The Rise of the New Groupthink. ‘Without great solitude, no serious work is possible’
“The mind is sharper and keener in seclusion and uninterrupted solitude. No big laboratory is needed in which to think. Originality thrives in seclusion free of outside influences beating upon us to cripple the creative mind. Be alone, that is the secret of invention; be alone, that is when ideas are born. That is why many of the earthly miracles have had their genesis in humble surroundings.”
— Nikola Tesla, Serbian-American inventor, mechanical engineer, and electrical engineer (1856-1943)
“Solitude is out of fashion. Our companies, our schools and our culture are in thrall to an idea I call the New Groupthink, which holds that creativity and achievement come from an oddly gregarious place. Most of us now work in teams, in offices without walls, for managers who prize people skills above all. Lone geniuses are out. Collaboration is in.
But there’s a problem with this view. Research strongly suggests that people are more creative when they enjoy privacy and freedom from interruption. And the most spectacularly creative people in many fields are often introverted, according to studies by the psychologists Mihaly Csikszentmihalyi and Gregory Feist. They’re extroverted enough to exchange and advance ideas, but see themselves as independent and individualistic. They’re not joiners by nature.
One explanation for these findings is that introverts are comfortable working alone — and solitude is a catalyst to innovation. As the influential psychologist Hans Eysenck observed, introversion fosters creativity by “concentrating the mind on the tasks in hand, and preventing the dissipation of energy on social and sexual matters unrelated to work.” In other words, a person sitting quietly under a tree in the backyard, while everyone else is clinking glasses on the patio, is more likely to have an apple land on his head. (Newton was one of the world’s great introverts: William Wordsworth described him as “A mind for ever/ Voyaging through strange seas of Thought, alone.”)
Solitude has long been associated with creativity and transcendence. “Without great solitude, no serious work is possible,” Picasso said. A central narrative of many religions is the seeker — Moses, Jesus, Buddha — who goes off by himself and brings profound insights back to the community.
Culturally, we’re often so dazzled by charisma that we overlook the quiet part of the creative process. (…)
In his memoir, Mr. [Steve] Wozniak offers this guidance to aspiring inventors:
“Most inventors and engineers I’ve met are like me … they live in their heads. They’re almost like artists. In fact, the very best of them are artists. And artists work best alone …. I’m going to give you some advice that might be hard to take. That advice is: Work alone… Not on a committee. Not on a team.”
And yet. The New Groupthink has overtaken our workplaces, our schools and our religious institutions. Anyone who has ever needed noise-canceling headphones in her own office or marked an online calendar with a fake meeting in order to escape yet another real one knows what I’m talking about. Virtually all American workers now spend time on teams and some 70 percent inhabit open-plan offices, in which no one has “a room of one’s own.” During the last decades, the average amount of space allotted to each employee shrank 300 square feet, from 500 square feet in the 1970s to 200 square feet in 2010.
Our schools have also been transformed by the New Groupthink. Today, elementary school classrooms are commonly arranged in pods of desks, the better to foster group learning. Even subjects like math and creative writing are often taught as committee projects. In one fourth-grade classroom I visited in New York City, students engaged in group work were forbidden to ask a question unless every member of the group had the very same question.
The New Groupthink also shapes some of our most influential religious institutions. Many mega-churches feature extracurricular groups organized around every conceivable activity, from parenting to skateboarding to real estate, and expect worshipers to join in. They also emphasize a theatrical style of worship — loving Jesus out loud, for all the congregation to see. “Often the role of a pastor seems closer to that of church cruise director than to the traditional roles of spiritual friend and counselor,” said Adam McHugh, an evangelical pastor and author of “Introverts in the Church.”
Some teamwork is fine and offers a fun, stimulating, useful way to exchange ideas, manage information and build trust.
But it’s one thing to associate with a group in which each member works autonomously on his piece of the puzzle; it’s another to be corralled into endless meetings or conference calls conducted in offices that afford no respite from the noise and gaze of co-workers. Studies show that open-plan offices make workers hostile, insecure and distracted. They’re also more likely to suffer from high blood pressure, stress, the flu and exhaustion. And people whose work is interrupted make 50 percent more mistakes and take twice as long to finish it. (…)
Privacy also makes us productive. In a fascinating study known as the Coding War Games, consultants Tom DeMarco and Timothy Lister compared the work of more than 600 computer programmers at 92 companies. They found that people from the same companies performed at roughly the same level — but that there was an enormous performance gap between organizations. What distinguished programmers at the top-performing companies wasn’t greater experience or better pay. It was how much privacy, personal workspace and freedom from interruption they enjoyed. Sixty-two percent of the best performers said their workspace was sufficiently private compared with only 19 percent of the worst performers. Seventy-six percent of the worst programmers but only 38 percent of the best said that they were often interrupted needlessly.
[Learning] Solitude can even help us learn. According to research on expert performance by the psychologist Anders Ericsson, the best way to master a field is to work on the task that’s most demanding for you personally. And often the best way to do this is alone. Only then, Mr. Ericsson told me, can you “go directly to the part that’s challenging to you. If you want to improve, you have to be the one who generates the move. Imagine a group class — you’re the one generating the move only a small percentage of the time.”
Conversely, brainstorming sessions are one of the worst possible ways to stimulate creativity. The brainchild of a charismatic advertising executive named Alex Osborn who believed that groups produced better ideas than individuals, workplace brainstorming sessions came into vogue in the 1950s. “The quantitative results of group brainstorming are beyond question,” Mr. Osborn wrote. “One group produced 45 suggestions for a home-appliance promotion, 56 ideas for a money-raising campaign, 124 ideas on how to sell more blankets.”
But decades of research show that individuals almost always perform better than groups in both quality and quantity, and group performance gets worse as group size increases. The “evidence from science suggests that business people must be insane to use brainstorming groups,” wrote the organizational psychologist Adrian Furnham. “If you have talented and motivated people, they should be encouraged to work alone when creativity or efficiency is the highest priority.”
The reasons brainstorming fails are instructive for other forms of group work, too. People in groups tend to sit back and let others do the work; they instinctively mimic others’ opinions and lose sight of their own; and, often succumb to peer pressure. The Emory University neuroscientist Gregory Berns found that when we take a stance different from the group’s, we activate the amygdala, a small organ in the brain associated with the fear of rejection. Professor Berns calls this “the pain of independence.”
The Internet: a place where we can be alone together— and this is precisely what gives it power
The one important exception to this dismal record is electronic brainstorming, where large groups outperform individuals; and the larger the group the better. The protection of the screen mitigates many problems of group work. This is why the Internet has yielded such wondrous collective creations. Marcel Proust called reading a “miracle of communication in the midst of solitude,” and that’s what the Internet is, too. It’s a place where we can be alone together — and this is precisely what gives it power.
My point is not that man is an island. Life is meaningless without love, trust and friendship.
And I’m not suggesting that we abolish teamwork. Indeed, recent studies suggest that influential academic work is increasingly conducted by teams rather than by individuals. (Although teams whose members collaborate remotely, from separate universities, appear to be the most influential of all.) The problems we face in science, economics and many other fields are more complex than ever before, and we’ll need to stand on one another’s shoulders if we can possibly hope to solve them.
But even if the problems are different, human nature remains the same. And most humans have two contradictory impulses: we love and need one another, yet we crave privacy and autonomy.
To harness the energy that fuels both these drives, we need to move beyond the New Groupthink and embrace a more nuanced approach to creativity and learning. Our offices should encourage casual, cafe-style interactions, but allow people to disappear into personalized, private spaces when they want to be alone. Our schools should teach children to work with others, but also to work on their own for sustained periods of time. And we must recognize that introverts like Steve Wozniak need extra quiet and privacy to do their best work.
Before Mr. Wozniak started Apple, he designed calculators at Hewlett-Packard, a job he loved partly because HP made it easy to chat with his colleagues. Every day at 10 a.m. and 2 p.m., management wheeled in doughnuts and coffee, and people could socialize and swap ideas. What distinguished these interactions was how low-key they were. For Mr. Wozniak, collaboration meant the ability to share a doughnut and a brainwave with his laid-back, poorly dressed colleagues — who minded not a whit when he disappeared into his cubicle to get the real work done.”
What Happened Before the Big Bang? The New Philosophy of Cosmology
Tim Maudlin: “There are problems that are fairly specific to cosmology. Standard cosmology, or what was considered standard cosmology twenty years ago, led people to the conclude that the universe that we see around us began in a big bang, or put another way, in some very hot, very dense state. And if you think about the characteristics of that state, in order to explain the evolution of the universe, that state had to be a very low entropy state, and there’s a line of thought that says that anything that is very low entropy is in some sense very improbable or unlikely. And if you carry that line of thought forward, you then say “Well gee, you’re telling me the universe began in some extremely unlikely or improbable state” and you wonder is there any explanation for that. Is there any principle that you can use to account for the big bang state?
This question of accounting for what we call the “big bang state” — the search for a physical explanation of it — is probably the most important question within the philosophy of cosmology, and there are a couple different lines of thought about it. One that’s becoming more and more prevalent in the physics community is the idea that the big bang state itself arose out of some previous condition, and that therefore there might be an explanation of it in terms of the previously existing dynamics by which it came about. There are other ideas, for instance that maybe there might be special sorts of laws, or special sorts of explanatory principles, that would apply uniquely to the initial state of the universe.
One common strategy for thinking about this is to suggest that what we used to call the whole universe is just a small part of everything there is, and that we live in a kind of bubble universe, a small region of something much larger. And the beginning of this region, what we call the big bang, came about by some physical process, from something before it, and that we happen to find ourselves in this region because this is a region that can support life. The idea being that there are lots of these bubble universes, maybe an infinite number of bubble universes, all very different from one another. Part of the explanation of what’s called the anthropic principle says, “Well now, if that’s the case, we as living beings will certainly find ourselves in one of those bubbles that happens to support living beings.” That gives you a kind of account for why the universe we see around us has certain properties. (…)
Newton would call what he was doing natural philosophy, that’s actually the name of his book: “Mathematical Principles of Natural Philosophy.” Philosophy, traditionally, is what everybody thought they were doing. It’s what Aristotle thought he was doing when he wrote his book called Physics. So it’s not as if there’s this big gap between physical inquiry and philosophical inquiry. They’re both interested in the world on a very general scale, and people who work in the foundations of physics, that is, the group that works on the foundations of physics, is about equally divided between people who live in philosophy departments, people who live in physics departments, and people who live in mathematics departments.
Q: In May of last year Stephen Hawking gave a talk for Google in which he said that philosophy was dead, and that it was dead because it had failed to keep up with science, and in particular physics. Is he wrong or is he describing a failure of philosophy that your project hopes to address?
Maudlin: Hawking is a brilliant man, but he’s not an expert in what’s going on in philosophy, evidently. Over the past thirty years the philosophy of physics has become seamlessly integrated with the foundations of physics work done by actual physicists, so the situation is actually the exact opposite of what he describes. I think he just doesn’t know what he’s talking about. I mean there’s no reason why he should. Why should he spend a lot of time reading the philosophy of physics? I’m sure it’s very difficult for him to do. But I think he’s just … uninformed. (…)
Q: Do you think that physics has neglected some of these foundational questions as it has become, increasingly, a kind of engine for the applied sciences, focusing on the manipulation, rather than say, the explanation, of the physical world?
Maudlin: Look, physics has definitely avoided what were traditionally considered to be foundational physical questions, but the reason for that goes back to the foundation of quantum mechanics. The problem is that quantum mechanics was developed as a mathematical tool. Physicists understood how to use it as a tool for making predictions, but without an agreement or understanding about what it was telling us about the physical world. And that’s very clear when you look at any of the foundational discussions. This is what Einstein was upset about; this is what Schrodinger was upset about.
Quantum mechanics was merely a calculational technique that was not well understood as a physical theory. Bohr and Heisenberg tried to argue that asking for a clear physical theory was something you shouldn’t do anymore. That it was something outmoded. And they were wrong, Bohr and Heisenberg were wrong about that. But the effect of it was to shut down perfectly legitimate physics questions within the physics community for about half a century. And now we’re coming out of that, fortunately.
Q And what’s driving the renaissance?
Maudlin: Well, the questions never went away. There were always people who were willing to ask them. Probably the greatest physicist in the last half of the twentieth century, who pressed very hard on these questions, was John Stewart Bell. So you can’t suppress it forever, it will always bubble up. It came back because people became less and less willing to simply say, “Well, Bohr told us not to ask those questions,” which is sort of a ridiculous thing to say.
Q: Are the topics that have scientists completely flustered especially fertile ground for philosophers? For example I’ve been doing a ton of research for a piece about the James Webb Space Telescope, the successor to the Hubble Space Telescope, and none of the astronomers I’ve talked to seem to have a clue as to how to use it to solve the mystery of dark energy. Is there, or will there be, a philosophy of dark energy in the same way that a body of philosophy seems to have flowered around the mysteries of quantum mechanics?
Maudlin: There will be. There can be a philosophy of anything really, but it’s perhaps not as fancy as you’re making it out. The basic philosophical question, going back to Plato, is “What is x?” What is virtue? What is justice? What is matter? What is time? You can ask that about dark energy - what is it? And it’s a perfectly good question.
There are different ways of thinking about the phenomena which we attribute to dark energy. Some ways of thinking about it say that what you’re really doing is adjusting the laws of nature themselves. Some other ways of thinking about it suggest that you’ve discovered a component or constituent of nature that we need to understand better, and seek the source of. So, the question — What is this thing fundamentally? — is a philosophical question, and is a fundamental physical question, and will lead to interesting avenues of
Q: One example of philosophy of cosmology that seems to have trickled out to the layman is the idea of fine tuning - the notion that in the set of all possible physics, the subset that permits the evolution of life is very small, and that from this it is possible to conclude that the universe is either one of a large number of universes, a multiverse, or that perhaps some agent has fine tuned the universe with the expectation that it generate life. Do you expect that idea to have staying power, and if not what are some of the compelling arguments against it?
