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Richard Feynman and The Connection Machine

Posted on Wednesday, February 8th, 02017 by Ahmed Kabil
link   Categories: Long Term Thinking, Technology, The Big Here   chat 0 Comments

One of the most popular pieces of writing on our site is Long Now co-founder Danny Hillis’ remembrance of building an experimental computer with theoretical physicist Richard Feynman. It’s easy to see why: Hillis’ reminisces about Feynman’s final years as they worked together on the Connection Machine are at once illuminating and poignant, and paint a picture of a man who was beloved as much for his eccentricity as his genius.

Photo by Faustin Bray

Photo by Faustin Bray

Richard Feynman and The Connection Machine

by W. Daniel Hillis for Physics Today

Reprinted with permission from Phys. Today 42(2), 78 (01989). Copyright 01989, American Institute of Physics.

One day when I was having lunch with Richard Feynman, I mentioned to him that I was planning to start a company to build a parallel computer with a million processors. His reaction was unequivocal, “That is positively the dopiest idea I ever heard.” For Richard a crazy idea was an opportunity to either prove it wrong or prove it right. Either way, he was interested. By the end of lunch he had agreed to spend the summer working at the company.

Richard’s interest in computing went back to his days at Los Alamos, where he supervised the “computers,” that is, the people who operated the mechanical calculators. There he was instrumental in setting up some of the first plug-programmable tabulating machines for physical simulation. His interest in the field was heightened in the late 1970’s when his son, Carl, began studying computers at MIT.

I got to know Richard through his son. I was a graduate student at the MIT Artificial Intelligence Lab and Carl was one of the undergraduates helping me with my thesis project. I was trying to design a computer fast enough to solve common sense reasoning problems. The machine, as we envisioned it, would contain a million tiny computers, all connected by a communications network. We called it a “Connection Machine.” Richard, always interested in his son’s activities, followed the project closely. He was skeptical about the idea, but whenever we met at a conference or I visited CalTech, we would stay up until the early hours of the morning discussing details of the planned machine. The first time he ever seemed to believe that we were really going to try to build it was the lunchtime meeting.

Richard arrived in Boston the day after the company was incorporated. We had been busy raising the money, finding a place to rent, issuing stock, etc. We set up in an old mansion just outside of the city, and when Richard showed up we were still recovering from the shock of having the first few million dollars in the bank. No one had thought about anything technical for several months. We were arguing about what the name of the company should be when Richard walked in, saluted, and said, “Richard Feynman reporting for duty. OK, boss, what’s my assignment?” The assembled group of not-quite-graduated MIT students was astounded.

After a hurried private discussion (“I don’t know, you hired him…”), we informed Richard that his assignment would be to advise on the application of parallel processing to scientific problems.

“That sounds like a bunch of baloney,” he said. “Give me something real to do.”

So we sent him out to buy some office supplies. While he was gone, we decided that the part of the machine that we were most worried about was the router that delivered messages from one processor to another. We were not sure that our design was going to work. When Richard returned from buying pencils, we gave him the assignment of analyzing the router.

The Machine

The router of the Connection Machine was the part of the hardware that allowed the processors to communicate. It was a complicated device; by comparison, the processors themselves were simple. Connecting a separate communication wire between each pair of processors was impractical since a million processors would require $10^{12]$ wires. Instead, we planned to connect the processors in a 20-dimensional hypercube so that each processor would only need to talk to 20 others directly. Because many processors had to communicate simultaneously, many messages would contend for the same wires. The router’s job was to find a free path through this 20-dimensional traffic jam or, if it couldn’t, to hold onto the message in a buffer until a path became free. Our question to Richard Feynman was whether we had allowed enough buffers for the router to operate efficiently.

During those first few months, Richard began studying the router circuit diagrams as if they were objects of nature. He was willing to listen to explanations of how and why things worked, but fundamentally he preferred to figure out everything himself by simulating the action of each of the circuits with pencil and paper.

In the meantime, the rest of us, happy to have found something to keep Richard occupied, went about the business of ordering the furniture and computers, hiring the first engineers, and arranging for the Defense Advanced Research Projects Agency (DARPA) to pay for the development of the first prototype. Richard did a remarkable job of focusing on his “assignment,” stopping only occasionally to help wire the computer room, set up the machine shop, shake hands with the investors, install the telephones, and cheerfully remind us of how crazy we all were. When we finally picked the name of the company, Thinking Machines Corporation, Richard was delighted. “That’s good. Now I don’t have to explain to people that I work with a bunch of loonies. I can just tell them the name of the company.”

The technical side of the project was definitely stretching our capacities. We had decided to simplify things by starting with only 64,000 processors, but even then the amount of work to do was overwhelming. We had to design our own silicon integrated circuits, with processors and a router. We also had to invent packaging and cooling mechanisms, write compilers and assemblers, devise ways of testing processors simultaneously, and so on. Even simple problems like wiring the boards together took on a whole new meaning when working with tens of thousands of processors. In retrospect, if we had had any understanding of how complicated the project was going to be, we never would have started.

‘Get These Guys Organized’

I had never managed a large group before and I was clearly in over my head. Richard volunteered to help out. “We’ve got to get these guys organized,” he told me. “Let me tell you how we did it at Los Alamos.”

Every great man that I have known has had a certain time and place in their life that they use as a reference point; a time when things worked as they were supposed to and great things were accomplished. For Richard, that time was at Los Alamos during the Manhattan Project. Whenever things got “cockeyed,” Richard would look back and try to understand how now was different than then. Using this approach, Richard decided we should pick an expert in each area of importance in the machine, such as software or packaging or electronics, to become the “group leader” in this area, analogous to the group leaders at Los Alamos.

Part Two of Feynman’s “Let’s Get Organized” campaign was that we should begin a regular seminar series of invited speakers who might have interesting things to do with our machine. Richard’s idea was that we should concentrate on people with new applications, because they would be less conservative about what kind of computer they would use. For our first seminar he invited John Hopfield, a friend of his from CalTech, to give us a talk on his scheme for building neural networks. In 1983, studying neural networks was about as fashionable as studying ESP, so some people considered John Hopfield a little bit crazy. Richard was certain he would fit right in at Thinking Machines Corporation.

What Hopfield had invented was a way of constructing an [associative memory], a device for remembering patterns. To use an associative memory, one trains it on a series of patterns, such as pictures of the letters of the alphabet. Later, when the memory is shown a new pattern it is able to recall a similar pattern that it has seen in the past. A new picture of the letter “A” will “remind” the memory of another “A” that it has seen previously. Hopfield had figured out how such a memory could be built from devices that were similar to biological neurons.

Not only did Hopfield’s method seem to work, but it seemed to work well on the Connection Machine. Feynman figured out the details of how to use one processor to simulate each of Hopfield’s neurons, with the strength of the connections represented as numbers in the processors’ memory. Because of the parallel nature of Hopfield’s algorithm, all of the processors could be used concurrently with 100\% efficiency, so the Connection Machine would be hundreds of times faster than any conventional computer.

