit for a while. Any board where attention is explicitly rewarded, whether in the form of (You)s or upboats, will almost by definition tend towards encouraging high volatility of beliefs and emotions. It sounds like you’ve been riding that wave a bit too long.

Also, learn to recognize fear mongering.

sen 3 Dec 2016 10:18 UTC
4 points
in reply to: DataPacRat’s comment on: Open thread, Nov. 28 - Dec. 04, 2016
I said you sound insane because of your paranoia, not because of what you wanted to do as a result of that paranoia. Whether or not you would be creating backups in other circumstances is irrelevant, except as an indicator of how paranoid you are. I don’t think such an indicator is necessary because your first two paragraphs already demonstrate what I see as an extreme level of paranoia, and so to me it’s irrelevant whether you already have backups of various sites. It’s perfectly reasonable for you to create backups given your beliefs. Those beliefs though I consider insane. The solution then is not to stop creating backups, as that would accomplish nothing. The solution is to stop browsing sites that are specifically designed to make you insane.

sen 3 Dec 2016 11:23 UTC
4 points
in reply to: DataPacRat’s comment on: Open thread, Nov. 28 - Dec. 04, 2016
You don’t place bets based solely on probabilities. You place bets based on probabilities, odds, timescales, investments, and alternative options. Specifically, you place bets to optimize for growth of principle with respect to time. What you’re doing is not placing a bet. If you were placing a bet and wanted feedback, it would have been appropriate to provide a lot more information, such as what you expect to gain from your bet, what you expect to lose in the negative case, what you’re hoping to optimize, what your expected costs are, and what alternatives you’re considering spending your time or money on. It’s not appropriate for you to provide any of that information because what you’re doing is not placing a bet.

What you’re doing is panicking and looking for an echo to tell you that your beliefs are sensible, that the world really is crashing, and that what you’re doing is justified. Your beliefs are not sensible, and the world isn’t really crashing. I don’t know if what you’re doing is justified, as that would require a lot more information, but honestly I think that’s irrelevant.

sen 3 Dec 2016 13:21 UTC
2 points
on: Making intentions concrete—Trigger-Action Planning
Reading through this, it seems completely obvious and intuitive, and yet I see a lot of “thumbs up” (or whatever the LW colloquialism is). For the sake of metaoptimization, I have to ask… Has this post actually helped anyone here? Reading through the comments, it seems again like everyone already knew this, and that people are just commenting with their own experiences. If this post didn’t actually help anyone here, the obvious follow-up question would be whether the “thumbs up” signal is actually conveying the intended meaning.

sen 4 Dec 2016 11:35 UTC
0 points
in reply to: ChristianKl’s comment on: Which areas of rationality are underexplored? - Discussion Thread
System 1 and 2 I don’t think are relevant since they’re not areas of rationality. It’s the difference between a design and an implementation. I don’t think this thread is about implementation optimizations, and I do see numerous threads on that topic.

Regarding double crux, I actually don’t see that when I browse through the recent threads, even going back several pages. Through the site search, I was able to find another post that links to a November 29th thread, which I think is the one you’re talking about.

Here’s an excerpt from that double crux thread.

Ideally, B is a statement that is somewhat closer to reality than A—it’s more concrete, grounded, well-defined, discoverable, etc. It’s less about principles and summed-up, induced conclusions, and more of a glimpse into the structure that led to those conclusions.

(It doesn’t have to be concrete and discoverable, though—often after finding B it’s productive to start over in search of a C, and then a D, and then an E, and so forth, until you end up with something you can research or run an experiment on).

That’s not out of context. The entire game description and recommendations are written with the focal point of increasing precision and making beliefs more concrete.

I want you to take the time to seriously consider whether you think I’m crazy for thinking that “increasing precision” and “making beliefs more concrete” could possibly be a bad thing when trying to understand how someone thinks. Think about what your gut reaction was when you read that. Think about what alternative there could be. Please don’t read on until you’re sure I’m just trolling so maybe you can see how screwed up this place this.

How about doing the exact opposite? How about making things less precise? How about throwing away useless structure and making it easier to reason by analogy, thereby letting people expose the full brunt of their intuition and experience that really leads to their beliefs? How about making beliefs less concrete, and therefore more abstract, more general, and easier to see relationships in other domains?