Maudlin: A lot of attention has been given to the fine tuning argument. Let me just say first of all, that the fine tuning argument as you state it, which is a perfectly correct statement of it, depends upon making judgments about the likelihood, or probability of something. Like, “how likely is it that the mass of the electron would be related to the mass of the proton in a certain way?” Now, one can first be a little puzzled by what you mean by “how likely” or “probable” something like that is. You can ask how likely it is that I’ll roll double sixes when I throw dice, but we understand the way you get a handle on the use of probabilities in that instance. It’s not as clear how you even make judgments like that about the likelihood of the various constants of nature (an so on) that are usually referred to in the fine tuning argument.
Now let me say one more thing about fine tuning. I talk to physicists a lot, and none of the physicists I talk to want to rely on the fine tuning argument to argue for a cosmology that has lots of bubble universes, or lots of worlds. What they want to argue is that this arises naturally from an analysis of the fundamental physics, that the fundamental physics, quite apart from any cosmological considerations, will give you a mechanism by which these worlds will be produced, and a mechanism by which different worlds will have different constants, or different laws, and so on. If that’s true, then if there are enough of these worlds, it will be likely that some of them have the right combination of constants to permit life. But their arguments tend not to be “we have to believe in these many worlds to solve the fine tuning problem,” they tend to be “these many worlds are generated by physics we have other reasons for believing in.”
If we give up on that, and it turns out there aren’t these many worlds, that physics is unable to generate them, then it’s not that the only option is that there was some intelligent designer. It would be a terrible mistake to think that those are the only two ways things could go. You would have to again think hard about what you mean by probability, and about what sorts of explanations there might be. Part of the problem is that right now there are just way too many freely adjustable parameters in physics. Everybody agrees about that. There seem to be many things we call constants of nature that you could imagine setting at different values, and most physicists think there shouldn’t be that many, that many of them are related to one another.
Physicists think that at the end of the day there should be one complete equation to describe all physics, because any two physical systems interact and physics has to tell them what to do. And physicists generally like to have only a few constants, or parameters of nature. This is what Einstein meant when he famously said he wanted to understand what kind of choices God had —using his metaphor— how free his choices were in creating the universe, which is just asking how many freely adjustable parameters there are. Physicists tend to prefer theories that reduce that number, and as you reduce it, the problem of fine tuning tends to go away. But, again, this is just stuff we don’t understand well enough yet.
Q: I know that the nature of time is considered to be an especially tricky problem for physics, one that physicists seem prepared, or even eager, to hand over to philosophers. Why is that?
Maudlin: That’s a very interesting question, and we could have a long conversation about that. I’m not sure it’s accurate to say that physicists want to hand time over to philosophers. Some physicists are very adamant about wanting to say things about it; Sean Carroll for example is very adamant about saying that time is real. You have others saying that time is just an illusion, that there isn’t really a direction of time, and so forth. I myself think that all of the reasons that lead people to say things like that have very little merit, and that people have just been misled, largely by mistaking the mathematics they use to describe reality for reality itself. If you think that mathematical objects are not in time, and mathematical objects don’t change — which is perfectly true — and then you’re always using mathematical objects to describe the world, you could easily fall into the idea that the world itself doesn’t change, because your representations of it don’t.
There are other, technical reasons that people have thought that you don’t need a direction of time, or that physics doesn’t postulate a direction of time. My own view is that none of those arguments are very good. To the question as to why a physicist would want to hand time over to philosophers, the answer would be that physicists for almost a hundred years have been dissuaded from trying to think about fundamental questions. I think most physicists would quite rightly say “I don’t have the tools to answer a question like ‘what is time?’ - I have the tools to solve a differential equation.” The asking of fundamental physical questions is just not part of the training of a physicist anymore.
Q: I recently came across a paper about Fermi’s Paradox and Self-Replicating Probes, and while it had kind of a science fiction tone to it, it occurred to me as I was reading it that philosophers might be uniquely suited to speculating about, or at least evaluating the probabilistic arguments for the existence of life elsewhere in the universe. Do you expect philosophers of cosmology to enter into those debates, or will the discipline confine itself to issues that emerge directly from physics?
Maudlin: This is really a physical question. If you think of life, of intelligent life, it is, among other things, a physical phenomenon — it occurs when the physical conditions are right. And so the question of how likely it is that life will emerge, and how frequently it will emerge, does connect up to physics, and does connect up to cosmology, because when you’re asking how likely it is that somewhere there’s life, you’re talking about the broad scope of the physical universe. And philosophers do tend to be pretty well schooled in certain kinds of probabilistic analysis, and so it may come up. I wouldn’t rule it in or rule it out.
I will make one comment about these kinds of arguments which seems to me to somehow have eluded everyone. When people make these probabilistic equations, like the Drake Equation, which you’re familiar with — they introduce variables for the frequency of earth-like planets, for the evolution of life on those planets, and so on. The question remains as to how often, after life evolves, you’ll have intelligent life capable of making technology.
What people haven’t seemed to notice is that on earth, of all the billions of species that have evolved, only one has developed intelligence to the level of producing technology. Which means that kind of intelligence is really not very useful. It’s not actually, in the general case, of much evolutionary value. We tend to think, because we love to think of ourselves, human beings, as the top of the evolutionary ladder, that the intelligence we have, that makes us human beings, is the thing that all of evolution is striving toward. But what we know is that that’s not true.
Obviously it doesn’t matter that much if you’re a beetle, that you be really smart. If it were, evolution would have produced much more intelligent beetles. We have no empirical data to suggest that there’s a high probability that evolution on another planet would lead to technological intelligence. There is just too much we don’t know.”
‘Human beings are learning machines,’ says philosopher (nature vs. nurture)
“The point is that in scientific writing (…) suggest a very inflexible view of human nature, that we are determined by our biology. From my perspective the most interesting thing about the human species is our plasticity, our flexibility. (…)
It is striking in general that human beings mistake the cultural for the natural; you see it in many domains. Take moral values. We assume we have moral instincts: we just know that certain things are right and certain things are wrong. When we encounter people whose values differ from ours we think they must be corrupted or in some sense morally deformed. But this is clearly an instance where we mistake our deeply inculcated preferences for natural law. (…)
Q: At what point with morality does biology stop and culture begin?
One important innate contribution to morality is emotions. An aggressive response to an attack is not learned, it is biological. The question is how emotions that are designed to protect each of us as individuals get extended into generalised rules that spread within a group. One factor may be imitation. Human beings are great imitative learners. Rules that spread in a family can be calibrated across a whole village, leading to conformity in the group and a genuine system of morality.
Nativists will say that morality can emerge without instruction. But with innate domains, there isn’t much need for instruction, whereas in the moral domain, instruction is extensive. Kids learn through incessant correction. Between the ages of 2 and 10, parents correct their children’s behaviour every 8 minutes or so of waking life. In due course, our little monsters become little angels, more or less. This gives us reason to think morality is learned.
Q: One of the strongest arguments for innateness comes from linguists such as Noam Chomsky, who argue that humans are born with the basic rules of grammar already in place. But you disagree with them?
Chomsky singularly deserves credit for giving rise to the new cognitive sciences of the mind. He was instrumental in helping us think about the mind as a kind of machine. He has made some very compelling arguments to explain why everybody with an intact brain speaks grammatically even though children are not explicitly taught the rules of grammar.
But over the past 10 years we have started to see powerful evidence that children might learn language statistically, by unconsciously tabulating patterns in the sentences they hear and using these to generalise to new cases. Children might learn language effortlessly not because they possess innate grammatical rules, but because statistical learning is something we all do incessantly and automatically. The brain is designed to pick up on patterns of all kinds.
Q: How hard has it been to put this alternative view on the table, given how Chomskyan thought has dominated the debate in recent years?
Chomsky’s views about language are so deeply ingrained among academics that those who take statistical learning seriously are subject to a kind of ridicule. There is very little tolerance for dissent. This has been somewhat limiting, but there is a new generation of linguists who are taking the alternative very seriously, and it will probably become a very dominant position in the next generation.
Q: You describe yourself as an “unabashed empiricist” who favours nurture over nature. How did you come to this position, given that on many issues the evidence is still not definitive either way?
Actually I think the debate has been settled. You only have to stroll down the street to see that human beings are learning machines. Sure, for any given capacity the debate over biology versus culture will take time to resolve. But if you compare us with other species, our degree of variation is just so extraordinary and so obvious that we know prior to doing any science that human beings are special in this regard, and that a tremendous amount of what we do is as a result of learning. So empiricism should be the default position. The rest is just working out the details of how all this learning takes place.
Q: What are the implications of an empirical understanding of human nature for the way we go about our lives. How should it affect the way we behave?
In general, we need to cultivate a respect for difference. We need to appreciate that people with different values to us are not simply evil or ignorant, and that just like us they are products of socialisation. This should lead to an increase in international understanding and respect. We also need to understand that group differences in performance are not necessarily biologically fixed. For example, when we see women performing less well than men in mathematics, we should not assume that this is because of a difference in biology.
Q: How much has cognitive science contributed to our understanding of what it is to be human, traditionally a philosophical question?
Cognitive science is in the business of settling long-running philosophical debates on human nature, innate knowledge and other issues. The fact that these theories have been churning about for a couple of millennia without any consensus is evidence that philosophical methods are better at posing questions than answering them. Philosophy tells us what is possible, and science tells us what is true.
Cognitive science has transformed philosophy. At the beginning of the 20th century, philosophers changed their methodology quite dramatically by adopting logic. There has been an equally important revolution in 21st-century philosophy in that philosophers are turning to the empirical sciences and to some extent conducting experimental work themselves to settle old questions. As a philosopher, I hardly go a week without conducting an experiment.
My whole working day has changed because of the infusion of science.”
— Jesse Prinz is a distinguished professor of philosophy at the City University of New York, specialising in the philosophy of psychology. He is a pioneer in experimental philosophy, using findings from the cognitive sciences, anthropology and other fields to develop empiricist theories of how the mind works. He is the author of The Emotional Construction of Morals (Oxford University Press, 2007), Gut Reactions (OUP, 2004) and Furnishing the Mind (MIT Press, 2002) and Beyond Human Nature: How culture and experience make us who we are, ‘Human beings are learning machines,’ says philosopher, NewScientist, Jan 20, 2012. (Illustration: Fritz Kahn, British Library)
Cognitive scientists develop new take on old problem: why human language has so many words with multiple meanings
“Why did language evolve? While the answer might seem obvious — as a way for individuals to exchange information — linguists and other students of communication have debated this question for years. Many prominent linguists, including MIT’s Noam Chomsky, have argued that language is, in fact, poorly designed for communication. Such a use, they say, is merely a byproduct of a system that probably evolved for other reasons — perhaps for structuring our own private thoughts.
As evidence, these linguists point to the existence of ambiguity: In a system optimized for conveying information between a speaker and a listener, they argue, each word would have just one meaning, eliminating any chance of confusion or misunderstanding. Now, a group of MIT cognitive scientists has turned this idea on its head. In a new theory, they claim that ambiguity actually makes language more efficient, by allowing for the reuse of short, efficient sounds that listeners can easily disambiguate with the help of context.
“Various people have said that ambiguity is a problem for communication,” says Ted Gibson, an MIT professor of cognitive science and senior author of a paper describing the research to appear in the journal Cognition. “But once we understand that context disambiguates, then ambiguity is not a problem — it’s something you can take advantage of, because you can reuse easy [words] in different contexts over and over again.” (…)
What do you ‘mean’?
For a somewhat ironic example of ambiguity, consider the word “mean.” It can mean, of course, to indicate or signify, but it can also refer to an intention or purpose (“I meant to go to the store”); something offensive or nasty; or the mathematical average of a set of numbers. Adding an ‘s’ introduces even more potential definitions: an instrument or method (“a means to an end”), or financial resources (“to live within one’s means”).
But virtually no speaker of English gets confused when he or she hears the word “mean.” That’s because the different senses of the word occur in such different contexts as to allow listeners to infer its meaning nearly automatically.
Given the disambiguating power of context, the researchers hypothesized that languages might harness ambiguity to reuse words — most likely, the easiest words for language processing systems. Building on observation and previous studies, they posited that words with fewer syllables, high frequency and the simplest pronunciations should have the most meanings.
To test this prediction, Piantadosi, Tily and Gibson carried out corpus studies of English, Dutch and German. (In linguistics, a corpus is a large body of samples of language as it is used naturally, which can be used to search for word frequencies or patterns.) By comparing certain properties of words to their numbers of meanings, the researchers confirmed their suspicion that shorter, more frequent words, as well as those that conform to the language’s typical sound patterns, are most likely to be ambiguous — trends that were statistically significant in all three languages.
To understand why ambiguity makes a language more efficient rather than less so, think about the competing desires of the speaker and the listener. The speaker is interested in conveying as much as possible with the fewest possible words, while the listener is aiming to get a complete and specific understanding of what the speaker is trying to say. But as the researchers write, it is “cognitively cheaper” to have the listener infer certain things from the context than to have the speaker spend time on longer and more complicated utterances. The result is a system that skews toward ambiguity, reusing the “easiest” words. Once context is considered, it’s clear that “ambiguity is actually something you would want in the communication system,” Piantadosi says.
Tom Wasow, a professor of linguistics and philosophy at Stanford University, calls the paper “important and insightful.”
“You would expect that since languages are constantly changing, they would evolve to get rid of ambiguity,” Wasow says. “But if you look at natural languages, they are massively ambiguous: Words have multiple meanings, there are multiple ways to parse strings of words. … This paper presents a really rigorous argument as to why that kind of ambiguity is actually functional for communicative purposes, rather than dysfunctional.”
Implications for computer science
The researchers say the statistical nature of their paper reflects a trend in the field of linguistics, which is coming to rely more heavily on information theory and quantitative methods.
“The influence of computer science in linguistics right now is very high,” Gibson says, adding that natural language processing (NLP) is a major goal of those operating at the intersection of the two fields.
