On Method: Computational Compressibility in Complex Natural and Cultural Phenomena

New working paper. Title above, abstract, contents, and introduction below:

Academia.edu: https://www.academia.edu/166054951/On_Method_Computational_Compressibility_in_Complex_Natural_and_Cultural_Phenomena
ResearchGate: https://www.researchgate.net/publication/404263330_On_Method_Computational_Compressibility_in_Complex_Natural_and_Cultural_Phenomena

Abstract: Various machine learning techniques have been used to develop models of complex systems from empirical data. Through discussions with Claude, this paper examines several examples, including: weather, protein folding, chess, language, asset pricing, ticket sales for movies, the 19th century English-language novel. These models differ from one another in various ways, but all are fundamentally descriptive in character. Explanations must necessarily reside with their respective disciplines. In some cases we already have fundamental accounts of the phenomena, while in other cases we do not. With respect to economics in particular, it is clear that such models reveal phenomena for which no explanations are currently available, presenting a challenge to economic theory.

Contents 

Part I: Computational Compressibility, Implications for Economics, Description and Explanation 5
Part II: Weather, Protein Folding, Chess, and Language 16
Part III: Interim Summary: Compressibility Without Reducibility 26
Part IV: Pricing Theory, Movies, 19th Century Novels, and Cultural Evolution 28
Part V: To Infinity and Beyond! – Hollywood Redux, Blockbusters, the Spreadsheet, Economics Going Forward 38

Introduction: Describing Computationally Compressible Systems 

This a transcription of a dialog I had with Claude 4.6 and 4.7 on April 21 - 23, 2026. While I started it with a specific case from Chapter 4 of Tyler Cowen’s recent monograph on marginalism ( The Marginal Revolution: Rise and Decline, and the Pending AI Revolution), now that the dialog has concluded with Chomsky’s distinction between descriptive and explanatory adequacy (Aspects of the Theory of Syntax), I realize that I’ve been thinking about the underlying issues for some time. While I read Aspects in about 1970, give or take a year, I didn’t think much about description as such until the 2000s, and then I was thinking about describing individual texts; but that’s not directly relevant to these cases in this paper. Then in the second decade of this century I began thinking about computational criticism, aka digital humanities, which typically involve some kind of statistical or machine learning investigation of a corpus of texts. In particular, I gave a great deal of attention to Macroanalysis (2013), where Matthew Jockers studied a corpus of roughly 3000 English-language novels published in the 19th century. That investigation culminated in a directed graph showing depicting relationships of close-similarity among the novels in the corpus. I decided that that graph, in effect, was fundamentally descriptive in character, depicting, in effect, the 19th century Anglophone Geist, or Spirit.

But Jockers’s graph wasn’t on my mind when I started my dialog with Claude. Rather, I was thinking about the distinction between computationally reducible and irreducible phenomena that Stephen Wolfram had introduced in his New Kind of Science (2001). As Claude notes in its summary of the dialog, “a reducible system admits shortcuts through its dynamics; an irreducible one must be simulated step by step.” My target was a paper about asset pricing that Cowen discussed in his monograph, which produced a model having 360,000 parameters but which defied intuitive understanding.

The weather is a canonical example of phenomenon that is computationally irreducible. Thus forecasting the weather generally involves running a simulation of the weather and stepping through it interval after interval. This requires enormous computing resources and takes time. But DeepMind has created a machine learning system, GraphCast, that abstracts over historical data in a way that allows more accurate forecasts with less compute. Thus the weather system is computationally irreducible, but it is also compressible.

I take that as my paradigm case of computational compressibility (pp. 16 ff.) and then move on to other examples: protein folding (another physical phenomenon, pp. 18 ff.), chess (human activity, pp. 22 ff.), and natural language (a different human activity, pp. 23 ff.), each of which is compressible using machine learning techniques. Each example sharpens and extends the idea of computational compressibility. At that point I asked Claude to summarize the discussion (pp. 26 ff)..

Then, and only then do I ask Claude to consider Cowen’s problematic example, AI Pricing Theory (pp. 28 ff.). In its analysis of the paper, Claude notice that it introduces something fundamentally new to the discussion, reflexivity. Asset pricing is done by a large group of actors over time who thus influence one another’s decisions. And that, in turn, brings up Arthur De Vany’s work on Hollywood Economics (pp. 30 ff.). De Vany discovered that box-office success cannot be predicted by such analytic variables as producer, screen writer, director, movie stars, or opening weekend box office. Rather the success of a film depends on a word-of-mouth cascade which cannot be predicted. That leads me, in turn, to suggest a thought experiment involve a hypothetical system capable to abstracting over entire films and developing a high-dimensional model which could be used to predict the success of individual films.

