Philippines

Article

Philippines is a recurring place in the Astral Codex Ten archive, appearing 17 times across 17 issues between May 21, 2021 and April 01, 2026. The archive places it in contexts such as “Some will cope (UK, France, Peru, Philippines)”; “In one region of the Philippines … 17 families control 78% of farmland”; “Its foils are always Thailand, Malaysia, and the Philippines”. It most often appears alongside Germany, India, China.

Metadata

Category: Places
Mention count: 17
Issue count: 17
First seen: May 21, 2021
Last seen: April 01, 2026

Appears In

- Germany (10 shared issues)
- India (9 shared issues)
- China (8 shared issues)
- France (8 shared issues)
- United States (8 shared issues)
- Brazil (7 shared issues)
- Canada (7 shared issues)
- Hong Kong (7 shared issues)
- Japan (7 shared issues)
- Russia (7 shared issues)
- Scott (7 shared issues)
- Singapore (7 shared issues)

External Links

Source Context

Recovered passages from the original issue text. When the raw archive preserved outbound links inside the source passage, they are listed directly under the quote.

Your Book Review: The Accidental Superpower

May 21, 2021 · Original source

The second half of The Accidental Superpower is filled with Zeihan’s predictions about what happens if the big thesis is right. Some states will fail, as they don’t have what’s needed to survive (Syria, Greece, Libya). Some will decentralize, as they’re in the same boat, just not as hard up (Russia, China). Some will merely decline, as they have some capacity to address challenges (Brazil, India, Canada). Some will cope (UK, France, Peru, Philippines). A few will join the US as “masters of the chaos,” as they have favorable geographies and other advantages (Australia, Argentina, Angola, Turkey, Indonesia, Uzbekistan).

Book Review: How Asia Works

June 28, 2021 · Original source

Undeveloped countries are mostly rural (for example, Korea was about 80% rural in 1950). Most people are farmers. Usually these countries are coming out of feudalism or colonialism or something and dominated by a few big landowners. In one region of the Philippines (Studwell's poster child for doing everything wrong) 17 families control 78% of farmland. Landowners hire peasants to work the land, then take most of the profit.

How Asia Works's success stories are always Japan, South Korea, Taiwan, and China. Its foils are always Thailand, Malaysia, and the Philippines. These last three countries resisted calls for land reform, sometimes violently (when a Thai economist published a study saying land reform was needed, the king responded by banning the study of economics!) But as usual, the Philippines takes last place. It passed a series of laws that had "LAND REFORM" in the title, but all managed to be completely useless. In particular, they often said that landlords had to give workers their land, or some other mutually-agreeable concession of equal value. Landlords were legally savvier than tenants, and had nearly unlimited power over them, and the Filipino government was too corrupt and useless to monitor any of this, so landlords found ways to get workers to agree to various things which were legally worker ownership but realistically meaningless. According to a 2007 survey, "seven out of ten 'beneficiaries' of land reform on the island of Negros said they were no better off than before reform". Filipino farming remains centralized and low-yield.

In contrast, Malaysia, Thailand, and the Philippines never achieved this level of growth. Studwell focuses his attention on Malaysia, the country that tried hardest. In 1981, Malaysia's new prime minister Mahathir bin Mohamed made a deliberate effort to imitate Japanese/Korean developmental success. Although he got a few things done, results could most charitably be described as "mixed". Studwell blames a few things.

Highlights From The Comments On "How Asia Works"

July 01, 2021 · Original source

Then we get the question on how widely his advice could be adopted. He briefly mentions in the Philippines section that, citing from memory, “Some people think they can do nothing, but condemning millions to poverty is no option at all” Well in some moral sense sure, but in a policy sense doing nothing is obviously an option. Elites have almost never pursued industrial policy for altruistic reasons: The success stories are all cases where elite interest lined up with the public interest. For example, South Korea was racially and ideologically homogeneous. Marshall Park in other words required industrialists to make SK rich and defended but didn’t care who they were. Studwell mentions that industrial policy failed in Malaysia in part because of affirmative action, and then ignores that point entirely. The East Asian countries that succeeded only did so because of their homogeneity allowing focus purely on industrialization, plus the fact that the US leaned heavily on them. It’s very unclear that a country could implement the Studwell program against the will of its elites. It isn’t the case that where there’s a will there’s a way. Finally, all of this requires a high quality bureaucracy, which East Asia has a long history of and the rest of the world lacks.

A negative outlook for the South Asian foils (Thailand, Malaysia, Philippines) seems unjustified considering how quickly they are growing at present despite not following the book's advice. Also they are still richer than their historically more-centrally-planned neighbors Vietnam and Cambodia. In the past 20 years, all the foils have increased their GDP per capita by at least 2.8x, which is a CAGR of at least 5.2%.

Because China, Vietnam, and Laos started so late, they don’t look great on the 1950 plot. They look a lot better on the 1990 plot, and it seems justified putting them in a separate “winner” category compared to “losers" Philippines/Indonesia/Malaysia/etc (South Korea, Taiwan, etc are already too rich to be able to grow fast by this point). If you switch the start date to 2005, then a lot of the gap closes.

Meetups Everywhere 2021: Times And Places

August 23, 2021 · Original source

CEBU CITY, PHILIPPINES (RSVP) Contact: Hampton, hampton[dot]moseley[at]gmail[dot]com Time: 2:30 PM, Saturday, September 25 Location: The Coffee Bean & Tea Leaf in IT park, across the street from The Walk. I will have a sign with ACX MEETUP on it. Coordinates: https://w3w.co/single.wisely.altitude

Inline links: RSVP, https://w3w.co/single.wisely.altitude

Open Thread 189

September 12, 2021 · Original source

1: I’m away this month visiting meetups, so expect posts here to be slightly lighter and further apart, sorry. Next on my schedule is Lisbon (Saturday 9/18 at 5) and Madrid (very tentatively 9/25 at 11), I’ll continue to provide updates. And check the master spreadsheet, where new meetups are still coming in (most recently Cebu in the Philippines).

Inline links: the master spreadsheet

Does Georgism Work, Part 3: Can Unimproved Land Value be Accurately Assessed Separately From Buildings?

