Link Bibliography

1. https://gwern.substack.com

2. “February 2018 News”, Gw­ern Bran­wen (2018-01-28):

   February 2018 gwern.net newsletter with 3 new essays and links on genetics/AI/psychology/economics, 1 book review, 1 movie review, and 7 pieces of music.

3. “Gwern.net newsletter archives”, Gw­ern Bran­wen (2013-12-01):

   Newsletter tag: archive of all issues back to 2013 for the gwern.net newsletter (monthly updates, which will include summaries of projects I’ve worked on that month (the same as the changelog), collations of links or discussions from my subreddit, and book/movie reviews.)

4. “Changelog”, Gw­ern Bran­wen (2013-09-15):

   This page is a changelog for Gw­ern.net: a monthly re­verse chrono­log­i­cal list of re­cent ma­jor writings/changes/additions.

   Fol­low­ing my writ­ing can be a lit­tle diffi­cult be­cause it is often so in­cre­men­tal. So every mon­th, in ad­di­tion to my reg­u­lar /r/Gwern sub­red­dit sub­mis­sions, I write up rea­son­ably-in­ter­est­ing changes and send it out to the mail­ing list in ad­di­tion to a com­pi­la­tion of links & re­views (archives).

5. “/r/gwern subreddit”, Gw­ern Bran­wen (2018-10-01):

   A subreddit for posting links of interest and also for announcing updates to gwern.net (which can be used as a RSS feed). Submissions are categorized similar to the monthly newsletter and typically will be collated there.

6. https://www.patreon.com/gwern

7. “Meta-analysis of genome-wide association studies for height and body mass index in ∼700,000 individuals of European ancestry”, Loic Yengo, Julia Sidorenko, Kathryn E. Kemper, Zhili Zheng, Andrew R. Wood, Michael N. Weedon, Timothy M. Frayling, Joel Hirschhorn, Jian Yang, Peter M. Visscher, the GIANT Consortium (2018-03-22):

   Genome-wide association studies (GWAS) stand as powerful experimental designs for identifying DNA variants associated with complex traits and diseases. In the past decade, both the number of such studies and their sample sizes have increased dramatically. Recent GWAS of height and body mass index (BMI) in ~250,000 European participants have led to the discovery of ~700 and ~100 nearly independent SNPs associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ~450,000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N~700,000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3,290 and 716 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of p<1 × 10⁻⁸), including 1,185 height-associated SNPs and 554 BMI-associated SNPs located within loci not previously identified by these two GWAS. The genome-wide significant SNPs explain ~24.6% of the variance of height and ~5% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were 0.44 and 0.20, respectively. From analyses of integrating GWAS and eQTL data by Summary-data based Mendelian Randomization (SMR), we identified an enrichment of eQTLs amongst lead height and BMI signals, prioritisting 684 and 134 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow up studies.

8. “A Polygenic Score for Higher Educational Attainment is Associated with Larger Brains”, Maxwell L. El­liott, Daniel W. Bel­sky, Kevin An­der­son, David L. Cor­co­ran, Tian Ge, An­nchen Kn­odt, Joseph A. Prinz, Karen Sug­den, Ben­jamin Williams, David Ire­land, Richie Poul­ton, Avshalom Caspi, Avram Holmes, Ter­rie Moffitt, Ah­mad R. Hariri (2018-03-23):

   People who score higher on intelligence tests tend to have larger brains. Twin studies suggest the same genetic factors influence both brain size and intelligence. This has led to the hypothesis that genetics influence intelligence partly by contributing to development of larger brains. We tested this hypothesis with molecular genetic data using discoveries from a genome-wide association study (GWAS) of educational attainment, a correlate of intelligence. We analyzed genetic, brain imaging, and cognitive test data from the UK Biobank, the Dunedin Study, the Brain Genomics Superstruct Project (GSP), and the Duke Neurogenetics Study (DNS) (combined n = 8,271). We measured genetics using polygenic scores based on published GWAS. We conducted meta-analysis to test associations among participants’ genetics, total brain volume (i.e., brain size), and cognitive test performance. Consistent with previous findings, participants with higher polygenic scores achieved higher scores on cognitive tests, as did participants with larger brains. Participants with higher polygenic scores also had larger brains. We found some evidence that brain size partly mediated associations between participants’ education polygenic scores and their cognitive test performance. Effect-sizes were larger in the population-based UK Biobank and Dunedin samples than in the GSP and DNS samples. Sensitivity analysis suggested this effect-size difference partly reflected restricted range of cognitive performance in the GSP and DNS samples. Recruitment and retention of population-representative samples should be a priority for neuroscience research. Findings suggest promise for studies integrating GWAS discoveries with brain imaging data to understand neurobiology linking genetics with individual differences in cognitive performance.