An Algorithm For Logarithms

Feynman worked out the program for computing Hopfield’s network on the Connection Machine in some detail. The part that he was proudest of was the subroutine for computing logarithms. I mention it here not only because it is a clever algorithm, but also because it is a specific contribution Richard made to the mainstream of computer science. He invented it at Los Alamos.

Consider the problem of finding the logarithm of a fractional number between 1.0 and 2.0 (the algorithm can be generalized without too much difficulty). Feynman observed that any such number can be uniquely represented as a product of numbers of the form $1 + 2^{-k]$, where $k$ is an integer. Testing each of these factors in a binary number representation is simply a matter of a shift and a subtraction. Once the factors are determined, the logarithm can be computed by adding together the precomputed logarithms of the factors. The algorithm fit especially well on the Connection Machine, since the small table of the logarithms of $1 + 2^{-k]$ could be shared by all the processors. The entire computation took less time than division.

Concentrating on the algorithm for a basic arithmetic operation was typical of Richard’s approach. He loved the details. In studying the router, he paid attention to the action of each individual gate and in writing a program he insisted on understanding the implementation of every instruction. He distrusted abstractions that could not be directly related to the facts. When several years later I wrote a general interest article on the Connection Machine for [Scientific American], he was disappointed that it left out too many details. He asked, “How is anyone supposed to know that this isn’t just a bunch of crap?”

Feynman’s insistence on looking at the details helped us discover the potential of the machine for numerical computing and physical simulation. We had convinced ourselves at the time that the Connection Machine would not be efficient at “number-crunching,” because the first prototype had no special hardware for vectors or floating point arithmetic. Both of these were “known” to be requirements for number-crunching. Feynman decided to test this assumption on a problem that he was familiar with in detail: quantum chromodynamics.

Quantum chromodynamics is a theory of the internal workings of atomic particles such as protons. Using this theory it is possible, in principle, to compute the values of measurable physical quantities, such as a proton’s mass. In practice, such a computation requires so much arithmetic that it could keep the fastest computers in the world busy for years. One way to do this calculation is to use a discrete four-dimensional lattice to model a section of space-time. Finding the solution involves adding up the contributions of all of the possible configurations of certain matrices on the links of the lattice, or at least some large representative sample. (This is essentially a Feynman path integral.) The thing that makes this so difficult is that calculating the contribution of even a single configuration involves multiplying the matrices around every little loop in the lattice, and the number of loops grows as the fourth power of the lattice size. Since all of these multiplications can take place concurrently, there is plenty of opportunity to keep all 64,000 processors busy.

To find out how well this would work in practice, Feynman had to write a computer program for QCD. Since the only computer language Richard was really familiar with was Basic, he made up a parallel version of Basic in which he wrote the program and then simulated it by hand to estimate how fast it would run on the Connection Machine.

He was excited by the results. “Hey Danny, you’re not going to believe this, but that machine of yours can actually do something [useful]!” According to Feynman’s calculations, the Connection Machine, even without any special hardware for floating point arithmetic, would outperform a machine that CalTech was building for doing QCD calculations. From that point on, Richard pushed us more and more toward looking at numerical applications of the machine.

By the end of that summer of 1983, Richard had completed his analysis of the behavior of the router, and much to our surprise and amusement, he presented his answer in the form of a set of partial differential equations. To a physicist this may seem natural, but to a computer designer, treating a set of boolean circuits as a continuous, differentiable system is a bit strange. Feynman’s router equations were in terms of variables representing continuous quantities such as “the average number of 1 bits in a message address.” I was much more accustomed to seeing analysis in terms of inductive proof and case analysis than taking the derivative of “the number of 1’s” with respect to time. Our discrete analysis said we needed seven buffers per chip; Feynman’s equations suggested that we only needed five. We decided to play it safe and ignore Feynman.

The decision to ignore Feynman’s analysis was made in September, but by next spring we were up against a wall. The chips that we had designed were slightly too big to manufacture and the only way to solve the problem was to cut the number of buffers per chip back to five. Since Feynman’s equations claimed we could do this safely, his unconventional methods of analysis started looking better and better to us. We decided to go ahead and make the chips with the smaller number of buffers.

Fortunately, he was right. When we put together the chips the machine worked. The first program run on the machine in April of 1985 was Conway’s game of Life.

Cellular Automata

The game of Life is an example of a class of computations that interested Feynman called [cellular automata]. Like many physicists who had spent their lives going to successively lower and lower levels of atomic detail, Feynman often wondered what was at the bottom. One possible answer was a cellular automaton. The notion is that the “continuum” might, at its lowest levels, be discrete in both space and time, and that the laws of physics might simply be a macro-consequence of the average behavior of tiny cells. Each cell could be a simple automaton that obeys a small set of rules and communicates only with its nearest neighbors, like the lattice calculation for QCD. If the universe in fact worked this way, then it presumably would have testable consequences, such as an upper limit on the density of information per cubic meter of space.

The notion of cellular automata goes back to von Neumann and Ulam, whom Feynman had known at Los Alamos. Richard’s recent interest in the subject was motivated by his friends Ed Fredkin and Stephen Wolfram, both of whom were fascinated by cellular automata models of physics. Feynman was always quick to point out to them that he considered their specific models “kooky,” but like the Connection Machine, he considered the subject sufficiently crazy to put some energy into.

There are many potential problems with cellular automata as a model of physical space and time; for example, finding a set of rules that obeys special relativity. One of the simplest problems is just making the physics so that things look the same in every direction. The most obvious pattern of cellular automata, such as a fixed three-dimensional grid, have preferred directions along the axes of the grid. Is it possible to implement even Newtonian physics on a fixed lattice of automata?

Feynman had a proposed solution to the anisotropy problem which he attempted (without success) to work out in detail. His notion was that the underlying automata, rather than being connected in a regular lattice like a grid or a pattern of hexagons, might be randomly connected. Waves propagating through this medium would, on the average, propagate at the same rate in every direction.

Cellular automata started getting attention at Thinking Machines when Stephen Wolfram, who was also spending time at the company, suggested that we should use such automata not as a model of physics, but as a practical method of simulating physical systems. Specifically, we could use one processor to simulate each cell and rules that were chosen to model something useful, like fluid dynamics. For two-dimensional problems there was a neat solution to the anisotropy problem since [Frisch, Hasslacher, Pomeau] had shown that a hexagonal lattice with a simple set of rules produced isotropic behavior at the macro scale. Wolfram used this method on the Connection Machine to produce a beautiful movie of a turbulent fluid flow in two dimensions. Watching the movie got all of us, especially Feynman, excited about physical simulation. We all started planning additions to the hardware, such as support of floating point arithmetic that would make it possible for us to perform and display a variety of simulations in real time.