If you convince someone that A really might not lead to B and that there are n experiments you could use to tell, whoopee do, they are literally never going to use that again. If you discover that you believe uniforms lead to bullying because you mentally model social dynamics as particle systems, and bullying as a problem that occurs in high-chaos environments, and that uniforms go a long way in cooling the system thereby reducing the chaos and bullying… That’s probably going to stick with you for a while, despite being a complete ungrounded non-sequitur.

sen 4 Dec 2016 14:21 UTC
1 point
in reply to: Kaj_Sotala’s comment on: Making intentions concrete—Trigger-Action Planning
That’s not what OP says, and it’s also a non-sequitur. Obviousness and intuitiveness does not imply that all goals should be turned to TAPs or that vague triggers shouldn’t be used. It’s obvious and intuitive to anyone that’s flown on an airplane that the Earth is spheroid, but that doesn’t mean I should use geodesics to compute the best way to get the the grocery store.

TAPs are useful for people that have problems following through with intentions. OP mentions three example indicators of such problems. If you don’t have problems following through, then there is no reason to “make all of your goals into TAPs” or “never think anything with a vague trigger”. Putting effort into solving a non-problem is a waste.

To answer your question, hell no. It’s clear why this would help certain people, but it’s certainly not optimal for people that can look ahead a bit into a future, keep things in mind for later, or… you know, stick to things. The general idea behind TAPs is that people are lazy when they have planning left to do, and they can’t remember to do things. Yes, I’ll set up notes sometimes, but for the vast majority of things, my brain just reminds me without any explicit triggers. It’s not like I feel lazy either, and I don’t feel off-put by non-concrete goals, not even a little.

I’m satisfied with my level of productivity, I don’t get discouraged by planning or non-concrete goals, and I don’t have trouble remembering to do things. TAPs has nothing to offer for me other than ideas on how to model certain aspects of people’s brains.

sen 5 Dec 2016 6:09 UTC
0 points
in reply to: NatashaRostova’s comment on: Beware of identifying with school of thoughts
Is there ever a case where priors are irrelevant to a distinction or justification? That’s the difference between pure Bayesian reasoning and alternatives.

OP gave the example of the function of organs for a different purpose, but it works well here. To a pure Bayesian reasoner, there is no difference between saying that the heart has a function and saying that the heart is correlated with certain behaviors, because priors alone are not sufficient to distinguish the two. Priors alone are not sufficient to distinguish the two because the distinction has to do with ideals and definitions, not with correlations and experience.

If a person has issues with erratic blood flow leading to some hospital visit, why should we look at the heart for problems? Suppose there were a problem found with the heart. Why should we address the problem at that level as opposed to fixing the blood flow issue in some more direct way? What if there was no reason for believing that the heart problem would lead to anything but the blood flow problem? What’s the basis for addressing the underlying cause as opposed to addressing solely the issue that more directly led to a hospital visit?

There is no basis unless you recognize that addressing underlying causes tends to resolve issues more cleanly, more reliably, more thoroughly, and more persistently than addressing symptoms, and that the underlying cause only be identified by distinguishing erroneous functioning from other abnormalities. Pure Bayesian reasoners can’t make the distinction because the distinction has to do with ideals and definitions, not with correlations and experience.

It’s really hard for me to see under what model of the world (correct) Bayesian analysis could be misleading.

If you wanted a model that was never misleading, you might as well use first order logic to explain everything. Or go straight for the vacuous case and don’t try to explain anything. That problem is that that doesn’t generalize well, and it’s too restrictive. It’s about broadening your notion of reasoning so that you consider alternative justifications and more applications.

sen 5 Dec 2016 6:56 UTC
0 points
in reply to: ChristianKl’s comment on: Making intentions concrete—Trigger-Action Planning
Fair enough, though I disagree with the idea of using the discussion board as a repository of information.

sen 6 Dec 2016 5:31 UTC
1 point
in reply to: NatashaRostova’s comment on: Beware of identifying with school of thoughts
The distinction between “ideal” and “definition” is fuzzy the way I’m using it, so you can think of them as the same thing for simplicity.

Symmetry is an example of an ideal. It’s not a thing you directly observe. You can observe a symmetry, but there are infinitely many kinds of symmetries, and you have some general notion of symmetry that unifies all of them, including ones you’ve never seen. You can construct a symmetry that you’ve never seen, and you can do it algorithmically based on your idea of what symmetries are given a bit of time to think about the problem. You can even construct symmetries that, at first glance, would not look like a symmetry to someone else, and you can convince that someone else that what you’ve constructed is a symmetry.

The set of natural numbers is an example of something that’s defined, not observed. Each natural number is defined sequentially, starting from 1.

Addition is an example of something that’s defined, not observed. The general notion of a bottle is an ideal.

In terms of philosophy, an ideal is the Platonic Form of a thing. In terms of category theory, an ideal is an initial or terminal object. In terms of category theory, a definition is a commutative diagram.