9. “Genetic correlation”, Eng­lish Wikipedia (2020-12-22):

   In multivariate quantitative genetics, a genetic correlation is the proportion of variance that two traits share due to genetic causes, the correlation between the genetic influences on a trait and the genetic influences on a different trait estimating the degree of pleiotropy or causal overlap. A genetic correlation of 0 implies that the genetic effects on one trait are independent of the other, while a correlation of 1 implies that all of the genetic influences on the two traits are identical. The bivariate genetic correlation can be generalized to inferring genetic latent variable factors across > 2 traits using factor analysis. Genetic correlation models were introduced into behavioral genetics in the 1970s–1980s.

10. /docs/iq/2018-huguet.pdf

11. “Copy number variation”, Eng­lish Wikipedia (2020-12-28):

    

    Copy number variation (CNV) is a phenomenon in which sections of the genome are repeated and the number of repeats in the genome varies between individuals. Copy number variation is a type of structural variation: specifically, it is a type of duplication or deletion event that affects a considerable number of base pairs. Approximately two-thirds of the entire human genome may be composed of repeats and 4.8–9.5% of the human genome can be classified as copy number variations. In mammals, copy number variations play an important role in generating necessary variation in the population as well as disease phenotype.

12. https://www.nature.com/articles/s41398-018-0217-4

13. “Genetic correlation”, Eng­lish Wikipedia (2020-12-22):

    In multivariate quantitative genetics, a genetic correlation is the proportion of variance that two traits share due to genetic causes, the correlation between the genetic influences on a trait and the genetic influences on a different trait estimating the degree of pleiotropy or causal overlap. A genetic correlation of 0 implies that the genetic effects on one trait are independent of the other, while a correlation of 1 implies that all of the genetic influences on the two traits are identical. The bivariate genetic correlation can be generalized to inferring genetic latent variable factors across > 2 traits using factor analysis. Genetic correlation models were introduced into behavioral genetics in the 1970s–1980s.

14. /docs/genetics/heritable/2018-bastarache.pdf

15. https://www.nytimes.com/2018/03/15/health/genetic-mutations-diagnosis.html

16. “Relationships between estimated autozygosity and complex traits in the UK Biobank”, Emma C. John­son, Luke M. Evans, Matthew C. Keller (2018-03-29):

    Inbreeding increases the risk of certain Mendelian disorders in humans but may also reduce fitness through its effects on complex traits and diseases. Such inbreeding depression is thought to occur due to increased homozygosity at causal variants that are recessive with respect to fitness. Until recently it has been difficult to amass large enough sample sizes to investigate the effects of inbreeding depression on complex traits using genome-wide single nucleotide polymorphism (SNP) data in population-based samples. Further, it is difficult to infer causation in analyses that relate degree of inbreeding to complex traits because confounding variables (e.g., education) may influence both the likelihood for parents to outbreed and offspring trait values. The present study used runs of homozygosity in genome-wide SNP data in up to 400,000 individuals in the UK Biobank to estimate the proportion of the autosome that exists in autozygous tracts—stretches of the genome which are identical due to a shared common ancestor. After multiple testing corrections and controlling for possible sociodemographic confounders, we found significant relationships in the predicted direction between estimated autozygosity and three of the 26 traits we investigated: age at first sexual intercourse, fluid intelligence, and forced expiratory volume in 1 second. Our findings for fluid intelligence and forced expiratory volume corroborate those of several published studies while the finding for age at first sexual intercourse was novel. These results may suggest that these traits have been associated with Darwinian fitness over evolutionary time, although there are other possible explanations for these associations that cannot be eliminated. Some of the autozygosity-trait relationships were attenuated after controlling for background sociodemographic characteristics, suggesting that care needs to be taken in the design and interpretation of ROH studies in order to glean reliable information about the genetic architecture and evolutionary history of complex traits.