December 11, 2021 · Original source

Gwartney says that when he was the assessment commissioner and chief executive officer in British Columbia, he had a staff of 690, and that this number has not changed significantly since then. British Columbia has a population of about 5 million, so that's 1 assessment officer for every 7,250 British Columbians. For context, the IRS has a staff size of 74,454, or about one IRS agent for every 4,425 Americans. I don't have data on how many property tax assessors the USA has in total, but the above slide suggests British Columbia's figure is on the high end. As for how you actually do assessments, sure, you can send out an army of assessors to value each and every property in your jurisdiction by hand. However, not only is that labor-intensive, it's also a recipe for inconsistency. Whatever method you're using to value properties needs to be consistent and standardized across all properties, so you don't have sharp discontinuities on the assessment map that are due solely to differences between Assessor Fred and Assessor Sally's personal methodologies. Thankfully, we're living in the modern age, and we have some fancy new tools at our disposal. 4. Modern Technology Georgists were doing split-rate assessments to allegedly good success long before the rise of the computer, such as J. J. Pastoriza's effort in setting up a Georgist tax regime in Houston, Texas in 1911. Today, we have spreadsheets, property value databases, GIS mapping visualizations, regression analysis, machine learning...the works. According to Gwartney, the Canadian province of British Columbia has revalued all its land and all its property on an annual basis simply by using computers and market analysis, ever since he first helped them set up their system back in 1975. Not every jurisdiction revalues their land this thoroughly and this often, but Gwartney says there is no significant technical or staffing barrier standing in the way. Gwartney has been retired for some time, so his seminar didn't cover all the latest cutting-edge techniques that have come out in the last few years. Let's look at some recent papers and see what new tools assessors have to play with. The first on my list is Land Value Appraisal Using Statistical Methods by Kolbe, Schulz, Wersing, and Werwatz (2019). This is a study on mass appraisal techniques using real estate transaction data from Berlin, Germany. It claims that not only are the results cheaper and faster to generate than those done by conventional property assessment methods, but they are also no less accurate than those done "by hand" by experts. Kolbe et al. assert that, provided you have access to high quality market transaction data, you can perform accurate and efficient mass appraisals of land values. They chose Berlin because it "has a very effective system of property transaction data collection and storage," in contrast to other parts of Germany. They cite some prior work by Almy (2014) studying Canada, the Netherlands, and the United States, suggesting that the assessment cost per property can be brought down to 20 Euros–25 times cheaper than what some other people (Fuest, et al. (2018)) assert. Given an average tax receipt of 2,000 Euros per property, this means that the assessment cost should represent only about 1% of the funds raised. Is that good? Let's take this assertion at face value for the moment and compare it to the cost of the IRS. Federal tax receipts in 2020 were $3.42 trillion, and operation costs for the IRS were $12.3 billion, or 0.36%. However, the IRS outsources most of the labor of tax preparation to the taxpayers themselves, with compliance costs estimated between $200 billion and $400 billion a year, to the delight of Intuit. Add that up and the total cost of federal tax collection to the economy is anywhere between 6-12% of the amount it raises. And what about sales tax? According to a 2006 report by PriceWaterHouseCoopers: The study finds that the national average annual state and local retail sales tax compliance cost in 2003 was 3.09 percent of sales tax collected for all retailers, 13.47 percent for small retailers, 5.20 percent for medium retailers, and 2.17 percent for large retailers So a compliance cost of 1% would be way more efficient in terms of cost collection than the other two most common forms of taxation, and taxpayers don't even have to do anything themselves, other than pay the bill. Alrighty, how about the accuracy? The authors cite two international examples, Australia and Lithuania, as among the few countries in the world that have both a Land Value Tax and statistical methods for mass appraisals. Hefferan and Boyd (2010) assert that objections to assessments from property owners in Australia are less than 1%. I'm willing to buy the improved efficiency claims just by taking a look at some methodologies. It seems reasonable that computerized records and algorithms can cut costs significantly; the real question is if you're trading off accuracy. The other papers I found on the subject are Bencure, et al (2019) in BayBay City, Philippines, Kilić, et al (2019) in Croatia, Yalpir & Unel (2017) in Konya, Turkey, and Raslanas et al. (2014) in Vilnius, Lithuania. Let's dive in and examine some methods. 5. Mass Appraisal Methods Here are some of the latest mass appraisal methods cribbed from the research papers listed above. All of these are based on taking market transaction data, plotting them out on a map, and running computations over them to estimate valuations for the properties you don't have known values for. Furthermore, all of these methods are able to value land and building values separately. Multiple Regression Analysis This paper by Yalpir and Unel out of Turkey gives a straightforward example of using Multiple Regression Analysis for land valuation. For those of you who didn't study math, let me explain regression analysis. This is a family of mathematical models where you basically take a data set, ask the question "what mathematical formula would best fit this data," choose a basic equation model, and then have a computer search for a set of coefficients that "best fit" that curve to the data with the least amount of error. The simplest example is using linear regression on a scatterplot of observed data points to fit a trend line. This is a common exercise in freshman physics and statistics classes. You can use more complicated versions of this numerical method to take a big bag of observations (real estate sales) and use "multiple regression" to tease out dependent variables (land value and improvements value) based on the independent variables (size, location, age, number of bedrooms) of your observations. In this case the team identified about a hundred different factors that can affect the price of a property: Then you create an entry for each property, fill in the values for each of those characteristics, and run it through the regressor. Take note of how many of these factors start with the words "proximity to." Each of these can be calculated automatically just by knowing where the property is on a map, and each of them is an independent contributor to the value of the property's location. The next step is to generate individual "index maps" that combine various related features into combined heat maps. Then you run everything through and see if it works. You can get the land share of the final value by combining the contributions of all the individual factors that you associate with "land," such as proximity to important things. In the verification section the authors say: As a result of the analysis, since the significance level (0.000) p <.05, corresponding to the F values in the ANOVA test, indicates that the regression analysis is appropriate and the models are significant. The criteria that make up the model account for about 85% of the market value and 15% cannot be explained for reasons such as economic, non-existent data and unearned income. Unfortunately, they don't say anything about how accurate their model is for assessing land values specifically. Otherwise, this is a pretty good example of using the Multiple Regression method for estimating the individual contributions of various factors to overall property values. Gwartney says Multiple Regression Analysis was a standard method he typically used, of which this specific paper is just one example. Nonparametric kernel regression This will be a method familiar to the programmers in the audience who have any experience with image processing algorithms. Here's an example from this old Gamasutra article: The basic idea here is to take a matrix of numbers, called a "kernel", and run that over every pixel in a source image. The kernel tells you how strongly to weight all of the source pixel's neighbors to compute a final result for that position. A simple "box blur" is a kernel where every value is 1 (meaning it averages the values of all neighboring pixels within a range). The more subtle gaussian blur illustrated above uses a two-dimensional normal distribution of values so that each pixel is most affected by those nearest to it. So let's apply the same principle to land valuations. If you have a map with lots of transaction data of pure land sales–defined as sales of either vacant land or teardown properties (where the building value is essentially zero)–then you can use a special kernel filter to smoothly interpolate land values across the region. So you basically have a smooth curve that mostly favors close-by points, tapers off a bit, and then disregards anything outside a certain distance entirely. The big assumption here is that land values change smoothly and do not change suddenly across very short distances. There are, in fact, locations with sharp jumps in value (any town with an "other side of the tracks," for instance). But for cases where we know a priori that land values change smoothly, this method is appropriate. No other prior restriction is placed on the form of the land value map, however, and this is why it's called "nonparametric." Here's an illustration. The outer box is the entire search distance that the kernel considers, and the circles represent the falloff of the curve itself. The size of the box is called the "bandwidth" and is set by the user. Everything outside of it will have zero influence on the kernel's output at any given location. This method operates on the same basic logic that I used when I hand-estimated the land value of that San Francisco house in Part I based on the value of the empty lot next door. However, it makes the whole procedure systematic. It can easily and accurately estimate the land value of a property with a big fat building on it simply by smoothly interpolating the known values of the nearby parking lots. Of course, it has limitations. First and foremost, it's a highly local operation, so if you have properties you're trying to value that don't have nearby pure land sales data, you can't really do much with this. Also, most people assume that city centers have less market transactions for undeveloped land than the countryside, as did I until I read that paper by Albouy in Part I. But in any case, this is just one method in your toolbox and might not be sufficient by itself. Its key advantage is that it works directly from true market data for land and doesn't need or want any other subjective data. In the end, basic kernel estimation just fills in the land value of unmeasured locations with a local weighted average of known locations. Nonparametric adaptive regression Kolbe, et al. build on the kernel regression method with a technique called Adaptive Weights Smoothing (AWS), which runs in several iterations and adds additional weight to any observed data points that are sufficiently close to the point being estimated. I'm not 100% sure about what all the math means, but it seems like it's basically a "smarter" version of the basic kernel method. Left: Nonparametric kernel regression, Right: Adaptive Weights Smoothing. I think the authors goofed and printed the same figure twice with different headings because they're identical if you overlay them in Photoshop. Semiparametric regression Now, the above two methods assume you have plenty of "pure" land sale records to work with. But if you're trying to work out prices in the city center, you've probably mostly got land and buildings mixed together. To do this effectively, we need more data, and this is where the "parameter" in "semiparametric" comes in. The model described in Kolbe et al. seems like a flavor of multiple regression analysis that takes the price, the location, and various characteristics of the building and feeds it into a regressor. But we've got "semi" parametric here. What does that mean? Well, if you already know how certain relationships between the data work a priori, it's better to enforce those relationships yourself rather than leave it to the computer. Here, we enforce the assumption that if two properties are right next to each other, then the value due to location is going to be essentially identical. This algorithm starts by ordering things geographically and then working out the differences in observed price by regressing on the difference between remaining property characteristics. In this method, the power of "location, location, location" is not something we're leaving to the regressor to discover by itself. Results of the Semiparametric regression method, we can see some significant differences from the simple kernel-based model. As you can see above, this gives you more detailed and likely more accurate results, and you're better able to assess the values of properties with buildings on them, even in the absence of pure land sales. This technique is more complicated and bakes in assumptions about the power of location, but otherwise doesn't assign subjective human weights to the various property characteristics. The chief human bias comes in the form of deciding which property characteristics are measured and made legible to the model in the first place. Okay great, but how accurate are the above three methods? Their main point of comparison is this thing called the "Bodenrichtwerte," or BRW. I think that means "ground-level-values" in English, and it's an expert-assessed map of land values for Berlin done the traditional way. The nonparametric kernel regression method has a correlation of 0.704 with the traditional method and has the added disadvantage that it's not able to produce estimates for the city center, only the outlying areas. Furthermore, the BRW map does show sharp discontinuities, which is another knock against the kernel method, at least for the city center. What about the iterative method? Kolbe et al. find that "the agreement between [Adaptive Weights Smoothing] land value estimates and, both, land prices and BRW land values is fairly good for all values of λ." Doing some quick checks, their values seem to be within about 85% of the BRW values. A different Kolbe et al. paper called Identifying Berlin's land value map using adaptive weights smoothing goes into more detail and claims to give "similar" values to that of the BRW. For the semiparametric method, they "found a strong positive correlation of 0.845" between their numbers and a previously expert-assessed set done using the traditional method. That sounds pretty good. It seems their margin for error is about plus or minus 15% compared to the traditional expert method. I'd like to see more direct comparisons against market transactions themselves, though, because if the prior expert assessments are wrong, then the main achievement here is improved efficiency, not accuracy. However, this method doesn't seem to be dramatically less accurate than the old way of doing things. The last three models came from the Berlin case study, where you have excellent market transaction data in an extremely wealthy and high-trust society. But what if you're trying to assess land in a developing nation with poor market transaction records, weak institutions, and widespread poverty? Innovative Land Valuation Model (iLVM) This is the particular name of the method described in Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches by Bencure, Tripathi, Miyazaki, Ninsawat, and Kim. They used BayBay City, Philippines as their case study. Whereas the previous models are very "hands-off" and let the computer work out the relationships between prices and property characteristics, here you get expert human opinion directly involved in building the model, baking in weights that directly embody judgments like "properties next to major roads are more valuable." These judgments are based on expert opinions that presumably come from observed experience but are a priori judgments nonetheless. Here, look at this big complicated flowchart. The "Analytic Hierarchy Process" in the box on the left is a particular kind of method for getting experts to set weights. The authors give this reason for using it: Despite criticism pinpointed by other scholars, the AHP remains the commonly used in many research fields and practical applications. This is because the AHP: (1) overcomes human difficulty in making simultaneous judgment among factors to be considered in the model; (2) is relatively simple as compared to other MCDA [multi-criteria decision analysis] methods; (3) is flexible to be integrated in various techniques such as programming, fuzzy logic, etc.; and (4) has the ability to check consistency in judgment After identifying a list of "factors" that can affect land value, they group them into taxonomical buckets: Note that certain factors like "Coastline" appear in multiple buckets; this captures the various influences a characteristic can have. For instance, land on the coast tends to be more economically valuable because of tourism, shipping, fishing, etc., so that goes under "economic." But land that's next to the coast is also more likely to flood, so it also goes under "environmental." And then there are various land use restrictions that apply specifically to coastal areas, so it goes under "legal" as well. In this way, a single factor like "the property is on the coastline" can have both positive and negative effects on land value (e.g., it's more economically valuable but it also might flood, and there are certain things you aren't allowed to do there). The next step is to set down some rules for how sensitive each factor is to location and distance. So here we can see that the economic benefit of being on the coast is most strongly felt if you're within half a kilometer of the ocean, but the environmental effect (e.g., risk of flooding) is most strongly felt when you're within 0.03 kilometers. And so on and so forth. Your experts help you work out all these rules. Note that for a few of these factors (such as land use and slope), you use metrics other than distance (e.g. land use classification and grade). Then you take all that stuff and assign everything a value between 0 and 5. Your team of experts then uses this table to come up with a set of weights for everything. What essentially comes out of this is a big linear equation with a bunch of coefficients for every one of your factors, which is then broadly fit to the observed market prices. When you're done, you can take any property on your list, multiply each of its characteristics by its respective weight, run that through your equation, and calculate the predicted price of the land. So how accurate is it? The authors compare it to standard Multiple Regression Analysis and claim it fares better. The Root Mean Square Error is quite a bit less than MRA. In addition, I think it's also saying that the MRA algorithm decided that only four of the factors were significant and basically ignored all the rest. By contrast, iLVM was able to maintain contributions from all the factors, because it doesn't leave that decision to the computer. I'm not 100% sure; it's not clear from the paper. The authors claim that about 67% of the variability is explained by their model, but they note that there are some areas where the model can be off by more than a factor of 1.0 in either the positive or negative direction. One thing that's kind of fun about this model is that you can make neat graphs like this that show the individual contribution of each factor: The main downside to this model is that it relies on a whole lot of subjective expert opinion and can be questioned on that basis. That said, it can be cheaply deployed in a transparent and consistent way across a large area. You can see why that's attractive for a developing nation with weak institutions and poor market transaction records; the argument is that this is a significant improvement over the former status quo. I wonder how well this model performs when you feed it better market transaction data, and how that would compare against all the others methods under identical conditions. More research is needed. Rather than drag you through a bunch more research papers, I'll just leave these others I found cited in the above studies: Killić et al. (2019) - Fuzzy expert system for land valuation in land consolidation processes