And that, in turn, led me to the work that Matthew Jockers had done on 19th century English-language novels (pp. 33 ff.), something that had not been on my mind when I began this dialog on April 21. Jockers used machine learning, albeit nothing so elaborate and computationally expensive as using a transformer to create an LLM – it only had roughly 600 parameters. What his model revealed, and what made it so fascinating to me, is that there is an inherent directionality to the production of novels over the course of a century. It’s not simply that later novels are systematically different from earlier ones, but that that difference has a direction in the 600-dimensional measurement space. What we’d really like to know, now, is a say to characterize that diction. The model shows us that there is a direction, but it doesn’t tell us what that direction is. Though the model is much simpler than that asset pricing model – it has three orders of magnitude fewer parameters – its significance is no more legible.

After that I have two discussions that are not based on existing models, but that do have implications for economists who want to study them. First, I consider the phenomenon of the blockbuster, arguing that it reveals audience preferences that had previously been unrecognized (pp. 41 ff.). Then I consider the spreadsheet (e.g. VisiCalc), which transformed the personal computer market from a small niche market into a large mainstream market (pp. 43 ff.). How do you create a model that allows you to predict markets that don’t even exist at the time you make your model? What kind of a problem is that? After that I took a brief look at Cowen’s argument in The Great Stagnation (pp. 45 ff.), where Claude remarked:

If the VisiCalc model is right, then what matters about ChatGPT and its successors is not primarily that they do existing things faster or cheaper—though they do—but whether they are constitutive technologies in the VisiCalc sense. Do they reorganize the space of possible wants, making new activities imaginable and practical that previously had no well-formed representation in anyone's preference space? With that I brought the exploration to a halt.

I then asked Claude to summarize the entire dialog, which I’ve placed immediately following these remarks (pp. XX ff), with a special emphasis on implications for economics (pp. 7 ff.). Then I introduce Chomsky’s distinction from the 1960s, description vs. explanation (pp. 9 ff.). Each of these cases involves a complex phenomenon that is irreducible, but can be compressed into a model that is descriptive in character. They have that in common. As for explanations, those must necessarily be specific to each phenomenon. Note that in some cases we have explanatory theories grounded in a fundamental understanding of the underlying system (weather, protein folding) while in others we do not (chess, asset pricing, cultural evolution).

Finally, I’ve added a coda from a different conversation with Claude (pp. 13 ff.), one I had with the AI that accompanied Cowen’s book. That conversation is about Hollywood Economics and Rational Ritual and argues that the factoring of intellectual space that we’ve inherited from the 19th century German university has outgrown its usefulness.

Mark Twain on New England weather, illustrations by ChatGPT

New England Weather was anthologized in The Literary World Seventh Reader, John Calvin Metcalf, editor (1919). Twain's essay is accompanied by James Russell Lowell's The First Snowfall.

There is a sumptuous variety about the New England weather that compels the stranger’s admiration—and regret. The weather is always doing something there; always attending strictly to business; always getting up new designs and trying them on the people to see how they will go. But it gets through more business in spring than in any other season. In the spring I have counted one hundred and thirty-six different kinds of weather within four and twenty hours. It was I who made the fame and fortune of the man who had that marvelous collection of weather on exhibition at the Centennial, which so astounded the foreigners. He was going to travel around the world and get specimens from all climes. I said, “Don’t do it; just come to New England on a favorable spring day.” I told him what we could do in the way of style, variety, and quantity. Well, he came, and he made his collection in four days. As to variety, he confessed that he got hundreds of kinds of weather that he had never heard of before. And as to quantity, after he had picked out and discarded all that was blemished in any way, he not only had weather enough, but weather to spare, weather to hire out, weather to sell, weather to deposit, weather to invest, and weather to give to the poor.

Old Probabilities has a mighty reputation for accurate prophecy and thoroughly deserves it. You take up the paper and observe how crisply and confidently he checks off what to-day’s weather is going to be on the Pacific, down South, in the Middle States, in the Wisconsin region. See him sail along in the joy and pride of his power till he gets to New England, and then see his tail drop. He doesn’t know what the weather is going to be in New England. Well, he mulls over it, and by and by he gets out something like this: “Probable northeast to southwest winds, varying to the southward and westward and eastward and points between; high and low barometer, swapping around from place to place; probable areas of rain, snow, hail, and drought, succeeded or preceded by earthquakes with thunder and lightning.” Then he jots down this postscript from his wandering mind, to cover accidents: “But it is possible that the program may be wholly changed in the meantime.” Yes, one of the brightest gems in the New England weather is the dazzling uncertainty of it. There is certain to be plenty of weather, but you never can tell which end of the procession is going to move first.

