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August 23, 2007

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I just wanted to say that this is the best damn blog I've read. The high level of regular, insightful, quality updates is stunning. Reading this blog, I feel like I've not just accumulated knowledge, but processes I can apply to continue to refine my understanding of how I think and how I accumulate further knowledge.

I am honestly surprised, with all the work the contributors do in another realms, that you are able to maintain this high level of quality output on a blog.

Recently I have been continuing my self-education in ontology and epistemology. Some sources are more rigorous than others. Reading Rand, for example, shows an author who seems to utilize "phlogiston" like mechanics to describe her ethical solutions to moral problems. Explanations that use convincing, but unbounded turns of phrase instead of a meaningful process of explanation. It can be very challenging to read and process new data and also maintain a lack of bias (or at least an awareness of bias, that can be accounted for and challenged). It requires a very high level of active, conscious information processing. Rereading, working exercises, and thinking through what a person is saying and why they are saying it. This blog has provided me lots of new tools to improve my methods of critical thinking.

Rock on.

I feel like I've not just accumulated knowledge, but processes I can apply to continue to refine my understanding of how I think and how I accumulate further knowledge.

You've warmed my heart for the day.

Great post and I agree with Brandon. Eliezer, I recommend you admin a message board (I've been recommending an overcomingbias message board for a while) but I think in particular you'd thrive in that environment due to your high posting volume and multiple threads of daily interest. I think you're a bit constrained intellectually, pedagogically, and speculatively by this format.

I think I've said this before, but there is some defense that can be made for the phlogiston theorists. Phlogiston is like an absence of oxygen in modern combustion theory. The falsifiable prediction that caused phlogiston to be abandoned was that phlogiston would have mass, whereas an absence of oxygen (what it was in reality) does not.

Could evolution be a fake explanation in that it doesn’t predict anything? I’m no creationist but what your explaining in regards to phlogiston seems to have a lot of similarity to evolution. Seems to me like no matter what the data is you can put the tag of evolution on it. Now I’m no expert on evolution so don’t flame me. Just a question on how evolution is different.

What TGGP said. Also, would an AI really be better at determining the falsifiability of a theory? It seems to me that, given a particular theory, an algorithm for determining the set of testable predictions thereof isn't going to be easy to optimize. How does the AI prove that one algorithm is better than another? Test it against a set of random theories?

C of A, TalkOrigins addresses your argument.

Phlogiston is not necessarily a bad thing. Concepts are utilized in reasoning to reduce and structure search space. Concepts can be placed in correspondence with multitude of contexts, selecting a branch with required properties, which correlate with its usage. In this case active 'phlogiston' concept correlates with presence of fire. Unifying all processes that exhibit fire under this tag can help in development of induction contexts. Process of this refinement includes examination of protocols which include 'phlogiston' concept. It's just not a causal model, which can rigorously predict nontrivial results through deduction.

Eliezer, we need more posts from you elucidating the importance of optimizing science, etc., as opposed to the current, functional elements of it. In my opinion people are wasting significant comment time responding to each of your posts by saying "hey, such-and-such that you criticized actually has some functionality".

An analogous principle operates in rigorous probabilistic reasoning about causality. ... We count each piece of evidence exactly once; no update message ever "bounces" back and forth. The exact algorithm may be found in Judea Pearl's classic "Probabilistic Reasoning ...

Actually, Pearl's algorithm only works for a tree of cause/effects. For non-trees it is provably hard, and it remains an open question how best to update. I actually need a good non-tree method without predictable errors for combinatorial market scoring rules.

In response to Hopefully Anonymous, I think there is a real difference between unfalsifiable pseudosciences and genuine scientific theories (both correct and incorrect). Coming up with methods to distinguish the two will be helpful for us in doing science. It is easy in hindsight to say how obviously wrong something is, it is another to understand why it is wrong and whether its wrongness could have been detected then with the information available as this could assist us later when we do not have all the information we would wish to.

Robin: Yes indeed. If you can find a cutset for the tree, or cluster a manageable set of variables, all is well and good. I suspect this is what happens with most real-life causal models.

But in general, finding a good non-tree method is not just NP-hard but AI-complete. It is the problem of modeling reality itself.

Robin Hanson said: "Actually, Pearl's algorithm only works for a tree of cause/effects. For non-trees it is provably hard, and it remains an open question how best to update. I actually need a good non-tree method without predictable errors for combinatorial market scoring rules."

To be even more precise, Pearl's belief propagation algorithm works for the so-called 'poly-tree graphs,' which are directed acyclic graphs without undirected cycles (e.g., cycles which show up if you drop directionality). The state of the art for exact inference in bayesian networks are various junction tree based algorithms (essentially you run an algorithm similar to belief propagation on a graph where you force cycles out by merging nodes). For large intractable networks people resort to approximating what they are interested in by sampling. Of course there are lots of approaches to this problem: bayesian network inference is a huge industry.

Very interesting. In computer networking, we deal with this same information problem, and the solution (not sending the information from the forward node back to the forward node) is referred to as Split Horizon.

Suppose that Node A can reach Network 1 directly - in one hop. So he tells his neighbor, Node B, "I can get to Network 1 in one hop!". Node B records "okay, I can get there in two hops then." The worry is that when Node A loses his connection to Network 1, he asks Node B how to get there, and Node B says "don't worry, I can get there in two hops!". This causes Node A to hand his traffic to Node B, who promptly turns it around and hands it back, and thus a loop is created. The solution, split horizon, is exactly as you say here: when you learn a piece of information, record which direction you learned it, and do not advertise that information back in that direction.

Thanks for the link Davis but it does not address the issue that is brought up in the original post. The examples given in your link were "retrodictions". To quote the original post...

“Thanks to hindsight bias, it's also not enough to check how well your theory "predicts" facts you already know. You've got to predict for tomorrow, not yesterday. It's the only way a messy human mind can be guaranteed of sending a pure forward message.”

I’m not arguing that evolution is pseudoscience. I’m just saying that evolution as an explanation could makes us think we understand more than we really do. Again I am no creationist, the data clearly does not fit the creationist explanation.

Another suberb post. I learn so much from your writings.

Is phlogiston theory so much worse than dark matter? Both are place-holders for our ignorance, but neither are completely mysterious, nor do they prevent further questions or investigation into their true nature. If people had an excellent phenomenological understanding of oxygen, but called it phlogiston and didn't know about atoms or molecules, I wouldn't discount that. Similarly, it can be very useful to use partial, vague and not-completely-satisfactory models, like dark matter.

Thanks Davis... for links dear....revolution is the process which make think more advance and more typical as well....anyways the sommensta re great i enjoyed here alot coz these topics are of my interest...

http://www.perfumesofparis.net/

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