Time for another business report, dedicated to the patron saint of sales— St. François de Sales.

The occasion is that total sales for all books, over nine years, have just kicked over 30,000.  Here’s a breakdown by title:


As you can see, nearly half of sales (almost 14,000) are due to the LCK alone. Of course, it’s been out for the longest. A chart of just the last 3 months looks similar, except

  • the Syntax Construction Kit is a much bigger slice, on par with the other linguistics books and the PCK
  • the China and India slices are a tad wider; they sell about three times the rate of the novels

Print books make up 61% of sales, which is about the same as it was in the last report. Print is not dead!

What else can I tell you?

  • People either don’t care for hardcovers, or don’t know they can buy them. (You can get hardcover versions of the LCK and the Lexipedia. And it’s worth it; they stand up to constant use much better.)
  • About 15% of sales are from outside the US.
  • I’ve had 11 clients for whom I created conlangs.
  • The smallest slice in the above chart, the Historical Atlas, is 100 copies, which is pretty good for a fictional history.



Connie-5I received my second proof, and my name is spelled correctly on both the cover and the title page, so I’ve approved it! The printers are standing by, ready to roll press for you, the reader.

Here’s my description page, and here’s the Amazon page. If you have no idea what syntax is, or what a syntax book is, start there.

If you’re an e-reader, you’ll have to wait a few days. The Kindle version takes some extra preparation, as all the nice Illustrator diagrams must be converted to the high-tech formats of the early 1990s, GIFs or JPEGs. I’ll mention it here when it’s done. (Oh wait. Another page says they accept PNGs. That’s late ’90s!)

Don’t be a drag who has a two-page syntax page in your grammar. All the cool kats will be tripping on the real syntax in this book.


I just finished Language acquisition and conceptual development, edited by Melissa Bowerman and Stephen Levinson (2001), and I want to write down what I learned while it’s still fresh in my mind.

You may recall the book report on Everett & Jackendoff and their feud over innatism. The issue there is Chomsky’s longstanding contention that language learning is far too hard for children, therefore they must have a head start: grammar and vocabulary are already hard-wired into their brains. All they have to do is figure out which of a small series of switches to flip to get the grammar (“oh! verbs go last here!”) and work out that dog means Inbuilt concept #293291.

This book is a report from the trenches of language acquisition; if anyone knows how it goes, these people do. I note, by the way, that this is one of the few fields dominated by women: 20 of the 30 authors of these papers are female. Yay for linguistics!

There is no knockout punch— unsurprisingly, there’s a lot we don’t know about how children learn languages. And this book, at least, doesn’t have too much to say about how children learn syntax, much less whether they do so using Minimalism, Arc Pair Grammar, Role & Reference Grammar, etc. It’s mostly about the first three years, the first words learned, and what that tells us about children’s conceptual system.

The biggest news seems to be:

  • Children understand things far earlier than was once supposed. E.g. Piaget thought that children didn’t acquire the notion of object permanence till 3 years or so; we now know they have it at 5 months. He also thought that children didn’t understand the concept of time till about 8; but in fact they are clearly able to remember and refer to past events, and anticipate and refer to future events, at not much more than 1 year of age.
  • At the same time: universal, basic concepts are more elusive than ever. Languages really do divide up conceptual space differently, and this is evident in children’s speech from the beginning.

The object permanence result is due to better, cleverer technique: rather than relying on the baby’s actions, we only check what they’re looking at. Basically: babies can be surprised, and look longer at unexpected outcomes. So you show them a doll being placed behind a screen, then remove the screen. They’re surprised if they see no doll there, or two dolls.

Many of the authors refer to Quine’s problem. Quine envisioned a linguist eliciting words from a native. A rabbit goes by, and the native says gavagai. Does this mean “rabbit”, or “hop”, or “fluffy tail”, or “unspecified set of rabbit parts”?

Now, the linguists can’t bring themselves to say that Quine is just being a jerk. But there’s a pretty clear answer to this problem: we aren’t tabulae rasae; we’re animals with a hundred-million-year evolutionary history of perceiving objects, especially moving objects, and double especially animals. Some things are very salient for humans— we’re built to see rabbits as objects with a characteristic shape, size, and activity pattern. We’re not built to focus on rabbit tails or miscellaneous rabbit parts.