Doing some quick checks, their values seem to be within about 85% of the BRW values. A different Kolbe et al. paper called Identifying Berlin's land value map using adaptive weights smoothing goes into more detail and claims to give "similar" values to that of the BRW. For the semiparametric method, they "found a strong positive correlation of 0.845" between their numbers and a previously expert-assessed set done using the traditional method. That sounds pretty good. It seems their margin for error is about plus or minus 15% compared to the traditional expert method. I'd like to see more direct comparisons against market transactions themselves, though, because if the prior expert assessments are wrong, then the main achievement here is improved efficiency, not accuracy. However, this method doesn't seem to be dramatically less accurate than the old way of doing things. The last three models came from the Berlin case study, where you have excellent market transaction data in an extremely wealthy and high-trust society. But what if you're trying to assess land in a developing nation with poor market transaction records, weak institutions, and widespread poverty? Innovative Land Valuation Model (iLVM) This is the particular name of the method described in Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches by Bencure, Tripathi, Miyazaki, Ninsawat, and Kim. They used BayBay City, Philippines as their case study. Whereas the previous models are very "hands-off" and let the computer work out the relationships between prices and property characteristics, here you get expert human opinion directly involved in building the model, baking in weights that directly embody judgments like "properties next to major roads are more valuable." These judgments are based on expert opinions that presumably come from observed experience but are a priori judgments nonetheless. Here, look at this big complicated flowchart. The "Analytic Hierarchy Process" in the box on the left is a particular kind of method for getting experts to set weights. The authors give this reason for using it: Despite criticism pinpointed by other scholars, the AHP remains the commonly used in many research fields and practical applications. This is because the AHP: (1) overcomes human difficulty in making simultaneous judgment among factors to be considered in the model; (2) is relatively simple as compared to other MCDA [multi-criteria decision analysis] methods; (3) is flexible to be integrated in various techniques such as programming, fuzzy logic, etc.; and (4) has the ability to check consistency in judgment After identifying a list of "factors" that can affect land value, they group them into taxonomical buckets: Note that certain factors like "Coastline" appear in multiple buckets; this captures the various influences a characteristic can have. For instance, land on the coast tends to be more economically valuable because of tourism, shipping, fishing, etc., so that goes under "economic." But land that's next to the coast is also more likely to flood, so it also goes under "environmental." And then there are various land use restrictions that apply specifically to coastal areas, so it goes under "legal" as well. In this way, a single factor like "the property is on the coastline" can have both positive and negative effects on land value (e.g., it's more economically valuable but it also might flood, and there are certain things you aren't allowed to do there). The next step is to set down some rules for how sensitive each factor is to location and distance. So here we can see that the economic benefit of being on the coast is most strongly felt if you're within half a kilometer of the ocean, but the environmental effect (e.g., risk of flooding) is most strongly felt when you're within 0.03 kilometers. And so on and so forth. Your experts help you work out all these rules. Note that for a few of these factors (such as land use and slope), you use metrics other than distance (e.g. land use classification and grade). Then you take all that stuff and assign everything a value between 0 and 5. Your team of experts then uses this table to come up with a set of weights for everything. What essentially comes out of this is a big linear equation with a bunch of coefficients for every one of your factors, which is then broadly fit to the observed market prices. When you're done, you can take any property on your list, multiply each of its characteristics by its respective weight, run that through your equation, and calculate the predicted price of the land. So how accurate is it? The authors compare it to standard Multiple Regression Analysis and claim it fares better. The Root Mean Square Error is quite a bit less than MRA. In addition, I think it's also saying that the MRA algorithm decided that only four of the factors were significant and basically ignored all the rest. By contrast, iLVM was able to maintain contributions from all the factors, because it doesn't leave that decision to the computer. I'm not 100% sure; it's not clear from the paper. The authors claim that about 67% of the variability is explained by their model, but they note that there are some areas where the model can be off by more than a factor of 1.0 in either the positive or negative direction. One thing that's kind of fun about this model is that you can make neat graphs like this that show the individual contribution of each factor: The main downside to this model is that it relies on a whole lot of subjective expert opinion and can be questioned on that basis. That said, it can be cheaply deployed in a transparent and consistent way across a large area. You can see why that's attractive for a developing nation with weak institutions and poor market transaction records; the argument is that this is a significant improvement over the former status quo. I wonder how well this model performs when you feed it better market transaction data, and how that would compare against all the others methods under identical conditions. More research is needed. Rather than drag you through a bunch more research papers, I'll just leave these others I found cited in the above studies: Killić et al. (2019) - Fuzzy expert system for land valuation in land consolidation processes

