LESSWRONG
LW

Learn about first and second derivatives and finding a maximum of a function. Then think about how you might find a maximum if you can only make little hops at a time.

Learn a little linear algebra (what a matrix inverse, determinant, etc. is). Understand the relationship between solving a system of linear equations and matrix inverse. Then think about what you might want to do if you have more equations than unknowns (can't invert exactly but can find something that's "as close to an inverse as possible" in some sense). A huge chunk of stuff that falls under the heading of "statistics/machine learning/neural networks/etc" is basically variations of that idea.

Some common computer science algorithms

Read Structure and Interpretation of Computer Programs: one of the highest concept/page density for computer science books.

Important algorithmic ideas are, in my opinion: hashing, dynamic programming/memoization, divide and conquer by recursion, splitting up tasks to be done in parallel, and locality (thin... (read more)

7coyotespike11y

+1 for Structure and Interpretation of Computer Programs (aka SICP, aka "the wizard book") - this is a legendary programming book. Here is an interactive version: https://xuanji.appspot.com/isicp/. I also agree on the important algorithmic ideas, with one addition: algorithmic analysis. Just as you can describe the movement of the planets with a few simple equations, and that's beautiful, you can describe any sequence of steps to finish a task as an algorithm. And you can mathematically analyze the efficiency of that sequence: as the task gets larger, do the number of steps required to finish it grow linearly, quadratically, logarithmically (we hope)? This is a broadly applicable and powerful idea, since pretty much everything (even learning) involves a sequence of steps or process.

2Capla11y

and Will you make top level comments for both of these, so that people can vote on them? (I can do it, but I figure you should get any karma from the upvotes)

4coyotespike11y

How flattering; I've now done so. Also, I very much like your approach to learning math by grounding it in concrete subjects. Many people say they learned calculus best by learning it alongside physics, since calculus appears much more concrete when you look at the velocity and arc of, say, a fired cannonball. Finally, here's an excellent article from Barbara Oakley, who learned math starting about age 26 after getting out of the Army. She's now an engineering prof, and teaches a MOOC called "Learning How to Learn" (I have not taken it, but I have reviewed the topics, and it appears to hit all the correct points): http://nautil.us/issue/17/big-bangs/how-i-rewired-my-brain-to-become-fluent-in-math-rd

2Capla11y

Thank you!

[-]Capla11y180

Basic statistics

1gedymin11y

Be careful here. Statistical intuition does not come naturally to humans - Kahneman and others have written extensively about this. Learning some mathematical facts (relatively simple to do) without learning the correct statistical intuitions (hard to do) may well have negative utility. Unjustified self confidence is an obvious outcome.

2Capla11y

Can you elaborate? What is the difference between "mathematical facts" and "statistical intuitions"? Can you give an example of each?

3ChristianKl11y

If you take the average introductory statistics textbook it tells you thinks that are true for normally distributed data. If you are faced with a real world problem that doesn't follow the normal distribution and try to apply statistical techniques proven to work for normal distributed data you are getting mistakes. Being good at statistical modelling means that you have an idea of what assumptions you can make about a certain data set and the kind of errors you will get when your assumptions don't match reality.

1gedymin11y

Example of a mathematical fact: a formula for calculating correlation coefficient. Example of a statistical intuition: knowing when to conclude that close-to-zero correlation implies independence. (To see the problem, see this picture for some datasets in which variables are uncorrelated, but not independent.)

4Lumifer11y

Not sure why are you calling this "intuition". Understanding that Pearson correlation attempts to measure a linear relationship and many relationships are not linear is just statistical knowledge, only a bit higher level than knowing the formula.

0Capla11y

Something to consider: what's a good field in which to learn basic statistics (sticking with the "learn by doing, when possible" theme) ?

4IlyaShpitser11y

If you want to learn statistics by doing, try to do what gwern does.