Early proposals were that children use some all-purpose generalizations: words are likely to refer to the most salient entities; words are normally not synonymous.

Going beyond this, there were assumptions that children would learn nouns before verbs, closed-class form words before content words, shape before materials, and that they would probably learn universal concepts first. This little list of assumptions turns out to be wrong: it depends on the language.

  • Many languages are far more verb-oriented than English. Kids still learn a lot of nouns, but sometimes the proportion of verbs is far higher.
  • Often very specific verbs are learned before abstract spatial words.
  • English children learn to pay the most attention to shape; Maya kids pay the most attention to material.

As for universal concepts, it’s worth looking in detail at an example provided by Levinson. The language is Tzeltal.

Pach-an-a bojch ta y-anil te karton-e.
bowl-put-cause.imp gourd at its-down cardboard-that

The intended translation is “Put the bowl behind the box.” But just about every detail in Tzeltal is different.

  • The shape and spatial information is largely encoded in the verb, not in nouns. Pach– means “place a bowl-shaped vessel upright on a surface.”
  • Corollary: the two NPs refer mostly to material. Bojch is really a word for a gourd; karton can refer to anything made of cardboard.
  • “Behind” is a relative term, which doesn’t exist in Tzeltal. Instead, an absolute frame of reference is used. “Downward” can refer to absolute height, but here it refers to horizontal location, because of a geographical particularity: Tzeltal territory is on a slope, so “downhill” also means “northward”.

Do children really master this system? Of course; they have a pretty good grasp of the slope system by age three. They also master a wide range of very specific verb forms rather than relying heavily, as English-speaking toddlers do, on “up/down”.

Another neat example: English toddlers quickly learn to distinguish “put ON” from “put IN”. Korean children divide up this semantic space quite differently, using at least seven verbs.

  • kkita means “fasten tightly”– this includes putting the top on a pen, placing Lego bricks together, putting a piece in a puzzle, placing a cassette in its box, or buttoning a button.
  • nehta means “place loosely”– e.g. put a book in a bag, or a toy in a box.
  • pwuchita is used for juxtaposing surfaces– e.g. placing a magnet on the fridge.
  • nohta is used for placing things on a horizontal surface.
  • for clothes, you have ssuta for hats, ipta for the body, sinta for the feet.

All this is fascinating because philosophers and linguists are apt to take English categories and assume they are universal concepts: UP, DOWN, IN, ON. Nope, they’re just projecting English words onto Mentalese. There is no stage where children use “universal” concepts before using language-specific ones. (Indeed, there’s evidence that children understand the language-specific concepts well before they can say the words.)

Does all this “affect how you think”? Of course. Levinson tells an amusing anecdote: he almost got his truck stuck in quicksand when his Australian Aborigine companion told him to “swerve north quick”. Levinson just couldn’t calculate where north was fast enough.

There’s also interesting tidbits like, did you know that there is a gradient between comitative and instrumental? It goes like this:

1 – give a show with a clown
2 – build a machine with an assistant
3 – captured the hill with his squad
4 – performed an act with an elephant
5 – the blind man crossed the street with his dog
6 – the officer caught the smuggler with a police dog
7 – won the appeal with a highly paid lawyer
8 – found the solution with a computer
9 – hunted deer with a rifle
10 – broke the window with a stone

In English, as you can see, we use “with” for all of these. In a multitude of languages, these meanings are divided up linearly. E.g.

  • Iraqi Arabic: 1-8 vs 9-10
  • Swahili: 1-6 vs 7-10
  • Slovak: 1-9 vs 10
  • Tamil: 1-2 vs 3-10

That’s pretty neat!

Anyway: there’s still a lot of argument on how exactly children learn, whether they start with particular cognitive abilities, whether they have particular linguistic abilities. Many authors point out that innatism doesn’t really help reduce the problem. E.g. to see if dog matches Inbuilt concept #293291, you pretty much have to have a sense of what a dog is. If you have that, what good is the inbuilt concept?