But, after all, there are at least two or three things about that weather (or, if you please, the effects produced by it) which we residents would not like to part with. If we hadn’t our bewitching autumn foliage, we should still have to credit the weather with one feature which compensates for all its bullying vagaries—the ice storm. Every bough and twig is strung with ice beads, frozen dewdrops, and the whole tree sparkles cold and white like the Shah of Persia’s diamond plume. Then the wind waves the branches, and the sun comes out and turns all those myriads of beads and drops to prisms that glow and burn and flash with all manner of colored fires; which change and change again, with inconceivable rapidity, from blue to red, from red to green, and green to gold. The tree becomes a spraying fountain, a very explosion of dazzling jewels, and it stands there the acme, the climax, the supremest possibility in art or nature, of bewildering, intoxicating, intolerable magnificence. One cannot make the words too strong. Month after month I lay up hate and grudge against the New England weather; but when the ice storm comes at last I say: “There, I forgive you now; you are the most enchanting weather in the world.”

Probabilistic weather forecasting with machine learning

Price, I., Sanchez-Gonzalez, A., Alet, F. et al. Probabilistic weather forecasting with machine learning. Nature (2024). https://doi.org/10.1038/s41586-024-08252-9

Abstract: Weather forecasts are fundamentally uncertain, so predicting the range of probable weather scenarios is crucial for important decisions, from warning the public about hazardous weather to planning renewable energy use. Traditionally, weather forecasts have been based on numerical weather prediction (NWP)1, which relies on physics-based simulations of the atmosphere. Recent advances in machine learning (ML)-based weather prediction (MLWP) have produced ML-based models with less forecast error than single NWP simulations2,3. However, these advances have focused primarily on single, deterministic forecasts that fail to represent uncertainty and estimate risk. Overall, MLWP has remained less accurate and reliable than state-of-the-art NWP ensemble forecasts. Here we introduce GenCast, a probabilistic weather model with greater skill and speed than the top operational medium-range weather forecast in the world, ENS, the ensemble forecast of the European Centre for Medium-Range Weather Forecasts4. GenCast is an ML weather prediction method, trained on decades of reanalysis data. GenCast generates an ensemble of stochastic 15-day global forecasts, at 12-h steps and 0.25° latitude–longitude resolution, for more than 80 surface and atmospheric variables, in 8 min. It has greater skill than ENS on 97.2% of 1,320 targets we evaluated and better predicts extreme weather, tropical cyclone tracks and wind power production. This work helps open the next chapter in operational weather forecasting, in which crucial weather-dependent decisions are made more accurately and efficiently.

The high value of improved hurricane forecasts

Renato Molina & Ivan Rudik, The Social Value of Hurricane Forecasts, NBER Working Papers #32548, June 2024, OI 10.3386/w32548.

Abstract: What is the impact and value of hurricane forecasts? We study this question using newly-collected forecast data for major US hurricanes since 2005. We find higher wind speed forecasts increase pre-landfall protective spending, but erroneous under-forecasts increase post-landfall damage and rebuilding expenditures. Our main contribution is a new theoretically-grounded approach for estimating the marginal value of forecast improvements. We find that the average annual improvement reduced total per-hurricane costs, inclusive of unobserved protective spending, by $700,000 per county. Improvements since 2007 reduced costs by 19%, averaging $5 billion per hurricane. This exceeds the annual budget for all federal weather forecasting.

H/t Tyler Cowen.

New AI-based weather forecasting is superior to traditional methods

Dan Stillman, Why your weather forecasts may soon become more accurate, Washington Post, Nov. 14, 2023.

Google DeepMind’s AI model, named “GraphCast,” was trained on nearly 40 years of historical data and can make a 10-day forecast at six-hour intervals for locations spread around the globe in less than a minute on a computer the size of a small box. It takes a traditional model an hour or more on a supercomputer the size of a school bus to accomplish the same feat. GraphCast was about 10 percent more accurate than the European model on more than 90 percent of the weather variables evaluated.

The study’s results are similar to those in an academic article published in August to the online database arXiv.

“To be competitive with arguably the best global prediction system, if not outperforming it, is astonishing,” Aaron Hill, lead developer of Colorado State University’s machine learning prediction system, said in an email. “You can safely add GraphCast to a growing list of AI-based weather prediction models that should see continued evaluation for their application in industry, research and operational forecasting.”