Inline links: Identifying Berlin's land value map using adaptive weights smoothing, Development of an Innovative Land Valuation Model (iLVM) for Mass Appraisal Application in Sub-Urban Areas Using AHP: An Integration of Theoretical and Practical Approaches, https://substackcdn.com/image/fetch/$s_!3CR3!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F0489e086-69ae-4840-b658-59fee6b3af44_2000x1672.png, https://substackcdn.com/image/fetch/$s_!fA0K!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F5d39769d-46e2-4891-92aa-cb3766068204_2000x978.png, https://substackcdn.com/image/fetch/$s_!phFK!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F5d1a519c-93d7-4bed-9577-7478fb239bca_1968x3548.png, https://substackcdn.com/image/fetch/$s_!XtLN!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Ff59cb148-e0da-456b-b205-973e04239be7_587x647.png, https://substackcdn.com/image/fetch/$s_!My3b!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fee7b484b-3be8-4363-bcb8-1cb4fb4a7c01_661x655.png, https://substackcdn.com/image/fetch/$s_!SqQA!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2F8a84b431-1250-427e-a67a-b3e2b8a3c0dd_896x623.png, https://substackcdn.com/image/fetch/$s_!qPOz!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fbucketeer-e05bbc84-baa3-437e-9518-adb32be77984.s3.amazonaws.com%2Fpublic%2Fimages%2Fb71526d0-736c-45d7-ad14-36e5670f78ab_1153x881.png, Killić et al. (2019)

Almy (2014) - Valuation and Assessment of Immovable Property Not to mention Fundamentals of Mass Appraisal, a literal textbook published by the IAAO, written by Gloudemans and Almy in 2011. I've only scratched the surface here. There are a whole lot more methodology papers out there, and this is just a sample of the ones I happened to come across. They seem to fall into either "hands-off" or "hands-on" approaches, depending on how much direct human judgment you want to bake into the system. So, can we accurately assess land and improvements separately? I think it's quite plausible but not a slam dunk. That said, if the objection is, "valuing land separately from improvements is fundamentally impossible, and we can never get better at it, so we shouldn't try," I think that's plainly ruled out. We clearly have a variety of methods at our disposal that seem reasonably accurate. Each of them has particular strengths and weaknesses, and each directly addresses shortcomings of prior methods. All of this implies that this is something we can continue to improve at. The big questions are whether we've already arrived at "good enough" and how tight our error tolerances need to be. And the operative phrase very much is "good enough." I don't know of anywhere in the world that currently has a 100% LVT policy, let alone an 85% LVT. The lower your LVT, the greater your margin of error becomes for not taxing more than the true land value. I know plenty of Georgists who would be ecstatic if they could get a 75% LVT, or even a 50% LVT, implemented in their area. Now, just because these assessment methods are available doesn't mean they're actually being used. Not everyone has Ted Gwartney as their assessor. Plenty of counties in my local area exclusively use the cost approach and will even apply a blanket "neighborhood factor" multiplier to up-assess swiftly appreciating areas. However, they apply that multiplier to the buildings rather than the land, which feels exactly backwards. The assessor hasn't raised the value of my land in years, while the assessed value of my house (which I am eminently qualified to tell you is an ever-degrading money pit) somehow continues to go up. Good assessment depends on having well trained staff, up-to-date methodology, and access to high quality market transaction data. I'm convinced, based on these papers and the IAAO's surveys, that assessment doesn't require a huge army of assessors poring over every aspect of citizens' properties. Furthermore, plenty of places already have property tax systems in place and are already paying the full cost of property assessments and property tax collection. Many of the methods described above seem capable of reducing property assessment costs by focusing on the land first and foremost and letting the building's value fall out as a residual, as Ted Gwartney insists. The cost also seems like something that, done properly, is only going to come down over time as fewer assessors are required. Another option is to keep staff sizes the same but use the emerging productivity gains to increase the frequency and quality of assessments. It also seems clear to me that Land Value Taxation is not more invasive and expensive than income and sales tax when you factor in the cost of compliance (not to mention the deadweight loss imposed on the economy). Countries that have implemented Land Value Taxes, such as Denmark, are already seeing some of the claims of Georgism borne out, as we discussed in Part II. This suggests to me that modern methods are probably "good enough," so long as assessors are well trained, abiding by current best practices, and able to access good market data. Given that Astral Codex Ten is a blog where ideas as lofty as full brain uploading, superhuman AI, and biological immortality are frequently discussed in earnest, it doesn't seem outlandish to suggest that human beings can probably use math and science to get better at estimating the market value of land relative to buildings. Conclusion By George, Unimproved Land Value can (probably) be accurately assessed. 6. Conclusions & Next Steps This concludes my three part series on the most common objections to Georgism. By George, the evidence has convinced me of three things: ✅ Land is a really big deal ✅ Land Value Tax cannot be passed on to tenants ✅ Unimproved Land Value can (probably) be accurately assessed I humbly submit that the case for Georgism survives a summary dismissal and can move on to a trial of the particulars. So where do we go from here? In the course of writing this series, I found a few subjects that someone should just go ahead and test already. Obviously, this would require research funding and smart people willing to do the work (hey, a guy can dream). These subjects are: 6.1. Assessment methods A lot of the methodology papers I read test one or two methods at a time in a particular case study. What I couldn't find was a study that tests every major mass appraisal method in one big cross comparison study, all in the same physical location using the same dataset. If we had this, we could get a better sense of their strengths and weaknesses without wondering what differences are due simply to one study being in Germany and the other in the Philippines. It seems the necessary ingredients are: An ideal test location with excellent property records and (ideally) a history of quality land value assessment and/or Land Value Tax

Inline links: Almy (2014), Fundamentals of Mass Appraisal

Predictions For 2022

February 01, 2022 · Original source

VOX PREDICTIONS 1. Democrats will lose their majorities in the House and Senate (95%): SELL TO 90% 2. Inflation in the US will average under three percent (80%): HOLD 3. Unemployment in the US will fall below four percent by November (80%): SELL to 60% if they mean in November, otherwise hold 4. Supreme Court will overturn Roe v. Wade (65%): SELL to 60% 5. Stephen Breyer will retire from the Supreme Court (55%): N/A 6. Emmanuel Macron will be reelected president of France (65%): HOLD 7. Jair Bolsonaro will be reelected president of Brazil (55%): SELL to 50% 8. Bongbong Marcos will be elected president of the Philippines (55%): BUY to 60% 9. Rebels will not capture Addis Ababa (55%): N/A 10. China will not reopen its borders in the first half of 2022 (80%): BUY to 90% 11. Chinese GDP will continue to grow for the first 3/4 of the year (95%): SELL to 90% 12. 20% of US kids between 0.5 and 5 years old will get at least one COVID vaccine by year's end (65%): HOLD 13. WHO will designate another Variant Of Concern by year's end (75%): HOLD 14. 12 billion COVID shots will be given out globally by 11/2022 (80%): HOLD 15. At least one country will have less than 10% of people vaccinated with two shots by 11/2022 (70%): BUY to 95% 16. A psychedelic drug will be decriminalized/legalized in at least one more US state (75%): HOLD 17. AI will discover a new drug promising enough for clinical trials (85%): HOLD 18. US govt will not renew the ban on funding gain-of-function research (60%): HOLD 19. The Biden administration will set the social cost of carbon at $100/ton or more (70%): HOLD 20. 2022 will be warmer than 2021 (80%): HOLD 21. Kenneth Branagh's Belfast will win Best Picture (55%): SELL to 30% 22. Norway will win the most medals at the 2022 Winter Olympics (60%): HOLD