You could try to save innatism by multiplying the number of inbuilt concepts. E.g. you include the 10 steps of the comitative/instrumental gradient, and both Korean and English positioning concepts, and both English and Tzeltal directional systems. But this is only complicating the child’s problem. Rather than finding quick matches between the words they hear and a small number of universal concepts, they have to consider hundreds or thousands of alternative conceptual systems.

It’s also worth pointing out that parents are far more helpful than Quine’s native informant. People don’t just say words at random. As Michael Tomasello emphasizes, language is often presented as a commentary on a situation the child already understands, such as moving toys around with her mother. There’s a lot of repetition; the parents’ language is emphatic and simplified; the parents are not trying to confuse the child with talk of bags of rabbit parts.

BTW, this is in theory the last book I’m consulting for my syntax book.  So, I’ll soon have a first draft, at least.


For those smart enough to check here for status updates…

  • The new board is kind of installed, but doesn’t work.
  • Bluehost guy didn’t know why.

Edit: The board is working again. The secret code is 676 for now. The whole sordid story is over on the board.

Which is progress, in a way, since yesterday when it totally didn’t work.

All this is after trying to import the old database from the old board.  Although I was following instructions on moving boards, the result was a disaster. The software and database are not independent; I expect it failed because the phpBB versions are different. Finally I gave up and tried reinstalling phpBB, but that didn’t work.

Sigh. Bottom line, still working on what should be the simplest part: getting a frigging blank board working.

I actually picked this up when it came out, but never finished it.  And I still haven’t, but I’m playing it again, and I’m almost done, so I’d might as well write a review.


I sure hope there’s a zipline so I can get down there

Re-reading my review of the reboot, I’m struck by how many things I didn’t like that they’ve now fixed:

  • Basically no Quicktime events. (There are a few “run away across a collapsing building” scenes, but they generally use moves you’ve mastered anyway.)
  • Not as many cutscenes in general. You mostly get to control your own camera!
  • No gallery of ‘friends’ who do nothing to help and are there mainly to get in trouble and/or get killed.
  • No snotty ‘friend’ who’s set up as the obvious betrayer.  (There is a betrayer, but they provided a plausible motivation this time.)

Almost all of the game is set in Siberia, looking for the lost city of Kitezh. Now, Kitezh is a city from Russian folklore which supposedly resisted the Mongol invasion by slipping into a lake.  A Rimsky-Korsakov opera has it becoming invisible instead.  Also it’s supposedly near Nizhny Novgorod, which is not in Siberia.

Whatevs. Here it’s founded by Byzantines, who’ve come from Syria, led by their Prophet, who seems to have the secret of eternal life.  An evil cult named Trinity wants this, and so does Lara Croft, but in a much nicer way.

On the plus side:

  • It’s really pretty; the mountains and the forests and the various ruins are very well done.
  • It has much bigger areas to explore, and you’re able to mess around all you like. (There were some small hubs in the first game, but there wasn’t much to do in each one, and the plot was always hurrying you on.)
  • I like Lara’s voice actress, Camilla Luddington. There’s much less character development this time, so most of the work of making Lara likeable comes down to the voice acting, and Luddington makes her sound earnest and concerned. OK, that may sound dull, but compare that to your basic space marine, who usually sounds indifferent and/or bombastic.
  • There is less emphasis on “hard enemies which mess with what you’ve learned so far”, which is fine by me. E.g. you have armored enemies, but you also have some good options against them.
  • The basic gameplay loop is fun.  Sneak around, shoot arrows or bullets at people, solve some physics puzzles, do some mild parkour. Everyone who’s passed through Kitezh– Trinity, the Byzantines, the Mongols, the Soviets– is fond of leaving ruins which can only be traversed with Lara’s particular gear– climbing pick, rope arrows, etc. Some places even give you a choice of route!

Honestly the open world aspect can be wearing.  There are optional tombs scattered around– you’d might as well do as many as you can, since they offer perks. There are challenges and optional missions and resources to pick up and animals to hunt and relics to find and… well, if you like that sort of thing, there’s a lot of it. The first time I played the game, I put it aside halfway through, and I think it’s because of all this cruft. I feel like I should do everything, but it becomes a chore.