    Author Summary

    Inbreeding is well known to increase the risk of rare, monogenic diseases, and there has been some evidence that it also affects complex traits, such as cognition and educational attainment. However, difficulties can arise when inferring causation in these types of analyses because of the potential for confounding variables (e.g., socioeconomic status) to bias the observed relationships between distant inbreeding and complex traits. In this investigation, we used single-nucleotide polymorphism data in a very large (n > 400,000) sample of seemingly outbred individuals to quantify the degree to which distant inbreeding is associated with 26 complex traits. We found robust evidence that distant inbreeding is inversely associated with fluid intelligence and a measure of lung function, and is positively associated with age at first sex, while other trait associations with inbreeding were attenuated after controlling for background sociodemographic characteristics. Our findings are consistent with evolutionary predictions that fluid intelligence, lung function, and age at first sex have been under selection pressures over time; however, they also suggest that confounding variables must be accounted for in order to reliably interpret results from these types of analyses.

17. https://www.nature.com/articles/s41539-018-0019-8

18. https://www.nature.com/articles/s41539-018-0021-1

19. “Mixed model association for biobank-scale data sets”, Po-Ru Loh, Gleb Kichaev, Steven Gazal, Armin P. Schoech, Alkes L. Price (2018-01-04):

    Biobank-based genome-wide association studies are enabling exciting insights in complex trait genetics, but much uncertainty remains over best practices for optimizing statistical power and computational efficiency in GWAS while controlling confounders. Here, we introduce a much faster version of our BOLT-LMM Bayesian mixed model association method— capable of running analyses of the full UK Biobank cohort in a few days on a single compute node—and show that it produces highly powered, robust test statistics when run on all 459K European samples (retaining related individuals). When used to conduct a GWAS for height in UK Biobank, BOLT-LMM achieved power equivalent to linear regression on 650K samples—a 93% increase in effective sample size versus the common practice of analyzing unrelated British samples using linear regression (UK Biobank documentation; Bycroft et al. bioRxiv). Across a broader set of 23 highly heritable traits, the total number of independent GWAS loci detected increased from 5,839 to 10,759, an 84% increase. We recommend the use of BOLT-LMM (retaining related individuals) for biobank-scale analyses, and we have publicly released BOLT-LMM summary association statistics for the 23 traits analyzed as a resource for all researchers.

20. “Quantitative analysis of population-scale family trees with millions of relatives”, Joanna Ka­planis, As­saf Gor­don, Tal Shor, Omer Weiss­brod, Dan Geiger, Mary Wahl, Michael Ger­shovits, Barak Markus, Mona Sheikh, Melissa Gym­rek, Gau­rav Bha­tia, Daniel G. MacArthur, Alkes L. Price, Yaniv Er­lich (2018-03-01):

    Family trees have vast applications in multiple fields from genetics to anthropology and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. Here, we collected 86 million profiles from publicly-available online data shared by genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of longevity by inspecting millions of relative pairs and to provide insights into the geographical dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data.

21. “Crowdsourcing big data research on human history and health: from genealogies to genomes and back again”, Alon Keinan, Alexan­dre Lussier (2018-04-12):

    Genealogies are likely the first, centuries-old “big data”, with their construction as old as human civilization itself. Globalization, and the identity crisis that ensued, turned many to online services, building family trees and investigating connections to historical records and other family trees [1]. An explosion has been underway since the beginning of the century in the number and usage of websites offering such genealogical services. About 130 million users combine to have created almost four billion profiles for family members across the three most popular websites of genealogy enthusiasts, Ancestry.com, MyHeritage, and Geni. More recent years have witnessed a similar rapid increase of genetic-based services that address the same need to learn about familial relationships and ancestry. These vast amounts of crowdsourced—and often crowdfunded (as users often pay for these services)—data offers ample scientific research opportunities that would otherwise require expansive collection. In a paper published today in Science, Kaplanis et al. [2, 3] introduce a genealogical dataset based on processing 86 million public Geni profiles. Armed with this crowdsourced dataset, they address fundamental research questions.

