Conversation
UNDERSTANDING IS A POOR SUBSTITUTE FOR CONVEXITY (ANTIFRAGILITY)
[12.12.12]
The point we will be making here is that logically, neither trial and error nor "chance" and serendipity can be behind the gains in technology and empirical science attributed to them. By definition chance cannot lead to long term gains (it would no longer be chance); trial and error cannot be unconditionally effective: errors cause planes to crash, buildings to collapse, and knowledge to regress.
NASSIM NICHOLAS TALEB, essayist and former mathematical trader, is Distinguished Professor of Risk Engineering at NYU’s Polytechnic Institute. He is the author the international bestseller The Black Swan and the recently published Antifragile: Things That Gain from Disorder. (US: Random House; UK: Penguin Press)
Nassim Nicholas Taleb's Edge Bio
UNDERSTANDING IS A POOR SUBSTITUTE FOR CONVEXITY (ANTIFRAGILITY)
Something central, very central, is missing in historical accounts of scientific and technological discovery. The discourse and controversies focus on the role of luck as opposed to teleological programs (from telos, "aim"), that is, ones that rely on pre-set direction from formal science. This is a faux-debate: luck cannot lead to formal research policies; one cannot systematize, formalize, and program randomness. The driver is neither luck nor direction, but must be in the asymmetry (or convexity) of payoffs, a simple mathematical property that has lied hidden from the discourse, and the understanding of which can lead to precise research principles and protocols.
Conversation
COLLECTIVE INTELLIGENCE
[11.21.12]
As all the people and computers on our planet get more and more closely connected, it's becoming increasingly useful to think of all the people and computers on the planet as a kind of global brain.
THOMAS W. MALONE is the Patrick J. McGovern Professor of Management at the MIT Sloan School of Management and the founding director of the MIT Center for Collective Intelligence. He was also the founding director of the MIT Center for Coordination Science and one of the two founding co-directors of the MIT Initiative on "Inventing the Organizations of the 21st Century".
Thomas W. Malone's Edge Bio Page
[31:45 minutes]
COLLECTIVE INTELLIGENCE
Pretty much everything I'm doing now falls under the broad umbrella that I'd call collective intelligence. What does collective intelligence mean? It's important to realize that intelligence is not just something that happens inside individual brains. It also arises with groups of individuals. In fact, I'd define collective intelligence as groups of individuals acting collectively in ways that seem intelligent. By that definition, of course, collective intelligence has been around for a very long time. Families, companies, countries, and armies: those are all examples of groups of people working together in ways that at least sometimes seem intelligent.
Conversation
WHAT DO ANIMALS WANT?
[10.31.12]
Whatever anybody says, I feel that the hard problem of consciousness is still very hard, and to try and rest your ethical case on proving something that has baffled people for years seems to me to be not good for animals. Much, much better to say let's go for something tangible, something we can measure. Are the animals healthy, do they have what they want? Then if you can show that, then that's a much, much better basis for making your decisions.
MARIAN STAMP DAWKINS is professor of animal behaviour at the University of Oxford, where she heads the Animal Behaviour Research Group. She is the author of Why Animals Matter.
Marian Stamp Dawkin's Edge Bio Page
[35 minutes]
The Reality Club: Nicholas Humphrey
WHAT DO ANIMALS WANT?
The questions I'm asking myself are really about how much we really know about animal consciousness. A lot of people think we do, or think that we don't need scientific evidence. It really began to worry me that people were basing their arguments on something that we really can't know about at all. One of the questions I asked myself was: how much do we really know? And is what we know the best basis for arguing for animal welfare? I've been thinking hard about that, and I came to the conclusion that the hard problem of consciousness is actually very hard. It's still there, and we kid ourselves if we think we've solved it.
Conversation
CONSTRUCTOR THEORY
A Conversation with [10.22.12]
There's a notorious problem with defining information within physics, namely that on the one hand information is purely abstract, and the original theory of computation as developed by Alan Turing and others regarded computers and the information they manipulate purely abstractly as mathematical objects. Many mathematicians to this day don't realize that information is physical and that there is no such thing as an abstract computer. Only a physical object can compute things.
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I think it's important to regard science not as an enterprise for the purpose of making predictions, but as an enterprise for the purpose of discovering what the world is really like, what is really there, how it behaves and why.