I’m enjoying it more now, mostly because I’ve given myself permission to skip anything I don’t feel like doing. Anyway, in general, this isn’t a Bethesda game, where the main questline is the dullest of them all.  They put the most work into the main story.

On the minus side–

  • I don’t mind dying to enemies– the fights always seem fair, and if I die it’s my fault and it’s usually easy to see why.  But I hate dying because I missed a jump, especially if it has fiddly positioning or timing to it. The game doesn’t even have the excuse of Mirror’s Edge, that it’s about split-second button presses. Rather than falling to her death, Lara should recover, like Batman.  (You could make her repeat the last bit, if you want mistakes to have a cost.)
  • The plot idea of ‘vindicating Dad’ is far less interesting than the first game, which moved Lara from frightened young girl to badass warrior woman. Once she’s that badass, there’s little she really needs, so the emotional temperature drops a bit.

There’s another sequel coming out later this year, so I hope I’ve put Trinity down by the time it comes out.

Edit: Finished it tonight… I really wasn’t far from the end. The final boss fight isn’t terribly hard, which is also fine by me.

Though they lost the character arc from the first game, I think the story here is a lot more meaningful. The story in the first game is more or less “try to escape this extremely dangerous island which Lara’s dad for some reason wanted to get to.” Here, it’s all related to having the secret of immortality… it’s not any more believable, but at least you can see that it has big consequences which explain why everyone is after it.




There’s been a lot of worry lately that robots will take all of our jobs. Should you be worried? Should you try to make friends with the robots so they treat you nicely?


This would be bad

Now, there’s a lot to say here, so here’s the tl;dr: no, this is only moderately worrisome. What you should worry about instead are:

Worries about automation go back to the beginning of the industrial revolution, two hundred years ago. But, with some major caveats, automation is good!  After 200 years,

  •  Life for the majority of people is far better. Before automation, 90% of the people lived by subsistence agriculture, one bad harvest or pestilence or war away from death. And those scourges came almost constantly.
  • Americans, as usual, focus on bad things in America, and don’t realize that these are boom times for most of the world. Global poverty is way down; it’s never been a better time to be Indian or Chinese.
  • Despite all the worries about machines taking our jobs— they haven’t. US unemployment is currently under 5%—  which is about as low as it’s gotten in my lifetime.
  • In general, pre-automation jobs sucked. There’s a tendency to romanticize lost jobs, but you really do not want to be a cotton picker, or a miner, or a laundrywoman, or a data entry typist.

The thing is, at any point in the last 200 years, an alarmist could concoct a tale of machine devastation. With modern farming techniques we don’t need 90% of the population to work on farms. Omigod that means 90% of the population will lose their jobs!  Only, this didn’t happen. Only 1.4% of the US population works on the farm today; the rest of the 90% found other jobs.

Now, the major caveat: this process sometimes goes smoothly, but sometimes is hella disruptive. It’s not pleasant when a middle-aged person has to change careers, whether it’s an 1800s agricultural worker, or a 1980s steelworker. Whole regions can be devastated and not know how to pick themselves up.

Jane Jacobs had a lot to say about what happens when the process goes well, and when it doesn’t. She calls the successful places city regions; as the name implies, these are always near big cities. In brief, this is the belt round a city where automation produces new opportunities as fast as it erodes old jobs. In a city region, there is new work to do, and it doesn’t take a lot of intervention for people to find it. (The books on India I recently read are also good introductions to this process. Poor people are amazingly entrepreneurial when they get the chance.)

You can’t count on everyone to live in a city region, but you can manage the disruption in other ways. This is where you need a strong economic safety net. You want people to be able to change jobs.  It’s not a huge exaggeration to say that the New Deal succeeded because it cushioned the disruption of industrialization. Stimulus spending spurred production and job creation; Social Security allowed people to move to where the jobs were without abandoning their old folks; unemployment insurance kept people going between jobs; the GI Bill trained people for new occupations. Europe went farther, with universal healthcare and free university education.

(Do you want a universal basic income?  Go for it, so long as you’re not actually looking to reduce government benefits. But it’s a good idea on its own; there’s no need to drag the robots into it.)

OK, but aren’t the robots different this time?  They can drive cars now! They can take your order at McDonalds! Surely all the jobs will disappear!

