Barry Nalebuff Explains the Prisoner’s Dilemma
Barry Nalebuff is the Milton Steinbach Professor of Management at the Yale School of Management. Professor Nalebuff has written on a wide variety of subjects ranging from strategy to pricing, bidding to bargaining, and innovation to incentives. He is an expert on game theory and has written extensively on its application for managers. His most recent book, The Art of Strategy, is an update of the best-selling Thinking Strategically, which explains the fundamentals of game theory using real world examples.
Professor Nalebuff's work on strategy focuses on the fundamental duality in business—the conflict between cooperating to create a pie and competing to divide it up—which he presents in Co-opetition. His book, Why Not?, focuses on providing a framework for problem solving and ingenuity. His work on product bundling was featured in the European Union's investigation of the proposed GE-Honeywell merger.
Question: What is the origin of the Prisoner’s Dilemma?
Barry Nalebuff: The Prisoner’s Dilemma goes back to two researchers at RAND, Merrill Flood and Melvin Dresher and it’s a classic story; you’ve seen God knows how many movies.
You interrogate two prisoners separately and the idea is that anyone who confesses is given some leniency, provided they confess first. And, of course, if neither of the two confesses. I have limited evidence against them so they do pretty well. And if they both confess, then the immunity isn’t worth anything.
However, each one is afraid that the other one is going to confess and so therefore confesses, and the police get all the information that they want.
This also raises a couple of things, one that the outcome of the game, in this case confess-confess, can be good or bad from the player’s perspectives, depending on who you are. If you are the prisoners, well, then it’s a disaster. If you are the police or society, it can be a good thing.
We see this with OPEC. The question is, how much oil that any one country choose to bring to the market. And it’s almost the case, no matter what quantities other members of OPEC supply, each one wants to put more on. And then, of course, when everyone does put more, the cartel is much less effective.
Recorded on: Oct 2, 2008
Barry Nalebuff explains game theory’s most famous corollary.
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Using machine-learning technology, the genealogy company My Heritage enables users to animate static images of their relatives.
- Deep Nostalgia uses machine learning to animate static images.
- The AI can animate images by "looking" at a single facial image, and the animations include movements such as blinking, smiling and head tilting.
- As deepfake technology becomes increasingly sophisticated, some are concerned about how bad actors might abuse the technology to manipulate the pubic.
My Heritage/Deep Nostalgia<p>But that's not to say the animations are perfect. As with most deep-fake technology, there's still an uncanny air to the images, with some of the facial movements appearing slightly unnatural. What's more, Deep Nostalgia is only able to create deepfakes of one person's face from the neck up, so you couldn't use it to animate group photos, or photos of people doing any sort of physical activity.</p>
My Heritage/Deep Nostalgia<p>But for a free deep-fake service, Deep Nostalgia is pretty impressive, especially considering you can use it to create deepfakes of <em>any </em>face, human or not. </p>
How long should one wait until an idea like string theory, seductive as it may be, is deemed unrealistic?
- How far should we defend an idea in the face of contrarian evidence?
- Who decides when it's time to abandon an idea and deem it wrong?
- Science carries within it its seeds from ancient Greece, including certain prejudices of how reality should or shouldn't be.
Plato used the allegory of the cave to explain that what humans see and experience is not the true reality.
Credit: Gothika via Wikimedia Commons CC 4.0<p>When scientists and mathematicians use the term <em>Platonic worldview</em>, that's what they mean in general: The unbound capacity of reason to unlock the secrets of creation, one by one. Einstein, for one, was a believer, preaching the fundamental reasonableness of nature; no weird unexplainable stuff, like a god that plays dice—his tongue-in-cheek critique of the belief that the unpredictability of the quantum world was truly fundamental to nature and not just a shortcoming of our current understanding. Despite his strong belief in such underlying order, Einstein recognized the imperfection of human knowledge: "What I see of Nature is a magnificent structure that we can comprehend only very imperfectly, and that must fill a thinking person with a feeling of humility." (Quoted by Dukas and Hoffmann in <em>Albert Einstein, The Human Side: Glimpses from His Archives</em> (1979), 39.)</p> <p>Einstein embodies the tension between these two clashing worldviews, a tension that is still very much with us today: On the one hand, the Platonic ideology that the fundamental stuff of reality is logical and understandable to the human mind, and, on the other, the acknowledgment that our reasoning has limitations, that our tools have limitations and thus that to reach some sort of final or complete understanding of the material world is nothing but an impossible, <a href="https://www.amazon.com/dp/B01K2JTGIA?tag=bigthink00-20&linkCode=ogi&th=1&psc=1" target="_blank" rel="noopener noreferrer">semi-religious dream</a>.</p>
A physicist creates an AI algorithm that predicts natural events and may prove the simulation hypothesis.
- Princeton physicist Hong Qin creates an AI algorithm that can predict planetary orbits.
- The scientist partially based his work on the hypothesis which believes reality is a simulation.
- The algorithm is being adapted to predict behavior of plasma and can be used on other natural phenomena.
Physicist Hong Qin with images of planetary orbits and computer code.
Credit: Elle Starkman
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The vaccine will shorten the "shedding" time.