5othercriteria11y

Or do the complete opposite. The impression I get of gwern is that he reads widely, thinks creatively, and experiments frequently, so he is constantly confronted with hypotheses that he has encountered or has generated. His use of statistics is generally confirmatory, in that he's using data to filter out unjustified hypotheses so he can further research or explore or theorize about the remaining ones. Another thing you can do with data is exploratory data analysis, using statistics to pull out interesting patterns for further consideration. The workflow for this might look more like: * Acquire (often multivariate) data from another researcher, source, or experiment. * Look at its marginal distributions to check your understanding of the system and catch really obvious outliers. * Maybe use tools like mixture modeling or Box-Cox transformation to clarify marginal distributions. * Use statistical tools like (linear, logistic, support vector, etc.) regression, PCA, etc., to find patterns in the data. * Do stuff with the resulting patterns: think up mechanisms, do confirmatory analysis, check literature, show them to other people, etc. A lot of what you get out of this process will be spurious, but seeing hypotheses that the data seemed to support go down in flames is a good way to convince yourself of the value of confirmatory analysis, and of tools for dealing with this multiple testing problem. I remember Gelman saying useful stuff like this, but it's been a while since I read that post so I might be mischaracterizing it. (Ilya, you know all of this, surely at a deeper level than I do. I'm just rhetorically talking to you as a means to dialogue at Capla. Gwern, hopefully my model of you is not too terrible.)

2Capla11y

I want to do that. Tell me how. I think I already read widely (at least compared to my meat-space peers and possibly compared to the typical LW reader), but I can do better. I am frequently complimented for asking creative questions, coming up with unusual ideas and solutions (again, in comparison to non-rationalists), but if there are ways to do this better, I want to hear them. However, I want to make regular experimentation a part of my life and don't really know how. I'm interning with a psych lab, and hope to work with some behavioral economists who run field-experiments. How do I gain proficiency with experimental methods and build the habit of running simple experiments regularly? I suppose that there's a certain kind of phenomenon that to the educated mind is automatically flagged as ripe for experimentation (I'm thinking of Feynman's curiosity about the ants in his room or Harry James Potter-Evans-Verres testing to find out what the optimal way to fight is, prior the the first battle), but I don't have that intuition, yet. Suggestions?

1Lumifer11y

That's usually called "data mining" and is a popular activity. Unfortunately many people think that's all they need and stop before the confirmatory phase.

2Capla11y

What does gwern do?

6Richard_Kennaway11y

This.

3AABoyles11y

Political Science! Since you're interested in election dynamics, 538's description of its model is a good place to get a punches-pulled look at how a statistical model is constructed.

0satt11y

I'll side-step the "field" part of the question and instead point to an undergraduate lecture course on data analysis which has some online notes and a series of exercises. It's worth pointing to something specific one could immediately start working on, because I think people underrate the trivial inconvenience of not knowing which specific book or course to consult. The course linked is not that basic, admittedly, but even if it's too advanced it should help highlight specific keywords & terms to look up on Google, Wikipedia, or textbooks.

[-]Capla11y170

Probability theory

[-]Capla11y100

Game theory

2TrE11y

Definitely involves math (since you asked).

[-]Capla11y80

Decision theory

5Vaniver11y

What do you mean by decision theory? One of the options I would call 'decision analysis,' and doesn't require much more than algebra and a basic understanding of probabilities. I wrote an introduction to it, and my current book recommendation on the subject is Decisive by the Heath brothers, and I remember it being much more about the psychology of decision-making (and practical heuristics you can apply) than math. A second option is "what's the difference between CDT and EDT?", but that's something you shouldn't really approach until you understand causal graphs, which you shouldn't really touch until you understand probabilities and graphical networks (like Bayes nets). A third option is "what are those exotic things they talk about on LW like TDT and UDT?", and I don't feel qualified to tell you the right way to approach that.

2Capla11y

I don't know what any of those acronyms are. I don't know much about decision theory, actually, except that at least 5 people on lesswrong think its worth learning.

3Vaniver11y

Alright; I'd recommend focusing your attention on decision analysis. The acronyms refer to 'philosophical positions' about the right way to make decisions; you are probably better off focusing on practical improvements you can make in your decision-making ability and what formal decision-making looks like (which gets called stuff like cost/benefit analysis more than it does decision theory).