Feynman the Explainer

In the meantime, we were having a lot of trouble explaining to people what we were doing with cellular automata. Eyes tended to glaze over when we started talking about state transition diagrams and finite state machines. Finally Feynman told us to explain it like this,

“We have noticed in nature that the behavior of a fluid depends very little on the nature of the individual particles in that fluid. For example, the flow of sand is very similar to the flow of water or the flow of a pile of ball bearings. We have therefore taken advantage of this fact to invent a type of imaginary particle that is especially simple for us to simulate. This particle is a perfect ball bearing that can move at a single speed in one of six directions. The flow of these particles on a large enough scale is very similar to the flow of natural fluids.”

This was a typical Richard Feynman explanation. On the one hand, it infuriated the experts who had worked on the problem because it neglected to even mention all of the clever problems that they had solved. On the other hand, it delighted the listeners since they could walk away from it with a real understanding of the phenomenon and how it was connected to physical reality.

We tried to take advantage of Richard’s talent for clarity by getting him to critique the technical presentations that we made in our product introductions. Before the commercial announcement of the Connection Machine CM-1 and all of our future products, Richard would give a sentence-by-sentence critique of the planned presentation. “Don’t say `reflected acoustic wave.’ Say [echo].” Or, “Forget all that `local minima’ stuff. Just say there’s a bubble caught in the crystal and you have to shake it out.” Nothing made him angrier than making something simple sound complicated.

Getting Richard to give advice like that was sometimes tricky. He pretended not to like working on any problem that was outside his claimed area of expertise. Often, at Thinking Machines when he was asked for advice he would gruffly refuse with “That’s not my department.” I could never figure out just what his department was, but it did not matter anyway, since he spent most of his time working on those “not-my-department” problems. Sometimes he really would give up, but more often than not he would come back a few days after his refusal and remark, “I’ve been thinking about what you asked the other day and it seems to me…” This worked best if you were careful not to expect it.

I do not mean to imply that Richard was hesitant to do the “dirty work.” In fact, he was always volunteering for it. Many a visitor at Thinking Machines was shocked to see that we had a Nobel Laureate soldering circuit boards or painting walls. But what Richard hated, or at least pretended to hate, was being asked to give advice. So why were people always asking him for it? Because even when Richard didn’t understand, he always seemed to understand better than the rest of us. And whatever he understood, he could make others understand as well. Richard made people feel like a child does, when a grown-up first treats him as an adult. He was never afraid of telling the truth, and however foolish your question was, he never made you feel like a fool.

The charming side of Richard helped people forgive him for his uncharming characteristics. For example, in many ways Richard was a sexist. Whenever it came time for his daily bowl of soup he would look around for the nearest “girl” and ask if she would fetch it to him. It did not matter if she was the cook, an engineer, or the president of the company. I once asked a female engineer who had just been a victim of this if it bothered her. “Yes, it really annoys me,” she said. “On the other hand, he is the only one who ever explained quantum mechanics to me as if I could understand it.” That was the essence of Richard’s charm.

A Kind Of Game

Richard worked at the company on and off for the next five years. Floating point hardware was eventually added to the machine, and as the machine and its successors went into commercial production, they were being used more and more for the kind of numerical simulation problems that Richard had pioneered with his QCD program. Richard’s interest shifted from the construction of the machine to its applications. As it turned out, building a big computer is a good excuse to talk to people who are working on some of the most exciting problems in science. We started working with physicists, astronomers, geologists, biologists, chemists — everyone of them trying to solve some problem that it had never been possible to solve before. Figuring out how to do these calculations on a parallel machine requires understanding of the details of the application, which was exactly the kind of thing that Richard loved to do.

For Richard, figuring out these problems was a kind of a game. He always started by asking very basic questions like, “What is the simplest example?” or “How can you tell if the answer is right?” He asked questions until he reduced the problem to some essential puzzle that he thought he would be able to solve. Then he would set to work, scribbling on a pad of paper and staring at the results. While he was in the middle of this kind of puzzle solving he was impossible to interrupt. “Don’t bug me. I’m busy,” he would say without even looking up. Eventually he would either decide the problem was too hard (in which case he lost interest), or he would find a solution (in which case he spent the next day or two explaining it to anyone who listened). In this way he worked on problems in database searches, geophysical modeling, protein folding, analyzing images, and reading insurance forms.

The last project that I worked on with Richard was in simulated evolution. I had written a program that simulated the evolution of populations of sexually reproducing creatures over hundreds of thousands of generations. The results were surprising in that the fitness of the population made progress in sudden leaps rather than by the expected steady improvement. The fossil record shows some evidence that real biological evolution might also exhibit such “punctuated equilibrium,” so Richard and I decided to look more closely at why it happened. He was feeling ill by that time, so I went out and spent the week with him in Pasadena, and we worked out a model of evolution of finite populations based on the Fokker Planck equations. When I got back to Boston I went to the library and discovered a book by Kimura on the subject, and much to my disappointment, all of our “discoveries” were covered in the first few pages. When I called back and told Richard what I had found, he was elated. “Hey, we got it right!” he said. “Not bad for amateurs.”

In retrospect I realize that in almost everything that we worked on together, we were both amateurs. In digital physics, neural networks, even parallel computing, we never really knew what we were doing. But the things that we studied were so new that no one else knew exactly what they were doing either. It was amateurs who made the progress.

Telling The Good Stuff You Know

Actually, I doubt that it was “progress” that most interested Richard. He was always searching for patterns, for connections, for a new way of looking at something, but I suspect his motivation was not so much to understand the world as it was to find new ideas to explain. The act of discovery was not complete for him until he had taught it to someone else.

I remember a conversation we had a year or so before his death, walking in the hills above Pasadena. We were exploring an unfamiliar trail and Richard, recovering from a major operation for the cancer, was walking more slowly than usual. He was telling a long and funny story about how he had been reading up on his disease and surprising his doctors by predicting their diagnosis and his chances of survival. I was hearing for the first time how far his cancer had progressed, so the jokes did not seem so funny. He must have noticed my mood, because he suddenly stopped the story and asked, “Hey, what’s the matter?”

I hesitated. “I’m sad because you’re going to die.”

“Yeah,” he sighed, “that bugs me sometimes too. But not so much as you think.” And after a few more steps, “When you get as old as I am, you start to realize that you’ve told most of the good stuff you know to other people anyway.”

We walked along in silence for a few minutes. Then we came to a place where another trail crossed and Richard stopped to look around at the surroundings. Suddenly a grin lit up his face. “Hey,” he said, all trace of sadness forgotten, “I bet I can show you a better way home.”

And so he did.

The 10,000-Year Geneaology of Myths

Posted on Wednesday, February 8th, 02017 by Ahmed Kabil
link   Categories: Clock of the Long Now, Long Term Science, Long Term Thinking, Seminars   chat 0 Comments

The “Shaft Scene” from the Paleolithic cave paintings in Lascaux, France.