The first thing I’d point out is, extrapolation is a crappy guide to the future. In 1890 you could predict that the cities of the future would be buried ten feet deep in horse manure. This didn’t happen.

Second, universal AI is a huge assumption. If you look at sf and pop-sci articles, humanoid robots are ten years away, and have been for a hundred years. The first robot story appear, Karl Čapek’s RUR, appeared in 1920. Basically, intelligence is a pretty hard problem, and researchers always underestimate it. It’s easy to feel (as I did when I was an undergraduate) that a pretty good AI would be just a few semesters of work. Well, it isn’t, or it’d be done by now!

Also, I spent years as a programmer, so I know just how stupid computers are. They are great tools, mind you! But I don’t think we should scare ourselves about their abilities, at least not yet.

The better question is, what sort of jobs can computers or robots do? The general answer: jobs that are

  • repetitive and predictable
  • expensive

Automation is not, er, automatic. It takes analysis, programming, and testing, and someone has to pay for all that. That’s why a repetitive assembly-line task, done by a high-pay union worker, is the first candidate for automation.  It’s barely worth it to replace a waiter (especially since they can be hired for far less than minimum wage).

(Driving is a weird case. I think AI driving is far less advanced than it seems. As in much of programming, you can cover 90% of the work of the program and still only be 10% done.  The unexpected or difficult cases take most of the effort.)

Let’s put it the other way. What jobs are probably safe from automation in this century? Some of these, I’d wager:

  • teacher
  • physician
  • nurse
  • CEO
  • programmer
  • athlete
  • writer
  • comics artist
  • prostitute
  • craft brewer
  • video game designer
  • marketing & sales
  • legislator
  • soldier
  • actor
  • day care worker
  • hair stylist
  • product designer
  • scientist
  • thug
  • organic produce farmer
  • architect
  • call center operator
  • plumber
  • robot designer
  • robot mechanic
  • robot debugger
  • cook
  • valet
  • monk/nun
  • preacher
  • personal trainer
  • psychologist
  • web designer
  • lawyer
  • burglar
  • drug dealer
  • cop
  • spammer
  • SEO farm writer
  • AI researcher
  • anti-AI pundit

Many of these jobs, though not all, involve what humans are best at: dealing with humans. I don’t think anyone cares that their cotton be hand-picked. I think it’ll be a long time before there’s a robot you would entrust your one-year-old to all day.

I have a friend who’s an architect. I’d say his work is at least half talking to clients, and managing building projects— i.e., managing other people (contractors and inspectors). There’s that human thing again. For making the actual plans, he already uses a computer. He can already produce a plan almost as fast as he can come up with an idea.

So the better question is not “Could a robot entirely do this job?” but “What could a computer-assisted person do in this job?” Lawyers, for instance, are often still stuck in the world of paper. Automation would allow them to take on more cases. (For good or for evil.)

I’ve purposely included some “bad jobs” on the list, because the point isn’t that “things will be fine.” But I’ll get back to the grim meathook future below.

I haven’t tried to anticipate what the new jobs of 2100 might be, but we can expect that there will be plenty of entirely new things. Over 200 years, we’ve moved from an agriculture economy, to a manufacturing economy, to a service economy.  I’ve suggested before that what’s next is a frivolity economy.

Another point that I think worriers-about-robots miss: Robots and programs cost money. As one datapoint: Bitcoin mining presently consumes as much energy as the entire nation of Tunisia.

Plus, if you’re really pessimistic about the uses of humans— then the cost of hiring a human will plummet. Humans can be raised quite cheaply, without the use of high-cost metals and rare earths, and they’re really pretty versatile.

I’ve written before about why humanoid robots are a dumb idea. I realize that many people really want them, but I’d answer that they only think they want them. You do not actually want a sentient android to be your sex worker, household cleaner, or driver, precisely because a sentient android can do what it wants, not what you want. Maybe you want a robot you can talk to— but speech is a terrible medium for giving technical instructions.