[-]Slider11y70

Computer Science: recursion

[-]Capla11y60

Information theory

[-]coyotespike11y40

I also agree with Ilya on the important algorithmic ideas, with one addition: algorithmic analysis. Just as you can describe the movement of the planets with a few simple equations, and that's beautiful, you can describe any sequence of steps to finish a task as an algorithm. And you can mathematically analyze the efficiency of that sequence: as the task gets larger, do the number of steps required to finish it grow linearly, quadratically, logarithmically (we hope)?

This is a broadly applicable and powerful idea, since pretty much everything (even learnin... (read more)

[-]Capla11y40

Epidemiology

[-]Anders_H11y170

As the token epidemiologist in the Less Wrong community, I should probably comment on this.

The utility of learning epidemiology will depend critically on what you mean by the word:

If you interpret "epidemiology" as the modern theory of causal inference and causal reasoning applied to health and medicine, then learning epidemiology is very useful, so much so that I believe that a course on causal reasoning should be required in high school. If you are interested in learning this material, my advisor is writing a book on Causal Inference in Epidemiology, part of which is freely available at http://www.hsph.harvard.edu/miguel-hernan/causal-inference-book/ . For more mathematically oriented readers, Pearl's book is also great.

If you interpret "epidemiology" to mean the material you will learn when taking a course called "Epidemiology", or to mean the methods used in most papers published in epidemiologic journals (ie endless Cox models, p-hacking, model selection algorithms and incoherent reasoning about confounding), then what you will get is a broken epistemology with negative utility. Stay far away from this - people who don't have the time to learn proper causal reasoning are better off with the heuristic "if it is not randomized, don't trust it" . This happens to be the mindset of most clinicians, and appropriately so.

[-]XFrequentist11y100

[Hey, I thought I was the token epidemiologist! ;) ]

I largely agree with Anders' comment (leave Pearl be for now; it's a difficult book), but there are some interesting non-causal mathy epidemiology topics that might suit your needs.

Concretely: study networks. Specifically, pick up the book Networks, Crowds, and Markets: Reasoning about a Highly Connected World (or download the free pdf, or take the free MOOC).

It presents a smooth slope of increasing mathematical sophistication (assuming only basic high school math at the outset), and is endlessly interesting as it gently builds and extends concepts. It eventually touches many of the topics you've indicated interest in (game theory, voting, epidemic dynamics, etc), giving you some powerful mathematical tools to reason with. Advanced sections are clearly marked as such, and can be passed over without losing coherence.

And hey, if the math in the advanced sections frustrates your understanding... that's basically what you've said you want!

0IlyaShpitser11y

If I was once employed by a Dept. of Epidemiology does that also make me the token epidemiologist? :) Epidemiology is defined to be things done by people in Departments of Epidemiology, correct?

0Lumifer11y

That makes you an expert on epidemiology, duh :-)

[-]TheMajor11y30

Does anyone with more experience than me foresee problems with this approach? Has this been tired before? How did it work?

I foresee (minor) problems. Nothing too serious, but it might be useful to be aware of the existence of problems with this approach. Most notably:

Many (sub)fields use only a single model out of a larger, overarching theory. Most of the times you want to skip the grand theory to get immediate results from a single model (so that's a plus for your approach), but sometimes having someone show you the similarities between different the

... (read more)

0Capla11y

Noted, but I do have the advantage of being able to ask. The next post will ask, "for each topic, formula, or problem on this list, what math do I need to know to understand and solve it?"

[-]Slider11y30

Computer Science: iteration

[-]Capla11y30

Economics optimization problems

1Vaniver11y

Microeconomics contains several useful tools for thinking about the world, but those tools are only somewhat driven by math. For example, let's consider "supply and demand." In a basic econ course, you'd focus on the math (which is basically just algebra). You'd have a increasing linear function for 'supply,' and a decreasing linear function for 'demand.' (Both represent the quantity supplied or demanded at a particular price.) You would then find the intersection of those two lines, and this is the "market price." You would then consider increasing or decreasing the supply or demand curve, and notice how this changes the intersection. In a basic econ philosophy book, you'd focus on the feedback mechanism that generates the lines discussed above. "Suppose there are more people who want to buy the good than there are sellers of the good at a particular price," the armchair philosopher would say, "and one of the buyers will craftily offer to pay more than the listed price in order to secure a spot in line." This would give you a sense of why the supply line is increasing and the demand line is decreasing. The idea of what an equilibrium means, and the math underpinning their study, seems useful. But for the math you're potentially better off looking at control theory / controls engineering / feedback systems / signal processing / there might be more names I'm forgetting. I don't think macroeconomic tools are personally relevant to you, but you can learn about the models in any standard undergraduate text, and it probably won't even require calculus. Numerical optimization is only kind of in economics. This is the class of problems where you have an exact statement of what you want ('this is my objective function, these are my constraints') and it just takes a lot of pushing numbers around to figure out what the best option is. Once you know linear algebra, you can figure out the basic approaches here, but I don't think this is particularly useful unless you're em