Piantadosi points out that ambiguity in natural language poses immense challenges for NLP developers. “Ambiguity is only good for us [as humans] because we have these really sophisticated cognitive mechanisms for disambiguating,” he says. “It’s really difficult to work out the details of what those are, or even some sort of approximation that you could get a computer to use.”
But, as Gibson says, computer scientists have long been aware of this problem. The new study provides a better theoretical and evolutionary explanation of why ambiguity exists, but the same message holds: “Basically, if you have any human language in your input or output, you are stuck with needing context to disambiguate,” he says.”
“We present a general information-theoretic argument that all efficient communication systems will be ambiguous, assuming that context is informative about meaning. We also argue that ambiguity additionally allows for greater ease of processing by allowing efficient linguistic units to be re-used. We test predictions of this theory in English, German, and Dutch. Our results and theoretical analysis suggest that ambiguity is a functional property of language that allows for greater communicative efficiency. (…)
Our results argue for a rational explanation of ambiguity and demonstrate that ambiguity is not mysterious when language is considered as a cognitive system designed in part for communication.”
“Compression is a fundamental goal of both human language and digital communication, yet natural language is very different from compression schemes employed by modern computers. We partly explain this difference using the fact that information theory generally assumes a common prior probability distribution shared by the encoder and decoder, whereas human communication has to be robust to the fact that a speaker and listener may have different prior beliefs about what a speaker may say. We model this information-theoretically using the following question: what type of compression scheme would be effective when the encoder and decoder have (boundedly) different prior probability distributions. The resulting compression scheme resembles natural language to a far greater extent than existing digital communication protocols. We also use information theory to justify why ambiguity is necessary for the purpose of compression.”
The Rise of Complexity. Scientists replicate key evolutionary step in life on earth
Green cells are undergoing cell death, a cellular division-of-labor—fostering new life.
“More than 500 million years ago, single-celled organisms on Earth’s surface began forming multi-cellular clusters that ultimately became plants and animals. (…)
The yeast “evolved” into multi-cellular clusters that work together cooperatively, reproduce and adapt to their environment—in essence, they became precursors to life on Earth as it is today. (…)
The finding that the division-of-labor evolves so quickly and repeatedly in these ‘snowflake’ clusters is a big surprise. (…) The first step toward multi-cellular complexity seems to be less of an evolutionary hurdle than theory would suggest.” (…)
“To understand why the world is full of plants and animals, including humans, we need to know how one-celled organisms made the switch to living as a group, as multi-celled organisms.” (…)
“This study is the first to experimentally observe that transition,” says Scheiner, “providing a look at an event that took place hundreds of millions of years ago.” (…)
The scientists chose Brewer’s yeast, or Saccharomyces cerevisiae, a species of yeast used since ancient times to make bread and beer because it is abundant in nature and grows easily.
They added it to nutrient-rich culture media and allowed the cells to grow for a day in test tubes.
Then they used a centrifuge to stratify the contents by weight.
As the mixture settled, cell clusters landed on the bottom of the tubes faster because they are heavier. The biologists removed the clusters, transferred them to fresh media, and agitated them again.
First steps in the transition to multi-cellularity: ‘snowflake’ yeast with dead cells stained red.
Sixty cycles later, the clusters—now hundreds of cells—looked like spherical snowflakes.
Analysis showed that the clusters were not just groups of random cells that adhered to each other, but related cells that remained attached following cell division.
That was significant because it meant that they were genetically similar, which promotes cooperation. When the clusters reached a critical size, some cells died off in a process known as apoptosis to allow offspring to separate.
The offspring reproduced only after they attained the size of their parents. (…)
Multi-cellular yeast individuals containing central dead cells, which promote reproduction.
“A cluster alone isn’t multi-cellular,”William Ratcliff says. “But when cells in a cluster cooperate, make sacrifices for the common good, and adapt to change, that’s an evolutionary transition to multi-cellularity.”
In order for multi-cellular organisms to form, most cells need to sacrifice their ability to reproduce, an altruistic action that favors the whole but not the individual. (…)
For example, all cells in the human body are essentially a support system that allows sperm and eggs to pass DNA along to the next generation.
Thus multi-cellularity is by its nature very cooperative.
“Some of the best competitors in nature are those that engage in cooperation, and our experiment bears that out. (…)
Evolutionary biologists have estimated that multi-cellularity evolved independently in about 25 groups.”
“Let’s rewind time back about 3.5 billion years. Our beloved planet looks nothing like the lush home we know today – it is a turbulent place, still undergoing the process of formation. Land is a fluid concept, consisting of molten lava flows being created and destroyed by massive volcanoes. The air is thick with toxic gasses like methane and ammonia which spew from the eruptions. Over time, water vapor collects, creating our first weather events, though on this early Earth there is no such thing as a light drizzle. Boiling hot acid rain pours down on the barren land for millions of years, slowly forming bubbling oceans and seas. Yet in this unwelcoming, violent landscape, life begins.
The creatures which dared to arise are calledcyanobacteria, or blue-green algae. They were the pioneers of photosynthesis, transforming the toxic atmosphere by producing oxygen and eventually paving the way for the plants and animals of today. But what is even more incredible is that they were the first to do something extraordinary – they were the first cells to join forces and create multicellular life. (…)
William Ratcliff and his colleagues at the University of Minnesota. In a PNAS paper published online this week, they show how multicellular yeast can arise in less than two months in the lab. (…)
All of their cultures went from single cells to snowflake-like clumps in less than 60 days. “Although known transitions to complex multicellularity, with clearly differentiated cell types, occurred over millions of years, we have shown that the first crucial steps in the transition from unicellularity to multicellularity can evolve remarkably quickly under appropriate selective conditions,” write the authors. These clumps weren’t just independent cells sticking together for the sake of it – they acted as rudimentary multicellular creatures. They were formed not by random cells attaching but by genetically identical cells not fully separating after division. Furthermore, there was division of labor between cells. As the groups reached a certain size, some cells underwent programmed cell death, providing places for daughter clumps to break from. Since individual cells acting as autonomous organisms would value their own survival, this intentional culling suggests that the cells acted instead in the interest of the group as a whole organism.
Given how easily multicellular creatures can arise in test tubes, it might then come as no surprise that multicellularity has arisen at least a dozen times in the history of life, independently in bacteria, plants and of course, animals, beginning the evolutionary tree that we sit atop today. Our evolutionary history is littered with leaps of complexity. While such intricacies might seem impossible, study after study has shown that even the most complex structures can arise through the meandering path of evolution. In Evolution’s Witness, Ivan Schwab explains how one of the most complex organs in our body, our eyes, evolved. (…)
Eyes are highly intricate machines that require a number of parts working together to function. But not even the labyrinthine structures in the eye present an insurmountable barrier to evolution.
Our ability to see began to evolve long before animals radiated. Visual pigments, like retinal, are found in all animal lineages, and were first harnessed by prokaryotes to respond to changes in light more than 2.5 billion years ago. But the first complex eyes can be found about 540 million years ago, during a time of rapid diversification colloquially referred to as the Cambrian Explosion. It all began when comb jellies, sponges and jellyfish, along with clonal bacteria, were the first to group photoreceptive cells and create light-sensitive ‘eyespots’. These primitive visual centers could detect light intensity, but lacked the ability to define objects. That’s not to say, though, that eyespots aren’t important – eyespots are such an asset that they arose independently in at least 40 different lineages. But it was the other invertebrate lineages that would take the simple eyespot and turn it into something incredible.
According to Schwab, the transition from eyespot to eye is quite small. “Once an eyespot is established, the ability to recognize spatial characteristics – our eye definition – takes one of two mechanisms: invagination (a pit) or evagination (a bulge).” Those pits or bulges can then be focused with any clear material forming a lens (different lineages use a wide variety of molecules for their lenses). Add more pigments or more cells, and the vision becomes sharper. Each alteration is just a slight change from the one before, a minor improvement well within bounds of evolution’s toolkit, but over time these small adjustments led to intricate complexity.
In the Cambrian, eyes were all the rage. Arthropods were visual trendsetters, creating compound eyes by using the latter approach, that of bulging, then combining many little bulges together. One of the era’s top predators, Anomalocaris, had over 16,000 lenses! So many creatures arose with eyes during the Cambrian that Andrew Parker, a visiting member of the Zoology Department at the University of Oxford, believes that the development of vision was the driver behind the evolutionary explosion. His ‘Light-Switch’ hypothesis postulates that vision opened the doors for animal innovation, allowing rapid diversification in modes and mechanisms for a wide set of ecological traits. Even if eyes didn’t spur the Cambrian explosion, their development certainly irrevocably altered the course of evolution.
Fossilized compound eyes from Cambrian arthropods (Lee et al. 2011)
Our eyes, as well as those of octopuses and fish, took a different approach than those of the arthropods, putting photo receptors into a pit, thus creating what is referred to as a camera-style eye. In the fossil record, eyes seem to emerge from eyeless predecessors rapidly, in less than 5 million years. But is it really possible that an eye like ours arose so suddenly? Yes, say biologists Dan-E. Nilsson and Susanne Pelger. They calculated a pessimistic guess as to how long it would take for small changes – just 1% improvements in length, depth, etc per generation – to turn a flat eyespot into an eye like our own. Their conclusion? It would only take about 400,000 years – a geological instant.
How does complexity arise in the first place
But how does complexity arise in the first place? How did cells get photoreceptors, or any of the first steps towards innovations such as vision? Well, complexity can arise a number of ways.
Each and every one of our cells is a testament to the simplest way that complexity can arise: have one simple thing combine with a different one. The powerhouses of our cells, called mitochondria, are complex organelles that are thought to have arisen in a very simple way. Some time around 3 billion years ago, certain bacteria had figured out how to create energy using electrons from oxygen, thus becoming aerobic. Our ancient ancestors thought this was quite a neat trick, and, as single cells tend to do, they ate these much smaller energy-producing bacteria. But instead of digesting their meal, our ancestors allowed the bacteria to live inside them as an endosymbiont, and so the deal was struck: our ancestor provides the fuel for the chemical reactions that the bacteria perform, and the bacteria, in turn, produces ATP for both of them. Even today we can see evidence of this early agreement – mitochondria, unlike other organelles, have their own DNA, reproduce independently of the cell’s reproduction, and are enclosed in a double membrane (the bacterium’s original membrane and the membrane capsule used by our ancestor to engulf it).
Over time the mitochondria lost other parts of their biology they didn’t need, like the ability to move around, blending into their new home as if they never lived on their own. The end result of all of this, of course, was a much more complex cell, with specialized intracellular compartments devoted to different functions: what we now refer to as a eukaryote.
Of course, while I focused this post on how complexity arose, it’s important to note that more complex doesn’t necessarily mean better. While we might notice the eye and marvel at its detail, success, from the viewpoint of an evolutionary lineage, isn’t about being the most elaborate. Evolution only leads to increases in complexity when complexity is beneficial to survival and reproduction.
Indeed, simplicity has its perks: the more simple you are, the faster you can reproduce, and thus the more offspring you can have. Many bacteria live happy simple lives, produce billions of offspring, and continue to thrive, representatives of lineages that have survived billions of years. Even complex organisms may favor less complexity – parasites, for example, are known for their loss of unnecessary traits and even whole organ systems, keeping only what they need to get inside and survive in their host. Darwin referred to them as regressive for seemingly violating the unspoken rule that more complex arises from less complex, not the other way around. But by not making body parts they don’t need, parasites conserve energy, which they can invest in other efforts like reproduction.
When we look back in an attempt to grasp evolution, it may instead be the lack of complexity, not the rise of it, that is most intriguing.”
“There appears to be no single memory store, but instead a diverse taxonomy of memory systems, each with its own special circuitry evolved to package and retrieve that type of memory. Memories are not static entities; over time they shift and migrate between different territories of the brain.
At the top of the taxonomical tree, a split occurs between declarative and non-declarative memories. Declarative memories are those you can state as true or false, such as remembering whether you rode a bicycle to work. Non-declarative memories are those that cannot be described as true or false, such as knowing how to ride a bicycle. A central hub in the declarative memory system is a brain region called the hippocampus. This undulating, twisted structure gets its name from its resemblance to a sea horse. Destruction of the hippocampus, through injury, neurosurgery or the ravages of Alzheimer’s disease, can result in an amnesia so severe that no events experienced after the damage can be remembered. (…)
A popular view is that during sleep your hippocampus “broadcasts” its recently captured memories to the neocortex, which updates your long-term store of past experience and knowledge. Eventually the neocortex is sufficient to support recall without relying on the hippocampus. However, there is evidence that if you need to vividly picture a scene in your mind, this appears to require the hippocampus, no matter how old the memory. We have recently discovered that the hippocampus is not only needed to reimagine the past, but also to imagine the future.
Pattern completion
Studying patients has taught us where memories might be stored, but not what physically constitutes a memory. The answer lies in the multitude of tiny modifiable connections between neuronal cells, the information-processing units of the brain. These cells, with their wispy tree-like protrusions, hang like stars in miniature galaxies and pulse with electrical charge. Thus, your memories are patterns inscribed in the connections between the millions of neurons in your brain. Each memory has its unique pattern of activity, logged in the vast cellular network every time a memory is formed.
It is thought that during recall of past events the original activity pattern in the hippocampus is re-established via a process that is known as “pattern completion”. During this process, the initial activity of the cells is incoherent, but via repeated reactivation the activity pattern is pieced together until the original pattern is complete. Memory retention is helped by the presence of two important molecules in our brain: dopamine and acetylcholine. Both help the neurons improve their ability to lay down memories in their connections. Sometimes, however, the system fails, leaving us unable to bring elements of the past to mind.
Of all the things we need to remember, one of the most essential is where we are. Becoming lost is debilitating and potentially terrifying. Within the hippocampus, and neighbouring brain structures, neurons exist that allow us to map space and find our way through it. “Place cells” provide an internal map of space; “head-direction cell” signal the direction we are facing, similar to an internal compass; and “grid cells” chart out space in a manner akin to latitude and longitude.