We’re way too influenced here by science fiction. We grew up thinking of the Jetsons’ robot maid, or C3PO. In fact, a bulky robot maid holding 19th century tools in her 21st century manipulators is awfully poor design. Consider all the household automation we already have: dishwashers, microwaves, vacuums, washing machines. Not a single one of them is humanoid, not a single one does its tasks as a human would. Honestly, automation of the house is almost done, compared to the year 1900. But if you want more, a better model would be the room-cleaning bots seen in The Fifth Element.

Here’s another way to think about the whole situation.  Again, 90% of the population used to be engaged in subsistence agriculture. That basically means that the entire population can do what the 10% did before. Or to put it another way, there are 325 million Americans. One way to explain our economy to someone from 1800 is that we’re as rich as a country of 3.25 billion people would have been in their time.

If we continue to automate predictable high-repetition tasks, maybe another 90% of current jobs disappear.  But the population will live like today’s 10% do. Their standard of living will be far higher, and their jobs on the whole more interesting than today’s. (Of course we’re writing sf at this point, so you’d might as well look at my attempt at an sf future.)

That doesn’t mean we won’t have a grim meathook future. Piketty has warned that our future might look like… the 19th century, when most income and wealth went to a tiny class— and not even a class of innovators and entrepreneurs, but a useless rentier elite. And of course right now as I write, a clown car of reactionaries is trying to take away tens of millions of people’s health care, while the clown-in-chief is demonizing trans men and women in uniform.

But that’s the thing: grim meathook future is a political choice. Automation is just a form of productivity increase— and productivity gains do not have to go entirely to the rich. They used to help out everyone.  Around 1980, American voters decided to stop helping out everyone, and help out only the top 10%.

If that continues, the future will be grim, robots or no.  But it’s not the nature of automation that is the threat. It’s whether we manage it under plutocracy or not.



If you’ve been following this blog, you may be thinking that I haven’t read much about India lately. On the contrary! I’ve been reading plenty, but a lot of it is pretty dry.

The exception is Tales of the Ten Princes (Daśa Kumāra Carita), by Daṇḍin, which I just finished. Your first question will undoubtedly be, why isn’t it Daśa Kumārāḥ, in the plural? Or even Daśānām Kumārāṇām, in the plural genitive? I’m pretty sure it’s because the title is a compound, i.e. Daśakumāracarita (दशकुमारचरित), and only the last root in a compound is declined.

Daṇḍin lived in Kāñcī, in the Tamil region, sometime around 700.  He’s also known for a work on literary stylistics, Kāvyādarśa. In that work he describes two ways of writing Sanskrit, the simpler style of the south, and the ornate style of the east. Ten princes is written mostly in the simpler style; perhaps to show his mastery of the ornate style, Daṇḍin also wrote a work (unfortunately lost) which, making use of the amazing number of synonyms in classical Sanskrit, is a simultaneous recounting of both the Rāmāyaṇa and the Mahābhārata.

So, on to the Princes. It’s basically a set of short stories linked by a framing device. In the frame, the king Rājahaṃsa loses his kingdom and escapes to a forest. However, his wife is pregnant, and there is a prophecy that the child will restore the kingdom. The boy grows up to be Rājavāhana, hero of the story. He grows up in the forest, and in a quick sequence, is joined by nine companions— sons of ministers as well as kings’ sons conveniently mislaid in the forest.

They grow up into strapping young lads, and finally go out seeking conquests.  Almost immediately Rājavāhana is invited into a quest in the netherworld. His companions separate and wander all over India seeking him. In each of the stories a prince comes to a city, falls in love, and by various manners becomes a king. Finally they all find each other and each narrates his story.  Then, of course, Rājavāhana regains his kingdom with their help, in effect becoming emperor, with his friends as kings under him.

The stories are short, unlikely, and a lot of fun.  They’re picaresque— indeed, many are cheerfully amoral. Though Rājavāhana himself is heroic, not a few of the princes resort to fraud, murder, or theft. It’s a good corrective if, like me, you’ve been reading rather a lot about Indian religions. There’s a whole lot of kāma (love) and plenty of artha (ambition), only a minimum of dharma (righteousness).

For example, the predicament of the prince Mantragupta is that his beloved, the princess Kanakalekhā, has been taken in a raid by the king of a neighboring land, Jayasiṃha. The princess pretends to be possessed by a yakṣa (a type of demon), but this will only put off the king temporarily.

