More on this book
Community
Kindle Notes & Highlights
Read between
June 10 - June 27, 2022
No matter what your accomplice does, it’s always better for you to defect. If your accomplice has ratted you out, ratting them out in turn will give you five years of your life back—you’ll get the shared sentence (five years) rather than serving the whole thing yourself (ten years). And if your accomplice has stayed quiet, turning them in will net you the full million dollars—you won’t have to split
A dominant strategy avoids recursion altogether, by being the best response to all of your opponent’s possible strategies—so you don’t even need to trouble yourself getting inside their head at all.
If everyone does the rational thing and follows the dominant strategy, the story ends with both of you serving five years of hard time—which, compared to freedom and a cool half million apiece, is dramatically worse for everyone involved.
the equilibrium for a set of players, all acting rationally in their own interest, may not be the outcome that is actually best for those players.
Quantifying the price of anarchy has given the field a concrete and rigorous way to assess the pros and cons of decentralized systems,
The problem isn’t that vacations aren’t attractive; the problem is that everyone wants to take slightly less vacation than their peers, producing a game whose only equilibrium is no vacation at all.
Scaling up this logic results in a potent argument for the role of government. In fact, many governments do have laws on the books mandating minimum vacations and limiting shop hours.
In both love and housing, though, we continue to encounter more options even after our optimal-stopping decision is made—so why not be ready to jump ship? Of course, knowing that the other party (be it spouse or landlord) is in turn prepared to jump ship would prevent many of the long-term investments (having children together, or laboriously moving in one’s belongings) that make those agreements worthwhile.
Love is like organized crime. It changes the structure of the marriage game so that the equilibrium becomes the outcome that works best for everybody.
“Something very important happens once somebody decides to follow blindly his predecessors independently of his own information signal, and that is that his action becomes uninformative to all later decision makers. Now the public pool of information is no longer growing.
Investors are said to fall into two broad camps: “fundamental” investors, who trade on what they perceive as the underlying value of a company, and “technical” investors, who trade on the fluctuations of the market. The rise of high-speed algorithmic trading has upset the balance between these two strategies,
Second, remember that actions are not beliefs; cascades get caused in part when we misinterpret what others think based on what they do.
a Vickrey auction has a number of attractive properties. And to an algorithmic game theorist in particular, one property especially stands out: the participants are incentivized to be honest. In fact, there is no better strategy than just bidding your “true value” for the item—exactly what you think the item is worth.
Even better, honesty remains the best policy regardless of whether the other bidders are honest themselves. In the prisoner’s dilemma, we saw how defection turned out to be the “dominant” strategy—the best move no matter whether your partner defected or cooperated. In a Vickrey auction, on the other hand, honesty is the dominant strategy. This is the mechanism designer’s holy grail. You do not need to strategize or recurse.
any game that can be played for you by agents to whom you’ll tell the truth, it says, will become an honesty-is-best game if the behavior you want from your agent is incorporated into the rules of the game itself.
The road to hell is paved with intractable recursions, bad equilibria, and information cascades.
Outcomes make news headlines—indeed, they make the world we live in—so it’s easy to become fixated on them. But processes are what we have control over.
If you wind up stuck in an intractable scenario, remember that heuristics, approximations, and strategic use of randomness can help you find workable solutions.
One of the implicit principles of computer science, as odd as it may sound, is that computation is bad: the underlying directive of any good algorithm is to minimize the labor of thought.
Likewise, seemingly innocuous language like “Oh, I’m flexible” or “What do you want to do tonight?” has a dark computational underbelly that should make you think twice. It has the veneer of kindness about it, but it does two deeply alarming things. First, it passes the cognitive buck: “Here’s a problem, you handle it.” Second, by not stating your preferences, it invites the others to simulate or imagine them. And as we have seen, the simulation of the minds of others is one of the biggest computational challenges a mind (or machine) can ever face.
Alternatively, you can try to reduce, rather than maximize, the number of options that you give other people—say, offering a choice between two or three restaurants rather than ten.
These approaches to the management of scarce shared resources mirror the distinction in computer science between “spinning” and “blocking.” When a processing thread requests a resource and can’t get it, the computer can either allow that thread to “spin”—to continue checking for the resource in a perpetual “Is it ready yet?” loop—or it can “block”: halt that thread, work on something else, and then come back around whenever the resource becomes free. To a computer scientist, this is a practical tradeoff: weighing the time lost to spinning against the time lost in context switching.
In the hard cases, the best algorithms are all about doing what makes the most sense in the least amount of time, which by no means involves giving careful consideration to every factor and pursuing every computation to the end. Life is just too complicated for that.