AI weather models have drawn increasing attention from government weather agencies because of their speed, efficiency and potential cost savings.

Traditional weather models, such as “the European,” operated by the European Center for Medium-Range Weather Forecasts (ECMWF) in Reading, Britain, and “the American,” by the National Oceanic and Atmospheric Administration, make forecasts based on complex mathematical equations. Such models underpin forecasts and lifesaving warnings worldwide but are expensive to run because they require tremendous amounts of computing power.

AI models use a different approach. They are first trained to recognize patterns in vast amounts of historical weather data, then generate forecasts by ingesting current conditions and applying what they learned from the historical patterns. The process is much less computationally intensive and can be completed in minutes or even seconds on much smaller computers.

However:

Researchers have expressed concerns about the ability of AI to accurately forecast extreme weather, in part because there are relatively few such events to learn from in the past. Yet GraphCast reduced cyclone forecast track errors by around 10 to 15 miles at a lead time of two to four days, improved forecasts of water vapor associated with atmospheric rivers by 10 to 25 percent, and provided more precise forecasts of extreme heat and cold five to 10 days ahead of time.

And:

Most experts, including the study’s authors, agree that traditional models aren’t about to be replaced by AI models, which still depend on the older models to supply training data and to generate the current conditions they use as a starting point to make a forecast.

Here's a link to the article in Science reporting the research underlying this article.

ChatGPT on predicting the weather [what are the limits of prediction for various phenomena?]

I’m interested in things we can predict and how we go about it. Newtonian mechanics gave us the means to predict the motion of the planets, so that’s now a solved problem – though I understand there are some nuances (chaos at the margins). Enormous effort goes into predicting the stock market. Much of that work is proprietary. In any event, I assume it’s an open problem.

What I’m really interested in is culture, such as Arthur De Vany’s work on predicting the success of a film once it’s released. He claims it’s impossible. I assume the same holds for books, records, and the like. Here we’ve got complex phenomena. Each title (movie, book, etc.) is an object of potentially unbounded complexity. The market is a population of people who have varying preferences for those complex objects. Perhaps the most interesting thing about this market place is that it has latent preferences (my coinage, though I wouldn’t be surprised if the concept, and even the term, is already in use; come to think of it, I’d be surprised if it isn’t). These preferences become visible only when an object is produced that satisfies them.

Now, one might observe of De Vany’s work that his method is crude. He trying to make predictions based on knowledge of producers, actors, directors, writers, and opening box-office. Maybe a more sophisticated set of variables, variables that tell us more about the films, would make prediction possible. Maybe, maybe some improvement is possible. But how much? What I’m wondering is whether or not these kinds of systems are computationally irreducible in the sense that Wolfram talks about. If so, then really accurate prediction would require a model as complex as the phenomenon itself.

All that’s by way of introduction. It provides context for this morning’s session with ChatGPT, where I asked it about weather prediction. My last question is about theoretical limits to accuracy. ChatGPT’s answers suggests that the weather system may ultimately be computationally irreducible. And, if you’re interested in long-term predictions, no matter how accurate your current model, if a large volcano blows the hurls enough dust into the atmosphere to affect the earth’s temperature, your predictions are now shot to hell.

* * * * *

I’m interested in how we predict the weather. I know we use computer models. I know that weather is chaotic, in the mathematical sense, that is, it is sensitive to small variations in initial conditions. I’m interested in two things in particular: 1) How accurate are the forecasts over time? For example what’s the accuracy of a 3-day forecast vs. a 10-day forecast? 2) What’s the size of the data grid on which the models are based? My impression is that the resolution of the grid is stated in kilometers.

Predicting the weather is a complex task, and it relies on a combination of observational data, computer models, and our understanding of atmospheric physics. Let me address your two questions:

1. Accuracy of Weather Forecasts Over Time:

• Weather forecasts are generally more accurate for shorter timeframes and become less accurate as you extend the forecast period. The accuracy of a forecast depends on various factors, including the region, weather conditions, and the lead time.
  
• Short-term forecasts (e.g., 1-3 days) tend to be more accurate, with high precision in predicting temperature, precipitation, and wind patterns. These forecasts are often reliable for planning daily activities.
  
• Medium-range forecasts (around 4-7 days) are less precise and are subject to greater uncertainty. While they can provide general trends, they may not be accurate for specific details.
  
• Long-range forecasts (beyond 7-10 days) are even less accurate and are more like climatological trends rather than detailed weather predictions.