For licensed London taxi drivers, it appears that navigating the labyrinth of London’s streets on a daily basis causes the density of grey matter in their posterior hippocampus to increase. Thus, the physical structure of your brain is malleable, depending on what you learn.
With impressive technical advances such as optogenetics, in which light beams excite or silence targeted groups of neurons, scientists are beginning to control memories at an unprecedented level.”
— Hugo Spiers is a neuroscientist and lecturer at the institute of behavioural neuroscience at University College London, What are memories made of?, The Guardian, Jan 14, 2012 (Illustration: Polly Becker)
A risk-perception: What You Don’t Know Can Kill You
“Humans have a perplexing tendency to fear rare threats such as shark attacks while blithely ignoring far greater risks like unsafe sex and an unhealthy diet. Those illusions are not just silly—they make the world a more dangerous place. (…)
We like to think that humans are supremely logical, making decisions on the basis of hard data and not on whim. For a good part of the 19th and 20th centuries, economists and social scientists assumed this was true too. The public, they believed, would make rational decisions if only it had the right pie chart or statistical table. But in the late 1960s and early 1970s, that vision of homo economicus—a person who acts in his or her best interest when given accurate information—was kneecapped by researchers investigating the emerging field of risk perception. What they found, and what they have continued teasing out since the early 1970s, is that humans have a hell of a time accurately gauging risk. Not only do we have two different systems—logic and instinct, or the head and the gut—that sometimes give us conflicting advice, but we are also at the mercy of deep-seated emotional associations and mental shortcuts. (…)
Our hardwired gut reactions developed in a world full of hungry beasts and warring clans, where they served important functions. Letting the amygdala (part of the brain’s emotional core) take over at the first sign of danger, milliseconds before the neocortex (the thinking part of the brain) was aware a spear was headed for our chest, was probably a very useful adaptation. Even today those nano-pauses and gut responses save us from getting flattened by buses or dropping a brick on our toes. But in a world where risks are presented in parts-per-billion statistics or as clicks on a Geiger counter, our amygdala is out of its depth.
A risk-perception apparatus permanently tuned for avoiding mountain lions makes it unlikely that we will ever run screaming from a plate of fatty mac ’n’ cheese. “People are likely to react with little fear to certain types of objectively dangerous risk that evolution has not prepared them for, such as guns, hamburgers, automobiles, smoking, and unsafe sex, even when they recognize the threat at a cognitive level,” says Carnegie Mellon University researcher George Loewenstein, whose seminal 2001 paper, “Risk as Feelings,” debunked theories that decision making in the face of risk or uncertainty relies largely on reason. “Types of stimuli that people are evolutionarily prepared to fear, such as caged spiders, snakes, or heights, evoke a visceral response even when, at a cognitive level, they are recognized to be harmless,” he says. Even Charles Darwin failed to break the amygdala’s iron grip on risk perception. As an experiment, he placed his face up against the puff adder enclosure at the London Zoo and tried to keep himself from flinching when the snake struck the plate glass. He failed.
The result is that we focus on the one-in-a-million bogeyman while virtually ignoring the true risks that inhabit our world. News coverage of a shark attack can clear beaches all over the country, even though sharks kill a grand total of about one American annually, on average. That is less than the death count from cattle, which gore or stomp 20 Americans per year. Drowning, on the other hand, takes 3,400 lives a year, without a single frenzied call for mandatory life vests to stop the carnage. A whole industry has boomed around conquering the fear of flying, but while we down beta-blockers in coach, praying not to be one of the 48 average annual airline casualties, we typically give little thought to driving to the grocery store, even though there are more than 30,000 automobile fatalities each year.
In short, our risk perception is often at direct odds with reality. All those people bidding up the cost of iodide? They would have been better off spending $10 on a radon testing kit. The colorless, odorless, radioactive gas, which forms as a by-product of natural uranium decay in rocks, builds up in homes, causing lung cancer. According to the Environmental Protection Agency, radon exposure kills 21,000 Americans annually.
David Ropeik, a consultant in risk communication and the author of How Risky Is It, Really? Why Our Fears Don’t Always Match the Facts, has dubbed this disconnect the perception gap. “Even perfect information perfectly provided that addresses people’s concerns will not convince everyone that vaccines don’t cause autism, or that global warming is real, or that fluoride in the drinking water is not a Commie plot,” he says. “Risk communication can’t totally close the perception gap, the difference between our fears and the facts.”
In the early 1970s, psychologists Daniel Kahneman, now at Princeton University, and Amos Tversky, who passed away in 1996, began investigating the way people make decisions, identifying a number of biases and mental shortcuts, or heuristics, on which the brain relies to make choices. Later, Paul Slovic and his colleagues Baruch Fischhoff, now a professor of social sciences at Carnegie Mellon University, and psychologist Sarah Lichtenstein began investigating how these leaps of logic come into play when people face risk. They developed a tool, called the psychometric paradigm, that describes all the little tricks our brain uses when staring down a bear or deciding to finish the 18th hole in a lighting storm.
Many of our personal biases are unsurprising. For instance, the optimism bias gives us a rosier view of the future than current facts might suggest. We assume we will be richer 10 years from now, so it is fine to blow our savings on a boat—we’ll pay it off then. Confirmation bias leads us to prefer information that backs up our current opinions and feelings and to discount information contradictory to those opinions. We also have tendencies to conform our opinions to those of the groups we identify with, to fear man-made risks more than we fear natural ones, and to believe that events causing dread—the technical term for risks that could result in particularly painful or gruesome deaths, like plane crashes and radiation burns—are inherently more risky than other events.
But it is heuristics—the subtle mental strategies that often give rise to such biases—that do much of the heavy lifting in risk perception. The “availability” heuristic says that the easier a scenario is to conjure, the more common it must be. It is easy to imagine a tornado ripping through a house; that is a scene we see every spring on the news, and all the time on reality TV and in movies. Now try imagining someone dying of heart disease. You probably cannot conjure many breaking-news images for that one, and the drawn-out process of atherosclerosis will most likely never be the subject of a summer thriller. The effect? Twisters feel like an immediate threat, although we have only a 1-in-46,000 chance of being killed by a cataclysmic storm. Even a terrible tornado season like the one last spring typically yields fewer than 500 tornado fatalities. Heart disease, on the other hand, which eventually kills 1 in every 6 people in this country, and 800,000 annually, hardly even rates with our gut. (…)
All the mental rules of thumb and biases banging around in our brain, the most influential in assessing risk is the “affect” heuristic. Slovic calls affect a “faint whisper of emotion” that creeps into our decisions. Simply put, positive feelings associated with a choice tend to make us think it has more benefits. Negative correlations make us think an action is riskier. One study by Slovic showed that when people decide to start smoking despite years of exposure to antismoking campaigns, they hardly ever think about the risks. Instead, it’s all about the short-term “hedonic” pleasure. The good outweighs the bad, which they never fully expect to experience.
Our fixation on illusory threats at the expense of real ones influences more than just our personal lifestyle choices. Public policy and mass action are also at stake. The Office of National Drug Control Policy reports that prescription drug overdoses have killed more people than crack and heroin combined did in the 1970s and 1980s. Law enforcement and the media were obsessed with crack, yet it was only recently that prescription drug abuse merited even an after-school special.
Despite the many obviously irrational ways we behave, social scientists have only just begun to systematically document and understand this central aspect of our nature. In the 1960s and 1970s, many still clung to the homo economicus model. They argued that releasing detailed information about nuclear power and pesticides would convince the public that these industries were safe. But the information drop was an epic backfire and helped spawn opposition groups that exist to this day. Part of the resistance stemmed from a reasonable mistrust of industry spin. Horrific incidents like those at Love Canal and Three Mile Island did not help. Yet one of the biggest obstacles was that industry tried to frame risk purely in terms of data, without addressing the fear that is an instinctual reaction to their technologies.
The strategy persists even today. In the aftermath of Japan’s nuclear crisis, many nuclear-energy boosters were quick to cite a study commissioned by the Boston-based nonprofit Clean Air Task Force. The study showed that pollution from coal plants is responsible for 13,000 premature deaths and 20,000 heart attacks in the United States each year, while nuclear power has never been implicated in a single death in this country. True as that may be, numbers alone cannot explain away the cold dread caused by the specter of radiation. Just think of all those alarming images of workers clad in radiation suits waving Geiger counters over the anxious citizens of Japan. Seaweed, anyone? (…)
All that media created a sort of feedback loop. Because people were seeing so many sharks on television and reading about them, the “availability” heuristic was screaming at them that sharks were an imminent threat.
“Certainly anytime we have a situation like that where there’s such overwhelming media attention, it’s going to leave a memory in the population,” says George Burgess, curator of the International Shark Attack File at the Florida Museum of Natural History, who fielded 30 to 40 media calls a day that summer. “Perception problems have always been there with sharks, and there’s a continued media interest in vilifying them. It makes a situation where the risk perceptions of the populace have to be continually worked on to break down stereotypes. Anytime there’s a big shark event, you take a couple steps backward, which requires scientists and conservationists to get the real word out.”
Then again, getting out the real word comes with its own risks—like the risk of getting the real word wrong. Misinformation is especially toxic to risk perception because it can reinforce generalized confirmation biases and erode public trust in scientific data. As scientists studying the societal impact of the Chernobyl meltdown have learned, doubt is difficult to undo. In 2006, 20 years after reactor number 4 at the Chernobyl nuclear power plant was encased in cement, the World Health Organization (WHO) and the International Atomic Energy Agency released a report compiled by a panel of 100 scientists on the long-term health effects of the level 7 nuclear disaster and future risks for those exposed. Among the 600,000 recovery workers and local residents who received a significant dose of radiation, the WHO estimates that up to 4,000 of them, or 0.7 percent, will develop a fatal cancer related to Chernobyl. For the 5 million people living in less contaminated areas of Ukraine, Russia, and Belarus, radiation from the meltdown is expected to increase cancer rates less than 1 percent. (…)
During the year following the September 11 attacks, millions of Americans opted out of air travel and slipped behind the wheel instead. While they crisscrossed the country, listening to breathless news coverage of anthrax attacks, extremists, and Homeland Security, they faced a much more concrete risk. All those extra cars on the road increased traffic fatalities by nearly 1,600. Airlines, on the other hand, recorded no fatalities.
It is unlikely that our intellect can ever paper over our gut reactions to risk. But a fuller understanding of the science is beginning to percolate into society. Earlier this year, David Ropeik and others hosted a conference on risk in Washington, D.C., bringing together scientists, policy makers, and others to discuss how risk perception and communication impact society. “Risk perception is not emotion and reason, or facts and feelings. It’s both, inescapably, down at the very wiring of our brain,” says Ropeik. “We can’t undo this. What I heard at that meeting was people beginning to accept this and to realize that society needs to think more holistically about what risk means.”
Ropeik says policy makers need to stop issuing reams of statistics and start making policies that manipulate our risk perception system instead of trying to reason with it. Cass Sunstein, a Harvard law professor who is now the administrator of the White House Office of Information and Regulatory Affairs, suggests a few ways to do this in his book Nudge: Improving Decisions About Health, Wealth, and Happiness, published in 2008. He points to the organ donor crisis in which thousands of people die each year because others are too fearful or uncertain to donate organs. People tend to believe that doctors won’t work as hard to save them, or that they won’t be able to have an open- casket funeral (both false). And the gory mental images of organs being harvested from a body give a definite negative affect to the exchange. As a result, too few people focus on the lives that could be saved. Sunstein suggests—controversially—“mandated choice,” in which people must check “yes” or “no” to organ donation on their driver’s license application. Those with strong feelings can decline. Some lawmakers propose going one step further and presuming that people want to donate their organs unless they opt out.
In the end, Sunstein argues, by normalizing organ donation as a routine medical practice instead of a rare, important, and gruesome event, the policy would short-circuit our fear reactions and nudge us toward a positive societal goal. It is this type of policy that Ropeik is trying to get the administration to think about, and that is the next step in risk perception and risk communication. “Our risk perception is flawed enough to create harm,” he says, “but it’s something society can do something about.””
“Defining life poses a challenge that’s downright philosophical. (…) When Portland State University biologist Radu Popa was working on a book about defining life, he decided to count up all the definitions that scientists have published in books and scientific journals. Some scientists define life as something capable of metabolism. Others make the capacity to evolve the key distinction. Popa gave up counting after about 300 definitions.
Things haven’t gotten much better in the years since Popa published Between Necessity and Probability: Searching for the Definition and Origin of Life in 2004. Scientists have unveiled even more definitions, yet none of them have been widely embraced. But now Edward Trifonov, a biologist at the University of Haifa in Israel (…) analyzed the linguistic structure of 150 definitions of life, grouping similar words into categories. He found that he could sum up what they all have in common in three words. Life, Trifonov declares, is simply self-reproduction with variations.
Trifonov argues that this minimal definition is useful because it encompasses both life as we know it and life as we may discover it to be. And as scientists tinker with self-replicating molecules, they may be able to put his definition to the test. It may be possible for them to create a system of molecules that meets the requirements. If it fails to come “alive,” it will show that the definition was missing something crucial about life. (…)
A number of the scientists who responded to Trifonov felt that his definition was missing one key feature or another, such as metabolism, a cell, or information. Eugene Koonin, a biologist at the National Center for Biotechnology Information, thinks that Trifonov’s definition is missing error correction. He argues that “self-reproduction with variation” is redundant, since the laws of thermodynamics ensure that error-free replication is impossible. “The problem is the exact opposite,” Koonin observes: if life replicates with too many errors, it stops replicating. He offers up an alternative: life requires “replications with an error rate below the sustainability threshold.”
Jack Szostak, a Nobel-prize winning Harvard biologist, simply rejects the search for any definition of life. “Attempts to define life are irrelevant to scientific efforts to understand the origin of life,” he writes (article PDF).
Szostak himself has spent two decades tinkering with biological molecules to create simple artificial life. Instead of using DNA to store genetic information and proteins to carry out chemical reactions, Szostak hopes to create cells that only contain single-stranded RNA molecules. Like many researchers, Szostak suspects that RNA-based life preceded DNA-based life. It may have even been the first kind of life on Earth, even if it cannot be found on the planet today.
