Russian mammoth ivory exports have been increasing steadily, averaging approximately 17 tonnes per year for 1991-2000 and averaging 60 tonnes per year for 2001-2013.
It is estimated that the mammoth ivory beneath the tundra has the potential to cover several hundred years’ worth of current elephant ivory sales.
That is from the Farah and Boyce paper cited here.
We propose the savanna theory of happiness, which suggests that it is not only the current consequences of a given situation but also its ancestral consequences that affect individuals’ life satisfaction and explains why such influences of ancestral consequences might interact with intelligence. We choose two varied factors that characterize basic differences between ancestral and modern life – population density and frequency of socialization with friends – as empirical test cases. As predicted by the theory, population density is negatively, and frequency of socialization with friends is positively, associated with life satisfaction. More importantly, the main associations of life satisfaction with population density and socialization with friends significantly interact with intelligence, and, in the latter case, the main association is reversed among the extremely intelligent. More intelligent individuals experience lower life satisfaction with more frequent socialization with friends. This study highlights the utility of incorporating evolutionary perspectives in the study of subjective well-being.
That is from Li and Kanazawa, via Neuroskeptic, file under speculative.
In a working paper released in December 2015, the economists Naima Farah and John R. Boyce find that the discovery and exchange of mammoth tusks is having a serious effect on the market for living elephant tusks. Since the collapse of the Soviet Union, they write, tusks from dead mammoths, found in the frozen Siberian tundra, have risen to account for as much as 20 percent of all ivory production. Crunching the numbers, they conclude, “Mammoth ivory trade may be saving elephants from extinction.” In the long run, however, it may be too optimistic to believe that such a laissez faire solution can forestall wild elephant extinction.
Most of the article, by Greg Rosalsky, deals with how researchers are using data (!) to determine whether woolly mammoths did indeed fall prey to the tragedy of the commons.
Language barriers while traveling the world may become a problem of the past with the advent of new technology. The latest craze in the tech world was recently unveiled at the 2016 Electronics Show: a wearable translator. The Japanese startup Logbar plans on releasing the portable translator called the “ili” this summer. The actual device looks like an Apple TV controller and is hung around your neck.
With the press of a button, the device is allegedly capable of simultaneous translation.
There is more here by David Grasso, including a video, from bold.global.
1. The subtitle is A New Theory of Chinese History, and volume one has just been translated and published from the Chinese.
2. The author, Dingxin Zhao, now is a Professor of Sociology at the University of Chicago.
3. The book has a curious 19th century air to its intellectual influences. The main argument uses Herbert Spencer to revise Michael Mann, a 20th century British sociologist who wrote on the sources of power. Lamarckian ideas are deployed frequently.
4. The Western model has had four independent power sources: states, churches, aristocracy, and the urban bourgeoisie.
5. Neither merchants nor religion had much of a strong, independent role in early Chinese politics. Only the state and the aristocracy were powerful actors.
6. In the model of this book, the dual forces of competition and institutionalization drive historical change. More than anything else, individuals maximize power.
7. The empowerment of economic power by ideology is the most fundamental feature of modernity.
8. “Three pivotal institutions of Western Zhou origin exerted an enduring impact on the history of China: the Mandate of Heaven, the kinship-based “feudal” system, and lineage law.” (p.79)
This is not an easy work to parse, but it is a book of substance and it reflects a considerable amount of careful thought.
Hanging up on annoying telemarketers is the easiest way to deal with them, but that just sends their autodialers onto the next unfortunate victim. Roger Anderson decided that telemarketers deserved a crueler fate, so he programmed an artificially intelligent bot that keeps them on the line for as long as possible.
Anderson, who works in the telecom industry and has a better understanding of how telemarketing call-in techniques work than most, first created a call-answering robot that tricked autodialers into thinking there was an actual person answering the phone. So instead of the machine automatically hanging up after ten seconds, a simple pre-recorded “hello?, hello?” message would have the call sent to a telemarketer who would waste a few precious moments until they realized there really wasn’t anyone there.
But Anderson then wondered just how long his robot could keep a telemarketer on the line for. It turns out, for surprisingly long.
…Here’s the best part: anyone can connect telemarketers calling and harassing them to Anderson’s auto-responding robot using the simple instructions he’s posted to his site…
There is more here, via HarpersNotes.
‘I’m just starting out as a collector,’ Sebastian begins. ‘I only own prehistoric shark teeth and I have a fossil of a prehistoric squid from way before the dinosaurs, and I got a Utahraptor bone shard I think from my kindergarten teacher, she’s an amateur palaeontologist who first got me into dinosaurs, and I have dinosaur poop but I think you should put in the word “coprolite”. That’s the technical term.’ He thinks some more. ‘Oh, I have mosasaur teeth, that’s a very cool prehistoric aquatic reptile. Imagine owning a T rex though! I would like to own any complete dinosaur, I don’t care which one.’