0Capla11y

I have what I think are very strong economic intuitions: it just makes sense to me and always has. I frequently think in terms of supply and demand. Do you think I would gain a great deal of value from the explicit mathematical formulations?

0Lumifer11y

Are your "very strong economic intuitions" correct and how do you know whether they are or not?

0Capla11y

They are informed by and consistent with the books I've read. I should note that I don't know much about macroeconomics. Keynesian economics (like Marxism) baffles me. I need a Keynesian to explain to me why there should be "leakages" in the banking system.

0Lumifer11y

Are they consistent with reality?

0Capla11y

I believe so, but I don't know how to check.

1Lumifer11y

Well, on which basis do you believe so? What I am getting at is that you were informed by some books and, unsurprisingly, found your knowledge consistent with these books. But that's no guarantee of correctness. There are a lot of books which teach and advocate views that range from not quite true to quite not true. The arbiter of correctness is reality. You said that you have correct economic intuitions. So let me ask again -- why do you believe they are correct, is that only because a book told you so?

2Capla11y

Let me be clarify: When I was very young, my dad t aught me the basis of economic concepts . They made sense to me. Latter, when I took economics classes, the instruction seemed obvious. I found myself having a feeling for the principles, in contrast with others that I spoke to. For instance, my peers were astonished to learn about the huge mark-up on "fancy" water. A high school friend commented that the water is obviously not worth that much. I knew that "value" is subjective, rather than being an intrinsic property of an object, and that the price, rather than reflecting that supposed real-value, was the result of the interaction of supply and demand. The water was "worth" exactly as much as it cost, by the simple fact that people would pay for it at that price. I read other economists. They sometimes expressed ideas from new perspectives, but the fundamental ideas were the same. I took college level classes. My confidence in my economic understanding parallels my confidence in evolution: the ideas make sense to me, and they are, to my knowledge, endorsed by the professionals who work in the field, yet I don't know of a way of verifying those concepts for myself, without relying on the testimony of those experts. I discount my economic knowledge somewhat, since economics in general seems more contentious than biology, and especially the foundation of biology. Does that clarify? What should I be asking?

1Lumifer11y

Well, it's pretty easy -- try to make some real-life forecasts. There is a lot of economic activity happening all around the world and enough of it is visible and documented. Take something you have an interest in, look at the current situation, explicitly apply the economic concepts you have, create a forecast (which you will be able to verify). Then see if your forecast worked out. Make a bunch of forecasts to get some diversification.

0Capla11y

Hmm... My first thought is "prediction is a b---- and macroeconomics is less predictable than anything else. Expecting to make predictions, and have them be right, is crazy." Now, I know there's a problem with that. (lthough, subjectively, I'm not doubting my knowledge any more than I was. I was going to type "Which makes me hugely suspect of my supposed knowledge", but then I realized that's not true and I was just signaling how "rational" I am (which, to go another level into meta-space, is, I think, what I'm doing right now, by telling you that I was going to falsely signal, then realized it and restrained myself)) Making predictions. That's a good idea. I don't have anything to lose except some false beliefs or some unfounded confidence in some beliefs. What are some simple questions of which I can predict answers?

1Lumifer11y

Not necessarily macroeconomics which are hard because if you can successfully predict things in that sphere, there is usually a simple way to monetize that ability. Look at your economic intuitions. What kind of claims do they make? What do they think they can predict?