22. “A genetic perspective on the relationship between eudaimonic –and hedonic well-being”, B.M.L. Basel­mans, M. Bar­tels (2018-03-15):

    Whether hedonism or eudaimonism are two distinguishable forms of well-being is a topic of ongoing debate. To shed light on the relation between the two, large-scale available molecular genetic data were leveraged to gain more insight into the genetic architecture of the overlap between hedonic and eudaimonic well-being. Hence, we conducted the first genome-wide association studies (GWAS) of eudaimonic well-being (N = ~108K) and linked it to a GWAS of hedonic well-being (N = ~ 222K). We identified the first two genome-wide significant independent loci for eudaimonic well-being and 6 independent loci for hedonic well-being. Joint analyses revealed a moderate phenotypic correlation (r = 0.53), but a high genetic correlation (r_g = 0.78) between eudaimonic and hedonic well-being. For both traits we identified enrichment in the frontal cortex -and cingulate cortex as well as the cerebellum to be top ranked. Bi-directional Mendelian Randomization analyses using two-sample MR indicated some evidence for a causal relationship from hedonic well-being to eudaimonic well-being whereas no evidence was found for the reverse. Additionally, genetic correlations patterns with a range of positive and negative related phenotypes were largely similar for hedonic –and eudaimonic well-being. Our results reveal a large genetic overlap between hedonism and eudaimonism.

23. https://whyevolutionistrue.wordpress.com/2018/03/04/a-human-chimera/

24. https://www.wired.com/story/live-forever-synthetic-human-genome/

25. http://pulitzercenter.org/reporting/right-not-know-when-ignorance-bliss-deadly

26. “Novel susceptibility loci and genetic regulation mechanisms for type 2 diabetes”, Angli Xue, Yang Wu, Zhihong Zhu, Futao Zhang, Kathryn E. Kemper, Zhili Zheng, Loic Yengo, Luke R. Lloyd-Jones, Julia Sidorenko, Yeda Wu, eQTLGen Consortium, Allan F. McRae, Peter M. Visscher, Jian Zeng, Jian Yang (2018-03-20):

    We conducted a meta-analysis of genome-wide association studies (GWAS) with ~16 million genotyped/imputed genetic variants in 62,892 type 2 diabetes (T2D) cases and 596,424 controls of European ancestry. We identified 139 common and 4 rare (minor allele frequency < 0.01) variants associated with T2D, 42 of which (39 common and 3 rare variants) were independent of the known variants. Integration of the gene expression data from blood (n = 14,115 and 2,765) and other T2D-relevant tissues (n = up to 385) with the GWAS results identified 33 putative functional genes for T2D, three of which were targeted by approved drugs. A further integration of DNA methylation (n = 1,980) and epigenomic annotations data highlighted three putative T2D genes (CAMK1D, TP53INP1 and ATP5G1) with plausible regulatory mechanisms whereby a genetic variant exerts an effect on T2D through epigenetic regulation of gene expression. We further found evidence that the T2D-associated loci have been under purifying selection.

27. https://www.fhi.ox.ac.uk/deciphering-chinas-ai-dream/

28. “The Surprising Creativity of Digital Evolution: A Collection of Anecdotes from the Evolutionary Computation and Artificial Life Research Communities”, Joel Lehman, Jeff Clune, Du­san Mi­s­e­vic, Christoph Adami, Lee Al­tenberg, Julie Beaulieu, Pe­ter J. Bent­ley, Samuel Bernard, Guil­laume Beslon, David M. Bryson, Pa­tryk Chrabaszcz, Nick Ch­eney, An­toine Cul­ly, Stephane Don­cieux, Fred C. Dy­er, Kai Olav Ellef­sen, Robert Feldt, Stephan Fis­cher, Stephanie For­rest, An­toine Frénoy, Chris­t­ian Gag­né, Leni Le Goff, Laura M. Grabowski, Babak Hod­jat, Frank Hut­ter, Lau­rent Keller, Ca­r­ole Knibbe, Pe­ter Krc­ah, Richard E. Lenski, Hod Lip­son, Robert Mac­Cur­dy, Car­los Maestre, Risto Mi­ikku­lainen, Sara Mitri, David E. Mo­ri­ar­ty, Jean-Bap­tiste Mouret, Anh Nguyen, Charles Ofria, Marc Parizeau, David Par­sons, Robert T. Pen­nock, William F. Punch, Thomas S. Ray, Marc Schoe­nauer, Eric Shul­te, Karl Sims, Ken­neth O. Stan­ley, François Tad­dei, Danesh Tara­pore, Si­mon Thibault, West­ley Weimer, Richard Wat­son, Ja­son Yosin­ski (2018-03-09):

