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<h1 id="post-title">Focus your Uncertainty</h1>
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<h1 id="anticipation-constraints">Anticipation-Constraints</h1>
<p>In Probability Theory, we use Random Experiments and outcomes and probabilities to model the world.</p>
<p>Our <a href>aim</a> is to maximize Predictive Power.</p>
<p>TODO: probability weight vs probability mass? (weight seems better)</p>
<p>Getting high Predictive Power - or being <em>right</em>, to put it plainly - involves putting as much probability mass on the right outcomes as possible. For this you need to <em>remove</em> probability mass from outcomes that won’t actually happen and put more weight on the outcome that will happen. This leads to a <em>focussing</em> of the probability distribution - a sharp spike around the outcome (or few outcomes you think will happen) and little or nothing around everything else.</p>
<p>This is the best case, of course. In reality, you want to make your probability distribution as sharp as possible.</p>
<p>What does this do? What are the practical benefits of getting “high Predictive Power”?</p>
<p>It pays to be certain - you can make more confident bets if you know you’re going to be right. You don’t have to <em>waste</em> time planning for scenarios that are not going to occur.</p>
<p>Also, having a sharp probability distribution makes your hypothesis more easily falsifiable?</p>
<p>Why is that a good thing? Because if you’re wrong you’ll know it immediately, and if you’re right, you can be reasonably sure that your hypothesis is a good one (it made an narrow good prediction - Bayes Theorem approves of it). Fail Faster!</p>
<h1 id="have-i-focussed-my-uncertainty">Have I focussed my uncertainty?</h1>
<p>Now we can modify our aim to be - Focus our Uncertainty. Make as narrow predictions as possible.</p>
<p>Also, if you don’t consider a Random Experiment at all, then you’re considered to be making random predictions for it - every outcome will be equally likely, leading to a flat probability distribution, which is bad news. That will lead to very low Predictive Power very quickly.</p>
<p>An ideal computer would consider all the Random Experiments for which its hypothesis makes a prediction. However, we don’t. We routinely miss the implications of our ideas until they hit us in the face (we worship this mystery under the name “creativity”).</p>
<p>So it’s useful to add another action of gathering more Random Experiments for which your hypothesis makes predictions.</p>
<p>We need to Focus our Uncertainty for the Random Experiments we already have and also seek out more Random Experiments for which our hypothesis makes predictions.</p>
<p>Making predictions for some Random Experiment is also called Anticipation-Constraining.</p>
<h1 id="anticipation-constraints-and-learning">Anticipation-Constraints and Learning</h1>
<p>What is Learning?</p>
<p>What do we mean when we want to learn from a textbook or a university course?</p>
<p>We want to know the truth about some subject - we to get high Predictive Power in that domain.</p>
<p>In other words, we want to Focus our Uncertainty in that area.</p>
<p>We want to acquire hypotheses that have high Predictive Power.</p>
<p>However, just knowing the name of hypothesis isn’t enough. Even knowing the definitions or the symbols in the formulas is not enough. If you can’t actually <em>use</em> your “hypothesis” to Anticipation-Constrain Random Experiments, then you haven’t really learnt anything.</p>
<p>Basically, every domain concerns itself with a class of problems. Economics is about problems relating to resource allocation. Physics is about problems relating to matter and energy, loosely speaking.</p>
<p>What does it mean to “solve a problem”?</p>
<p>Well, we are <em>uncertain</em> about the action that will give us the desired outcome (faster cars, tastier food, healthier bodies, whatever). These are our Random Experiments.</p>
<p>If we could know exactly which kind of car design to try out, or which combination of spices to put into our dish, or which medicines to take, we could easily gain what we want.</p>
<p><strong>Problem-solving</strong> = Predicting (narrowly and accurately) which attempt will give the desired results</p>
<p>So, that’s all a domain is: a bunch of problems that people are uncertain about (or were uncertain about) and some hypotheses that can make narrow and accurate predictions about those problems.</p>
<p>If the hypotheses (or theories or models or whatever they call it) in a domain don’t really tell you sharply and accurately what’s going to happen in the important Random Experiments, then it is a worthless domain.</p>