The “Shaft Scene” from the Paleolithic cave paintings in Lascaux, France.

ONE OF THE MOST FAMOUS SCENES in the Paleolithic cave paintings in Lascaux, France depicts a confrontation between a man and a bison. The bison appears fixed in place, stabbed by a spear. The man has a bird’s head and is lying prone on the ground. Scholars have long puzzled over the pictograph’s meaning, as the narrative scene it depicts is one of the most complex yet discovered in Paleolithic art.

To understand what is going on in these scenes, some scholars have started to re-examine myths passed down through oral traditions, which some evidence suggest may be far older than previously thought. Myths still hold relevance today by allowing us to frame our actions at a civilizational level as part of a larger story, something that we hope to be able to accomplish with the idea of the “Long Now.”

Historian Julien d’Huy recently proposed an intriguing hypothesis[subscription required]: the cave painting of the man & bison could be telling the tale of the Cosmic Hunt, a myth that has surfaced with the same basic story structure in cultures across the world, from the Chukchi of Siberia to the Iroquois of the Northeastern United States. D’Huy uses comparative mythology combined with new computational modeling technologies to reconstruct a version of the myth that predates humans’ migration across the Bering Strait. If d’Huy is correct, the Lascaux painting would be one of the earliest depictions of the myth, dating back an estimated 20,000 years ago.

The Greek telling of the Cosmic Hunt is likely most familiar to today’s audiences. It recounts how the Gods transformed the chaste and beautiful Callisto into a bear, and later, into the constellation Ursa Major. D’Huy suggests that in the Lascaux painting, the bison isn’t fixed in place because it has been killed, as many experts have proposed, but because it is a constellation.

Comparative mythologists have spilled much ink over how myths like Cosmic Hunt can recur in civilizations separated by thousands of miles and thousands of years with many aspects of their stories intact. D’huy’s analysis is based off the work of anthropologist Claude Levi-Strauss, who posited that these myths are similar because they have a common origin. Levi-Strauss traced the evolution of myths by applying the same techniques that linguists used to trace the evolution of words. D’Huy provides new evidence for this approach by borrowing recently developed computational statistical tools from evolutionary biology.  The method, called phylogenetic analysis, constructs a family tree of a myth’s discrete elements, or “mythemes,” and its evolution over time:

Mythical stories are excellent targets for such analysis because, like biological species, they evolve gradually, with new parts of a core story added and others lost over time as it spreads from region to region.  […] Like genes, mythemes are heritable characteristics of “species” of stories, which pass from one generation to the next and change slowly.

A phylogenetic tree of the Cosmic Hunt shows its evolution over time

This new evidence suggests that the Cosmic Hunt has followed the migration of humans across the world. The Cosmic Hunt’s phylogenetic tree shows that the myth arrived in the Americas at different times over the course of several millennia:

One branch of the tree connects Greek and Algonquin versions of the myth. Another branch indicates passage through the Bering Strait, which then continued into Eskimo country and to the northeastern Americas, possibly in two different waves. Other branches suggest that some versions of the myth spread later than the others from Asia toward Africa and the Americas.

Myths may evolve gradually like biological species, but can also be subject to the same sudden bursts of evolutionary change, or punctuated equilibrium. Two structurally similar myths can diverge rapidly, d’Huy found, because of “migration bottlenecks, challenges from rival populations, or new environmental and cultural inputs.”

Neil Gaiman

Neil Gaiman, in his talk “How Stories Last” at Long Now in 02015, imagined stories in similarly biological terms—as living things that evolve over time and across mediums. The ones that persist are the ones that outcompete other stories by changing:

Do stories grow? Pretty obviously — anybody who has ever heard a joke being passed on from one person to another knows that they can grow, they can change. Can stories reproduce? Well, yes. Not spontaneously, obviously — they tend to need people as vectors. We are the media in which they reproduce; we are their petri dishes… Stories grow, sometimes they shrink. And they reproduce — they inspire other stories. And, of course, if they do not change, stories die.

Throughout human history, myths functioned to transmit important cultural information from generation to generation about shared beliefs and knowledge. “They teach us how the world is put together,” said Gaiman, “and the rules of living in the world.” If the information isn’t clothed in a compelling narrative garb—a tale of unrequited love, say, or a cunning escape from powerful monsters— the story won’t last, and the shared knowledge dies along with it. The stories that last “come in an attractive enough package that we take pleasure from them and want them to propagate,” said Gaiman.

Sometimes, these stories serve as warnings to future generations about calamitous events. Along Australia’s south coast, a myth persists in an aboriginal community about an enraged ancestor called Ngurunderi who chased his wives on foot to what is today known as Kangaroo Island. In his anger, Ngurunderi made the sea levels rise and turned his wives into rocks.

Kangaroo Island, Australia

Linguist Nicholas Reid and geologist Patrick Nunn believe this myth refers to a shift in sea levels that occurred thousands of years ago. Through scientifically reconstructing prehistoric sea levels, Reid and Nunn dated the myth to 9,800 to 10,650 years ago, when a post-glacial event caused sea levels to rise 100 feet and submerged the land bridge to Kangaroo Island.

“It’s quite gobsmacking to think that a story could be told for 10,000 years,” Reid said. “It’s almost unimaginable that people would transmit stories about things like islands that are currently underwater accurately across 400 generations.”

Gaiman thinks that this process of transmitting stories is what fundamentally allows humanity to advance:

Without the mass of human knowledge accumulated over millennia to buoy us up, we are in big trouble; with it, we are warm, fed, we have popcorn, we are sitting in comfortable seats, and we are capable of arguing with each other about really stupid things on the internet.

Atlantic national correspondent James Fallows, in his talk “Civilization’s Infrastructure” at Long Now in 02015, said such stories remain essential today. In Fallows’ view, effective infrastructure is what enables civilizations to thrive. Some of America’s most ambitious infrastructure projects, such as the expansion of railroads across the continent, or landing on the moon, were spurred by stories like Manifest Destiny and the Space Race. Such myths inspired Americans to look past their own immediate financial interests and time horizons to commit to something beyond themselves. They fostered, in short, long-term thinking.

James Fallows, left, speaking with Stewart Brand at Long Now

For Fallows, the reason Americans haven’t taken on grand and necessary projects of infrastructural renewal in recent times is because they struggle to take the long view. In Fallows’ eyes, there’s a lot to be optimistic about, and a great story to be told:

The story is an America that is not in its final throes, but is going through the latest version in its reinvention in which all the things that are dire now can be, if not solved, addressed and buffered by individual talents across the country but also by the exceptional tools that the tech industry is creating. There’s a different story we can tell which includes the bad parts but also —as most of our political discussion does not—includes the promising things that are beginning too.