    Biological evolution provides a creative fount of complex and subtle adaptations, often surprising the scientists who discover them. However, because evolution is an algorithmic process that transcends the substrate in which it occurs, evolution’s creativity is not limited to nature. Indeed, many researchers in the field of digital evolution have observed their evolving algorithms and organisms subverting their intentions, exposing unrecognized bugs in their code, producing unexpected adaptations, or exhibiting outcomes uncannily convergent with ones in nature. Such stories routinely reveal creativity by evolution in these digital worlds, but they rarely fit into the standard scientific narrative. Instead they are often treated as mere obstacles to be overcome, rather than results that warrant study in their own right. The stories themselves are traded among researchers through oral tradition, but that mode of information transmission is inefficient and prone to error and outright loss. Moreover, the fact that these stories tend to be shared only among practitioners means that many natural scientists do not realize how interesting and lifelike digital organisms are and how natural their evolution can be. To our knowledge, no collection of such anecdotes has been published before. This paper is the crowd-sourced product of researchers in the fields of artificial life and evolutionary computation who have provided first-hand accounts of such cases. It thus serves as a written, fact-checked collection of scientifically important and even entertaining stories. In doing so we also present here substantial evidence that the existence and importance of evolutionary surprises extends beyond the natural world, and may indeed be a universal property of all complex evolving systems.

29. “World Models”, David Ha, Jür­gen Schmid­hu­ber (2018-03-27):

    We explore building generative neural network models of popular reinforcement learning environments. Our world model can be trained quickly in an unsupervised manner to learn a compressed spatial and temporal representation of the environment. By using features extracted from the world model as inputs to an agent, we can train a very compact and simple policy that can solve the required task. We can even train our agent entirely inside of its own hallucinated dream generated by its world model, and transfer this policy back into the actual environment.

    An interactive version of this paper is available at https://worldmodels.github.io/

30. https://worldmodels.github.io/

31. “ViZDoom: A Doom-based AI Research Platform for Visual Reinforcement Learning”, Michał Kemp­ka, Marek Wyd­much, Grze­gorz Runc, Jakub Toczek, Wo­j­ciech Jaśkowski (2016-05-06):

    The recent advances in deep neural networks have led to effective vision-based reinforcement learning methods that have been employed to obtain human-level controllers in Atari 2600 games from pixel data. Atari 2600 games, however, do not resemble real-world tasks since they involve non-realistic 2D environments and the third-person perspective. Here, we propose a novel test-bed platform for reinforcement learning research from raw visual information which employs the first-person perspective in a semi-realistic 3D world. The software, called ViZDoom, is based on the classical first-person shooter video game, Doom. It allows developing bots that play the game using the screen buffer. ViZDoom is lightweight, fast, and highly customizable via a convenient mechanism of user scenarios. In the experimental part, we test the environment by trying to learn bots for two scenarios: a basic move-and-shoot task and a more complex maze-navigation problem. Using convolutional deep neural networks with Q-learning and experience replay, for both scenarios, we were able to train competent bots, which exhibit human-like behaviors. The results confirm the utility of ViZDoom as an AI research platform and imply that visual reinforcement learning in 3D realistic first-person perspective environments is feasible.

32. https://research.google.com/pubs/archive/46507.pdf

33. https://www.nytimes.com/2018/03/19/health/nih-biobank-genes.html

34. https://web.stanford.edu/~dbroock/published%20paper%20PDFs/kalla_broockman_minimal_persuasive_effects_of_campaign_contact_in_general_elections_evidence_from_49_field_experiments.pdf

35. http://faculty.som.yale.edu/jameschoi/icm.pdf

36. “From the Concept of Totipotency to Biofortified Cereals”, Ingo Potrykus (2015-04-01):