0Capla11y

That if somthign is hard to get or make it will cost more than if its easy to get or make. That some consumption is conscious consumption and provides a signalling benefit which swamps the direct material benefit. Higher order goods have a longer delay to pay-off in the form of first order goods, but the progression to higher and higher order goods and the specialization that drives it, is economic growth. People like variety in their consumption because of LMD in their utility functions. If the price of a good rises, the prices of its compliment goods will fall slightly and the prices of its substitute goods will rice slightly (since people will buy more of substitute and less of the complement and supply & demand. Economies of scale. There's no such thing as a free lunch. (If you know that diminishing marginal returns are pervasive and allow for trade between actors, pretty much all of classical economics follows by implication.)

[-]Capla11y30

The math of natural selection and evolution

1Toggle11y

Depends whether you want to go genetics, or a more theoretical approach. But complete expert-level knowledge of the subject would probably include fitness landscapes, network theory, a basic understanding of what's fashionably called 'big data' (for bioinformatics), linear algebra (including a familiarity with the dynamics of formally unsolvable systems and chaos theory), and a LOT of probability theory.

1Capla11y

Price's Equation

2othercriteria11y

That is important destination but maybe too subtle a starting point. Start with ecological models for inter-species interactions (predation, competition, mutualism, etc.) where there are more examples and the patterns are simpler, starker, and more intuitive. Roughly, death processes may depend on all involved populations but birth processes depend on each species separately. Then move to natural selection and evolution, intra-species interactions, where the birth processes for each genotype may depend on populations of all the different genotypes, and death processes depend on the phenotypes of all the different populations.

0Capla11y

Do you have a curriculum that works through these? It can either be an already existing textbook or class or just a list of concepts.

2othercriteria11y

It's been a while since I've thought about how to learn ecology, but maybe check out Ben Bolker's Ecological Models and Data in R? It would also be a decent way to start to learn how to do statistics with R.

[-]coyotespike11y20

As Ilya recommended, a great choice for programming in general is the legendary Structure and Interpretation of Computer Programs (aka SICP, aka "the wizard book"). Here is an interactive version: https://xuanji.appspot.com/isicp/. (You can find solutions to the problems here, but of course use sparingly if at all: http://community.schemewiki.org/?sicp-solutions)

If you benefit from more instruction than a solo journey through SICP, I cannot recommend highly enough MIT's Introduction to Computer Programming course, which remains one of the best ed... (read more)

[-]Capla11y20

What other sorts of math do economists use?

[-]Capla11y20

The Schrödinger equation

[-]leplen11y110

I don't think that trying to solve the Schrödinger equation itself is particularly useful. The SE is a partial differential equation, and there's a whole logic of differential equations and boundary conditions, etc. that provides context for the SE. If you're serious about trying to understand quantum mechanics, I think the concept of Hilbert space/abstract vector spaces/linear algebra in general is a bigger conceptual shift than just being able to solve the particle in a box in function space. It's also just a really useful set of concepts that makes learning things like optimization, coordinate/fourier transforms, etc. easier/more intuitive.

Until I had the wave function explained to me as some vector in a high dimensional space that we could map into x-space or p-space or Lz-space I don't think I really had a good grasp on quantum mechanics. This is anecdote not data, your mileage may vary.

2TrE11y

I'm just making a similar experience.

3TheMajor11y

Try the wave equation first? If you want to think of particles like waves it might be useful to know what a wave is. Note that you already need to have heard of a respectable chunk of calculus to solve this equation.

1Toggle11y

The Schrodinger equation itself can be understood as simply a particular instance of integral calculus. If I recall my undergrad days correctly, you didn't even need linear algebra. Once you know the calculus, quantum waveforms don't require a whole lot of additional mathematical insight.

[-]Toggle11y10

This is an interesting approach, but I am wondering if reversing the process wouldn't be more helpful. The problem is that you can't just jump from propositional logic to chaos theory, since you must first learn algebra, calculus, and linear algebra. So an unordered list of mathematical subjects has some pitfalls.