Meetups Everywhere 2022: Times & Places

August 26, 2022 · Original source

BRISBANE, AUSTRALIA Contact: Jarred Filmer, jarred[dot]filmer[at]gmail[dot]com Time: Saturday, September 10, 7:00 PM Location: 52 McCaul Street Taringa (house) Coordinates: 5R4JFXXQ+P8 Event link(s): LessWrong, Facebook event Group info: We used to meet once a month years ago, but now just meet whenever there's a Meetups Everywhere :) Notes: Snacks will be provided but dinner will not be, would recommend eating before you come CANBERRA, AUSTRALIA Contact: Andy Bachler, Andy[dot]Bachler[at]gmail[dot]com Time: Wednesday, August 31, 5:30 PM Location: Badger & Co pub at ANU. Central location, parking free after 5pm, might be loud, sorry! Coordinates: 4RPFP4FC+34 Event link(s): LessWrong, Eventbrite Notes: Parking area just to the north of the pub, over the river, is free after 5pm! GOLD COAST (SOUTH), AUSTRALIA Contact: Lerancan, lerancan[at]gmail[dot]com Time: Sunday, September 11, 2:00 PM Location: A picnic table, Wyberba Street Reserve, Tugun Coordinates: 5R3MVF5W+555 Event link(s): LessWrong Notes: Email me in case of bad weather/you can't find me/you can't make that time etc. MELBOURNE, AUSTRALIA Contact: Ryan, xgravityx[at]hotmail[dot]com Time: Friday, September 2, 6:00 PM Location: Beer Deluxe Federation Square Coordinates: 4RJ65XM9+3Q Event link(s): LessWrong, Facebook event Group info: We're officially the Less Wrong Melbourne social meetup group, though our members include the broader rationalist community. We meet once a month for casual discussion (and beers for those so inclined). Please join our Facebook group to see the meeting invite; there you will see a WhatsApp group link - please join that group too to ensure timely updates in case of changes (Facebook notifications don't work reliably for this). Notes: Please RSVP to the meeting invite on the Facebook group so that I can make an appropriate booking. PERTH, AUSTRALIA Contact: Madge, madgech[at]gmail[dot]com Time: Sunday, September 25, 2:00 PM Location: Russell Square, Northbridge, corner of Shenton and Aberdeen St. There will be some sort of ACX meetup sign. Coordinates: 4PWQ3V34+W6 Event link(s): LessWrong, Facebook event Group info: I run one meetup per year, if someone else wants to take over please do Notes: Please RSVP on LessWrong or Facebook SYDNEY, AUSTRALIA Contact: Eliot, Redeliot[at]gmail[dot]com Time: Thursday, September 15, 6:00 PM Location: City of Sydney rsl, lvl 2 in the fishbowl Coordinates: 4RRH46F4+983 Event link(s): LessWrong, Meetup.com Group info: We meet monthly WOLLONGONG, AUSTRALIA Contact: Jason, jason[dot]bowkettblogs[at]gmail[dot]com Time: Saturday, September 3, 12:00 PM Location: UOW Library Coordinates: 4RQGHVVH+69 Event link(s): LessWrong CHENGDU, CHINA Contact: Alex, acx[dot]chengdu[at]gmail[dot]com Time: Thursday, September 15, 7:00 PM Location: Chef Wenwu Hot & Spicy Jianghu Food (Yulin store)/文武大厨·热辣江湖菜(玉林店). I (a foreigner) will be wearing a green shirt. Coordinates: 8P26J3C5+462 Event link(s): LessWrong Notes: Please RSVP at the above email address, I will give you my Wechat contact if you're interested in attending. Open to time/date/location changes, so let me know if the proposed event doesn't work for you! Can be a bilingual event; all welcome. 有双语交流的可能性。如果想来的话，请提前发给我个电子邮件。 HONG KONG Contact: Nathan, nathan[at]xevarion[dot]org Time: Saturday, September 10, 1:00 PM Location: The Catalyst, 2 Po Yan Street, Sheung Wan. Big wooden door. Coordinates: 862M74PW+6XP Event link(s): LessWrong BANGALORE, INDIA Contact: Nihal, propwash[at]duck[dot]com, Discord: propwash#4648 Time: Sunday, September 18, 4:00 PM Location: Matteo Coffea, Church Street Coordinates: 7J4VXJF4+PR Event link(s): LessWrong Group info: We're the longest active group in Asia — we've been meeting monthly for the last 4 years, discussing ACX posts, LW content with a diverse and friendly group of people. Check our website for more info. Notes: Please RSVP on LessWrong to help me be better prepared. HYDERABAD, INDIA Contact: Vatsal, vmehra[at]pm[dot]me, Whatsapp: +919944430856 (username: Vim) Time: Sunday, September 11, 5:00 PM Location: The Weekend Cafe, Plot No D, 3, Vikrampuri Colony, beside vac's bakery, Vikrampuri Colony, Lane, Secunderabad, Telangana, 500015, India Coordinates: 7J9WFF4X+5P Event link(s): LessWrong Group info: Our rationality meetup group has been around for about 3 months and we discuss articles and exercises (eg. CFAR handbook) that can help us improve epistemic and instrumental rationality. MUMBAI, INDIA Contact: PB, e2y94n1nv[at]relay[dot]firefox[dot]com Time: Sunday, October 9, 4:00 PM Location: Jamjar Diner, Versova Coordinates: 7JFJ4RM6+5W Event link(s): LessWrong Notes: Please RSVP on LessWrong or via email so I can plan activities accordingly. NEW DELHI, INDIA Contact: Suryansh Tyagi, suryanshtyagiphone[at]gmail[dot]com, WhatsApp/phone +919997299972 Time: Sunday, September 11, 5:00 PM Location: Select CityWalk Mall, Saket. Where inside the mall depends on the number of people interested. Coordinates: 7JWVG6H9+8H Event link(s): LessWrong Notes: Please either send me an email or message me on WhatsApp if you want to attend. Any suggestions/changes are welcome. UDAIPUR, RAJASTHAN, INDIA Contact: Shailendra Paliwal, acx-meetup-2022[at]shailendra[dot]me Time: Saturday, September 10, 7:00 PM Location: We'll be at Doodh Talai near Pichola Lake and I'll be wearing a gray t-shirt carrying a sign ACX Meetup Coordinates: 7JPMHM9M+HG Event link(s): LessWrong Notes: Please RSVP on LessWrong so that I can plan ahead UBUD, BALI, INDONESIA Contact: William Ubud, Napaproject[at]gmail[dot]com Time: Tuesday, August 30, 6:00 PM Location: PARQ Ubud Coordinates: 6P3QG789+F7 Event link(s): LessWrong TOKYO, JAPAN Contact: Harold Godsoe, hgodsoe[at]gmail[dot]com Time: Saturday, October 8, 10:00 AM Location: Near Nakameguro station - RSVP for details Coordinates: 8Q7XJPV2+QFP Event link(s): LessWrong, Meetup.com Notes: ACX Tokyo meets monthly since Sept 2021. Our meetups are in English, so far. To join in, feel free to get in touch in any of the many ways to do so (email, Meetup.com). It's useful to be in contact before coming to an event, to help with that first leap of faith. KUALA LUMPUR, MALAYSIA Contact: Yi-Yang, yi[dot]yang[dot]chua[at]gmail[dot]com, LessWrong profile Time: Saturday, September 17, 2:00 PM Location: I'll be in Lisette's Bangsar, which is a 5-minute walk from Bangsar LRT. I'll be wearing a pale green t-shirt and carrying an ACX sign. Coordinates: 6PM34MHH+VW Event link(s): LessWrong AUCKLAND, NEW ZEALAND Contact: Jonathan De Wet, jonpdw[at]gmail[dot]com Time: Saturday, September 3, 6:30 PM Location: 32 Stanley Ave Milford, Auckland Coordinates: 4VMP6QH4+86 Event link(s): LessWrong, Facebook event Notes: It’s a dinner party! Please RSVP on FB so I know how much food to make DUNEDIN, NEW ZEALAND Contact: Gavin, bisga673[at]student[dot]otago[dot]ac[dot]nz Time: Saturday, September 3, 3:00 PM Location: Picnic tables outside of St. David's lecture theatre on Otago University campus. I'll make a sign with ACX meetup. Coordinates: 4V6G4GP7+GM5 Event link(s): LessWrong Notes: There is no Dunedin group as far as I'm aware of, but I'd be keen to meet other likeminded people and organise group hangouts occasionally. WELLINGTON, NEW ZEALAND Contact: Ben W, benwve[at]gmail[dot]com Time: Tuesday, September 27, 5:30 PM Location: Rutherford House, Bunny Street, Wellington. Room MZ05, which is on the mezzanine floor Coordinates: 4VCPPQCH+FGC Event link(s): LessWrong Notes: We're running the event this time in partnership with Effective Altruism Wellington LAPU LAPU, CEBU, PHILIPPINES Contact: Dave, tokkolizard[at]tutanota[dot]com Time: Sunday, September 4, 2:00 PM Location: Starbucks in Mactan Newtown, there will be a sign with ACX MEETUP on it. Coordinates: 7Q268257+4F Event link(s): LessWrong Notes: Please RSVP by mail so I know if I need to set up a bigger meeting place SINGAPORE Contact: Jonathan Ng, jonathan[dot]ng1[at]gmail[dot]com, Telegram @derpy Time: Tuesday, September 6, 6:30 PM Location: Tanjong Pagar MRT gantry, I'll be wearing the dark blue EA Global 2022 jumper Coordinates: 6PH57RGW+J8 Event link(s): LessWrong

Who Predicted 2022?

January 24, 2023 · Original source

Sheer luck - for instance, I’d talked to some people from the Philippines about Bongbong Marcos a few days before entering the competition, and was surprised by how enthusiastic they were about him. I suspect this made me more bullish on his chances of being elected than most of the other entrants.