<h1 id="naive-realism-and-confusion">Naive Realism and Confusion</h1>
<p>However, humans aren’t very good at this kind of Anticipation-Constraining.</p>
<p>One problem is that we don’t think of all the Random Experiments - possible scenarios where we may be uncertain. This is hard in itself.</p>
<p>Things are made worse by the fact that we do sometimes think vaguely about a bunch of Random Experiments but then convince ourselves that we have Predictive Power about them when we don’t.</p>
<p>We don’t think about all the Random Experiments but we think we do. And for the few Random Experiments that we consider, we think we have accurate narrow predictions for them when we don’t.</p>
<p>This failure mode is due to our old nemesis: Naive Realism.</p>
<p>Most of the time all we have are <em>passwords</em>. Why does a light bulb glow? “Electricity”. Why does the Earth revolve around the Sun instead of just doing some other random thing? “Gravity”.</p>
<p>They don’t really let us make accurate and narrow predictions for many Random Experiments. For example, with a light bulb, what will happen if I remove the glass bulb? Will it still glow? There’s still electricity, right? What if I replace the “tungsten filament” with copper? I have no clue what will happen in these scenarios. I have a very low Predictive Power in this area - a poor understanding of what’s really going on.</p>
<hr />
<p>The only remedy for Naive Realism: empirical tests.</p>
<p>Test out your “knowledge” level. Take a bunch of problems and see if you really are able to solve them (i.e., make narrow and accurate predictions for them).</p>
<hr />
<p>So, we can easily fool ourselves into thinking that we have “learnt” some ideas when we haven’t. I go deeper into this in <a href>The Technique Trap</a>.</p>
<h1 id="what-should-i-anticipate">What should I anticipate?</h1>
<p>Think in terms of Anticipation-Constraints.</p>
<p>Ask not what you should believe, ask what you should anticipate.</p>
<p>(<a href>Make your Beliefs pay Rent in Anticipated Consequences</a>)</p>
<p>We usually try to think in terms of “definitions”.</p>
<blockquote>
<p>If a tree falls in a forest and nobody hears it, did it make a sound?</p>
</blockquote>
<p>This question has sparked heated debates. Most answers revolve around the “make a sound” part. What is the <em>definition</em> of “making a sound”, people ask?</p>
<p>One person might say that making a sound is about having sound waves in the air. Therefore, the tree did make a sound.</p>
<p>Another person might say that making a sound is about having the sensation of hearing in some brain. Therefore, the tree did not make a sound.</p>
<p>TODO: Quote the entire passage?</p>
<p>But, as Eliezer points out, do these two persons have any difference in anticipation?</p>
<p>If they capture some of the sound waves with a sound recorder, do they expect to hear something different when they replay it?</p>
<p>Take any such experiment. They have no real differences of anticipation at all.</p>
<p>This is how definitions can lead you astray. They make you <em>think</em> you have massive differences of opinion when in reality you have none.</p>
<h1 id="aim-learn-everything-important">Aim: Learn everything important</h1>
<p>We want to learn everything we need to achieve our goals. That is, we want to be able to solve our important problems (through narrow and accurate predictions).</p>
<p>We need to acquire hypotheses that make narrow and accurate predictions for important Random Experiments. Then, we need to carefully look at all the implications of our hypotheses.</p>
<p>Naive Realism thwarts us in both goals.</p>
<hr />
<p>Right now, we care about getting hypotheses that somebody else has already developed. I’ll talk about generating and testing new hypotheses in another essay.</p>
<p><strong>Central Question</strong>: How can we actually learn things?</p>
<p>The Technique Trap was about how Naive Realism prevents us from getting performance from techniques.</p>
<p>Now, just substitute “Predictive Power” and “ideas” instead of “performance” and “techniques”.</p>
<p>The Learning Trap (?) is about how Naive Realism prevents us from getting Predictive Power from ideas.</p>
<p>The same problems recur as in the Technique Trap.</p>