I didn’t say these things weren’t influenced by past observations and correlations. I said past observations and correlations were irrelevant for distinguishing them. Meaning, for example, you can distinguish between more natural numbers than your past experiences should allow.

sen 6 Dec 2016 6:19 UTC
1 point
in reply to: WhySpace_duplicate0.9261692129075527’s comment on: Which areas of rationality are underexplored? - Discussion Thread
“Group” is a generalization of “symmetry” in the common sense.

I can explain group theory pretty simply, but I’m going to suggest something else. Start with category theory. It is doable, and it will give you the magical ability of understanding many math pages on Wikipedia, or at least the hope of being able to understand them. I cannot overstate how large an advantage this gives you when trying to understand mathematical concepts. Also, I don’t believe starting with group theory will give you any advantage when trying to understand category theory, and you’re going to want to understand category theory if you’re interested in reasoning.

When I was getting started with category theory, I went back and forth between several pages (Category Theory, Functor, Universal Property, Universal Object, Limits, Adjoint Functors, Monomorphism, Epimorphism). Here are some of the insights that made things click for me:
- An “object” in category theory corresponds to a set in set theory. If you’re a programmer, it’s easier to think of a single categorical object as a collection (class) of OOP objects. It’s also valid and occasionally useful to think of a single categorical object as a single OOP object (e.g., a collection of fields).
- A “morphism” in category theory corresponds to a function in set theory. If you think of a categorical object as a collection of OOP objects, then a morphism takes as input a single OOP object at a time.
- It’s perfectly valid for a diagram to contain the same categorical object twice. Diagrams only show relations, and it’s perfectly valid for an OOP object to be related to another OOP object of the same class. When looking at commutative diagrams that seem to contain the same categorical object twice, think of them as distinct categorical objects.
- Diagrams don’t only show relationships between OOP objects. They can also show relationships between categorical objects. For example, a diagram might state that there is a bijection between two categorical objects.
- You’re not always going to have a natural transformation between two functors of the same category.
- When trying to understand universal properties, the following mapping is useful (look at the diagrams on Wikipedia): A is the Platonic Form of Y, U is a fire that projects only some subset of the aspects of being like A.
- The duality between categorical objects and OOP objects is critical to understanding the difference between any diagram and its dual (reversed-morphisms). Recognizing this makes it much easier to understand limits and colimits.
Once you understand these things, you’ll have the basic language down to understand group theory without much difficulty.

sen 6 Dec 2016 7:12 UTC
0 points
in reply to: NatashaRostova’s comment on: Beware of identifying with school of thoughts
I don’t understand what point you’re making with the computer, as we seem to be in complete agreement there. Nothing about the notion of ideals and definitions suggests that computers can’t have them or their equivalent. It’s obvious enough that computers can represent them, as you demonstrated with your example of natural numbers. It’s obvious enough that neurons and synapses can encode these things, and that they can fire in patterned ways based on them because… well that’s what neurons do, and neurons seem to be doing to bulk of the heavy lifting as far as thinking goes.

Where we disagree is in saying that all concepts that our neurons recognize are equivalent and that they should be reasoned about in the same way. There are clearly some notions that we recognize as being valid only after seeing sufficient evidence. For these notions, I think bayesian reasoning is perfectly well-suited. There are also clearly notions we recognize as being valid for which no evidence is required. For these, I think we need something else. For these notions, only usefulness is required, and sometimes not even that. Bayesian reasoning cannot deal with this second kind because their acceptability has nothing to do with evidence.

You argue that this second kind is irrelevant because these things exist solely in people’s minds. The problem is that the same concepts recur again and again in many people minds. I think I would agree with you if we only ever had to deal with a physical world in which people’s minds did not matter all that much, but that’s not the world we live in. If you want to be able to reliably convey your ideas to others, if you want to understand how people think at a more fundamental level, if you want your models to be useful to someone other than yourself, if you want to develop ideas that people will recognize as valid, if you want to generalize ideas that other people have, if you want your thoughts to be integrated with those of a community for mutual benefit, then you cannot ignore these abstract patterns because these abstract patterns constitute such a vast amount of how people think.

It also, incidentally, has a tremendous impact on how your own brain thinks and the kinds of patterns your brain lets you consciously recognize. If you want to do better generalizing your own ideas in reliable and useful ways, then you need to understand how they work.

For what it’s worth, I do think there are physically-grounded reasons for why this is so.

sen 6 Dec 2016 9:11 UTC
0 points
in reply to: ChristianKl’s comment on: Making intentions concrete—Trigger-Action Planning
Information on the discussion board is front-facing for some time, then basically dies. Yes, you can use the search to find it again, but that becomes less reliable as discussion of TAPs increases. It’s also antithetical to the whole idea behind TAP.

The wiki is better suited for acting as a repository of information.