If there existed some graph of dependencies in mathematical knowledge (basically a Diablo skill tree, or Khan Academy with lower resultion), we could note on each node any applications like quantum theory, music theory, and so on. This would he... (read more)

0Capla11y

Where did you get the Khan Academy image? Could I see the full version? A dependencies graph would be of great philosophical and practical interest. After compiling a list of topics, the next step is to figure our what math underpins each one, and order them according to increasing assumed mathematical knowledge.

3coyotespike11y

Sadly, you can no longer see the full version on Khan Academy. https://khanacademy.zendesk.com/hc/en-us/articles/203353750-Where-is-the-Knowledge-Map-Star-Map-math-overview- The Exercise Dashboard is not as helpful for highlighting dependencies: https://www.khanacademy.org/exercisedashboard You may be able to find other knowledge maps; Khan wasn't the first to have the idea. I like Kaj's idea as well. I compared the curricula of several majors at MIT to come up with a core curriculum, useful across engineering, computer science, and biology.

3Kaj_Sotala11y

You could assemble a partial dependency graph by looking at the course pages of different math departments and noting which courses are listed as prerequisites for more advanced courses.

0Capla11y

Yes, although that wouldn't include the applied math that I'm looking for.

1Toggle11y

The main problem with the Khan version is that it got huge; they were subdividing things down to about the level of an individual half-hour lesson. For someone interested in wider strategic planning, something like this would be a bit more reasonable, as long as you added in the annotations. This book is also reviewed as a good way to conceptualize the macrostructure of mathematical reasoning, although I can't vouch for it personally.

[-]Artaxerxes11y10

Consider giving people an optional Karma balance sink post, so they can downvote to even out your Karma after voting on an option if they feel like it. Or not, just a suggestion.

0Capla11y

Thanks for the suggestion.

-11Capla11y

[-]Capla11y10

The math of music

2tgb11y

The book Music and Mathematics is a bit of a jumble of essays from various people. It's not a coherent whole or anything, BUT, if you are the least bit interested in this subject I would strongly you recommend reading the first essay (i.e. chapter). A lot of "music theory" mumbles around circles of fifths and chord progressing and things with vague pretenses of being math, but that chapter includes the only fundamental mathematical idea in the entire book. Conveniently that chapter, and some others, are here.

[-]Capla11y10

The math that makes reddit work

2Punoxysm11y

I am not sure exactly what you mean, but if you mean the math of its article rankings then it's important to note that the amount of very early upvotes, the subreddit a post is posted to, the name and preview thumbnail, and the time of day it is posted all have tremendous impact on its ikelihood to hit the front page. http://cseweb.ucsd.edu/~jmcauley/pdfs/icwsm13.pdf

3Capla11y

THAT is what I mean. What is that algorithm?

3Punoxysm11y

The algorithm combines recency and total upvotes. It also takes subreddit origin into account so that the totally massive subreddits don't dominate completely. All the code is open-source if you want to go really deep, but basically upvotes + time penalty.

[-]Capla11y10

The math of neural networks

0[anonymous]11y

Have you read Margaret Boden's The creative mind: myths and mechanisms? It might make studying relevant math more rewarding.

0Capla11y

Nope. What can you tell me about it?

0[anonymous]11y

I haven't read it all - got sidetracked by exams and then stuff - but the first half is pretty good. Substantial and understandable (yum-yum, like a rich stew) even to people from different fields (I'm a botanist, with maybe only a bit more than the common knowledge of brain chemistry and very vague ideas about computers.) It can be downloaded as a .pdf from http://cs.oswego.edu/~malloy/Courses/Files/COG468/mythsmechanisms.pdf Try the first chapter (In a Nutshell) and see if it goes for you.

[-]Capla11y00

I found this.

http://abstrusegoose.com/275

I want this kind of insight, or at least the low hanging fruit.

[-]Capla11y00

What are the foundations of bayesianism?

1Lumifer11y

The Bayes' theorem, of course.

[-]Capla11y00

Deriving probabilities from causal diagrams ?

[-]ChristianKl11y00

The math of governing the dynamics of political elections

The problem with focusing on the math of elections is that it often makes the person ignore politics of elections. In this video there for example the claims that First Past the Post Voting always results in a two party system. In the UK you have a third party with the Liberal Party. Canada also has multiple parties in it's parliament despite First Past the Post Voting.