2. Grid Resolution of Weather Models:

• Numerical weather models divide the atmosphere into a three-dimensional grid to simulate the behavior of the atmosphere. The resolution of these grids is indeed stated in kilometers and is known as the "grid spacing" or "grid resolution."
  
• High-resolution models have smaller grid spacing (e.g., 1-10 kilometers) and can provide more detailed forecasts, especially for local or short-term weather events. These models are typically used for regional weather predictions.
  
• Coarser-resolution models have larger grid spacing (e.g., 10-50 kilometers or more) and are used for global weather forecasts, as they cover larger geographic areas. They are less detailed but can provide information on large-scale weather patterns.
  
• The choice of grid resolution depends on the specific goals of the forecast and the available computational resources. Weather agencies use a hierarchy of models with different resolutions to cater to various forecasting needs.

It's important to note that advances in technology and improved understanding of atmospheric processes have led to better weather predictions over the years. Weather forecast accuracy has significantly improved for short-term forecasts, and even medium-range forecasts have seen improvements. However, long-range forecasting remains challenging due to the chaotic nature of the atmosphere and the limitations of current modeling capabilities.

What’s the largest computers used in weather forecasting? How long does it take to run a simulation? Are there any weather models so complicated that it takes almost as much time to run the model as it takes for the weather to evolve?

Weather forecasting requires powerful supercomputers to run the complex numerical models that simulate the atmosphere. Some of the largest computers used in weather forecasting include:

1. IBM Blue Gene: The IBM Blue Gene supercomputers, like Blue Gene/P and Blue Gene/Q, have been used for weather modeling. These supercomputers are known for their high processing power and are used by various meteorological agencies.
2. Cray Supercomputers: Cray has also provided supercomputing solutions for weather forecasting, such as the Cray XT series and the Cray XC40 series. These supercomputers are designed to handle large-scale simulations.
3. Fujitsu Supercomputers: The Japanese Meteorological Agency (JMA) has used Fujitsu's supercomputers, such as the K computer, for high-resolution weather modeling.
4. Various National Supercomputing Centers: Many countries operate their own supercomputing centers for weather forecasting, and these centers use some of the world's most powerful supercomputers. For example, the European Centre for Medium-Range Weather Forecasts (ECMWF) uses its own supercomputer for global weather prediction.

The time it takes to run a weather simulation depends on several factors, including the model's resolution, the complexity of the simulation, and the available computational resources. High-resolution regional models, which cover smaller geographic areas, generally require less time compared to global models. On average, it may take anywhere from several minutes to a few hours to run a single simulation.

Hurricane Harvey update

Here's a podcast featuring Tim Morton, speaking from the flood. He and his family are OK, though a bit wet. He's discussing the hurricane and philosophy. "But is it a hyperobject, Tim?" Tim gets this question ALL the time now, as though he's adjudicator-in-chief for hyperobects. He is not, and, yes, Hurricane Harvey is a hyperobject.

Ep. #87 - Hurricane Harvey (feat. Timothy Morton) https://t.co/NLhd0Wq0sx

— Bill Benzon (@bbenzon) August 28, 2017

Hey, Professor Tim? Is this flotilla of fire ants a hyper object?

Fire ants form a protective island as they float out the #Houston flood pic.twitter.com/UBORwAzA4R

— Omar Villafranca (@OmarVillafranca) August 27, 2017

"You're on your own, Grasshopper."

Frankenstein, it seems, was born in a volcano

In today's NYTimes:

In April 1815, the most powerful volcanic blast ]Mt. Tambora in Indonesia] in recorded history shook the planet in a catastrophe so vast that 200 years later, investigators are still struggling to grasp its repercussions. It played a role, they now understand, in icy weather, agricultural collapse and global pandemics — and even gave rise to celebrated monsters.

The monsters in question were of the imaginary kind:

The story also comes alive in local dramas, none more important for literary history than the birth of Frankenstein’s monster and the human vampire. That happened on Lake Geneva in Switzerland, where some of the most famous names of English poetry had gone on a summer holiday.

By 1816, Switzerland, landlocked and famously rugged, was beginning to reel from the bad weather and failed crops. Starving mobs stormed bakeries after bread prices soared. The book recounts a priest’s distress: “It is terrifying to see these walking skeletons devour the most repulsive foods with such avidity.”

That June, the cold and stormy weather sent the English tourists inside a lakeside villa to warm themselves by a fire and exchange ghost stories. Mary Shelley, then 18, was part of a literary coterie that included Percy Shelley, her future husband, as well as Lord Byron. Wine flowed, as did laudanum, a form of opium. Candles flickered.