A view of the underground site of The Clock looking up at the spiral stairs currently being cut

When Danny Hillis proposed building a 10,000 year clock, he wanted to create a myth that stood the test of time. Writing in 01998, Long Now co-founder Stewart Brand noted the trend of short-term thinking taking hold in civilization, and proposed the myth of the Clock of the Long Now:

Civilization is revving itself into a pathologically short attention span. The trend might be coming from the acceleration of technology, the short-horizon perspective of market-driven economics, the next-election perspective of democracies, or the distractions of personal multi-tasking. All are on the increase. Some sort of balancing corrective to the short-sightedness is needed-some mechanism or myth which encourages the long view and the taking of long-term responsibility, where ‘long-term’ is measured at least in centuries. Long Now proposes both a mechanism and a myth.

Long Business: A Family’s Secret to a Millennia of Sake-Making

Posted on Tuesday, February 7th, 02017 by Ahmed Kabil
link   Categories: Long Term Thinking, The Interval   chat 0 Comments

The Sudo family has been making sake for almost 900 years in Japan’s oldest brewery. Genuemon Sudo, who is the 55th generation of his family to carry on the tradition, said that at the root of Sudo’s longevity is a commitment to protecting the natural environment:

Sake is made from rice. Good rice comes from good soil. Good soil comes from fresh and high-quality water. Such water comes from protecting our trees. Protecting the natural environment makes excellent sake.

The natural environment of the Sudo brewery was tested as never before during the 02011 earthquake and subsequent nuclear meltdown. The ancient trees surrounding the brewery absorbed the quake’s impact, saving it from destruction. The water in the wells, which the Sudo family feared was poisoned by nuclear radiation, was deemed safe after radioactive analysis.

Damaged by the quake but not undone, the Sudo brewery continues a family tradition  almost a millennia in the making, with the trees, as Genuemon Sudo put it, “supporting us every step of the way.”

In looking at the list of the world’s longest-lived institutions, it is hard to ignore that many of them provide tangible goods to people, such as a room to sleep, or a libation to drink. Studying places like the Sudo brewery was part of the inspiration for creating The Interval, our own space that inspires long-term thinking.

Edge Question 02017

Posted on Friday, January 20th, 02017 by Ahmed Kabil
link   Categories: Long Term Science, Long Term Thinking, Technology   chat 0 Comments

Spiders 2013 by Katinka Matson

It’s been an annual tradition since 01998: with a new year comes a new Edge question.

Every January, John Brockman presents the members of his online salon with a question that elicits discussion about some of the biggest intellectual and scientific issues of our time. Previous iterations have included prompts such as “What should we be worried about?” or  “What do you think about machines that think?” The essay responses – in excess of a hundred each year – offer a wealth of insight into the direction of today’s cultural forces, scientific innovations, and global trends.

This year, Brockman asks:

What scientific term or concept ought to be more widely known?

The extensive collection of answers includes contributions by several Long Now Board members, fellows, and past (and future!) SALT speakers:

George Dyson, who spoke at Long Now in 02013, says the Reynolds Number from fluid dynamics can be applied to non-traditional domains to understand why things might go smoothly for a while, and then all of a sudden don’t.

Long Now Board Member Stewart Brand says genetic rescue can help threatened wildlife populations by restoring genetic diversity.

Priyamvada Natarajan, who spoke at Long Now in 02016, describes how the bending of light, or gravitational lensing, is a consequence of Einstein’s re-conceptualization of gravity in his theory of relativity.

Samuel Arbesman, who spoke at the Interval in 02016, says “magical” self-replicating computer programs known as quines underscore the limits of mathematics and computer science while demonstrating that reproduction isn’t limited to the domain of the biological.

Michael Shermer, who spoke at Long Now in 02015, says the very human tendency to be “preternaturally pessimistic” has an evolutionary basis. Negativity bias, which can be observed across all domains of life, is a holdover from an evolutionary past where existence was more dangerous, so over-reacting to threats offered more of a pay-off than under-reacting.

Long Now Board Member Brian Eno sets his sights on confirmation bias after a particularly divisive election season playing out on social media revealed that more information does not necessarily equal better decisions.

George Church of Long Now’s Revive and Restore says that while DNA may be one of the most widely known scientific terms, far too few people understand the DNA in their own bodies. With DNA tests as low as $499, Church says there’s no reason not to get your DNA tested, especially when it could allow for preventative measures when it comes to genetic diseases.

Brian Christian, who spoke at Long Now in 02016, argues that human culture progresses via the retention of youthful traits into adulthood, a process known as neoteny.

Long Now Board Member Kevin Kelly argues that the best way to steer clear of failure is by letting go of success once it is achieved, thereby avoiding premature optimization.

Seth Lloyd, who spoke at Long Now in 02016, explains the accelerating spread of digital information using a centuries-old scientific concept from classical mechanics called the virial theorem.

Long Now Board Member Danny Hillis unpacks impedance matching, or adding elements to a system so that it accepts energy more efficiently. He predicts a future where impedance matching could help cool the earth by adding tiny particles of dust to our stratosphere that would reflect away the sun’s infrared waves.

Steven Pinker, who spoke at Long Now in 02012, argues that the meaning of life and human purpose lies in the second law of thermodynamics. Pinker believes our deeply-engrained habit of under-appreciating the universe’s tendency towards disorder is “a major source of human folly.”

Long Now Board Member Paul Saffo says that at the heart of today’s biggest challenges, from sustaining mega-cities to overpopulation to information overload, are hidden laws of scale described by Haldane’s Rule of the Right Size.

Martin Rees, who spoke at Long Now in 02010, says we may be living in a multiverse.

These are just a few of this year’s thought-provoking answers; you can read the full collection here.

Breakthrough Listen Initiative Wants to Hear From You

Posted on Tuesday, August 9th, 02016 by Andrew Warner
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We have received an email from Jill Tarter, former director of the Center for SETI research, on a new outreach on behalf of the Breakthrough Listen Initiative. They want to hear from the general public on their ideas for new approaches for finding evidence of extraterrestrial technological civilizations. They are looking for 1 page descriptions, with specific attention paid to:

  • New parameter space to be explored;
  • Hardware and/or software required;
  • Current status of any prototyping or trial runs;
  • Any technology barriers at this time;
  • Scale of the effort – estimates of resources, time to completion, and costs;
  • Any other scientific opportunities enabled by this new approach.

Descriptions that reach Jill Tarter by 15 August, 2016 will be incorporated into the subcommittee’s deliberations later that week. Please send your approach to newideas4seti@seti.org.

Craters & Mudrock: Tools for Imagining Distant Future Finlands

Posted on Tuesday, July 5th, 02016 by Vincent Ialenti
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 Lake LappajärviLake Lappajärvi (Photo Credit: Hannu Oksa)
About 73 million years ago a meteorite crashed into what is now Finland’s Southern Ostrobothnia region. Today, serene Lake Lappajärvi rests in the twenty-three kilometer wide crater made in the distant past blast’s wake. Locals still enjoy boating to Lappajärvi’s Kärnänsaari: an island formed by the Cretaceous meteorite collision’s melt-rock. Paddling there is an encounter with Finland’s landscape’s deep history.