Book Review: The Geography Of Madness

February 22, 2023 · Original source

She reminded me that yesterday she was unusually grumpy, so much so that she had apologized to me for it and tried to come up with explanations - and then later yesterday she had her period. Meanwhile, Bures’ counterargument is - what? That it sounds kind of sexist to accuse female hormones of making women overly emotional? Hasn’t he ever heard of stereotype accuracy? That people asked their doctors to be treated for it more often after they knew it was considered a medical condition, and was treatable? That seems to have a much simpler explanation! That there are no biomarkers? There are inconsistent biomarkers that work sometimes but not other times, just like for schizophrenia, epilepsy, cancer, and half the other conditions in medicine. That these conditions don’t occur in most cultures? From here: A World Health Organization (WHO) study on menstruation (1981) surveyed 5,322 women from Egypt, India, Indonesia, Jamaica, Korea, Mexico, Pakistan, Philippines, United Kingdom and Yugoslavia. . . The majority of women in all cultures report some premenstrual physical discomfort in addition to negative mood changes, however fewer women report mood change than physical change. The main cross-cultural difference was in the prevalence of specific symptoms. Immigrants to the United States report more PMDD the longer they’re here? True (source), but it’s a matter of degree, and seems more true of the PMDD diagnosis than specific symptoms. The diagnosis requires impairment, which is subjective. I imagine an immigrant from a culture where mental disorders are unthinkable - something that only happens to a few psychos in asylums - and where you work 12-hour days in sweatshops. Someone asks her “hey, has this mental disorder ever prevented you from working?”, and she says no, because obviously you grit your teeth and work through the symptoms. And I imagine an American seeing the same question and saying “Yeah, I did decide I had to take a couple of sick days because of that.” I’m not saying this definitely happened, just that it’s a possibility. Meanwhile, this entire area of study is a mess. The “PMDD is culture-bound” hypothesis was originally invented by feminist scholars trying to argue that the diagnosis was a sexist attempt to pathologize women as overemotional and untrustworthy (this is also where Bures got his “it’s just hysteria by a different name” idea). See for example here and here, the second of which says that “the feminist argument is that if women are angry/distressed, it is for good reason, not due to pathology”. Bures somehow swallowed and repeated this, and then some feminists on Vox wrote an article attacking him as a “male writer” who was denying women’s lived experiences of PMS and stereotyping them as stupid and gullible. Neither side has an argument beyond “I can think of a reason it would be sexist for people to disagree with me” and neither side will acknowledge that the other side is also feminists basing their argument entirely on how it would be sexist to disagree with them. Everything in every area of social science has been like this for at least the past twenty years. But also, this highlights the difficulties with declaring something culture-bound. How do you know if something’s culture-bound, vs. people don’t notice it or mention it if they don’t have a name for it? How do you know if something’s culture-bound vs. some cultures consider it too embarrassing or taboo to think about? How do you know if something’s culture-bound, vs. people will go to doctors about it if they think doctors can treat it, and otherwise they won’t? I’ll discuss these questions more later, but I want to finish Bures’ argument. He gestures at a few other possible candidates for culture-bound mental disorders, including repetitive strain injury and chronic pain. But he quickly moves on to a long section that tries to establish the reality of “voodoo death”, ie the thing where if you believe you are going to die hard enough, you actually die. I think most arguments for voodoo death are pretty bad, and I didn’t find Bures’ convincing. But bonus points for referencing a study claiming that chronically stressed people only die at higher rates if they believe chronic stress is bad for them, and if not then they don’t (this is not really how I interpret the abstract, but I haven’t looked closely) Is it weird to stay on the crazy train long enough to agree that cultural effects are strong enough to make you think witches are stealing your penis, and then get off it once people start talking about voodoo death? I think no - these are very different situations. Believing in koro can make you hallucinate that your penis is shrunken or gone, but no belief, however strong, can (directly) remove your penis itself. Culture → beliefs is fine; culture → reality is a step I’m not willing to take. V. Since I rejected Bures’ PMDD example, I want to digress to what I think is a stronger argument: anorexia, which Ethan Watters discusses in his book Crazy Like Us. Anorexia was mostly unknown in the West, until becoming “trendy” in the mid-1800s. During that period, doctors reported high prevalence of anorexia among “hysterics”, but the fad ended after about ten or twenty years, and it went back to being basically unknown. In 1983, famous singer Karen Carpenter died of anorexia, thrusting it back into the national news, and suddenly lots of people (in the West) were anorexic again. Meanwhile, foreign doctors who trained in the West went back to their home countries, searched far and wide for it, and found almost nothing. The few cases they did see didn’t resemble the typical Western version at all - for example, one Hong Kong psychiatrist was able to find a woman who refused to eat out of grief when a boyfriend left her, but she didn’t think she was fat, or feel any cultural pressure to be thinner. The absence of anorexia abroad was especially surprising since anorexics tend to end up in the hospital with extremely noticeable malnutrition that doesn’t really mimic anything else. It’s not really possible to hide severe anorexia the way you can hide severe depression. In 1994, Hong Kong got its own Karen Carpenter - a young girl died of anorexia, setting off a national panic and many public awareness campaigns. Near-instantly, anorexia rates shot up to the same level as the West, with the appropriate number of people presenting to hospital ERs with severe malnutrition. This story raises a lot of questions. For example: where did the first anorexics (Karen Carpenter, the girl in Hong Kong) come from? Why anorexia and not something else? And how come knowing about anorexia makes it spread so quickly? VI. Past this point I’m using this review to discuss my own thoughts, not Bures’ or Watters’. “Culture-bound” is less all-or-nothing than you’d think. Look hard enough, and you’ll find people having “culture-bound syndromes” from cultures they’ve never heard of. Ntouros et al in Thessaloniki describe “koro-like symptoms in two Greek men”. One, a paranoid schizophrenic: . . . reported for the first time a sensation that his penis retracts into the abdomen and a fear that it will subsequently be lost. This would be accompanied by anxiety and sadness pertaining only to the loss itself. He would then proceed to search manually for his penis and masturbate. No pleasure was gained by masturbation, but the anxiety would be lifted. Romero et al describe a case of koro in "an intellectually disabled Caucasian patient" in Spain. They write that "although it is widely regarded as an epidemic in South-east Asia, there are some isolated cases in other cultures as well." Wilson and Agin describe a 29 year old white male from New York, "not exposed to the Chinese culture”, who went to the doctor with a five month history of worrying that his genitals were retracting into his body: Sometimes, he would manually reaffirm the presence of his genitals. Occasionally he would, in private, remove his garments and visually confirm the presence of his genitals. On one occasion, while taking the train home from work, he experienced an acute exacerbation of these symptoms. His pain increased from 3/10 to 10/10, and he felt as if his genitals had fully retracted within his belly. Upon reaching his hometown, he immediately went to the local hospital emergency room where examinations for inguinal hernia, urinary tract infection, proctitis, prostatitis, and testicular disorders proved negative. He improved significantly on the anti-anxiety medication desipramine. Chowdhury surveys the evidence on koro and divides the condition into two types: culture-bound and non-culture-bound. The culture-bound type usually goes in large epidemics, hundreds to thousands of people, in koro-believing parts of Africa and Asia; the victims were usually previously psychologically normal. The non-culture-bound type hits a few scattered individuals, is not contagious, and can happen anywhere - Greece, Spain, America. Some patients are psychologically normal, but there are a disproportionate number of schizophrenics, drug users, brain damage victims, and other previously-mentally-ill people. Other culture-bound illnesses seem to be like this too. Running amok has been big in Malaysia for 300 years. The Columbine shooters seem to have been autocthonous American cases, equivalent to that one New Yorker who got koro - before their fame inscribed amok onto the US collective consciousness the same way Karen Carpenter’s inscribed anorexia. Japan’s jikoshu-kyofu affects occasional victims in the US under the name olfactory reference syndrome. Watters admits there were a tiny handful of unusual anorexia cases in Hong Kong before Westernization. And even that Indian there’s-a-lizard-in-my-skin condition differs only in species from delusional parasitosis. Delusional parasitosis - the false belief that you are infested with parasites and can feel them crawling in your skin - is actually an especially interesting case. Two groups are disproportionately represented among patients: menopausal women and cocaine addicts. Relatedly, two biological conditions that can sometimes cause weird skin sensations that feel like crawling insects are . . . menopause and cocaine use. So there’s no mystery here. But, also represented among delusional parasitosis patients are the roommates and family members of these people. The index case hallucinates insects for a well-understood biological reason; their close contacts hallucinate insects through social contagion. So a unified theory of these conditions might be: Some people have the condition for a normal biological or psychiatric reason. For example, someone might believe a lizard is crawling under their skin because they use cocaine, which causes hallucinatory crawling sensations. Or someone might believe their penis is missing because they’re schizophrenic, which makes them naturally hallucination-prone.