To my surprise, Sebastian doesn’t see an emotional difference between owning dinosaur toys and owning real dinosaurs, and he hints at a dimensionless state he enters using imagination. ‘If I look at a toy giganotosaurus, it feels the same as looking at a real giganotosaurus, which I have only seen once in a museum. I really see the same thing when I’m looking at my toy. I forget that the real dinosaur is way bigger. My toy is just as big in my mind.’
That is from an interesting Laurie Gwen Shapiro Aeon article, hat tip goes to Anecdotal.
As tech firms and law enforcement experiment with radio jammers and net-wielding interceptor drones to take down rogue quadcopters, police in the Netherlands are trialling a simpler solution: eagles. The country’s law enforcement has teamed up with a raptor training company named Guard From Above to see if birds of prey can be used to safely intercept quadcopters.
In the video demonstration above, an eagle is seen easily plucking what looks like a DJI Phantom out of the air. However, it’s not clear how dangerous this is for the bird.
File under “But at a price.”
The article is here, with an excellent video, and for the pointer I thank @alsoyourbrother.
That is a new paper by Ali Faraji-Rad and Michel Tuan Pham, here is the abstract:
Uncertainty is an unavoidable part of human life. How do states of uncertainty influence the way people make decisions? We advance the proposition that states of uncertainty increase the reliance on affective inputs in judgments and decisions. In accord with this proposition, results from six studies show that the priming of uncertainty (vs. certainty) consistently increases the effects of a variety of affective inputs on consumers’ judgments and decisions. Primed uncertainty is shown to amplify the effects of the pleasantness of a musical soundtrack (study 1), the attractiveness of a picture (study 2), the appeal of affective attributes (studies 3 and 4), incidental mood states (study 6), and even incidental states of disgust (study 5). Moreover, both negative and positive uncertainty increase the influence of affect in decisions (study 4). The results additionally show that the increased reliance on affective inputs under uncertainty does not necessarily come at the expense of a reliance on descriptive attribute information (studies 2 and 5), and that the increased reliance on affect under uncertainty is distinct from a general reliance on heuristic or peripheral cues (study 6).
The pointer is from Cass Sunstein on Twitter. File under “The culture that is Iowa”?
Although Stockfish and Komodo have differences in their evaluation scales—happily less pronounced than they were 1 and 2 years ago—they agree that the world’s elite made six times more large errors when on the lower side of equality.
We don’t know how general this phenomenon is, but interestingly it seems to hold much more strongly for top players than for weak players. That is from chess of course.
Here is much more detail from Ken Regan, along with some suggested hypotheses and resolutions.
From Foreign Affairs. Here is my bottom line:
His latest entry into this debate [over stagnation], The Rise and Fall of American Growth, is likely to be the most interesting and important economics book of the year. It provides a splendid analytic take on the potency of past economic growth, which transformed the world from the end of the nineteenth century onward.
In a nutshell, Gordon is probably right about the past, but wrong about the future. My greatest reservation about the work is that Gordon thinks he can predict future rates of productivity growth with considerable accuracy:
…although Gordon focuses on the demographic challenges the United States faces, he never considers that today, thanks to greater political and economic freedom all over the world, more individual geniuses have the potential to contribute to global innovation than ever before.
…many past advances came as complete surprises. Although the advents of automobiles, spaceships, and robots were widely anticipated, few foretold the arrival of x-rays, radio, lasers, superconductors, nuclear energy, quantum mechanics, or transistors. No one knows what the transistor of the future will be, but we should be careful not to infer too much from our own limited imaginations.
And here is the “oops” aspect of the book:
What Gordon neglects to mention, however, is that he is also the author of a 2003 Brookings essay titled “Exploding Productivity Growth,” in which he optimistically predicted that productivity in the United States would grow by 2.2 to 2.8 percent for the next two decades, most likely averaging 2.5 percent a year; he even suggested that a three percent rate was possible.
…Gordon offers a brief history of the evolution of his views on productivity. Yet he does not mention the 2003 essay, nor does he explain why he has changed his mind so dramatically. He also fails to cite other proponents of the stagnation thesis, even though…their work predates his book.
Nonetheless this is a tract well worth reading. Again, here is my entire review.
The excellent Susan Athey addresses that question on Quora, here is one excerpt:
Machine learning is a broad term; I’m going to use it fairly narrowly here. Within machine learning, there are two branches, supervised and unsupervised machine learning. Supervised machine learning typically entails using a set of “features” or “covariates” (x’s) to predict an outcome (y). There are a variety of ML methods, such as LASSO (see Victor Chernozhukov (MIT) and coauthors who have brought this into economics), random forest, regression trees, support vector machines, etc. One common feature of many ML methods is that they use cross-validation to select model complexity; that is, they repeatedly estimate a model on part of the data and then test it on another part, and they find the “complexity penalty term” that fits the data best in terms of mean-squared error of the prediction (the squared difference between the model prediction and the actual outcome). In much of cross-sectional econometrics, the tradition has been that the researcher specifies one model and then checks “robustness” by looking at 2 or 3 alternatives. I believe that regularization and systematic model selection will become a standard part of empirical practice in economics as we more frequently encounter datasets with many covariates, and also as we see the advantages of being systematic about model selection.