Life, Szostak suspects, arose through a long series of steps, as small molecules began interacting with each other, replicating, getting enveloped into cells, and so on. Once there were full-blown cells that could grow, divide, and evolve, no one would deny that life had come to exist on Earth. But it’s pointless to try to find the precise point along the path where life suddenly sprang into being and met an arbitrary definition. “None of this matters, however, in terms of the fundamental scientific questions concerning the transitions leading from chemistry to biology,” says Szostak.
It’s conceivable that Mars has Earth-like life, either because one planet infected the other, or because chemistry became biology along the same path on both of them. In either case, Curiosity [rover] may be able to do some good science when it arrives at Mars this summer. But if it’s something fundamentally different, even the most sophisticated machines may not be able to help us until we come to a decision about what we’re looking for in the first place.”
Nicholas Carr on Books That Are Never Done Being Written
“I recently got a glimpse into the future of books. A few months ago, I dug out a handful of old essays I’d written about innovation, combined them into a single document, and uploaded the file to Amazon’s Kindle Direct Publishing service. Two days later, my little e-book was on sale at Amazon’s site. The whole process couldn’t have been simpler.
Then I got the urge to tweak a couple of sentences in one of the essays. I made the edits on my computer and sent the revised file back to Amazon. The company quickly swapped out the old version for the new one. I felt a little guilty about changing a book after it had been published, knowing that different readers would see different versions of what appeared to be the same edition. But I also knew that the readers would be oblivious to the alterations. (…)
When Johannes Gutenberg invented movable type a half-millennium ago, he also gave us immovable text. Before Gutenberg, books were handwritten by scribes, and no two copies were exactly the same. (…) With the arrival of the letterpress, thousands of identical copies could enter the marketplace simultaneously. The publication of a book, once a nebulous process, became an event.
A new set of literary workers coalesced in publishing houses, collaborating with writers to perfect texts before they went on press. The verb “to finalize” became common in literary circles, expressing the permanence of printed words. (…)
Beyond giving writers a spur to eloquence, what the historian Elizabeth Eisenstein calls “typographical fixity” served as a cultural preservative. It helped to protect original documents from corruption, providing a more solid foundation for the writing of history. It established a reliable record of knowledge, aiding the spread of science. It accelerated the standardization of everything from language to law. The preservative qualities of printed books, Ms. Eisenstein argues, may be the most important legacy of Gutenberg’s invention.
Once digitized, a page of words loses its fixity. It can change every time it’s refreshed on a screen. A book page turns into something like a Web page, able to be revised endlessly after its initial uploading. There’s no technological constraint on perpetual editing, and the cost of altering digital text is basically zero. As electronic books push paper ones aside, movable type seems fated to be replaced by movable text.
That’s an attractive development in many ways. It makes it easy for writers to correct errors and update facts. (…)
Even literary authors will be tempted to keep their works fresh. Historians and biographers will be able to revise their narratives to account for recent events or newly discovered documents. Polemicists will be able to bolster their arguments with new evidence. Novelists will be able to scrub away the little anachronisms that can make even a recently published story feel dated.
But as is often the case with digitization, the boon carries a bane. The ability to alter the contents of a book will be easy to abuse. School boards may come to exert even greater influence over what students read. They’ll be able to edit textbooks that don’t fit with local biases. Authoritarian governments will be able to tweak books to suit their political interests. And the edits can ripple backward. Because e-readers connect to the Internet, the works they contain can be revised remotely, just as software programs are updated today. Movable text makes a lousy preservative.
Such abuses can be prevented through laws and software protocols. What may be more insidious is the pressure to fiddle with books for commercial reasons. Because e-readers gather enormously detailed information on the way people read, publishers may soon be awash in market research. They’ll know how quickly readers progress through different chapters, when they skip pages, and when they abandon a book.
The promise of stronger sales and profits will make it hard to resist tinkering with a book in response to such signals, adding a few choice words here, trimming a chapter there, maybe giving a key character a quick makeover. What will be lost, or at least diminished, is the sense of a book as a finished and complete object, a self-contained work of art.
Not long before he died, John Updike spoke eloquently of a book’s “edges,” the boundaries that give shape and integrity to a literary work and that for centuries have found their outward expression in the indelibility of printed pages. It’s those edges that give a book its solidity, allowing it to stand up to the vagaries of fashion and the erosions of time. And it’s those edges that seem fated to blur as the words of books go from being stamped permanently on sheets of paper to being rendered temporarily on flickering screens.”
Scientists recreate evolution of complexity using ‘molecular time travel’
“Much of what living cells do is carried out by “molecular machines” – physical complexes of specialized proteins working together to carry out some biological function. (…)
In a study published early online on January 8, in Nature, a team of scientists from the University of Chicago and the University of Oregon demonstrate how just a few small, high-probability mutations increased the complexity of a molecular machine more than 800 million years ago. By biochemically resurrecting ancient genes and testing their functions in modern organisms, the researchers showed that a new component was incorporated into the machine due to selective losses of function rather than the sudden appearance of new capabilities.
“Our strategy was to use ‘molecular time travel’ to reconstruct and experimentally characterize all the proteins in this molecular machine just before and after it increased in complexity,” said the study’s senior author Joe Thornton, PhD, professor of human genetics and evolution & ecology at the University of Chicago, professor of biology at the University of Oregon, and an Early Career Scientist of the Howard Hughes Medical Institute.
“By reconstructing the machine’s components as they existed in the deep past,” Thornton said, “we were able to establish exactly how each protein’s function changed over time and identify the specific genetic mutations that caused the machine to become more elaborate.” (…)
To understand how the ring increased in complexity, Thornton and his colleagues “resurrected” the ancestral versions of the ring proteins just before and just after the third subunit was incorporated. To do this, the researchers used a large cluster of computers to analyze the gene sequences of 139 modern-day ring proteins, tracing evolution backwards through time along the Tree of Life to identify the most likely ancestral sequences. They then used biochemical methods to synthesize those ancient genes and express them in modern yeast cells. (…)
Thornton’s research group has helped to pioneer this molecular time-travel approach for single genes; this is the first time it has been applied to all the components in a molecular machine.
The group found that the third component of the ring in Fungi originated when a gene coding for one of the subunits of the older two-protein ring was duplicated, and the daughter genes then diverged on their own evolutionary paths.
The pre-duplication ancestor turned out to be more versatile than either of its descendants: expressing the ancestral gene rescued modern yeast that otherwise failed to grow because either or both of the descendant ring protein genes had been deleted. In contrast, each resurrected gene from after the duplication could only compensate for the loss of a single ring protein gene.
The researchers concluded that the functions of the ancestral protein were partitioned among the duplicate copies, and the increase in complexity was due to complementary loss of ancestral functions rather than gaining new ones. By cleverly engineering a set of ancestral proteins fused to each other in specific orientations, the group showed that the duplicated proteins lost their capacity to interact with some of the other ring proteins. Whereas the pre-duplication ancestor could occupy five of the six possible positions within the ring, each duplicate gene lost the capacity to fill some of the slots occupied by the other, so both became obligate components for the complex to assemble and function.
“It’s counterintuitive but simple: complexity increased because protein functions were lost, not gained,” Thornton said. “Just as in society, complexity increases when individuals and institutions forget how to be generalists and come to depend on specialists with increasingly narrow capacities.” (…)
“The mechanisms for this increase in complexity are incredibly simple, common occurrences,” Thornton said. “Gene duplications happen frequently in cells, and it’s easy for errors in copying to DNA to knock out a protein’s ability to interact with certain partners. It’s not as if evolution needed to happen upon some special combination of 100 mutations that created some complicated new function.”
Thornton proposes that the accumulation of simple, degenerative changes over long periods of times could have created many of the complex molecular machines present in organisms today. Such a mechanism argues against the intelligent design concept of “irreducible complexity,” the claim that molecular machines are too complicated to have formed stepwise through evolution.
“I expect that when more studies like this are done, a similar dynamic will be observed for the evolution of many molecular complexes,” Thornton said.
“These really aren’t like precision-engineered machines at all,” he added. “They’re groups of molecules that happen to stick to each other, cobbled together during evolution by tinkering, degradation, and good luck, and preserved because they helped our ancestors to survive.”
Why Do Languages Die? Urbanization, the state and the rise of nationalism
“The history of the world’s languages is largely a story of loss and decline. At around 8000 BC, linguists estimate that upwards of 20,000 languages may have been in existence. Today the number stands at 6,909 and is declining rapidly. By 2100, it is quite realistic to expect that half of these languages will be gone, their last speakers dead, their words perhaps recorded in a dusty archive somewhere, but more likely undocumented entirely. (…)
The problem with globalization in the latter sense is that it is the result, not a cause, of language decline. (…) It is only when the state adopts a trade language as official and, in a fit of linguistic nationalism, foists it upon its citizens, that trade languages become “killer languages.” (…)
Most importantly, what both of the above answers overlook is that speaking a global language or a language of trade does not necessitate the abandonment of one’s mother tongue. The average person on this planet speaks three or four languages. (…)
The truth is, most people don’t “give up” the languages they learn in their youth. (…) To wipe out a language, one has to enter the home and prevent the parents from speaking their native language to their children.
Given such a preposterous scenario, we return to our question — how could this possibly happen?
One good answer is urbanization. If a Gikuyu and a Giryama meet in Nairobi, they won’t likely speak each other’s mother tongue, but they very likely will speak one or both of the trade languages in Kenya — Swahili and English. Their kids may learn a smattering of words in the heritage languages from their parents, but by the third generation any vestiges of those languages in the family will likely be gone. In other cases, extremely rural communities are drawn to the relatively easier lifestyle in cities, until sometimes entire villages are abandoned. Nor is this a recent phenomenon.
The first case of massive language die-off was probably during the Agrarian (Neolithic) Revolution, when humanity first adopted farming, abandoned the nomadic lifestyle, and created permanent settlements. As the size of these communities grew, so did the language they spoke. But throughout most of history, and still in many areas of the world today, 500 or fewer speakers per language has been the norm. Like the people who spoke them, these languages were constantly in flux. No language could grow very large, because the community that spoke it could only grow so large itself before it fragmented. The language followed suit, soon becoming two languages. Permanent settlements changed all this, and soon larger and larger populations could stably speak the same language. (…)
“In primitive times every migration causes not only geographical but also intellectual separation of clans and tribes. Economic exchanges do not yet exist; there is no contact that could work against differentiation and the rise of new customs. The dialect of each tribe becomes more and more different from the one that its ancestors spoke when they were still living together. The splintering of dialects goes on without interruption. The descendants no longer understand one other.… A need for unification in language then arises from two sides. The beginnings of trade make understanding necessary between members of different tribes. But this need is satisfied when individual middlemen in trade achieve the necessary command of language.”
— Ludwig von Mises, Nation, State, and Economy (Online edition, 1919; 1983), Ludwig von Mises Institute, p. 46–47.
Thus urbanization is an important factor in language death. To be sure, the wondrous features of cities that draw immigrants — greater economies of scale, decreased search costs, increased division of labor — are all made possible with capitalism, and so in this sense languages may die for economic reasons. But this is precisely the type of language death that shouldn’t concern us (unless you’re a linguist like me), because urbanization is really nothing more than the demonstrated preferences of millions of people who wish to take advantage of all the fantastic benefits that cities have to offer.
In short, these people make the conscious choice to leave an environment where network effects and sociological benefits exist for speaking their native language, and exchange it for a greater range of economic possibilities, but where no such social benefits for speaking the language exist. If this were the only cause of language death — or even just the biggest one — then there would be little more to say about it. (…)
Far too many well-intentioned individuals are too quick to substitute their valuations for those of the last speakers of indigenous languages this way. Were it up to them, these speakers would be resigned to misery and poverty and deprived of participation in the world’s advanced economies in order that their language might be passed on. To be sure, these speakers themselves often fall victim to the mistaken ideology that one language necessarily displaces or interferes with another.
Although the South African Department of Education is trying to develop teaching materials in the local African languages, for example, many parents are pushing back; they want their children taught only in English. In Dominica, the parents go even further and refuse to even speak the local language, Patwa, to their children.[1] Were they made aware of the falsity of this notion of language displacement, perhaps they would be less quick to stop speaking their language to their children. But the decision is ultimately theirs to make, and theirs alone.
Urbanization, however, is not the only cause of language death. There is another that, I’m sad to say, almost none of the linguists who work on endangered languages give much thought to, and that is the state. The state is the only entity capable of reaching into the home and forcibly altering the process of language socialization in an institutionalized way.
How? The traditional method was simply to kill or remove indigenous and minority populations, as was done as recently as 1923 in the United States in the last conflict of the Indian War. More recently this happens through indirect means — whether intentional or otherwise — the primary method of which has been compulsory state schooling.
There is no more pernicious assault on the cultural practices of minority populations than a standardized, Anglified, Englicized compulsory education. It is not just that children are forcibly removed from the socialization process in the home, required to speak an official language and punished (often corporally) for doing otherwise. It is not just that schools redefine success, away from those things valued by the community, and towards those things that make someone a better citizen of the state. No, the most significant impact of compulsory state education is that it ingrains in children the idea that their language and their culture is worthless, of no use in the modern classroom or society, and that it is something that merely serves to set them apart negatively from their peers, as an object of their vicious torment.
But these languages clearly do have value, if for no other reason than simply because people value them. Local and minority languages are valued by their speakers for all sorts of reasons, whether it be for use in the local community, communicating with one’s elders, a sense of heritage, the oral and literary traditions of that language, or something else entirely. Again, the praxeologist is not in a position to evaluate these beliefs. The praxeologist merely notes that free choice in language use and free choice in association, one not dictated by the edicts of the state, will best satisfy the demand of individuals, whether for minority languages or lingua francas. What people find useful, they will use.