Lappajärvi has caught the attention of safety case experts working on radioactive waste management company Posiva Oy’s underground dump for used-up nuclear fuel at Olkiluoto, Western Finland. These experts are tasked with predicting how Posiva’s repository will interact with the region’s rocks, groundwater, ecosystems, and populations throughout nuclear waste’s multi-millennial time spans of dangerous radioactivity. From 02012 to 02014, I spent thirty-two months in Finland conducting anthropological research on how safety case experts see the world, how they relate to one another, and how they reckon with various spans of time in their professional lives.

When I returned to my home institution Cornell University in August 02014, I wrote a three-article series for NPR’s Cosmos & Culture blog. In it I described how safety case experts envisioned Finnish landscapes changing over the next ten thousand years. I explained how they study a present-day ice sheet in Greenland and a uranium deposit in Southern Finland as analogues to help them think about Finland’s far future ice sheets and nuclear waste deposits. I suggested that, in this moment of global environmental uncertainty some call the Anthropocene, it becomes a pressing societal task to embrace long-termist “deep time thinking.”

I continue this line of thought here by exploring how safety case experts study prehistoric places – like Lappajärvi crater-lake – to forecast how Finland will change one million years hence. I present these prehistoric places as tools for imagining distant future worlds. I advocate that societies at large use these tools to do intellectual exercises, imagination workouts, or thought experiments to cultivate their own deep time thinking skills. Doing so is crucial on a damaged planet wracked by environmental crisis.

Safety case experts make mathematical models of how the Olkiluoto repository might endure or fall apart in the extreme long-term. They assess the nuclear waste dump’s physical strengths. This is the crux of their work. However, they also develop more qualitative, speculative, quirky approaches in their Complementary Considerations report. A hodgepodge of scientific evidence and PR tools aimed at persuading various audiences of the facility’s safety, this report plays a supporting role in their broader safety argument. And it contains a fascinating thought experiment: a section called “The Evolution of the Repository System Beyond A Million Years in the Future” (p197-200).

OlkiluotoFinland’s nuclear waste repository at Olkiluoto (Photo Credit: Posiva Oy)
Complementary Considerations explains how Lappajärvi crater-lake kept its form throughout numerous past Ice Age glaciation and post-Ice Age de-glaciation periods. It tells a story of “fairly stable conditions and slow surface processes” over millions of years. In light of this, safety case experts expect only limited erosion and landmass movement throughout the repository’s multimillion-year futures. Lappajärvi’s deep histories are, in this way, taken as windows into Olkiluoto’s deep futures. From this angle, safety case experts argue that Posiva’s repository can, like Lappajärvi’s crater, withstand the waxing and waning of future Ice Ages’ ice sheets advancing and retreating.

Safety case experts also use prehistoric Littleham mudstone in Devon, England as a tool for forecasting Finland’s far futures. In Devon one can find copper that has survived over 170 million years without corroding away. The copper was long encased in the sedimentary rock. Complementary Considerations predicts a similar fate for the huge copper canisters Posiva will use to secure Finland’s nuclear waste. It also suggests that – because Littleham mudstone is more abrasive to copper than is the bentonite clay to surround Posiva’s canisters – the canister copper might see even rosier futures.

Safety case experts see the distant pasts of mudstone and copper in England as tools for envisioning the distant futures of bentonite and canisters in Finland. They see the distant pasts of a Southern Ostrobothnian crater-lake as tools for envisioning the distant futures of an Olkiluoto repository’s local geology. Deep time forecasts are, in this way, made through techniques of analogy. Visions of far future worlds emerge from analogies across time (extrapolating from long pasts to reckon long futures) and analogies across space (extrapolating across distant locales sometimes thousands of miles apart).

Yet, as safety case experts and their critics both cautioned me, one should not take these deep time analogies too seriously. There are, of course, limits to what, say, native copper in mudrock in Devon can really tell us about manufactured copper pieces in clayin Olkiluoto. Differences between repository conditions and these prehistoric places are, for many, simply too vast to make reasonable analogies between them.

But I am only half-interested in whether these techniques ought to persuade us of Posiva’s repository’s safety. I let the engineers, geologists, chemists, metallurgists, ecosystems modelers, and regulatory authorities sort that out. Instead, I find a unique intellectual opportunity in them. I wonder: can safety case experts’ techniques be retooled to help populations reposition their everyday lives within broader horizons of time? Can farsighted organizations like The Long Now Foundation help inspire general long-term thinking?

One does not have to be a Nordic nuclear waste expert to benefit from the deep time toolkits I present here. An educated public can too reflect on how analogical reasoning can stretch one’s imaginative horizons further forward and backward across time. For example, many drive through rural regions where stratigraphic rock layers are visible on highways carved into rocky hills. When doing so, why not visualize what the surrounding landscape might have looked like in each of the past times the rock faces’ layers respectively represent? Are the imageries that come to mind drawn from forest, mountain, desert, or snowy environments out there in the world today? What analogical resources did your mind tap to imagine distant past worlds? What might these landscapes’ far futures look like if they were to have, say, Sahara-like conditions? What about Amazonian rainforest-like conditions?

Posiva FacilityThe tunnel into Posiva’s underground research facility ONKALO (Photo Credit: Posiva Oy)
Straining to imagine present-day landscapes in such radically different states – in ways inspired by encounters with the deep time of Earth’s everyday environments – can be an intellectual calisthenics strengthening one’s long-termist intuitions. It can serve as an imaginative mental workout for prepping one’s mind for better adopting the farsightedness necessary to think more clearly about today’s climate change, biodiversity, Anthropocene, sustainability, or human extinction challenges.

Scenes in which radically long time horizons enter practical planning, policy, or regulatory projects – with Finland’s nuclear waste repository safety case work as but one example – can be sources of tools, techniques, and inspiration for thinking more creatively across wider time spans. And groups that advocate long-termism like The Long Now Foundation have a key role to play in disseminating these tools, techniques, and inspirations publically in this moment of planetary uncertainty.

Vincent Ialenti is a National Science Foundation Graduate Research Fellow and a PhD Candidate in Cornell University’s Department of Anthropology. He holds an MSc in “Law, Anthropology & Society” from the London School of Economics.

Visualization of 5,000 Years of War

Posted on Wednesday, March 16th, 02016 by Andrew Warner
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1100Lab has developed a visualization mapping all of the battles in Wikipedia in the last 5,000 years. Their blog details how they compiled the data, as well as other projects by the Netherlands based research and development firm.

Edge Question 02016

Posted on Tuesday, January 12th, 02016 by Andrew Warner
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double_tulips_2016.640

It’s been an annual tradition since 01998: with a new year comes a new Edge question.