<p>We acquire too many “ideas” (or theories or models) and judge them by how convincing they sound rather than their empirical Predictive Power. We don’t master those ideas - we don’t interpret the instructions correctly (textbooks, lectures, etc.) and we don’t test our Predictive Power using a large variety of Random Experiments - we just <em>think</em> we have understood those ideas. Worst of all, we don’t actually <em>use</em> the ideas we have to make narrow and accurate predictions, especially when the implications are surprising and impactful. We just use the Net Benefits of the ideas we have to feel superior to others and thus go into Affective Death Spirals.</p>
<hr />
<p>How do we counteract these problems?</p>
<p>We need to be careful about what we acquire. It should have been proven to increase Predictive Power.</p>
<p>We need to actually master the ideas and not delude ourselves about our understanding.</p>
<p>Finally, we need to actually apply the ideas we have instead of keeping them locked like treasures. We need to actively seek out valuable implications that people haven’t figured out yet.</p>
<h1 id="rationalists-taboo">Rationalist’s Taboo</h1>
<p>Learning involves not just acquiring hypotheses that give good Predictive Power, but also rejecting and refining hypotheses that don’t.</p>
<p>A technique that can help us achieve the above is Rationalist’s Taboo.</p>
<p>It will help us actually learn ideas and gain Predictive Power instead of fooling ourselves.</p>
<p>Here is my version of the game.</p>
<p>Take a statement. Taboo the important words in the statement - the ones that <em>seem</em> to make sense to you. That is, replace the important verbs, nouns, and adjectives in it with nonsense words. Look at the statement again and <em>now</em> talk about what you expect to see.</p>
<hr />
<blockquote>
<p><strong>Statement</strong>: India is a democracy.</p>
</blockquote>
<p>That seems like a fine statement. India <em>is</em> a democracy. What’s there to see in this statement?</p>
<p>Well, we don’t talk in terms of definitions. We talk in terms of Anticipation-Constraints.</p>
<p>Taboo “India” and “democracy”. We can’t use those words. Let’s replace them by “Foobar” and “bazbaz”.</p>
<blockquote>
<p><strong>Tabooed Statement #1</strong>: Foobar is a bazbaz.</p>
</blockquote>
<p>How’s that statement looking now?</p>
<p>Now that we’ve got the apparently-meaningful stuff out of the way, let’s look at what the statement actually <em>predicts</em>.</p>
<p>First, we need to list out the Random Experiments that this statement is talking about. Then, we need to see what outcome it narrowly predicts - where it puts its probability mass. It can be helpful to look at what it <em>forbids</em> - what it says will definitely not happen.</p>
<p>It says Foobar is a bazbaz. So, Foobar will have the characteristic of bazbaz if we go and check it out.</p>
<p>Remember, we don’t care about the <em>definition</em> of a democracy, we care about what it tells us to anticipate.</p>
<p>So, the person talking about the “democracy” can’t hide behind the definitions from Wikipedia or wherever. He has to stick his neck out and say what specifically he expects to see.</p>
<p>By democracy, maybe he’s only talking about how the head of the state is appointed. Maybe he predicts that the head will be decided through popular elections, and <em>not</em> based on birth or the vote of a group of elites. Elected through popular elections; any other way is <em>forbidden</em>.</p>
<p>(Note that “head of the state” needs Tabooing, but it’ll do for now.)</p>
<p>So, India’s head of state will decided through popular elections, and not any other way (in normal circumstances).</p>
<p>That seems a clear enough sentence, right?</p>
<p>Wait a minute. What is “India”? Wikipedia might give us the definition, but that’s not what we want. We want the Anticipation-Constraints.</p>
<p>So, the head of state in New Delhi - the Prime Minister - will be decided through popular elections <em>conducted where</em>?</p>
<p>What is the narrow prediction here? Where does he predict the elections <em>will</em> be conducted and where it definitely <em>won’t</em>?</p>
<p>Let’s say that from all the villages and towns and cities within the boundaries as laid down in the official map, the votes will be counted; and definitely will not be counted from any other places other than these.</p>
<p>This is just one particular Random Experiment (how is the head of the state appointed?). But already we can see how narrow it is. It talks <em>only</em> about the elections and the places where votes are accepted. It doesn’t talk about what a great country India is or how beautiful the system of democracy is or how fair and just the system is, none of that.</p>
<h1 id="questions-of-fact">Questions of Fact</h1>