By contrast, the state values none of these things. For the state, the goal is to bind individuals to itself, to an imagined homogeneous community of good citizens, rather than their local community. National ties trump local ones in the eyes of the state. Free choice in association is disregarded entirely. And so the state forces many indigenous people to become members of a foreign community, where they are a minority and their language is scorned, as in the case of boarding schools. Whereas at home, mastering the native language is an important part of functioning in the community and earning prestige, and thus something of value, at school it becomes a black mark and a detriment. Given the prisonlike way schools are run, and how they exhibit similar intense (and sometimes dangerous) pressures from one’s peers, minority-language-speaking children would be smart to disassociate themselves as quickly as possible from their cultural heritage.
Mises himself, though sometimes falling prey to common fallacies regarding language like linguistic determinism and ethnolinguistic isomorphism, was aware of this distinction between natural language decline and language death brought on by the state. (…)
This is precisely what the Bureau of Indian Affairs accomplished by coercing indigenous children into attending boarding schools. Those children were cut off from their culture and language — their nation — until they had effectively assimilated American ideologies regarding minority languages, namely, that English is good and all else is bad.
Nor is this the only way the state affects language. The very existence of a modern nation-state, and the ideology it encompasses, is antithetical to linguistic diversity. It is predicated on the idea of one state, one nation, one people. In Nation, State, and Economy, Mises points out that, prior to the rise of nationalism in the 17th and 18th centuries, the concept of a nation did not refer to a political unit like state or country as we think of it today.
A “nation” instead referred to a collection of individuals who share a common history, religion, cultural customs and — most importantly — language. Mises even went so far as to claim that “the essence of nationality lies in language.”[2] The “state” was a thing apart, referring to the nobility or princely state, not a community of people (hence Louis XIV’s famous quip, “L’état c’est moi.”).[3] In that era, a state might consist of many nations, and a nation might subsume many states.
The old blurry linguistic borders became inconvenient for nationalists. To build nations strong enough to win themselves a state, the people of a would-be nation needed to be welded together with a clear sense of community. Speaking a minority dialect or refusing to assimilate to a standard wouldn’t do.[4]
Mises himself elaborated on this point. Despite his belief in the value of a liberal democracy, which would remain with him for the rest of his life, Mises realized early on that the imposition of democracy over multiple nations could only lead to hegemony and assimilation:
In polyglot territories, therefore, the introduction of a democratic constitution does not mean the same thing at all as introduction of democratic autonomy. Majority rule signifies something quite different here than in nationally uniform territories; here, for a part of the people, it is not popular rule but foreign rule. If national minorities oppose democratic arrangements, if, according to circumstances, they prefer princely absolutism, an authoritarian regime, or an oligarchic constitution, they do so because they well know that democracy means the same thing for them as subjugation under the rule of others.[5]
From the ideology of nationalism was also born the principle of irredentism, the policy of incorporating historically or ethnically related peoples into the larger umbrella of a single state, regardless of their linguistic differences. As Greene points out, for example,
By one estimate, just 2 or 3 percent of newly minted “Italians” spoke Italian at home when Italy was unified in the 1860s. Some Italian dialects were as different from one another as modern Italian is from modern Spanish.[6]
This in turn prompted the Italian statesman Massimo D’Agelizo (1798–1866) to say, “We have created Italy. Now we need to create Italians.” And so these Italian languages soon became yet another casualty of the nation-state.
Mises once presciently predicted that,
If [minority nations] do not want to remain politically without influence, then they must adapt their political thinking to that of their environment; they must give up their special national characteristics and their language.[7]
This is largely the story of the world’s languages. It is, as we have seen, the history of the state, a story of nationalistic furor, and of assimilation by force. Only when we abandon this socialist and utopian fantasy of one state, one nation, one people will this story begin to change.”
[1] Amy L. Paugh, Playing With Languages: Children and Change in a Caribbean Village (2012), Berghahn Books. [2] Ludwig von Mises, Human Action: A Treatise on Economics (Scholar’s Edition, 2010) Auburn, AL: Ludwig von Mises Institute, p.37. [3] “I am the state.” [4] Robert Lane Greene, You Are What You Speak: Grammar Grouches, Language Laws, and the Politics of Identity (Kindle Edition, 2011), Delacorte Press, p. 132. [5] Mises, Nation, State, and Economy, p. 77. [6] Greene, You Are What You Speak, p. 141. [7] Mises, Nation, State, and Economy, p. 77.
“Isn’t language loss a good thing, because fewer languages mean easier communication among the world’s people? Perhaps, but it’s a bad thing in other respects. Languages differ in structure and vocabulary, in how they express causation and feelings and personal responsibility, hence in how they shape our thoughts. There’s no single purpose “best” language; instead, different languages are better suited for different purposes.
For instance, it may not have been an accident that Plato and Aristotle wrote in Greek, while Kant wrote in German. The grammatical particles of those two languages, plus their ease in forming compound words, may have helped make them the preeminent languages of western philosophy.
Another example, familiar to all of us who studied Latin, is that highly inflected languages (ones in which word endings suffice to indicate sentence structure) can use variations of word order to convey nuances impossible with English. Our English word order is severely constrained by having to serve as the main clue to sentence structure. If English becomes a world language, that won’t be because English was necessarily the best language for diplomacy.”
— Jared Diamond, American scientist and author, currently Professor of Geography and Physiology at UCLA, The Third Chimpanzee: The Evolution & Future of the Human Animal, Hutchinson Radius, 1991.
“It is commonly agreed by linguists and anthropologists that the majority of languages spoken now around the globe will likely disappear within our lifetime. The phenomenon known as language death has started to accelerate as the world has grown smaller. “This extinction of languages, and the knowledge therein, has no parallel in human history. K. David Harrison’s book is the first to focus on the essential question, what is lost when a language dies? What forms of knowledge are embedded in a language’s structure and vocabulary? And how harmful is it to humanity that such knowledge is lost forever?”
Edward Glaeser: ‘Cities Are Making Us More Human’ Illustration: “The elevated sidewalk: How it will solve city transportation problems”, Scientific American, vol. 109 (July-Dec 1913)
“As opposed to the conventional wisdom, Harvard economist Edward Glaeser believes urbanization to be a solution to many unanswered problems, such as pollution, depression and a lack of creativity. (…)
Living around trees and living in low density areas may end being actually quite harmful for the environment, whereas living in high-rise buildings and urban core may end up being quite kind to the environment. (…)
People who live in urban apartments all typically use less electricity at home and less energy at home heating than people who live in larger suburban or rural homes. A single family detached house uses on average 83% more electricity than urban apartments do within the United States. (…)
Q: How are cities making us smarter?
Glaeser: I think the most important thing cities do today is to allow the creation of new ideas. Chains of collaborative brilliance have always been responsible for human kind’s greatest hits. We have seen this in cities for millennia – Socrates and Plato bickered on an Athenian street corner; we saw it again in Florence with the ideas that went from Brunelleschi to Donatello to Masaccio to Filippino Lippi and to the Florentine Renaissance. It helps us to know each other, learn from each other and to collectively create something great. In some sense, cities are making us more human.
Our greatest asset as a species is the ability to learn from the people around us. We come out of the womb with this remarkable ability to take in information from those people – parents, peers, teachers – that are near us. Cities enable us to get smart by being around other smart people. I think this explains why cities have not become obsolete over the past thirty years. (…) We have just crossed the half-way point where more than 50% of humanity lives in cities. (…) Source: Ethan Zuckerman, Desperately Seeking Serendipity, 12.V.2011
These facts are related to the role cities play today, a role very much tied to the generation of information. Globalization and new technologies did make the industrial city obsolete, at least in the West. But they also increased the idea of returns of human capital and innovation. You could sell something on the other side of the planet because you could produce it on the other side of the planet. By making knowledge more valuable, they made cities more important. That is why they continue to play the incredibly important role of connecting people, enabling them to learn from one another at close distances. (…)
I also want to emphasize that cities are often places of significant and often positive political change. One thing that those countries need is political change, which is much more likely to come out of an organized urban group than it is to come from a dispersed agricultural population. (…)
If you compare countries that are more than 50% urbanized with countries that are less than 50% urbanized, incomes are five times higher in the more urbanized countries and infant mortality rates are less than a third in the more urbanized countries. The path of rural poverty really is awful. (…)”
“The pattern, and it alone, brings into being and causes to pass away and confers purpose, that is to say, value and meaning, on all there is. To understand is to perceive patterns. (…) To make intelligible is to reveal the basic pattern.”
“One of the most wonderful things about the emerging global superbrain is that information is overflowing on a scale beyond what we can wrap our heads around. The electronic, collective, hive mind that we know as the Internet produces so much information that organizing this data — and extracting meaning from it — has become the conversation of our time.
Sanford Kwinter’s Far From Equilibrium tackles everything from technology to society to architecture under the thesis that creativity, catharsis, transformation and progressive breakthroughs occur far from equilibrium. So even while we may feel overwhelmed and intimidated by the informational overload and radical transformations of our times, we should, perhaps, take refuge in knowing that only good can come from this. He writes:
“(…) We accurately think of ourselves today not only as citizens of an information society, but literally as clusters of matter within an unbroken informational continuum: “We are all,” as the great composer Karlheinz Stockhausen once said, “transistors, in the literal sense. We send, receive and organize [and] so long as we are vital, our principle work is to capture and artfully incorporate the signals that surround us.” (…)
Clay Shirky often refers to the “Cognitive Surplus,” the overflowing output of the billion of minds participating in the electronic infosphere. A lot of this output is silly, but a lot of it is meaningful and wonderful. The key lies in curation; which is the result of pattern-recognition put into practice. (…)
Matt Ridley’s TED Talk, “When Ideas Have Sex” points to this intercourse of information and how it births new thought-patterns. Ideas, freed from the confines of space and time by the invisible, wireless metabrain we call The Internet, collide with one another and explode into new ideas; accelerating the collective intelligence of the species. Creativity thrives when minds come together. The last great industrial strength creative catalyst was the city: It is no coincidence than when people migrate to cities in large numbers, creativity and innovation thrives.
Now take this very idea and apply it to the web: the web essentially is a planetary-scale nervous system where individual minds take on the role of synapses, firing electrical pattern-signals to one another at light speed — the net effect being an astonishing increase in creative output. (…)
Ray Kurzweil too, expounds on this idea of the power of patterns:
“I describe myself as a patternist, and believe that if you put matter and energy in just the right pattern you create something that transcends it. Technology is a good example of that: you put together lenses and mechanical parts and some computers and some software in just the right combination and you create a reading machine for the blind. It’s something that transcends the semblance of parts you’ve put together. That is the nature of technology, and it’s the nature of the human brain.
Biological molecules put in a certain combination create the transcending properties of human intelligence; you put notes and sounds together in just the rightcombination, and you create a Beethoven symphony or a Beatles song. So patterns have a power that transcends the parts of that pattern.”
R. Buckminster Fuller refers to us as “pattern integrities.” “Understanding order begins with understanding patterns,” he was known to say E.J. White, who worked with Fuller, says that:
“For Fuller, the thinking process is not a matter of putting anything into the brain or taking anything out; he defines thinking as the dismissal of irrelevancies, as the definition of relationships” — in other words, thinking is simultaneously a form of filtering out the data that doesn’t fit while highlighting the things that do fit together… We dismiss whatever is an “irrelevancy” and retain only what fits, we form knowledge by ‘connecting the dots’… we understand things by perceiving patterns — we arrive at conclusions when we successfully reveal these patterns. (…)
Fuller’s primary vocation is as a poet. All his disciplines and talents — architect, engineer, philosopher, inventor, artist, cartographer, teacher — are just so many aspects of his chief function as integrator… the word “poet” is a very general term for a person who puts things together in an era of great specialization when most people are differentiating or taking things apart… For Fuller, the stuff of poetry is the patterns of human behavior and the environment, and the interacting hierarchies of physics and design and industry. This is why he can describe Einstein and Henry Ford as the greatest poets of the 20th century.” (…)
In a recent article in Reality Sandwich, Simon G Powell proposed that patterned self-organization is a default condition of the universe:
“When you think about it, Nature is replete with instances of self-organization. Look at how, over time, various exquisitely ordered patterns crystallise out of the Universe. On a macroscopic scale you have stable and enduring spherical stars, solar systems, and spiral galaxies. On a microscopic scale you have atomic and molecular forms of organization. And on a psychological level, fed by all this ambient order and pattern, you have consciousness which also seems to organise itself into being (by way of the brain). Thus, patterned organisation of one form or another is what nature is proficient at doing over time.
This being the case, is it possible that the amazing synchronicities and serendipities we experience when we’re doing what we love, or following our passions — the signs we pick up on when we follow our bliss- represent an emerging ‘higher level’ manifestation of self-organization? To make use of an alluring metaphor, are certain events and cultural processes akin to iron filings coming under the organising influence of a powerful magnet? Is serendipity just the playing out on the human level of the same emerging, patterned self-organization that drives evolution?”
Barry Ptolemy’s film Transcendent Man reminds us that the universe has been unfolding in patterns of greater complexity since the beginning of time. Says Ptolemy:
“First of all we are all patterns of information. Second, the universe has been revealing itself as patterns of information of increasing order since the big bang. From atoms, to molecules, to DNA, to brains, to technology, to us now merging with that technology. So the fact that this is happening isn’t particularly strange to a universe which continues to evolve and unfold at ever accelerating rates.”
“Networks are everywhere. The brain is a network of nerve cells connected by axons, and cells themselves are networks of molecules connected by biochemical reactions. Societies, too, are networks of people linked by friendships, familial relationships and professional ties. On a larger scale, food webs and ecosystems can be represented as networks of species. And networks pervade technology: the Internet, power grids and transportation systems are but a few examples. Even the language we are using to convey these thoughts to you is a network, made up of words connected by syntactic relationships.”
‘For decades, we assumed that the components of such complex systems as the cell, the society, or the Internet are randomly wired together. In the past decade, an avalanche of research has shown that many real networks, independent of their age, function, and scope, converge to similar architectures, a universality that allowed researchers from different disciplines to embrace network theory as a common paradigm.”