…in general ML prediction models are built on a premise that is fundamentally at odds with a lot of social science work on causal inference. The foundation of supervised ML methods is that model selection (cross-validation) is carried out to optimize goodness of fit on a test sample. A model is good if and only if it predicts well. Yet, a cornerstone of introductory econometrics is that prediction is not causal inference, and indeed a classic economic example is that in many economic datasets, price and quantity are positively correlated. Firms set prices higher in high-income cities where consumers buy more; they raise prices in anticipation of times of peak demand. A large body of econometric research seeks to REDUCE the goodness of fit of a model in order to estimate the causal effect of, say, changing prices. If prices and quantities are positively correlated in the data, any model that estimates the true causal effect (quantity goes down if you change price) will not do as good a job fitting the data. The place where the econometric model with a causal estimate would do better is at fitting what happens if the firm actually changes prices at a given point in time—at doing counterfactual predictions when the world changes. Techniques like instrumental variables seek to use only some of the information that is in the data – the “clean” or “exogenous” or “experiment-like” variation in price—sacrificing predictive accuracy in the current environment to learn about a more fundamental relationship that will help make decisions about changing price. This type of model has not received almost any attention in ML.
The answer is interesting, though difficult, throughout. Here are various Susan Athey writings, on machine learning. Here are other Susan Athey answers on Quora, recommended. Here is her answer on whether machine learning is “just prediction.”
Tic-tac-toe fell in 1952, checkers in 1994, chess in 1997 and it now looks like Go, the ancient Chinese game that has a search space many, many times greater than chess, has fallen to a new AI from Google.
…our program AlphaGo achieved a 99.8% winning rate against other Go programs, and defeated the human European Go champion by 5 games to 0. This is the first time that a computer program has defeated a human professional player in the full-sized game of Go, a feat previously thought to be at least a decade away.
Importantly, AlphaGo isn’t based primarily on searching a huge space but on deep neural networks that learned first from human players and then from simulated play with itself. The techniques, therefore, are not limited to Go.
AlphaGo will face its greatest challenge in March.
AlphaGo’s next challenge will be to play the top Go player in the world over the last decade, Lee Sedol. The match will take place this March in Seoul, South Korea.
Win or lose, I will bet that Lee Sedol is the last human champion the world will ever know.
That is the title of the new and forthcoming Robin Hanson book, due out in May. I was asked to supply a blurb, and offered two possibilities. One was:
“Robin Hanson is one of our most original and important thinkers. This is his book.”
The ostensible premise of the book is that people have become computer uploads, and we have an entirely new society to think about: how it works, what problems it has, and how it evolves. One key point about this new world is individuals can be copied. But this is more than just a nerdy tech book, it is also:
- Straussian commentary on the world we actually live in. We are already something-or-other, uploaded into humans,and very often Robin is describing our world in cloaked fashion, albeit with some slight tweaks to parameters for the purpose of moral illumination.
- A reminder of how strange everything is, and how we use self-deception to come to terms with that strangeness. It’s a mock of all those who believe in individual free will.
- An attempt to construct a fully rational theology, proving by various deductions that God is not fully benevolent in the traditional sense.
- An extended essay on the impossibility of avoiding theology, given the imposition of competitive constraints on a world where production and copying are possible. And ultimately it is a theodicy, though it will not feel that way to Westerners, Jews, Christians, or Muslims. It hearkens back to medieval theology, Descartes, and the idea of living in God’s possibly terrifying simulation.
- A satire on the rest of social science, and how we try to explain and predict the future.
- A meta-level growth model in which energy alone matters and the “fixed factor” assumptions of other models are relativized. Copying is taken seriously, besides how special are you anyway? In the meantime, we learn just how much of the world we know depends upon the presence of various fixed factors. But surely that is temporary!
- A challenge to our notions of wherein the true value of a life resides.
I hope enough readers pick up on some of this. And yes, there is a chapter on sex, love, and affairs.
It is hard to excerpt from this book, but here is one short bit:
Compared with humans, ems fear much less the death of the particular copy that they now are. Ems instead fear “mind theft,” that is, the theft of a copy of their mental state. Such a theft is both a threat to the economic order, and a plausible route to personal destitution or torture. While a few ems offer themselves as open source and free to copy, most ems work hard to prevent mind theft. Most long-distance physical travel is “beam me up” electronic travel, but done carefully to prevent mind theft.
I am wildly enthusiastic about everything the Robin upload does, and some of his copies are better yet. Here is the book’s home page.
Addendum: Here is Robin Hanson’s response.