Every January, John Brockman presents the members of his online salon with a question that elicits discussion about some of the biggest intellectual and scientific issues of our time. Previous iterations have included prompts such as “What should we be worried about?” or “What scientific concept would improve everybody’s cognitive toolkit?” The essay responses – in excess of a hundred each year – offer a wealth of insight into the direction of today’s cultural forces, scientific innovations, and global trends.

This year, Brockman asks:

WHAT DO YOU CONSIDER THE MOST INTERESTING RECENT [SCIENTIFIC] NEWS? WHAT MAKES IT IMPORTANT?

Scientific topics receiving prominent play in newspapers and magazines over the past several years include molecular biology, artificial intelligence, artificial life, chaos theory, massive parallelism, neural nets, the inflationary universe, fractals, complex adaptive systems, superstrings, biodiversity, nanotechnology, the human genome, expert systems, punctuated equilibrium, cellular automata, fuzzy logic, space biospheres, the Gaia hypothesis, virtual reality, cyberspace, and teraflop machines. … Unlike previous intellectual pursuits, the achievements of the third culture are not the marginal disputes of a quarrelsome mandarin class: they will affect the lives of everybody on the planet.

You might think that the above list of topics is a preamble for the Edge Question 2016, but you would be wrong. It was a central point in my essay, “The Third Culture,” published 25 years ago in The Los Angeles Times, 1991 (see below). The essay, a manifesto, was a collaborative effort, with input from Stephen Jay Gould, Murray Gell-Mann, Richard Dawkins, Daniel C. Dennett, Jared Diamond, Stuart Kauffman, Nicholas Humphrey, among other distinguished scientists and thinkers. It proclaimed:

The third culture consists of those scientists and other thinkers in the empirical world who, through their work and expository writing, are taking the place of the traditional intellectual in rendering visible the deeper meanings of our lives, redefining who and what we are.

“The wide appeal of the third-culture thinkers,” I wrote, “is not due solely to their writing ability; what traditionally has been called ‘science’ has today become ‘public culture.’Stewart Brand writes that ‘Science is the only news. When you scan through a newspaper or magazine, all the human interest stuff is the same old he-said-she-said, the politics and economics the same sorry cyclic dramas, the fashions a pathetic illusion of newness, and even the technology is predictable if you know the science. Human nature doesn’t change much; science does, and the change accrues, altering the world irreversibly.’ We now live in a world in which the rate of change is the biggest change.” Science has thus become a big story, if not the big story: news that will stay news.

This is evident by the continued relevance today of the scientific topics in the 1991 essay that were all in play before the Web, social media, mobile communications, deep learning, big data. Time for an update. …

Contributors include: Long Now President Stewart Brand, Long Now Board Members Kevin Kelly, Peter Schwartz, Paul Saffo, and many of our past Seminar speakers.

 

 

 

 

Sweden’s Minister of the Future

Posted on Tuesday, December 8th, 02015 by Andrew Warner
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Sweden’s Minister of the Future, Kristina Persson, has been tasked with expanding the temporal horizons of government plans and “constantly remind others to include the long-term in the decision making process.”

The idea behind the creation of such a ministry was a simple one: for Sweden to remain competitive tomorrow, it might, unfortunately, have to take unpopular steps today—and since politics and politicians, given elections and interests, tend to focus on the short-term, a watchdog for the long-term was needed.

It’s easier said than done, as politics show us every day. Can you think of a politician willing to risk re-election for a better future they cannot benefit from? Most probably wouldn’t.

Read the full interview by Alberto Mucci at Vice Motherboard.

The Artangel Longplayer Letters: Manuel Arriaga writes to Giles Fraser

Posted on Wednesday, November 18th, 02015 by Andrew Warner
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dysonIn May, John Burnside  wrote a letter to Manuel Arriga as part of the Artangel Longplayer Letters series. The series is a relay-style correspondence: The first letter was written by Brian Eno to Nassim Taleb. Nassim Taleb then wrote to Stewart Brand, and Stewart wrote to Esther Dyson, who wrote to Carne Ross, who wrote to John Burnside, who wrote to Manuel Arriaga. Manuel’s response is now addressed to Giles Fraser, a priest, professor, and journalist who studies contemporary ethics, who will respond with a letter to a recipient of his choosing.

The discussion thus far has focused on the extent and ways government and technology can foster long-term thinking. You can find the previous correspondences here.


From: Manuel Arriaga, New York
To: Giles Fraser, London
16 November 2015

Dear Giles,

Reading the earlier letters in this exchange, it strikes me that the issue of long-term thinking is twofold. Its challenges make themselves felt at two very different levels: the individual and the collective.

As individuals we are notoriously prone to myopic decision-making. The work of cognitive psychologists such as Tversky and Kahneman, whom Stewart Brand quoted in his letter, abundantly documents the biases that plague each of us as we try to act “rationally”. When the temporal horizon expands and making a good decision today depends on properly weighing benefits and costs that are far into the future, we do a particularly poor job. It doesn’t help that, when we look into the more distant future, such consequences are probabilistic rather than certain.

A second, distinct problem has to do with collective decision making. How can we, as a society, adequately handle issues that have long-term consequences? Obviously, different people will list different concerns, but there is a widespread perception that our political life is too caught up in the ephemeral, all the while neglecting to pay proper attention to a number of looming structural challenges.

Why does this distinction between the individual and the collective matter? Because the pathologies that afflict us as a society are not simply the sum – nor the inevitable consequence – of our limitations as individuals. Instead, we have put in place specific procedures and collective decision-making mechanisms that ensure that our individual-level myopia will be amplified when we collectively make decisions. (It is in this sense that, as Esther Dyson wrote, “long-term thinking and collective action are two sides of the same coin.”) Our political system(s) almost seems designed to take our innate biases and ensure that, as a society, we act in a way that would make the most foolhardy and impulsive teenager seem wise by comparison.

Consider elections, perhaps one of the most celebrated institutions of modern times – the only widely-accepted way for the public to delegate power into the hands of a small number of politicians. This provides a way to hold those we elect accountable and gives (some measure of) protection against authoritarian abuses of power.

However, as is painfully evident in 2015, elections also foster shortsightedness in a myriad of ways. Politicians are immersed in the media and electoral cycles, unable to extend their vision beyond the dual horizons of the day’s media coverage and the forthcoming election. Citizens are invited to pick representatives (and occasionally to vote on ballot measures) with little to no serious reflection and on the basis of a wholly inadequate information diet. Finally, journalists find themselves working in an ever-accelerating environment, where they often feel that careful, in-depth coverage of policy issues no longer has a place and must be sacrificed at the altar of sensationalism, high ratings and social media buzz. To borrow Brian Eno’s phrase, the whole system seems geared towards “increasingly short nows”.