— Albert-László Barabási , physicist, best known for his work in the research of network theory, and Eric Bonabeau, Scale-Free Networks, Scientific American, April 14, 2003.
“Coral reefs are sometimes called “the cities of the sea”, and part of the argument is that we need to take the metaphor seriously: the reef ecosystem is so innovative because it shares some defining characteristics with actual cities. These patterns of innovation and creativity are fractal: they reappear in recognizable form as you zoom in and out, from molecule to neuron to pixel to sidewalk. Whether you’re looking at original innovations of carbon-based life, or the explosion of news tools on the web, the same shapes keep turning up. (…) When life gets creative, it has a tendency to gravitate toward certain recurring patterns, whether those patterns are self-organizing, or whether they are deliberately crafted by human agents.”
— Steven Johnson, author of Where Good Ideas Come From, cited by Jason Silva
“Network systems can sustain life at all scales, whether intracellularly or within you and me or in ecosystems or within a city. (…) If you have a million citizens in a city or if you have 1014 cells in your body, they have to be networked together in some optimal way for that system to function, to adapt, to grow, to mitigate, and to be long term resilient.”
“Recognizing this super-connectivity and conductivity is often accompanied by blissful mindbody states and the cognitive ecstasy of multiple “aha’s!” when the patterns in the mycelium are revealed. That Googling that has become a prime noetic technology (How can we recognize a pattern and connect more and more, faster and faster?: superconnectivity and superconductivity) mirrors the increased speed of connection of thought-forms from cannabis highs on up. The whole process is driven by desire not only for these blissful states in and of themselves, but also as the cognitive resource they represent.The devices of desire are those that connect,” because as Johnson says “chance favors the connected mind”.
“The mind thinks its thoughts in ‘Mentalese,’ codes them in the local natural language, and then transmits them (say, by speaking them out loud) to the hearer. The hearer has a Cryptographer in his head too, of course, who thereupon proceeds to decode the ‘message.’ In this picture, natural language, far from being essential to thought, is merely a vehicle for the communication of thought.”
“According to one school of philosophy, our thoughts have a language-like structure that is independent of natural language: this is what students of language call the language of thought (LOT) hypothesis. According to the LOT hypothesis, it is because human thoughts already have a linguistic structure that the emergence of common, natural languages was possible in the first place. (…)
Many - perhaps most - psychologists end up concluding that ordinary people do not use the rules of logic in everyday life.
There is an alternative way of seeing this: that there is a language of thought, and that it has a more logical form than ordinary natural language. This view has an added bonus: it tells us that, if you want to express yourself more clearly and more effectively in natural language, then you should express yourself in a form that is closer to computational logic - and therefore closer to the language of thought. Dry legalese never looked so good.”
“In philosophy of mind, the language of thought hypothesis (LOTH) put forward by American philosopher Jerry Fodor describes thoughts as represented in a “language” (sometimes known as mentalese) that allows complex thoughts to be built up by combining simpler thoughts in various ways. In its most basic form the theory states that thought follows the same rules as language: thought has syntax.
Using empirical data drawn from linguistics and cognitive science to describe mental representation from a philosophical vantage-point, the hypothesis states that thinking takes place in a language of thought (LOT): cognition and cognitive processes are only ‘remotely plausible’ when expressed as a system of representations that is “tokened” by a linguistic or semantic structure and operated upon by means of a combinatorial syntax.Linguistic tokens used in mental language describe elementary concepts which are operated upon by logical rules establishing causal connections to allow for complex thought. Syntax as well as semantics have a causal effect on the properties of this system of mental representations.
These mental representations are not present in the brain in the same way as symbols are present on paper; rather, the LOT is supposed to exist at the cognitive level, the level of thoughts and concepts. LOTH has wide-ranging significance for a number of domains in cognitive science. It relies on a version of functionalist materialism, which holds that mental representations are actualized and modified by the individual holding the propositional attitude, and it challenges eliminative materialism and connectionism. It implies a strongly rationalist model of cognition in which many of the fundamentals of cognition are innate. (…)
Some philosophers have argued that our public language is our mental language, that a person who speaks English thinks in English. Others contend that people who do not know a public language (e.g. babies, aphasics) can think, and that therefore some form of mentalese must be present innately. (…)
Tim Crane, in his book The Mechanical Mind, states that, while he agrees with Fodor, his reason is very different. A logical objection challenges LOTH’s explanation of how sentences in natural languages get their meaning. That is the view that “Snow is white” is TRUE if and only if P is TRUE in the LOT, where P means the same thing in LOT as “Snow is white” means in the natural language. Any symbol manipulation is in need of some way of deriving what those symbols mean. If the meaning of sentences is explained in terms of sentences in the LOT, then the meaning of sentences in LOT must get their meaning from somewhere else. There seems to be an infinite regress of sentences getting their meaning. Sentences in natural languages get their meaning from their users (speakers, writers). Therefore sentences in mentalese must get their meaning from the way in which they are used by thinkers and so on ad infinitum. This regress is often called the homunculus regress.
Daniel Dennett accepts that homunculi may be explained by other homunculi and denies that this would yield an infinite regress of homunculi. Each explanatory homunculus is “stupider” or more basic than the homunculus it explains but this regress is not infinite but bottoms out at a basic level that is so simple that it does not need interpretation. John Searle points out that it still follows that the bottom-level homunculi are manipulating some sorts of symbols.
LOTH implies that the mind has some tacit knowledge of the logical rules of inference and the linguistic rules of syntax (sentence structure) and semantics (concept or word meaning). If LOTH cannot show that the mind knows that it is following the particular set of rules in question then the mind is not computational because it is not governed by computational rules. Also, the apparent incompleteness of this set of rules in explaining behavior is pointed out. Many conscious beings behave in ways that are contrary to the rules of logic. Yet this irrational behavior is not accounted for by any rules, showing that there is at least some behavior that does not act in accordance with this set of rules.”
“LOTH is an hypothesis about the nature of thought and thinking with propositional content. As such, it may or may not be applicable to other aspects of mental life. Officially, it is silent about the nature of some mental phenomena such as experience, qualia, sensory processes, mental images, visual and auditory imagination, sensory memory, perceptual pattern-recognition capacities, dreaming, hallucinating, etc. To be sure, many LOT theorists hold views about these aspects of mental life that sometimes make it seem that they are also to be explained by something similar to LOTH.
For instance, Fodor (1983) seems to think that many modular input systems have their own LOT to the extent to which they can be explained in representational and computational terms. Indeed, many contemporary psychological models treat perceptual input systems in just these terms. There is indeed some evidence that this kind of treatment might be appropriate for many perceptual processes. But it is to be kept in mind that a system may employ representations and be computational without necessarily satisfying any or both of the clauses in (B) above in any full-fledged way. Just think of finite automata theory where there are plenty of examples of a computational process defined over states or symbols which lack full-blown syntactic and/or semantic structural complexity. (…)
Whether sensory or perceptual processes are to be treated within the framework of full-blown LOTH is again an open empirical question. It might be that the answer to this question is affirmative. If so, there may be more than one LOT realized in different subsystems or mechanisms in the mind/brain. So LOTH is not committed to there being a single representational system realized in the brain, nor is it committed to the claim that all mental representations are complex or language-like, nor would it be falsified if it turns out that most aspects of mental life other than the ones involving propositional attitudes don’t require a LOT.
Similarly, there is strong evidence that the mind also exploits an image-like representational medium for certain kinds of mental tasks. LOTH is non-committal about the existence of an image-like representational system for many mental tasks other than the ones involving propositional attitudes. But it is committed to the claim that propositional thought and thinking cannot be successfully accounted for in its entirety in purely imagistic terms. It claims that a combinatorial sentential syntax is necessary for propositional attitudes and a purely imagistic medium is not adequate for capturing that.”
Infinite Stupidity. Social evolution may have sculpted us not to be innovators and creators as much as to be copiers
A review of some big events
“Obviously one of the big events in our history was the origin of our planet, about 4.5 billion years ago. And what’s fascinating is that about 3.8 billion years ago, only about seven or eight hundred million years after the origin of our planet, life arose. That life was simple replicators, things that could make copies of themselves. And we think that life was a little bit like the bacteria we see on earth today. It would be the ancestors of the bacteria we see on earth today.
That life ruled the world for 2 billion years, and then about 1.5 billion years ago, a new kind of life emerged. These were the eukaryotic cells. They were a little bit different kind of cell from bacteria. And actually the kind of cells we are made of. And again, these organisms that were eukaryotes were single-celled, so even 1.5 billion years ago, we still just had single-celled organisms on earth. But it was a new kind of life.
It was another 500 million years before we had anything like a multicellular organism, and it was another 500 million years after that before we had anything really very interesting. So, about 500 million years ago, the plants and the animals started to evolve. And I think everybody would agree that this was a major event in the history of the world, because, for the first time, we had complex organisms.
After about 500 million years ago, things like the plants evolved, the fish evolved, lizards and snakes, dinosaurs, birds, and eventually mammals. And then it was really just six or seven million years ago, within the mammals, that the lineage that we now call the hominins arose. And they would be direct descendants of us. And then, within that lineage that arose about six or seven million years ago, it was only about 200,000 years ago that humans finally evolved.
Idea of idea evolution
And so, this is really just 99.99 percent of the way through the history of this planet, humans finally arose. But in that 0.01 percent of life on earth, we’ve utterly changed the planet. And the reason is that, with the arrival of humans 200,000 years ago, a new kind of evolution was created. The old genetical evolution that had ruled for 3.8 billion years now had a competitor, and that new kind of evolution was ideas.
It was a true form of evolution, because now ideas could arise, and they could jump from mind to mind, without genes having to change. So, populations of humans could adapt at the level of ideas. Ideas could accumulate. We call this cumulative cultural adaptation. And so, cultural complexity could emerge and arise orders and orders of magnitude faster than genetic evolution.
Now, I think most of us take that utterly for granted, but it has completely rewritten the way life evolves on this planet because, with the arrival of our species, everything changed. Now, a single species, using its idea evolution, that could proceed apace independently of genes, was able to adapt to nearly every environment on earth, and spread around the world where no other species had done that. All other species are limited to places on earth that their genes adapt them to. But we were able to adapt at the level of our cultures to every place on earth. (…)
If we go back in our lineage 2 million years or so, there was a species known as Homo erectus. Homo erectus is an upright ape that lived on the African savannah. It could make tools, but they were very limited tools, and those tools, the archaeological record tells us, didn’t change for about 1.5 million years. That is, until about the time they went extinct. That is, they made the same tools over and over and over again, without any real changes to them.
If we move forward in time a little bit, it’s not even clear that our very close cousins that we know are related to us 99.5 or 99.6 percent in the sequences of their genes, the Neanderthals, it’s not even clear that they had what we call idea evolution. Sure enough, their tools that they made were more complex than our tools. But the 300,000 or so years that they spent in Europe, their toolkit barely changed. So there’s very little evolution going on.
So there’s something really very special about this new species, humans, that arose and invented this new kind of evolution, based on ideas. And so it’s useful for us to ask, what is it about humans that distinguishes them? It must have been a tiny genetic difference between us and the Neanderthals because, as I said, we’re so closely related to them genetically, a tiny genetic difference that had a vast cultural potential.
That difference is something that anthropologists and archaeologists call social learning. It’s a very difficult concept to define, but when we talk about it, all of us humans know what it means. And it seems to be the case that only humans have the capacity to learn complex new or novel behaviors, simply by watching and imitating others. And there seems to be a second component to it, which is that we seem to be able to get inside the minds of other people who are doing things in front of us, and understand why it is they’re doing those things. These two things together, we call social learning.
Many people respond that, oh, of course the other animals can do social learning, because we know that the chimpanzees can imitate each other, and we see all sorts of learning in animals like dolphins and the other monkeys, and so on. But the key point about social learning is that this minor difference between us and the other species forms an unbridgeable gap between us and them. Because, whereas all of the other animals can pick up the odd behavior by having their attention called to something, only humans seem to be able to select, among a range of alternatives, the best one, and then to build on that alternative, and to adapt it, and to improve upon it. And so, our cultures cumulatively adapt, whereas all other animals seem to do the same thing over and over and over again.
Even though other animals can learn, and they can even learn in social situations, only humans seem to be able to put these things together and do real social learning. And that has led to this idea evolution. What’s a tiny difference between us genetically has opened up an unbridgeable gap, because only humans have been able to achieve this cumulative cultural adaptation. (…)
I’m interested in this because I think this capacity for social learning, which we associate with our intelligence, has actually sculpted us in ways that we would have never anticipated. And I want to talk about two of those ways that I think it has sculpted us. One of the ways has to do with our creativity, and the other has to do with the nature of our intelligence as social animals.
One of the first things to be aware of when talking about social learning is that it plays the same role within our societies, acting on ideas, as natural selection plays within populations of genes. Natural selection is a way of sorting among a range of genetic alternatives, and finding the best one. Social learning is a way of sifting among a range of alternative options or ideas, and choosing the best one of those. And so, we see a direct comparison between social learning driving idea evolution, by selecting the best ideas —we copy people that we think are successful, we copy good ideas, and we try to improve upon them — and natural selection, driving genetic evolution within societies, or within populations.
I think this analogy needs to be taken very seriously, because just as natural selection has acted on genetic populations, and sculpted them, we’ll see how social learning has acted on human populations and sculpted them.
What do I mean by “sculpted them”? Well, I mean that it’s changed the way we are. And here’s one reason why. If we think that humans have evolved as social learners, we might be surprised to find out that being social learners has made us less intelligent than we might like to think we are. And here’s the reason why.
If I’m living in a population of people, and I can observe those people, and see what they’re doing, seeing what innovations they’re coming up with, I can choose among the best of those ideas, without having to go through the process of innovation myself. So, for example, if I’m trying to make a better spear, I really have no idea how to make that better spear. But if I notice that somebody else in my society has made a very good spear, I can simply copy him without having to understand why.