Mantragupta finds a way, however. He goes to the king’s city and pretends to be a powerful ascetic, one who knows all the Vedas, can cure all illnesses, and has supernatural powers. Jayasiṃha is taken in; he comes to see the sage and asks for help with the yakṣaridden maiden. Mantragupta agrees to help: the king must merely bathe in a certain pool, and he will be transformed into a body which the girl will find irresistible. He must have his army secure the pool first, of course. The king agrees.  (However unlikely the strategies proposed in this book, the other characters invariably go along.)

But Mantragupta has previously made a secret recess in the pool which has an underwater exit. When the king comes and waits in the water, Mantragupta comes out, strangles him, and hides the body in the recess. He comes out, pretending to be the king in his new body.  He rescues his princess and enjoys his new kingdom.

In another chapter, there’s an amusing passage where a king’s friend give him advice that is exactly contrary to Kauṭilya or Manu. E.g., one of the traditional sins of kings was gambling. The friend gives this advice:

Gambling too has merits. The renunciation of quantities of wealth, as if it is no more than straw, gives an incomparable liberality of the temperament. The uncertainty of gain or loss makes the heart impervious to joy or sorrow. The capacity increases for wrath, the prime fount of valor. The observation of exceedingly subtle legerdemain with dice and sleights of hand provides an infinite sharpness to the intellect. Concentration on one subject assures an exceptional single-mindedness.  Delight increases in daring, the companion of enterprise. Competition with the strong-minded makes for self-confidence, indomitability and magnanimity.

Of course the king is being led to his doom, but the extended argument makes for a nice parody of moralistic authors.

Similarly playful: one chapter is told without any labial consonants, as the narrating prince has a sore lip, from too much lovemaking.  Take that, Georges Perec!  (The translator doesn’t even attempt this in English, though Wikipedia suggests that another recent translation does.)

Most of the princes fall in love at first sight with a woman, and this is always reciprocated. One, indeed, gets the woman to fall in love by sending her a portrait of himself. This gives Daṇḍin the chance to grow effusive over the women. As one prince says:

All the limbs of this maiden are pure in complexion and without any blemish. They are neither too gross nor too meagre, not too long or too short. The inner sides of her fingers are pink, and the palms of her hands bear many auspicious signs like the barley grain, the fish, the lotus, and the jar. Her ankles are even. Her feet are plump and unmarked by veins. Her well-rounded calves so merge into ample thighs that the knees are hardly noticeable. The bottom is smooth, perfectly divided, beautifully dimpled and round as a wheel. The navel is small, a little low and deep. A triple line adorns the abdomen. A large bosom with upturned nipples covers the breast. The shoulders slope smoothly into supple arms. The fingernails have the fine gloss of gems. The fingers are tapering, soft, and copper-hued. 

Her neck is slender and graceful like a conchshell. Her face is like a lotus flower, with lips red and rounded, nose like a flower bud, handsome chin and shapely temples. Her forehead shines like the crescent moon and her wavy hair like a line of sapphires. Her dark eyebrows are arched and well-separated, and her eye are bright and wide with a glance both merry and languorous. Her ears are ornamented only with loops of pale lotus sets. Her abundant hair is dark and fragrant and simply dressed.

It’s interesting to compare this description with temple statues, which depict the same kind of very curvy body.

One prince finds that his lover is already married, producing an ethical dilemma:

My purpose is almost accomplished, but sleeping with another’s wife will hurt dharma. However, the compilers of the scriptures permit this if both artha and kāma are attained at the same time. I am committing this transgression to free my parents from jail. That should neutralize any sin, and may also reward me with some fraction of dharma.

Fortunately for him, Ganeśa himself appears in a  dream and tells him to proceed.

About the only negative to these stories is that they’re almost weightless.  The characters are vivid and range from princes to ascetics to thieves to courtesans to Jain monks to Greek sailors to jungle warriors, the plots are amusing, but it’s hard to remember them an hour later.  And the cities, though they’re scattered all over India, the cities all melt together.  But these are tales built to entertain, and they still do, 1300 years later.

If you do pick this up, try to get the modern translation by Aditya N. D. Haksar.



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