Inline links: stereotype accuracy, inconsistent biomarkers that work sometimes but not other times, here, source, here, here, and then some feminists on Vox wrote an article, chronic pain, are pretty bad, a study, Ethan Watters discusses in his book, Ntouros et al, Romero et al, Wilson and Agin, surveys the evidence, olfactory reference syndrome, delusional parasitosis

Your Book Review: How the War Was Won

August 09, 2024 · Original source

Data from HtWWW, recreated to improve image quality. German oil shortages caused exactly the same training problem Japan had faced, with a slightly different but similarly disastrous outcome. Japanese training and production problems led to planes not arriving where they were supposed to in fighting condition (perhaps as few as 10% were actually combat capable when they arrived!) For Germany, training shortfalls meant annihilation for their air force as inexperienced pilots were forced to fight numerically and qualitatively superior American and British pilots. German monthly aircraft lost/damaged rates increased from 52.5% in January 1944 to 96.3% in June. One particularly illuminating episode illustrates how these problems manifested for Germany. The German air force had a reserve of 800 aircraft to counter the D-Day landings. The pilots of that force were used to only flying under expert control systems in Germany (countering bombing raids). When they went to France, they had trouble navigating and often landed on the wrong fields. Ultimately, they were poorly prepared to fight. The head of German fighter command was certain that the entire reserve did not destroy even two dozen Allied aircraft. American/British Airpower Decided the Outcome of Land Battles Beyond the strategic effects of bombing, tactical airpower (i.e., airplanes attacking land forces) gave an insurmountable advantage to the western Allies’ land forces. After D-Day, the Germans had a very strong defensive position in the hedgerows of northwest France. Allied aircraft literally carpet bombed one of the strongest divisions in the German army out of existence, with 70% casualties in one day. That division would normally have approximately 200 AFVs. At the end of that one day of bombing, it had 14. The Battle of the Bulge, the last offensive by the Germans to drive back the western Allies’ advance, was almost pathetic in its hopelessness. We Americans tend to focus on the hard fighting at the outset of the battle, and the stout resistance of the 101st Airborne at Bastogne. Knowing that airpower would make their attack impossible, the Germans timed the battle for bad weather and prayed it lasted as long as possible. Prayer was really the only option. Once the skies inevitably cleared after a little over a week of bad weather, more than 2,000(!) Allied bombers destroyed the German offensive. With most logistical support wiped out, one famous German division had to abandon all its vehicles and walk back to Germany. Criticism of HtWWW as a Book: Love the Data, (Mostly) Don’t Care About the People My single biggest criticism of HtWWW is O’Brien spends a lot of time (I would estimate 20% of the book) discussing the relative importance and influence of various people in the United States and United Kingdom. The section on Doug MacArthur is worth a longer digression, which I have included below. The problem is that focusing on personnel is almost completely irrelevant to the main argument of the book. For example, it is modestly interesting that Franklin Roosevelt, consistent with advice from Harry Hopkins and Admiral Ernest King, focused America’s productive effort on air and sea power. It is not at all central to the argument that air and sea power won the war. The fact that these particular people thought it was a good idea to build planes and ships matters less than the outcome that the U.S. did exactly that. I am very much interested in World War II history, and on an interestingness scale of 1-10, I found this discussion to be at about a 4. The central argument of the book about German and Japanese production was a consistent 10. Sidenote: MacArthur Was a Disastrous General In the part of the book focused on personnel, the one discussion that hit around a 9 or 10 was of Douglas MacArthur and the invasion of the Philippines. MacArthur was the American general commanding the defense of the Philippines. The Japanese conquered the Philippines, and MacArthur slipped away to Australia, heroically vowing, “I shall return.” He did in December 1944, and some of the worst fighting of the war took place, with massive casualties for the Americans, Japanese, and Filipino civilians. Fighting was still ongoing in the Philippines when the war ended in August 1945. The Americans took more than 220,000 casualties, the Japanese 430,000. Estimates vary on Filipino civilian deaths, but 750,000 is a credible middle of the road estimate. O’Brien’s contribution here was pointing out the strategic pointlessness of MacArthur’s invasion. The big American strategy in the western Pacific was to penetrate the Japanese defensive line of islands to link up with China. The northern Marianas Islands also were within heavy bomber range of Japan, and so would allow for efficient, effective bombing. (Bombing Japan from bases in China were logistically impractical, with virtually all materials being flown in over the Himalayas—another fascinating logistics discussion in this book.) The Americans had already conquered the Marianas Islands and had total air and sea dominance in the western Pacific. The forces the Japanese had in the Philippines could have been simply left to wither, as they had been on other islands bypassed by the island-hopping campaign. So, why did the Philippines invasion happen? The inescapable conclusion is that MacArthur was too politically formidable to risk angering, and he personally wanted to invade the Philippines to make good on his promise to return. Not coincidentally, the Philippines also offered some prospect of an extended land campaign where MacArthur could improve his reputation after his disastrous original defense of the Philippines. Also relevant, in O’Brien’s words: “MacArthur [] dazzled Roosevelt with tales of easy victories and grateful Filipinos and American voters.” Criticisms of HtWWW’s Central Argument I think it is clear from the data that O’Brien’s argument, that air and sea power played a more important role than land battles in deciding the war, is fundamentally right. Still, one can raise a few objections. Individual naval battles were capable of destroying a significant percentage of overall production. O’Brien discusses the Battle of Midway, where the Japanese lost four aircraft carriers (37 percent of their navy’s aircraft carriers at the time, 22 percent of all carriers they had during the war). This point doesn’t really disprove O’Brien’s core argument—it is basically a footnote saying that individual naval battles are more likely to matter than individual land battles. Politics and psychology matter tremendously in war, sometimes more than productive effort. O’Brien tacitly acknowledges this in the V-2 weapons discussion when he notes that the Germans spent all this money and effort on a psychological salve to the trauma of Allied bombing. The Japanese did ultimately surrender after the atomic bombings. (Or, if you are more on the revisionist end of the spectrum, they surrendered after the Soviets declared war.) France surrendered after a few disastrous battles. The productive effort lens might be useful, but subject to important caveats. Why Does the Conventional Narrative Focus on Battles? A perfect companion book to HtWWW would examine why military historians and the broader public have focused inordinately on battles. Here are some plausible factors: Battles are more dramatic. Propaganda during the war focused on battles so that there would be more inherent drama. Working twelve hour shifts in a factory to win the great battle is probably psychologically easier than thinking your work is going to disappear into an inchoate slog.

Your Book Review: Nine Lives

September 13, 2024 · Original source

[This is one of the finalists in the 2024 book review contest, written by an ACX reader who will remain anonymous until after voting is done. I’ll be posting about one of these a week for several months. When you’ve read them all, I’ll ask you to vote for a favorite, so remember which ones you liked] Cats have nine lives but they don’t get involved in jungle wars in the Philippines Aimen Dean (pseudonym) compares himself to the proverbial cat: he has nine lives, surviving every impossible situation and starting new lives under strange new conditions.

(The jungle war in the Philippines sounded cool in the section title, but his brief stint there at 18 is actually one of the least exciting stories of his life: it was mostly a frozen conflict and the jihadists spent their time playing beach volleyball.)

Anyways, the End Times are here. (According to a Pew Research poll, more than half of Muslims believe that the Mahdi will arrive within their lifetime, and this belief is universally accepted among jihadists). This means that you should contribute to fulfilling the prerequisite prophecies as quickly as possible. Nothing else really matters. The young Dean travels to the Philippines to fight in the Muslim independence movement there, but later gets embarrassed about this as a wasteful diversion: the Philippines don’t feature in any of the prophecies, so it’s not important.

Inline links: Pew Research poll

Your Review: Project Xanadu - The Internet That Might Have Been

September 19, 2025 · Original source

Of course, they didn’t, and so Engelbart spent two years faffing around in the Philippines. He lived on a remote island with nothing to do but read and read and read. He spent his first five days camping out by a little stilt hut with a sign reading “Red Cross Library”—and in the Red Cross Library, there was a copy of the September 1945 issue of LIFE magazine in which Vannevar Bush’s description of the memex had been reprinted.

I think we can, in large part, trace it back to Doug Engelbart, who, by blind, dumb luck, found himself on a remote Philippine island for two years with nothing to do but hang out in a big hut full of magazines. And there he happened to read Vannevar Bush’s essay, and then, fifteen years later, the thought happened to pop back into his head, and he happened to be a little better positioned, a little better at technology than Ted Nelson, and so he happened to make comprehensive hypertext a highly-visible reality before anyone else.