    I was a college teacher when opportunity opened a path into academia. A fascination with totipotency channeled me into research on tissue culture. As I was more interested in contributions to food security than in scientific novelty, I turned my attention to the development of genetic modification technology for cereals. From my cell culture experience, I had reasons not to trust Agrobacterium for that purpose, and I developed direct gene transfer instead. In the early 1990s, I became aware of the problem of micronutrient deficiency, particularly vitamin A deficiency in rice-eating populations. Golden Rice, which contains increased amounts of provitamin A, was probably instrumental for the concept of biofortification to take off. I realized that this rice would remain an academic exercise if product development and product registration were not addressed, and this is what I focused on after my retirement. Although progress is slowly being made, had I known what this pursuit would entail, perhaps I would not have started. Hopefully Golden Rice will reach the needy during my lifetime. [Keywords: Golden Rice, biofortification, genetic engineering, public good, GMO regulation, Autobiography]

37. “Golden rice”, Eng­lish Wikipedia (2020-12-28):

    

    Golden rice is a variety of rice produced through genetic engineering to biosynthesize beta-carotene, a precursor of vitamin A, in the edible parts of rice. It is intended to produce a fortified food to be grown and consumed in areas with a shortage of dietary vitamin A, a deficiency which each year is estimated to kill 670,000 children under the age of 5 and cause an additional 500,000 cases of irreversible childhood blindness. Rice is a staple food crop for over half of the world's population, providing 30–72% of the energy intake for people in Asian countries, and becoming an effective crop for targeting vitamin deficiencies.

38. https://www.theringer.com/2018/3/21/17130490/predictit-politics-elections-gambling

39. https://www.newyorker.com/magazine/2018/04/02/are-we-already-living-in-virtual-reality

40. https://www.nature.com/articles/s41598-018-23044-8

41. /docs/iq/2017-jerrim.pdf

42. https://www.nytimes.com/2018/03/15/technology/saudi-arabia-hacks-cyberattacks.html

43. “Trusted computing base”, Eng­lish Wikipedia (2020-12-22):

    The trusted computing base (TCB) of a computer system is the set of all hardware, firmware, and/or software components that are critical to its security, in the sense that bugs or vulnerabilities occurring inside the TCB might jeopardize the security properties of the entire system. By contrast, parts of a computer system outside the TCB must not be able to misbehave in a way that would leak any more privileges than are granted to them in accordance to the security policy.

44. https://www.newyorker.com/magazine/2014/10/13/tales-trash

45. “Taxi Industry Regulation, Deregulation, and Reregulation: The Paradox of Market Failure”, Paul Stephen Dempsey (1996):

    During the last fifteen years, Congress has deregulated, wholly or partly, a number of infrastructure industries, including most modes of transport—airlines, motor carriers, railroads, and intercity bus companies. Deregulation emerged in a comprehensive ideological movement which abhorred governmental pricing and entry controls as manifestly causing waste and inefficiency, while denying consumers the range of price and service options they desire.

    In a nation dedicated to free market capitalism, governmental restraints on the freedom to enter into a business or allowing the competitive market to set the price seem fundamentally at odds with immutable notions of economic liberty. While in the late 19^th and early 20^th Century, market failure gave birth to economic regulation of infrastructure industries, today, we live in an era where the conventional wisdom is that government can do little good and the market can do little wrong.

    Despite this passionate and powerful contemporary political/economic ideological movement, one mode of transportation has come full circle from regulation, through deregulation, and back again to regulation—the taxi industry. American cities began regulating local taxi firms in the 1920s. Beginning a half century later, more than 20 cities, most located in the Sunbelt, totally or partially deregulated their taxi companies. However, the experience with taxicab deregulation was so profoundly unsatisfactory that virtually every city that embraced it has since jettisoned it in favor of resumed economic regulation.

    Today, nearly all large and medium-sized communities regulate their local taxicab companies. Typically, regulation of taxicabs involves: (1) limited entry (restricting the number of firms, and/or the ratio of taxis to population), usually under a standard of "public convenience and necessity," [PC&N] (2) just, reasonable, and non-discriminatory fares, (3) service standards (e.g., vehicular and driver safety standards, as well as a common carrier obligation of non-discriminatory service, 24-hour radio dispatch capability, and a minimum level of response time), and (4) financial responsibility standards (e.g., insurance).