<p>When you start talking in terms of Anticipation-Constraints, not definitions, all questions become <em>questions of fact</em>. Much of the moral, subjective tone drains out of conversations.</p>
<p>India is a “great country”? What exactly do you mean? No. What exactly do you <em>anticipate</em> to see?</p>
<p>Do you expect to see it near the top of the global list of countries by GDP (per capita, nominal) as published by the UN, and <em>not</em> in the middle or in the bottom? Do you expect it to have less than 1% of its population below $2 per day of income? Do you expect it to have won the Cricket World Cup in recent times and to have not lost horribly?</p>
<p>It all boils down to questions of fact. There is no <em>vague</em>, <em>mysterious</em> essence of India that is “great”. You can pin down <em>exactly</em> what is true.</p>
<p><strong>TODO</strong>: This is what PG talks about in “What you can’t say”. He says that instead of saying whether something is “X-ist” (racist, communist, etc.), ask if it is true. It is a question of fact. It is either true or not. If it is true (i.e., if it makes accurate predictions), then you shouldn’t try to obscure it by calling it names. And if it isn’t true, then don’t even bother trying to argue about the names, just point out the facts.</p>
<blockquote>
<p><strong>TODO</strong>: Understanding is the first step…</p>
</blockquote>
<p>If it is true but not desirable, you can work to change it. But first, you must accept what is true.</p>
<blockquote>
<p>Litany of Gendlin. - What is true is already so</p>
</blockquote>
<p>This applies even to seemingly subjective personal questions. Is this a good movie or not? Well, I like it and that’s all that matters.</p>
<p>No. We need to taboo “like”. What do you really anticipate when you watch this movie?</p>
<p>Will you laugh a lot? That is a question of fact - it can be measured.</p>
<p>Will you forget your worries? That is a question of fact - we can measure your stress level, and even your self-reported well-being during or after the movie (assuming you don’t lie).</p>
<p>Will you get a warm feeling inside when others say they like this movie too? That too is a question of fact. Your brain is part of this world. Your emotions can be measured too.</p>
<blockquote>
<p>I don’t like that guy.</p>
</blockquote>
<p>If you mean you get a bad feeling when you think about him, cool, that’s an anticipation.</p>
<p>If you mean that he is “bad for you” somehow, you need to be more specific. How do you anticipate that he will be bad for you? What do you forbid? Do you expect him to actively take decisions that will hurt you (and <em>not</em> take any decisions that will benefit you)?</p>
<h1 id="taboo-important-topics">Taboo important topics</h1>
<p>Check whether it gets you out of the Learning Trap.</p>
<p>Also, if you predict this, you must <em>forbid</em> that.</p>
<p>Don’t just ask what it <em>implies</em>, ask what it forbids.</p>
<p><strong>Important</strong>: Ask everyone everywhere, “What do you forbid?”</p>
<p>The strength of your theory is not what you can explain but what you <em>can’t</em>.</p>
<h1 id="testing-the-learning-trap-hypothesis">Testing the Learning Trap Hypothesis</h1>
<p>This hypothesis claims that you can actually get more Predictive Power by thinking in terms of Anticipation-Constraints.</p>
<p>In fact, it forbids the possibility that you can learn without doing so. (Really?)</p>
<p>Prove it.</p>
<hr />
<p>This whole notion of “Think in terms of Anticipation-Constraints” is very vague. Let’s be more specific.</p>
<p><strong>Taboo Algorithm</strong>:</p>
<ul>
<li><p>List out the Random Experiments for that this hypothesis apparently constrains.</p></li>
<li><p>For each Random Experiment, ask what it predicts narrowly and what it <em>forbids</em>.</p></li>
</ul>
<p>You may list out a class of Random Experiments instead of talking about individual ones, if they have things in common.</p>
<p>What would that look like? I don’t think you’re being clear here. I don’t think you or anyone has ever used this “technique” before. Beware of the Technique Trap, Mr. SPK.</p>
<p>Use the Counter, SPK. Actually use your above “technique” 25 times before you talk about it.</p>
<h1 id="tackling-the-learning-trap">Tackling the Learning Trap</h1>
<p>There are two things we want to tackle:</p>
<ul>
<li><p>Actually learn stuff</p>
<p>If someone has Predictive Power in an important area, you need to get that too.</p>
<p>Learn a theory or hypothesis that someone else and be able to predict things just as well as they do.</p>
<p>You should be able to solve all the problems they are able to solve.</p>