Needless to say, we should be doing the opposite. We should be devising collective governance mechanisms that bring out the best in our thinking, creating ways to make decisions that will help us, as a society, overcome our innate myopia and the biases that plague our reasoning. The good news is that I sincerely believe that we have at our disposal a concrete, albeit little known, way to do just that. Its wider adoption promises to make the collective more, rather than less, intelligent than the individual – in short, the kind of change of method that would, as Carne Ross put it, be “tantamount to changing the outcome” in matters of policy that require deep long-term political thinking.

One way to achieve this is through a practice known as citizen deliberation: the use of large panels of randomly selected people to carefully reflect and decide on complex policy matters. Unlike professional politicians, such a representative sample of ordinary citizens has all the incentives – and close to none of the disincentives – to properly think through the long-term consequences of different policy choices. Furthermore, if the deliberation process were rigorously conducted, these citizen panels would be able to see through the “ideology and ghost stories,” as Stewart Brand puts it, that typically plague such decisions.

Greater use of citizen deliberation in policy making could be a powerful antidote to many of the ills we have been identifying. However, in my short book Rebooting Democracy: A Citizen’s Guide to Reinventing Politics, a specific concern over our difficulty in making reasoned long-term choices prompted me to suggest a blueprint for a particular kind of institution. A “Long Now Citizens’ Assembly” (the name was meant as a not-so-subtle nod to the inspiring work of the Long Now Foundation) would be a large citizen panel that would convene every ten years. These citizens would be tasked with defining a collective political vision, thereby setting out some key choices in terms of the direction their nation, region or city should take, subject to approval in a referendum. The decade between meetings would make it unambiguously clear that the panel existed in a different temporal plane from that of electoral party politics.

Although citizen deliberation dates back to ancient Greece, the idea of involving ordinary citizens in real-world policy making invariably comes as a shock to many. However, skepticism dissipates as people come to understand how citizen deliberation works in practice. The citizen panel carries out an in-depth study and analysis of the issue(s) at hand, including consultations with policy makers, interest groups, scientific experts and others. They deliberate, at length and with the assistance of skilled facilitators, about the available policy choices and their possible impact. The process has nothing in common with the rowdy scenes and uninformed shouting matches that characterized, for example, the town hall meetings on healthcare reform in the United States back in 2009.

A commonly-voiced concern is whether ordinary citizens have what it takes – are they intelligent enough to address complex policy issues? Here, too, doubts prove unfounded. Stanford Professor James Fishkin, one of the world’s foremost experts on citizen deliberation, writes that “the public is very smart if you give them a chance. If people think their voice actually matters, they’ll do the hard work, really study, … ask the experts smart questions and then make tough decisions. When they hear the experts disagreeing, they’re forced to think for themselves. About 70% change their minds in the process.” He assures us that “citizens can become better informed and master the most complex issues of state government if they are given the chance.”

The promise of citizen deliberation is that it could free policy making from the well-known biases that plague professional politicians. Ordinary citizens, chosen at random and for a single, non-renewable term, can act – just like a jury in court – in what they perceive to be the true long-term public interest, free from the pressures of facing reelection. They don’t have to worry about how necessary-but-unpopular measures will adversely impact their popularity ratings.

But perhaps the most exciting aspect is that none of this is idle, academic speculation. Recent experiences show how well citizen deliberation works in practice. In 2004, a randomly-chosen panel of 160 citizens was tasked by the government of the Canadian province of British Columbia with reforming the province’s electoral system. After drawing on the input of a wide variety of experts, consulting the public, and deliberating at length, the British Columbia Citizens’ Assembly on Electoral Reform ended up suggesting a type of electoral system that, in the words of Professor David Farrell, a renowned expert on electoral systems, “politicians, given a choice, would probably least like to see introduced but which voters, given a choice, should choose.” The assembly’s proposal was later approved by 58% of the popular vote in a referendum, yet regrettably failed to meet the strict requirements imposed by the provincial government for its results to be considered binding, and therefore has yet to be implemented.

Similarly encouraging results are reported from the U.S. state of Oregon. Since 2010, citizen deliberation has been used to assist Oregon voters in state-wide ballot initiatives. In a process known as the “Citizen Initiative Review,” a panel of about twenty-five randomly chosen Oregonians is tasked with carefully researching and deliberating on the ballot measure up for a vote. At the end of this process, an accessible and highly informative set of “key findings”, as well as an indication of how many panelists ultimately supported and opposed the proposed measure, are presented as a “citizens’ statement” in the pamphlet that voters receive in the mail before a ballot. Research confirms that this citizens’ statement not only makes voters better informed, but also has a substantial influence on the voting behavior of those who read it.

In his letter, John Burnside rightly wonders if – in light of the substantial social change that would be required just to bring rampant environmental destruction under control – it might be too optimistic to place that much faith in the abilities of our fellow citizens. When one pauses to consider what is at stake and how far we are from attaining that goal, it is impossible not to share his concern. Yet, I can think of no other collective decision making system better equipped to handle such a challenge. After all, the kind of major lifestyle changes that seem necessary are utterly indefensible by professional politicians seeking (re)election. We can also hope for the success of NGOs and other groups in civil society trying to promote greater environmental awareness, yet their odds of effecting major changes seem awfully limited as long as our so-called democracies remain deaf to voices other than those stemming from powerful economic interests (or, perhaps just as depressingly, focus groups). Our best hope perhaps lies in the abilities of ordinary citizens to collectively engage with these difficult issues and then share their findings with the broader public.

Giles, in this letter I deliberately adopted an “engineering” perspective – that of a self-confessed geek who asks himself how we might reform a system so that it can generate what I consider to be better outcomes. I did so aware of the violent oversimplification entailed in this process, any hopes of true change ultimately depending on our values and how they come to evolve over time.

As argued above, I believe that citizen deliberation offers us a powerful way to cut through the everyday froth, to reflect on and articulate what our values truly are and which reforms are needed so that, together, we can build a future that is true to those values. Yet, this is at best a tiny piece of the puzzle. I very much look forward to seeing where you will choose to take this conversation next.

All my best,

Manuel.


Manuel Arriaga is a visiting research professor at New York University and a fellow at the University of Cambridge. In 2014, he published Rebooting Democracy: A Citizen’s Guide to Reinventing Politics, which, by the end of the same year, had become the #1 best-selling book on democracy on Amazon UK. He is currently working on a film project on democratic innovations. More information about his work can be found at  http://www.rebootdemocracy.org.

Giles Fraser is a priest of the Church of England and a journalist. He is currently the parish priest at St Mary’s, Newington, near the Elephant and Castle, London, and writes a weekly Saturday column Loose Canon for The Guardian, as well as appearing frequently on BBC Radio 4. He is a regular contributor on Thought for the Day and a panellist on The Moral Maze. He is visiting professor in the anthropology department at the London School of Economics. He was previously Canon Chancellor of St Paul’s Cathedral and director of the St Paul’s Institute from 2009 until his resignation in October 2011. As Canon Chancellor, Fraser was a residentiary canon with special responsibility for contemporary ethics and engagement with the City of London as a financial centre.