What this means is that social learning may have set up a situation in humans where, over the last 200,000 years or so, we have been selected to be very, very good at copying other people, rather than innovating on our own. We like to think we’re a highly inventive, innovative species. But social learning means that most of us can make use of what other people do, and not have to invest the time and energy in innovation ourselves.
Now, why wouldn’t we want to do that? Why wouldn’t we want to innovate on our own? Well, innovation is difficult. It takes time. It takes energy. Most of the things we try to do, we get wrong. And so, if we can survey, if we can sift among a range of alternatives of people in our population, and choose the best one that’s going at any particular moment, we don’t have to pay the costs of innovation, the time and energy ourselves. And so, we may have had strong selection in our past to be followers, to be copiers, rather than innovators.
This gives us a whole new slant on what it means to be human, and I think, in many ways, it might fit with some things that we realize are true about ourselves when we really look inside ourselves. We can all think of things that have made a difference in the history of life. The first hand axe, the first spear, the first bow and arrow, and so on. And we can ask ourselves, how many of us have had an idea that would have changed humanity? And I think most of us would say, well, that sets the bar rather high. I haven’t had an idea that would change humanity. So let’s lower the bar a little bit and say, how many of us have had an idea that maybe just influenced others around us, something that others would want to copy? And I think even then, very few of us can say there have been very many things we’ve invented that others would want to copy.
This says to us that social evolution may have sculpted us not to be innovators and creators as much as to be copiers, because this extremely efficient process that social learning allows us to do, of sifting among a range of alternatives, means that most of us can get by drawing on the inventions of others.
The formation of social groups
Now, why do I talk about this? It sounds like it could be a somewhat dry subject, that maybe most of us are copiers or followers rather than innovators. And what we want to do is imagine that our history over the last 200,000 years has been a history of slowly and slowly and slowly living in larger and larger and larger groups.
Early on in our history, it’s thought that most of us lived in bands of maybe five to 25 people, and that bands formed bands of bands that we might call tribes. And maybe tribes were 150 people or so on. And then tribes gave way to chiefdoms that might have been thousands of people. And chiefdoms eventually gave way to nation-states that might have been tens of thousands or even hundreds of thousands, or millions, of people. And so, our evolutionary history has been one of living in larger and larger and larger social groups.
What I want to suggest is that that evolutionary history will have selected for less and less and less innovation in individuals, because a little bit of innovation goes a long way. If we imagine that there’s some small probability that someone is a creator or an innovator, and the rest of us are followers, we can see that one or two people in a band is enough for the rest of us to copy, and so we can get on fine. And, because social learning is so efficient and so rapid, we don’t need all to be innovators. We can copy the best innovations, and all of us benefit from those.
But now let’s move to a slightly larger social group. Do we need more innovators in a larger social group? Well, no. The answer is, we probably don’t. We probably don’t need as many as we need in a band. Because in a small band, we need a few innovators to get by. We have to have enough new ideas coming along. But in a larger group, a small number of people will do. We don’t have to scale it up. We don’t have to have 50 innovators where we had five in the band, if we move up to a tribe. We can still get by with those three or four or five innovators, because all of us in that larger social group can take advantage of their innovations.
Language is the way we exchange ideas
And here we can see a very prominent role for language. Language is the way we exchange ideas. And our eyes allow us to see innovations and language allows us to exchange ideas. And language can operate in a larger society, just as efficiently as it can operate in a small society. It can jump across that society in an instant.
You can see where I’m going. As our societies get larger and larger, there’s no need, in fact, there’s even less of a need for any one of us to be an innovator, whereas there is a great advantage for most of us to be copiers, or followers. And so, a real worry is that our capacity for social learning, which is responsible for all of our cumulative cultural adaptation, all of the things we see around us in our everyday lives, has actually promoted a species that isn’t so good at innovation. It allows us to reflect on ourselves a little bit and say, maybe we’re not as creative and as imaginative and as innovative as we thought we were, but extraordinarily good at copying and following.
If we apply this to our everyday lives and we ask ourselves, do we know the answers to the most important questions in our lives? Should you buy a particular house? What mortgage product should you have? Should you buy a particular car? Who should you marry? What sort of job should you take? What kind of activities should you do? What kind of holidays should you take? We don’t know the answers to most of those things. And if we really were the deeply intelligent and imaginative and innovative species that we thought we were, we might know the answers to those things.
And if we ask ourselves how it is we come across the answers, or acquire the answers to many of those questions, most of us realize that we do what everybody else is doing. This herd instinct, I think, might be an extremely fundamental part of our psychology that was perhaps an unexpected and unintended, you might say, byproduct of our capacity for social learning, that we’re very, very good at being followers rather than leaders. A small number of leaders or innovators or creative people is enough for our societies to get by.
Now, the reason this might be interesting is that, as the world becomes more and more connected, as the Internet connects us and wires us all up, we can see that the long-term consequences of this is that humanity is moving in a direction where we need fewer and fewer and fewer innovative people, because now an innovation that you have somewhere on one corner of the earth can instantly travel to another corner of the earth, in a way that it would have never been possible to do 10 years ago, 50 years ago, 500 years ago, and so on. And so, we might see that there has been this tendency for our psychology and our humanity to be less and less innovative, at a time when, in fact, we may need to be more and more innovative, if we’re going to be able to survive the vast numbers of people on this earth.
That’s one consequence of social learning, that it has sculpted us to be very shrewd and intelligent at copying, but perhaps less shrewd at innovation and creativity than we’d like to think. Few of us are as creative as we’d like to think we are. I think that’s been one perhaps unexpected consequence of social learning.
Another side of social learning I’ve been thinking about - it’s a bit abstract, but I think it’s a fascinating one -goes back again to this analogy between natural selection, acting on genetic variation, and social learning, acting on variation in ideas. And any evolutionary process like that has to have both a sorting mechanism, natural selection, and what you might call a generative mechanism, a mechanism that can create variety.
We all know what that mechanism is in genes. We call it mutation, and we know that from parents to offspring, genes can change, genes can mutate. And that creates the variety that natural selection acts on. And one of the most remarkable stories of nature is that natural selection, acting on this mindlessly-generated genetic variation, is able to find the best solution among many, and successively add those solutions, one on top of the other. And through this extraordinarily simple and mindless process, create things of unimaginable complexity. Things like our cells, eyes and brains and hearts, and livers, and so on. Things of unimaginable complexity, that we don’t even understand and none of us could design. But they were designed by natural selection.
Where do ideas come from?
Now let’s take this analogy of a mindless process and take - there’s a parallel between social learning driving evolution at the idea level and natural selection driving evolution at the genetic level - and ask what it means for the generative mechanism in our brains.
Well, where do ideas come from? For social learning to be a sorting process that has varieties to act on, we have to have a variety of ideas. And where do those new ideas come from?
The idea that I’ve been thinking about, that I think is worth contemplating about our own minds is what is the generative mechanism? If we do have any creativity at all and we are innovative in some ways, what’s the nature of that generative mechanism for creating new ideas?
This is a question that’s been asked for decades. What is the nature of the creative process? Where do ideas come from? And let’s go back to genetic evolution and remember that, there, the generative mechanism is random mutation.
Now, what do we think the generative mechanism is for idea evolution? Do we think it’s random mutation of some sort, of ideas? Well, all of us think that it’s better than that. All of us think that somehow we can come up with good ideas in our minds. And whereas natural selection has to act on random variation, social learning must be acting on directed variation. We know what direction we’re going.
But, we can go back to our earlier discussion of social learning, and ask the question, well, if you were designing a new hand axe, or a new spear, or a new bow and a new arrow, would you really know how to make a spear fly better? Would you really know how to make a bow a better bow? Would you really know how to shape an arrowhead so that it penetrated its prey better? And I think most of us realize that we probably don’t know the answers to those questions. And that suggests to us that maybe our own creative process rests on a generative mechanism that isn’t very much better than random itself.
And I want to go further, and suggest that our mechanism for generating ideas maybe couldn’t even be much better than random itself. And this really gives us a different view of ourselves as intelligent organisms. Rather than thinking that we know the answers to everything, could it be the case that the mechanism that our brain uses for coming up with new ideas is a little bit like the mechanism that our genes use for coming up with new genetic variance, which is to randomly mutate ideas that we have, or to randomly mutate genes that we have.
Now, it sounds incredible. It sounds insane. It sounds mad. Because we think of ourselves as so intelligent. But when we really ask ourselves about the nature of any evolutionary process, we have to ask ourselves whether it could be any better than random, because in fact, random might be the best strategy.
Genes could never possibly know how to mutate themselves, because they could never anticipate the direction the world was going. No gene knows that we’re having global warming at the moment. No gene knew 200,000 years ago that humans were going to evolve culture. Well, the best strategy for any exploratory mechanism, when we don’t know the nature of the processes we’re exploring, is to throw out random attempts at understanding that field or that space we’re trying to explore.
And I want to suggest that the creative process inside our brains, which relies on social learning, that creative process itself never could have possibly anticipated where we were going as human beings. It couldn’t have anticipated 200,000 years ago that, you know, a mere 200,000 years later, we’d have space shuttles and iPods and microwave ovens.
What I want to suggest is that any process of evolution that relies on exploring an unknown space, such as genes or such as our neurons exploring the unknown space in our brains, and trying to create connections in our brains, and such as our brain’s trying to come up with new ideas that explore the space of alternatives that will lead us to what we call creativity in our social world, might be very close to random.
We know they’re random in the genetic case. We think they’re random in the case of neurons exploring connections in our brain. And I want to suggest that our own creative process might be pretty close to random itself. And that our brains might be whirring around at a subconscious level, creating ideas over and over and over again, and part of our subconscious mind is testing those ideas. And the ones that leak into our consciousness might feel like they’re well-formed, but they might have sorted through literally a random array of ideas before they got to our consciousness.
Karl Popper famously said the way we differ from other animals is that our hypotheses die in our stead; rather than going out and actually having to try out things, and maybe dying as a result, we can test out ideas in our minds. But what I want to suggest is that the generative process itself might be pretty close to random.
Putting these two things together has lots of implications for where we’re going as societies. As I say, as our societies get bigger, and rely more and more on the Internet, fewer and fewer of us have to be very good at these creative and imaginative processes. And so, humanity might be moving towards becoming more docile, more oriented towards following, copying others, prone to fads, prone to going down blind alleys, because part of our evolutionary history that we could have never anticipated was leading us towards making use of the small number of other innovations that people come up with, rather than having to produce them ourselves.
The interesting thing with Facebook is that, with 500 to 800 million of us connected around the world, it sort of devalues information and devalues knowledge. And this isn’t the comment of some reactionary who doesn’t like Facebook, but it’s rather the comment of someone who realizes that knowledge and new ideas are extraordinarily hard to come by. And as we’re more and more connected to each other, there’s more and more to copy. We realize the value in copying, and so that’s what we do.
And we seek out that information in cheaper and cheaper ways. We go up on Google, we go up on Facebook, see who’s doing what to whom. We go up on Google and find out the answers to things. And what that’s telling us is that knowledge and new ideas are cheap. And it’s playing into a set of predispositions that we have been selected to have anyway, to be copiers and to be followers. But at no time in history has it been easier to do that than now. And Facebook is encouraging that.
And then, as corporations grow … and we can see corporations as sort of microcosms of societies … as corporations grow and acquire the ability to acquire other corporations, a similar thing is happening, is that, rather than corporations wanting to spend the time and the energy to create new ideas, they want to simply acquire other companies, so that they can have their new ideas. And that just tells us again how precious these ideas are, and the lengths to which people will go to acquire those ideas.
A tiny number of ideas can go a long way, as we’ve seen. And the Internet makes that more and more likely. What’s happening is that we might, in fact, be at a time in our history where we’re being domesticated by these great big societal things, such as Facebook and the Internet. We’re being domesticated by them, because fewer and fewer and fewer of us have to be innovators to get by. And so, in the cold calculus of evolution by natural selection, at no greater time in history than ever before, copiers are probably doing better than innovators. Because innovation is extraordinarily hard. My worry is that we could be moving in that direction, towards becoming more and more sort of docile copiers.
But, these ideas, I think, are received with incredulity, because humans like to think of themselves as highly shrewd and intelligent and innovative people. But I think what we have to realize is that it’s even possible that, as I say, the generative mechanisms we have for coming up with new ideas are no better than random.
And a really fascinating idea itself is to consider that even the great people in history whom we associate with great ideas might be no more than we expect by chance. I’ll explain that. Einstein was once asked about his intelligence and he said, “I’m no more intelligent than the next guy. I’m just more curious.” Now, we can grant Einstein that little indulgence, because we think he was a pretty clever guy.
What does curiosity mean?
But let’s take him at his word and say, what does curiosity mean? Well, maybe curiosity means trying out all sorts of ideas in your mind. Maybe curiosity is a passion for trying out ideas. Maybe Einstein’s ideas were just as random as everybody else’s, but he kept persisting at them.
And if we say that everybody has some tiny probability of being the next Einstein, and we look at a billion people, there will be somebody who just by chance is the next Einstein. And so, we might even wonder if the people in our history and in our lives that we say are the great innovators really are more innovative, or are just lucky.
Now, the evolutionary argument is that our populations have always supported a small number of truly innovative people, and they’re somehow different from the rest of us. But it might even be the case that that small number of innovators just got lucky. And this is something that I think very few people will accept. They’ll receive it with incredulity. But I like to think of it as what I call social learning and, maybe, the possibility that we are infinitely stupid.”
— Mark Pagel, Professor of Evolutionary Biology, Reading University, England and The Santa Fe Institute, Infinite Stupidity, Edge, Dec 16, 2011 (Illustration by John S. Dykes)
See also:
☞ Mark Pagel: How language transformed humanity
Biologist Mark Pagel shares an intriguing theory about why humans evolved our complex system of language. He suggests that language is a piece of “social technology” that allowed early human tribes to access a powerful new tool: cooperation. — Mark Pagel: How language transformed humanity, TED.com, July 2011
Maudlin: There will be. There can be a philosophy of anything really, but it’s perhaps not as fancy as you’re making it out. The basic philosophical question, going back to 