In this moody atmosphere, Mary Shelley came up with her lurid tale of Frankenstein, which she published two years later. And Lord Byron hit on the outline of the modern vampire tale, published later by a compatriot as “The Vampyre.” The freakish weather also inspired Byron’s apocalyptic poem “Darkness.”

All this is detailed in a book, Tambora: The Eruption that Changed the World, by Gillen D’Arcy Wood,

The Dutch on Water: Let It Flow

The New York Times, How to Think Like the Dutch in a Post-Sandy World:

In the Netherlands, a man named Henk Ovink offered to be Donovan’s guide. Ovink was the director of the office of Spatial Planning and Water Management, meaning, essentially, that it was his job to keep the famously waterlogged country dry. As he learned about various Dutch innovations, Donovan was struck by the fact that Ovink looked at water as much in cultural as in engineering terms, which was a function of the centuries-old need of the Dutch to act together for protection.

Think like a community:

Beyond that, Ovink feared that politics might undermine any chance to encourage new thinking about water management. “When I mentioned climate change to one official,” he said, “she almost hit me.” He characterized some of the wishful thinking he believed he would be dealing with as: “Don’t hire a Dutchman — believe in angels.”

Dutch battles against water led his country to develop a communal society. To this day, Water Boards, which date to the Middle Ages, are a feature of every region, and they guide long-term infrastructural planning. American individualism, on the other hand, has yielded a system in which each municipality has a great deal of autonomy, making regional cooperation difficult.

Pacific Avenue in Jersey City, the day after Sandy

Remembering Sandy

Hemlock in the River

A Photo Essay in Which Your Author Contemplates the Nature of Graffiti and Continues Around the Same Old Circle

The river of life, the ever-flowing Heraclitean river, you know, the one you can’t step into twice–that's what I'm talking about. As for Hemlock, it was awhile before I even knew it was Hemlock (I forget who told me). But there’s someone else up on that wall too, someone beneath Hemlock.

This is how the wall looked when I first saw it, though I would have been approaching from the East rather than the West, as in this shot:

Here’s a bit more context:

They–that is, the images of interest–are on the wall at the far right and to the rear, where sunlight has rendered them invisible to the camera. That elegant Remix piece (by Raels) at the lower left is now under three or four layers of paint. The masonry and steel superstructure supports Route 139 as it heads off toward the Pulaski Skyway.

That’s right, we’re down in the Bergen Arches, a man-made trench cut through Jersey City’s Bergen Hill early in the 20th Century. It’s been closed off at the East end and three of the four train tracks ripped out. It’s abandoned (not quite) and gone to seed (most wonderfully).

Here’s that wall straight on:

It was one of my favorites when I first saw it in the summer of 2007 and remains so six years later, though it’s now a bit more worn.

Just WHAT is it? Well, sure, it’s graffiti, we know that. But what’s IN the graffiti, what’s it represent?

The Anthropocene in Pictures

That's us, or rather our impact on the physical disposition of the planet. Here's how it goes:

the Pleistocene, 2.5 million years ago to 12 thousand years ago, saw the emergence of humans from clever apes. 

the Holocene, 12 thousand years ago to to 1800 AD, from agriculture to industry and the emergence of loosely integrate world order of human commerce and exchange; 

and now the Anthropocene, 1800 AD the present and into the future, when industry started pumping CO2 into the atmosphere, little knowing the consequences.

The anthropocene is mapped out in images of the earth at Globaïa. H/t Tim Morton.

What Have I Learned from Sandy? Resilience Begins in Responsibility

I phrase it as a question because, though, considered as a weather event, hurricane Sandy is over and done with, as a psycho-cultural-historical event, it is only in the early phases of its life. In an earlier post (Thoughts on Sandy: We Must Change Our Ways, NOW) I talked about the need to restructure our world:

We have to rethink and restructure. We have to decouple and downsize. Otherwise we’re committing suicide by “civilization” and technology.

That idea isn’t new to me. It’s been with me in one form or another for a long time.

But, whatever lessons Sandy has for others—and I hope her lessons have been deep ones—I’m beginning to think that she does have a lesson for me, a lesson about self-reliance, community, and their interdependence. Still, I’m not sure. It’s too soon to tell. In any event, before I get around to a tentative account of THAT lesson, I want first to talk about some other lessons.