2AABoyles11y

"If a field involves math, and you cannot do the math, you are not qualified to comment on that field." The "politics of elections" are explicable mathematically using formal methods like game theory. That said, it's social science, which is naturally inexact, which makes it difficult. If you want to criticize it, how about learning the math yourself?

0ChristianKl11y

I trust people who are actually successful at politics over people sitting in academia when it comes to explaining me how politics works. As far as the ability to do the math of the field goes, when I was a kid I did the d'hondt calculation to get the amount of seats particular election results would produce while being at a election party and most of the people with public offices at the party had never run d'hondt.

1AABoyles11y

OK, props to you for working through a mathematical model of an election. But I find your criticism about the ability of elected officials to be wanting: Do you expect your plumber to well-versed in the mathematics of hydrodynamics?

0ChristianKl11y

The point is that elected officials are the experts at politics. If they have no use for knowing the math, then the math is not central.

1AABoyles11y

So you're criticizing Capla's interest in models of electoral dynamics because politicians don't do it. That misses the point: electoral dynamics are mathematically explicable. They don't cause electoral outcomes, so an in-depth understanding of them is of limited value to candidates themselves. But trusting the explanations of the politicians as to why they won an election is a genuinely terrible approach to understanding anything but the politician's own beliefs.

1Lumifer11y

The campaigns of candidates employ political consultants many of whom are statisticians of the Nate Silver kind. All high-level campaigns do a lot of data mining and analysis.

0ChristianKl11y

I don't deny that there's polling and trying to predict the effect of political messaging based on statistical models. On the other hand running a campaign and doing public policy are two different things. *I would also note that I don't live in the US but in Germany, and we don't have exactly the same political system.

0Capla11y

Isn't that like saying psychology is useless since humans have "free will"? It may not be perfectly predictive, but it's still interesting and useful to know what the underlying math and incentives tend to. In any case, if there are major exceptions that deviate form the mathematical political optima, I want to know why that is.

1ChristianKl11y

The problem isn't uselessness it's that people think they understand more than they do and make a lot of silly mistakes because they are overconfident that their models matter. In particular people it makes people underrate the value of the public debate and complex coalition building and focus to much on elections as if they are the only way that public policy get's decided. Whether or not humans have free will is also arguable.

2AABoyles11y

Is your disagreement with Capla's interest in electoral dynamics, or with Political Science writ large?

0ChristianKl11y

I think plenty of people in political science departments misunderstand politics because they are in their ivory tower. On the other hand that doesn't mean that everybody in political science doesn't know what they are talking about.

2AABoyles11y

"In particular people it makes people underrate the value of the public debate and complex coalition building and focus to[sic] much on elections as if they are the only way that public policy get's[sic] decided." There's a lot more to political science than non-causal models predicting elections. Coalition-building, to borrow your example, is a particularly rich topic of study.

3ChristianKl11y

Here my core concern isn't so much political science but people from a STEM mindset trying to understand politics and then focusing their energies on easily modeled processes and thereby misunderstand the complexity of politics. If you want to know more about my position see the discussion on http://lesswrong.com/lw/krp/three_methods_of_attaining_change/ .

3AABoyles11y

Ah, the sweet smell of common ground! I definitely agree with this.

0Capla11y

It's in quotes for a reason.

[-]Capla11y00

"Sacred Geometry"

1Capla11y

I'm surprised that this is the first thing to get a point. [My surprise is flagged for consideration with regards to what it means about my model] Who else is it that even knows what sacred geometry is?

0Capla11y

It looks like the fault with my model was inferring to much from a single data point.

[-]Capla11y00

Chaos theory

[-]Capla11y00

The math of politics

3Punoxysm11y

Median Voter Theorem is a good one. If, in a two-person race, you will vote for whomever is is closest to your position, even if they're only a tiny deviation from he median towards you and their opponent is a tiny deviation away from you, then candidates are incentivized to cater as closely as possible to the median voter. Many factors complicate this in real life elections, but it's still a good concrete start.

3AABoyles11y

Applied Game Theory!

2AABoyles11y

This is mostly just applied Game Theory for theoretical political science and Basic Statistics for empirical political science, so I upvoted those instead.

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