<p>If you study an introductory economics textbook, you should be able to solve all the basic supply and demand problems and marginal thinking problems.</p></li>
<li><p>Getting new Predictive Power using existing hypotheses</p>
<p>See consequences that have others have missed. Make non-obvious connections.</p>
<p>Recognize when an old hypothesis tells you useful things about a new problem you’re stuck on.</p>
<p>After studying the economics textbook, you should be able to see how Scarcity Power allows your favourite movie stars to charge more than run-of-the-mill actors. TODO</p>
<p>Squeeze maximum juice out of existing hypotheses.</p></li>
</ul>
<p>So, get the Predictive Power other people have and then get some more.</p>
<p><strong>Note</strong>: We’ll tackle the problem of coming up with new and improved hypotheses in another essay.</p>
<p>Learning = getting the Predictive Power other people have</p>
<p>Applying what you’ve learned = getting some more Predictive Power using hypotheses you have</p>
<h1 id="other-stuff">Other stuff</h1>
<p>100% probability exactly. Not more.</p>
<p>Conservation of Expected Evidence.</p>
<p>Hindsight Bias - you think your theory put more probability weight on it than it did.</p>
<p>Maybe talk about Making History Available;</p>
<p>Maybe Science as Curiosity-Stopper - <em>you</em> need to know the answers, <em>you</em> need to be predict and forbid outcomes. So what if the answer is out there on Wikipedia somewhere? Doesn’t matter. <em>You</em> need to know.</p>
<h1 id="notes">Notes</h1>
<ul>
<li><p>Practice being in situations where you have <em>no clue</em> what’s going on</p>
<p>Where you really have no idea what’s going to happen next.</p>
<p>Like when I had some symptoms - was it this disease? That one? was it caused by this? Or that? Or something else? What will happen if I change this behaviour? Will it go away? No idea!</p>
<blockquote>
<p><a href>TODO: Tools shatter in your hand… quote</a></p>
</blockquote></li>
</ul>
<h1 id="essay-checklist">Essay Checklist</h1>
<h2 id="specify-surprise">Specify + Surprise</h2>
<p><strong>Important</strong>: Difference between my hypothesis and conventional hypotheses. Specified explicitly.</p>
<ul>
<li><p>Technical explanations (aka narrow predictions)</p>
<p>This is where the game is.</p>
<p>Why? Pays off.</p>
<p>Falsifiability. This is nothing but learning <em>quickly</em>.</p>
<p>TODO: Need examples.</p>
<p>TODO: What are NOT narrow predictions? Technical explanation of technical explanation. This, not that.</p>
<p>Why non-technical predictions / opinions suck!</p></li>
<li><p><strong>Problem-solving</strong> = Predicting (narrowly and accurately) which attempt will give the desired results</p></li>
<li><p>Learning = Making narrow and accurate predictions for the problems in a domain</p>
<p>i.e., being able to solve the problems in that domain.</p></li>
<li><p>Naive Realism => We think we have considered all the Random Experiments, and we think we Anticipation-Constrain them when we don’t.</p></li>
<li><p>The Learning Trap Hypothesis</p></li>
<li><p>No definition, only Anticipation-Constraints. What do you forbid?</p></li>
<li><p>It’s all a question of fact.</p></li>
<li><p>The Taboo Algorithm</p></li>
</ul>
<h2 id="organize">Organize</h2>
<p>Summaries</p>
<ul>
<li><p>Iteration #1: Technical explanations rock for predicting accurately and learning quickly (falsifiability). We don’t catch all that our ideas imply and thus get poor solutions.</p></li>
<li><p>Iteration #3: You have learnt a topic if and only if you’re able to make accurate and narrow predictions for the problems in that area. Because of Naive Realism, we don’t think about all the Random Experiments we need to but we think we do. And for the few that we do consider, we think we have accurate narrow predictions when we don’t.</p></li>
<li><p>Iteration #4: We fall into the Learning Trap where we acquire ideas indiscriminately, neglect to master them, and then fail to apply them at all because we are misled by Naive Realism. Rationalist’s Taboo.</p></li>
<li><p>Iteration #6: Talk in terms of Anticipation-Constraints, <em>not</em> definitions. Taboo the important parts of the sentence. Don’t ask just what it implies, ask what it forbids. Then, all questions becomes questions of <em>fact</em>.</p></li>
<li><p>Iteration #8: Aim in Learning - Get the Predictive Power other people have and then get some more.</p></li>
</ul>
<div class="info">Created: June 12, 2015</div>
<div class="info">Last modified: September 28, 2019</div>
<div class="info">Status: finished</div>
<div class="info"><b>Tags</b>: Anticipation-Constraints</div>
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