Sputnik, Martial Law, Berlin Wall

These lessons are personal lessons, though not entirely so. They are lessons about the intersection of my life with the larger currents of history. As such, I don’t expect that these historical events will have the same or similar significance for others, though they might. Briefly, these are the events:

• 1957: The Russians launched Sputnik, the first man-made satellite to circle the earth
• 1968 1969: Martin Luther King was assassinated, riots broke out, and martial law was declared in Baltimore
• 1989: Berlin Wall came down and set the stage for the reunification of Germany

I was ten years old in 1957 and was fascinated by outer space, rockets, and such—a fascination stoked, no doubt, by various TV programs by Walt Disney and films such as Forbidden Planet (1956). The launching of Sputnik marks the first time my dreams and fantasies met-up with history.

The launching of Sputnik was certainly a world historical event. Shorn of politics, it was the first time that humans stepped off of the earth to function in outer space, if only indirectly. But of course, we can’t divorce Sputnik from Cold War politics, nor did I do so as a ten-year old. I knew, in my ten-year old way, that it was important for America to beat the Russians in the space race that Sputnik had catalyzed.

However, by the mid-1960s I had decided that, if the Cold War was in fact a real and pressing international conflict, it was a conflict dominated by a military-industrial complex that was more interested in preserving itself than in preserving the peace. The war in Vietnam had made me a pacifist and the counter-culture had almost made me a hippie.

Almost. I wore hippie clothing, listened to the Beatles, the Doors, and Jimi Hendrix, and smoked weed—yes, I inhaled. But I never made it to full-out hippiedom. I was too much of an intellectual for that.

And when Martin Luther King was assassinated in 1968, riots broke out in Baltimore, where I was attending The Johns Hopkins University. The riots took place in East Baltimore, far from the North Charles Street campus of university (though the medical school and hospital were in East Baltimore), but that made no difference when martial law was declared. The whole city was put on lock-down. Curfew was 4PM and National Guard vehicles and men patrolled the streets. Of course I had to break curfew, along with some of my hippie (and non-hippie) friends.

Could New York City Protect Itself with Barriers?

In the past few years proposals have been floated to protect the NYC area from flodding by putting large gates into New York Bay. The gates would lie in the seafloor most of the time but would be raised into place during a major storm, such as Sandy. The NYTimes writes:

[Brian A. Colle] said that if gates had been placed in strategic spots like the Arthur Kill, between Staten Island and New Jersey, they would have protected some of the areas that were swamped by floodwaters, including the edges of Lower Manhattan, low-lying areas of Brooklyn and Queens and the western part of Staten Island, as well as Jersey City and Hoboken, N.J.

“The idea is that you raise these barriers, and anywhere inside of that you’re basically protected,” Dr. Colle said, adding, “With a solid barrier, we basically can have business as usual in Lower Manhattan.”

But vexing questions remain. Would industries tolerate immense disruption from the construction of barriers in the city’s busy waterways? Would residents object to the marring of vistas? With climate change advancing, can scientists accurately predict the size of hurricanes that the sea gates would one day have to withstand?

And where would the $10 billion-plus in construction money come from? Even a study — taking into account the complexity of New York waterways, projections in the rise in sea levels and other factors — would take years and millions of dollars.

The scientists and engineers who have worked on conceptual designs for the city say a comprehensive study is needed on what would be the most effective locations and the most practical type of barriers — whether they swing close like a driveway gate or pivot up from the ocean floor, for example.

A feasibility study by the Army Corps of Engineers, which would have jurisdiction, would require authorization from Congress.

“A lot of things need to be taken into consideration before we throw up a giant wall,” said Chris Gardner, a spokesman for the corps.

What strikes me as that time to do the preliminary study and then the actual construction is on the order of significant climate change due to global warming. Thus one can imagine that the climate is changing fast enough that such gates would be obsolete by the time they're built. These gates make sense ONLY if we take major steps to stop pumping CO2 into the atmosphere. Otherwise it's just more wishful techno-dreaming.

Apocalypse Now: Sandy Dominates My Weltanschauung

Big word that: Weltanschauung. It means world view, a comprehensive top-to-bottom, right-to-left, inside-and-outside account of the world. Well, hurricane Sandy changed my world. She deserves a Big Word.

Now, physically, me and my stuff are now OK. But as I type this I hear noise created by the generator at the firehouse behind my building. The street it faces doesn't have electric power. That firehouse is my polling place today. I assume the generator will power the electronic voting machine.

Tomorrow we're supposed to get an ordinary winter storm, one of those storms that're variously delightful, if you like snow, or merely irritating if you don't. But this one is coming on top of Sandy, and Jersey City has not even remotely recovered from her. So the impact of this new storm could be harmful, especially for those still without power.