The Fatima Sun Miracle: Much More Than You Wanted To Know

October 01, 2025 · Original source

Maybe one video which is actually good. A good representative of the first category is this video from 2023: The quality is very high. You can see everything clearly - at least on the ground. The crowds are obviously seeing something. The videographer interviews some people in the crowds and they say that the sun is spinning. But the sun itself just looks like a bright smudge. The videographer apologizes constantly for this and seems to think that if he could film it clearly, we would all agree it was spinning. Here’s another one like this: Same videographer, different witnesses, same story. We move on to the second category, videos that claim to capture the phenomenon but look more like a cell phone camera having a stroke. Here’s one from 2009: Some sample testimonials from the comments section: I, along with many others at the same time in Medjugorje, was able to look at the sun with the naked eye, in the summer, under a clear sky, without any discomfort. And we watched it spin this way and that like a top, to everyone's amazement. Then, when it stopped, it hurt your eyes and blinded you even if you tried to look at it with two or three pairs of sunglasses. It was incredible and I'll never forget it. Seeing it on camera doesn't have much of an effect on me, and I think it might be due to the lens not reacting well to sunlight. But seeing it with the naked eye is incredible. … I saw the solar phenomenon in Medjugorje with my dear late mother, along with a large group of people, in 2010. It was a wonderful and unforgettable spectacle. It was possible to watch it without discomfort. It pulsated and changed colors around me, red, blue, green... incredible. I've read various explanations, but none can match or explain what I saw. Only something from the sky could have created it. I rule out an atmospheric phenomenon. From what I've heard, it doesn't happen often, but only every now and then. … When the digital camera's sensor becomes saturated with excess light, the image is interrupted for an instant, that is, for the time necessary to bring the semiconductors responsible for recording the image back into operation. In fact, this phenomenon is visible only through the camera and not directly, as one might understand from the comments in the video. Therefore, it is a physical phenomenon linked to the control and measurement equipment (the camera itself) which malfunctions due to its incorrect use, namely pointing the lens towards a source that is too bright, such as the sun. … One thing is certain: if this solar phenomenon is due to the Virgin Mary, the epileptics who were there were not happy. … At least a few of the people who have seen the miracle in person describe the video as not completely foreign to their experience. I’m still a little skeptical because of even worse videos like this one: The sun seems to be expanding whenever he raises the camera, and shrinking whenever he lowers it. This is some kind of auto-brightness adjust. If it wasn’t, and there was a real miracle going on, at least one member of the crowd would be watching it instead of praying quietly. The best video I could find of the Benin City, Nigeria, 2017 miracle is also in the cell-phone-stroke category: …and here is another one from the same miracle (remember, there was a crowd of 30,000+ for this one) where the sun seems completely normal. But that brings us to the third category, the one video which is actually good. In 2000, God told a prayer group in the Philippines to build a very big church. If it was meant as a divine test, they passed: Since then, people have reported miracles at the site regularly. Most interesting for our purposes, some say that the Miracle of the Sun occurs there every Divine Mercy Sunday (the Sunday after Easter). I’m not sure this is right - I can only find evidence of it occurring in about a third of years - but that’s still a pretty good record. Here is the miracle from 2010 (starts at 3:11): Although the sun isn’t vastly clearer than any of the other videos, it’s obvious in this one that the oohs and aahs of the crowd match up with the pulses recorded on video - so it doesn’t seem like it can just be a camera failure. A more experienced critic on Reddit agrees: I would have expected that having dozens of videos of the sun miracle would finally clarify things. Instead, they’ve only gotten more confusing. The part that should be most easily captured even on blurry cell phone footage - the sun changing color and staining everything around different colors - is totally absent. Yet it seems like something must be happening to impress all of these crowds, and that the camera is able to capture some of it. 3.4: Any Little Maid That Walks In Good Thoughts Apart What updates should we make based on all these other miracles? First, we must discard our exotic meteorologic hypotheses. It might be barely possible for a rare dust storm, or a perfectly-timed ice whirlwind, to coincide with a prophecied apparition once. For it to do so every time a little girl says she sees the Virgin Mary defies belief. Second, we may want to rule out the actual Virgin Mary, at least insofar as she can be considered allied with the Catholic Church. It seems that sun miracles are common even at apparitions which the Church denounces as misguided or heretical; surely the Virgin would not want to confuse people by lending miraculous signs to false prophecies. (a true believer may posit that the miracles associated with real apparitions were caused by the Virgin, those associated with fake apparitions were caused by demons, and those that were neither - like Salema Manoel on her car ride home - were the demons again, trying to confuse us. I can only cite the usual prior against conspiracy theories; the conspirators being demons hardly makes things better.) This seems to leave illusions/hallucinations as a leading candidate. We previously came up with three arguments that seemed to rule these out: Dalleur and others have collected testimonies from people many miles from the Fatima crowd, which seems to rule out mass suggestion and demand and objective explanation.

Inline links: very big church, https://substackcdn.com/image/fetch/$s_!G6AX!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fc7d5cfca-90bf-4eef-8e0c-11fa9b9adae7_673x864.png, agrees, https://substackcdn.com/image/fetch/$s_!78rP!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd982d2b8-1324-480d-9c04-31a57201ebbd_896x234.png

Since then, people have reported miracles at the site regularly. Most interesting for our purposes, some say that the Miracle of the Sun occurs there every Divine Mercy Sunday (the Sunday after Easter). I’m not sure this is right - I can only find evidence of it occurring in about a third of years - but that’s still a pretty good record. Here is the miracle from 2010 (starts at 3:11): Although the sun isn’t vastly clearer than any of the other videos, it’s obvious in this one that the oohs and aahs of the crowd match up with the pulses recorded on video - so it doesn’t seem like it can just be a camera failure. A more experienced critic on Reddit agrees: I would have expected that having dozens of videos of the sun miracle would finally clarify things. Instead, they’ve only gotten more confusing. The part that should be most easily captured even on blurry cell phone footage - the sun changing color and staining everything around different colors - is totally absent. Yet it seems like something must be happening to impress all of these crowds, and that the camera is able to capture some of it. 3.4: Any Little Maid That Walks In Good Thoughts Apart What updates should we make based on all these other miracles? First, we must discard our exotic meteorologic hypotheses. It might be barely possible for a rare dust storm, or a perfectly-timed ice whirlwind, to coincide with a prophecied apparition once. For it to do so every time a little girl says she sees the Virgin Mary defies belief. Second, we may want to rule out the actual Virgin Mary, at least insofar as she can be considered allied with the Catholic Church. It seems that sun miracles are common even at apparitions which the Church denounces as misguided or heretical; surely the Virgin would not want to confuse people by lending miraculous signs to false prophecies. (a true believer may posit that the miracles associated with real apparitions were caused by the Virgin, those associated with fake apparitions were caused by demons, and those that were neither - like Salema Manoel on her car ride home - were the demons again, trying to confuse us. I can only cite the usual prior against conspiracy theories; the conspirators being demons hardly makes things better.) This seems to leave illusions/hallucinations as a leading candidate. We previously came up with three arguments that seemed to rule these out: Dalleur and others have collected testimonies from people many miles from the Fatima crowd, which seems to rule out mass suggestion and demand and objective explanation.

Inline links: agrees, https://substackcdn.com/image/fetch/$s_!78rP!,f_auto,q_auto:good,fl_progressive:steep/https%3A%2F%2Fsubstack-post-media.s3.amazonaws.com%2Fpublic%2Fimages%2Fd982d2b8-1324-480d-9c04-31a57201ebbd_896x234.png

Someone who understands videography (or maybe someone who specifically doesn’t understand videography, idk) should do lots of experiments with videotaping the sun on normal days and see whether they can replicate the pulsing effect seen in the Medjugorje, Benin City, and Philippines video. If it’s just a natural artifact of trying to record the sun with a cell phone camera, it should pretty easy to replicate. If you try this, please email me your results whether or not you’re able to produce the effect.

ACX Grants Results 2025

October 13, 2025 · Original source

Micaella Rogers and Tom Daniels, $50K, for lead-acid battery recycling. Unsafe lead-acid battery recycling is a major contributor to global lead burden; it’s hard to figure out how literally and causally to take the highest estimates of damage, but they suggest up to 350,000 deaths per year and $170 billion in lost productivity. Some governments have curtailed this problem by making customers pay a deposit along with a new battery, which they get back when they return the battery to a safe recycling facility. Micaella and Tom’s organization wants to advise the Philippines government on how to do the same.

Inline links: Micaella and Tom’s organization

Highlights From The Comments On Fatima

October 24, 2025 · Original source

In the original post, I mentioned some videos of modern sun miracles. Most of them seemed like obvious cell phone camera failures, but I included one from the Philippines that seemed slightly better, mostly because the changes in the sun seemed to correspond to reactions from the crowd. But commenters were skeptical.

My conclusion: I was always ready to admit that you could get a sun expanding or contracting with camera movements. The Philippines video slightly impressed me because I couldn’t see the camera movements, and I thought that the sun changes corresponded to crowd reactions. But I admit I didn’t watch it very closely, because I hate watching videos and can’t bring myself to do more than skim them at high speed. Since people who did watch it more closely say that they noticed camera movements, non-correlations with the crowd, and reasons to think that the videographer might be reacting to the crowd rather than the crowd reacting to the sun, I’m now satisfied that it’s not worth taking seriously, and that the video evidence for the miracle is wholly negative.

I don’t find the 2010 Phillipines video any better than the other ones. Eyeballing it, the sky appears much brighter as the camera points down, and more normal as it points up, plus some delay as the camera adjusts. There clearly seems to be brightness correction going on in the camera that stops when the camera points away from the sun entirely (6:00) and restarts when the camera turns back toward the sun (6:25).

Meetups Everywhere Spring 2026: Times & Places

April 01, 2026 · Original source

Contact: June Contact Info: +64273410265 Time: Saturday, April 11th, 2:00 PM Location: Aro Park Coordinates: https://plus.codes/4VCPPQ39+V8 Notes: RSVPing to my email is helpful, but not mandatory Philippines MANILA Contact: Steve Kuhn Contact Info: steve[@]sportspredict[.]com Time: Thursday, April 23rd, 1:00 PM Location: Thie High Street Lounge, Shangri La Hotel, BGC, Manila Coordinates: https://plus.codes/7Q63H22W+XP

Inline links: https://plus.codes/4VCPPQ39+V8, https://plus.codes/7Q63H22W+XP

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Astral Codex Ten

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