DAVID DEUTSCH is a Physicist at the University of Oxford. His research in quantum physics has been influential and highly acclaimed. He is the author of The Beginning of Infinity and The Fabric of Reality.
David Deutsch's Edge Bio Page
REALITY CLUB: Arnold Trehub, Harold Levey
[47 minutes]
CONSTRUCTOR THEORY
Some considerable time ago we were discussing my idea, new at the time, for constructor theory, which was and is an idea I had for generalizing the quantum theory of computation to cover not just computation but all physical processes. I guessed and still guess that this is going to provide a new mode of description of physical systems and laws of physics. It will also have new laws of its own which will be deeper than the deepest existing theories, such as quantum theory and relativity. At the time, I was very enthusiastic about this, and what intervened between then and now is that writing a book took much longer than I expected. But now I'm back to it, and we're working on constructor theory and, if anything, I would say it's fulfilling its promise more than I expected and sooner than I expected.
One of the first rather unexpected yields of this theory has been a new foundation for information theory. There's a notorious problem with defining information within physics, namely that on the one hand information is purely abstract, and the original theory of computation as developed by Alan Turing and others regarded computers and the information they manipulate purely abstractly as mathematical objects. Many mathematicians to this day don't realize that information is physical and that there is no such thing as an abstract computer. Only a physical object can compute things.
On the other hand, physicists have always known that in order to do the work that the theory of information does within physics, such as informing the theory of statistical mechanics, and thereby, thermodynamics (the second law of thermodynamics), information has to be a physical quantity. And yet, information is independent of the physical object that it resides in.
Conversation
HOW TO WIN AT FORECASTING
[12.6.12]
The question becomes, is it possible to set up a system for learning from history that's not simply programmed to avoid the most recent mistake in a very simple, mechanistic fashion? Is it possible to set up a system for learning from history that actually learns in our sophisticated way that manages to bring down both false positive and false negatives to some degree? That's a big question mark.
Nobody has really systematically addressed that question until IARPA, the Intelligence Advanced Research Projects Agency, sponsored this particular project, which is very, very ambitious in scale. It's an attempt to address the question of whether you can push political forecasting closer to what philosophers might call an optimal forecasting frontier. That an optimal forecasting frontier is a frontier along which you just can't get any better.
PHILIP E. TETLOCK is Annenberg University Professor at the University of Pennsylvania (School of Arts and Sciences and Wharton School). He is author of Expert Political Judgment: How Good Is It? How Can We Know?
Philip Tetlock's Edge Bio Page
[46.50 minutes]
INTRODUCTION
by Daniel Kahneman
Philip Tetlock’s 2005 book Expert Political Judgment: How Good Is It? How Can We Know? demonstrated that accurate long-term political forecasting is, to a good approximation, impossible. The work was a landmark in social science, and its importance was quickly recognized and rewarded in two academic disciplines—political science and psychology. Perhaps more significantly, the work was recognized in the intelligence community, which accepted the challenge of investing significant resources in a search for improved accuracy. The work is ongoing, important discoveries are being made, and Tetlock gives us a chance to peek at what is happening.
Tetlock’s current message is far more positive than his earlier dismantling of long-term political forecasting. He focuses on the near term, where accurate prediction is possible to some degree, and he takes on the task of making political predictions as accurate as they can be. He has successes to report. As he points out in his comments, these successes will be destabilizing to many institutions, in ways both multiple and profound. With some confidence, we can predict that another landmark of applied social science will soon be reached.
Daniel Kahneman, recipient of the Nobel Prize in Economics, 2002, is the Eugene Higgins Professor of Psychology Emeritus at Princeton University and author of Thinking Fast and Slow.
HOW TO WIN AT FORECASTING
A Conversation with Philip Tetlock
There's a question that I've been asking myself for nearly three decades now and trying to get a research handle on, and that is why is the quality of public debate so low and why is it that the quality often seems to deteriorate the more important the stakes get?
About 30 years ago I started my work on expert political judgment. It was the height of the Cold War. There was a ferocious debate about how to deal with the Soviet Union. There was a liberal view; there was a conservative view. Each position led to certain predictions about how the Soviets would be likely to react to various policy initiatives.