Given this, it is thus something of a shock when I go online to the various places I haunt and discuss and find that those discussions aren't dominated by Sandy. Why not? Because most folks don't live in an area that's been crushed by Sandy. She doesn't dominate their Weltanschuung.

For me and my neighbors, we got a taste of the apocalypse. For the rest of the world, life goes on.

Thoughts on Sandy: We Must Change Our Ways, NOW

I didn’t really think much about hurricane Sandy until I went grocery shopping on Sunday afternoon, October 28. The fact that Irene hadn’t hit Jersey City as bad as had been predicted meant little about Sandy. And I knew that. But still, how bad could it be? So I didn’t stock up on batteries, candles, and non-perishable food. Thus it’s a matter of luck that I had enough to get through four-and-a-half days without power.

Of course, I also had friends, June Jones in particular. A number of people met at her place for meals. She was cooking up a storm. Without power the food in her freezer would spoil quickly. She decided to cook it up and had her friends and family over.

Thanks, June!

And then there’s my friends at the Villain. But I’m getting ahead of myself.

So I got home from shopping on Sunday afternoon and spent some more time on my Halloween costume: Trash Master. I was coming down the home stretch on it and figured it would be ready in plenty of time for the Halloween party we were throwing for the kids in the garden.

Did some more work on the costume on Monday and more this and that. Took some photos of wind whipping through the garden (see above) and planned my work for the rest of the week. Around 8:30 PM or so that evening the power flickered and then went out. But it came back in a minute or so. Every once in awhile I could feel the building shake. At 9:05 PM the power went out again, and didn’t come back.

Not to worry. I was ready for bed anyhow—I’m going to bed early these days, and getting up early, too, as always. I figured the power would be back when I woke up, or later that day.

I woke up Monday morning to darkness. I had some breakfast, grabbed my camera, and hit the streets by 6:45 AM. Very few lights were on anywhere. That was NOT a good sign, not good at all. Oh, some big buildings had lights on, buildings with generators no doubt. But mostly things were dark, in Jersey City AND in Manhattan.

40 Days and Nights of Techno Hubris: Titanic

It’s 7:30 in the AM this Monday in late October, just before All Hallows Eve, and my thoughts turn to Titanic, not the ship, but a folk poem about that ship. It's a poem about water, lots of it, and that's one reason it comes to mind.

I’ve published it here before, back in May of 2010, but it’s time to bump it up to the top of the list, along with a new introduction. Why?

Here’s why.

Lafayette by the Bay

I live in the Lafayette neighborhood of Jersey City, NJ, less than a half-mile from the Hudson Rive and the New York Bay. Sometime in the next 24 hours there's going to be a storm surge in that bay and part of Jersey City is going to be flooded. Probably not my part, but, in those immortal words of Thomas Fats Waller, “one never knows, do one?”

Whatever flooding there is, and there WILL be some, will be driven by hurricane Sandy. Last year it was Irene. Irene wasn't as bad as predicted, at least not in my neighborhood–though Communipaw Avenue had 3 or 4 inches of water near Garfield, just a few blocks from me. But it was bad enough, and inflicted considerable damage inland in small towns and hamlets that were wrecked by raging rivers.

I want to blame this one on anthropogenic climate chaos, aka global warming. But that's tricky. There were hurricaines, and nasty one, long before us industrious industrial humans started messing with the climate. Not knowingly, not intentionally mind you, no more so that those ancient humans desiccated North Africa until it became the Sahara Dessert. Be messing with the environment we did, not doubt about it.

The thing, we can't blame any specific weather even on global warming, because all of the weather, all the time, 24/7/365 (366 in leap years) is affected. It's the general tempo and temperature that's affected, not specific events.

There are, of course, those who imagine techno-fixes for this mess. Let's pump some sulpher into the atmosphere, they say, it'll blot out the sun just enough to set things right. Any maybe we should all hold on to our lucky rabbit's foot while doing it, 'cause we're going to need all the luck we can get.

No, I fear that putting our faith in techno-fixes is just going to make things worse. We're not that powerful, not that knowledgeable. So let's be wise. Let’s listen to the poets of Titanic, which is, among other things, about techno hubris. And water, lots of water.

What's Titanic?

Toasts

Titanic is a toast, a form of boasting narrative in the African-American oral tradition that is a precursor to rap and hip-hop. If you go to this YouTube video you can hear Rudy Ray Moore recite a version from Dolomite.