    This article explores the legal, historical, economic, and philosophical bases of regulation and deregulation in the taxi industry, as well as the empirical results of taxi deregulation. The paradoxical metamorphosis from regulation, to deregulation, and back again, to regulation is an interesting case study of the collision of economic theory and ideology, with empirical reality. We begin with a look at the historical origins of taxi regulation.

    [Keywords: Urban Transportation, Taxi Industry, Common Carrier, Mass Transit, Taxi Industry Regulation, Taxi Deregulation, Reregulation, Taxicab Ordinance, PUC, Open Entry, Reglated Entry, Operating Efficiency, Destructive Competition, Regulated Competition, Cross Subsidy, Cream Skimming, PC&N, Pollution, Cabs]

46. http://www.econlib.org/library/Columns/y2008/Mungerbus.html

47. /docs/economics/2016-michaels.pdf

48. https://archive.org/stream/Fantastic_v20n02_1970-12_LennyS-cape1736#page/n85/mode/2up

49. “R. A. Lafferty”, Eng­lish Wikipedia (2020-12-28):

    

    Raphael Aloysius Lafferty was an American science fiction and fantasy writer known for his original use of language, metaphor, and narrative structure, as well as for his etymological wit. He also wrote a set of four autobiographical novels, In a Green Tree; a history book, The Fall of Rome; and several novels of historical fiction.

50. https://www.amazon.com/McNamaras-Folly-Low-IQ-Troops-Vietnam-ebook/dp/B0108H60MG/

51. /Book-reviews#mcnamaras-folly-gregory-2015

52. /docs/iq/1991-laurence-lowaptitudemeninthemilitary.pdf

53. /docs/philo/1973-kubose-zenkoans.pdf

54. /docs/history/1979-vogel-japanasnumberone.pdf

55. “Ezra Vogel”, Eng­lish Wikipedia (2020-12-28):

    

    Ezra Feivel Vogel was an American academic who became the Henry Ford II Professor of the Social Sciences at Harvard University. He wrote prolifically on modern Japan, China, Korea, and other topics related to Asia.

56. /Book-reviews#japan-as-number-one-lessons-for-america-vogel-1999

57. “Ready Player One (film)”, Eng­lish Wikipedia (2020-12-28):

    

    Ready Player One is a 2018 American science fiction action-adventure film directed by Steven Spielberg, from a screenplay by Zak Penn and Ernest Cline. Based on Cline's 2011 novel of the same name, it stars Tye Sheridan, Olivia Cooke, Ben Mendelsohn, Lena Waithe, T.J. Miller, Simon Pegg, and Mark Rylance. Set in 2045, much of humanity uses the virtual world OASIS to escape the real world. Orphaned teenager Wade Watts (Sheridan) finds clues to a hidden game that promises OASIS' ownership to the winner, and he and four allies try to complete it before a corporation run by businessman Nolan Sorrento (Mendelsohn) can do so.

58. /Movie-reviews#ready-player-one

59. “Moulin Rouge!”, Eng­lish Wikipedia (2020-12-28):

    

    Moulin Rouge! is a 2001 jukebox musical romantic drama film directed, co-produced, and co-written by Baz Luhrmann. The film tells the story of a young English poet/writer, Christian, who falls in love with the star of the Moulin Rouge, cabaret actress and courtesan Satine. It uses the musical setting of the Montmartre Quarter of Paris, France. The film is the third part of Luhrmann's "Red Curtain Trilogy," following Strictly Ballroom and Romeo + Juliet.

60. “Hells Angels (manga)”, Eng­lish Wikipedia (2020-12-28):

    

    Hells Angels is a Japanese manga series written and illustrated by Sin'Ichi Hiromoto. It was serialized in Shueisha's Ultra Jump from 2002 to 2004 and compiled into three tankōbon volumes.

61. /Anime-reviews#hells

62. “Gwern.net newsletter (Substack subscription page)”, Gw­ern Bran­wen (2013-12-01):

    Subscription page for the monthly gwern.net newsletter. There are monthly updates, which will include summaries of projects I’ve worked on that month (the same as the changelog), collations of links or discussions from my subreddit, and book/movie reviews. You can also browse the archives since December 2013.

63. https://old.reddit.com/r/gwern/.rss