Refugees selling the food of their homeland to get a start in a new life is, by now, a cliché. Khaled (in the photo) joined their ranks a year ago. But cliché or not, selling food is an important way to give people work to do, wages, and hope. If it’s happening on your doorstep, which it is, and the food is good, which it is, what’s a hungry podcaster to do? Go there, obviously, and report back. Which is why, a couple of weeks ago, I found myself, microphone in hand, waiting patiently in line for a falafel wrap.
Truth be told, there aren’t that many Syrian refugees in Italy. The most recent official statistics put the total at around 5000 with a little over 600 in Rome. Hummustown is helping a few of them.
One promise of ride-hailing companies like Uber and Lyft was fewer cars clogging city streets. But studies suggest the opposite: that ride-hailing companies are pulling riders off buses, subways, bicycles and their own feet and putting them in cars instead.
And in what could be a new wrinkle, a service by Uber called Express Pool now is seen as directly competing with mass transit.
Uber and Lyft argue that in Boston, for instance, they complement public transit by connecting riders to hubs like Logan Airport and South Station. But they have not released their own specific data about rides, leaving studies up to outside researchers.
And the impact of all those cars is becoming clear, said Christo Wilson, a professor of computer science at Boston’s Northeastern University, who has looked at Uber’s practice of surge pricing during heavy volume.
“The emerging consensus is that ride-sharing (is) increasing congestion,” Wilson said.
It’s interesting that the “simple” story peddled by ridesharing companies is the one that’s most believed. Outside studies like this are certainly both wanted and needed.
It’s always seemed to me that these companies weren’t quite doing what they said they were from a simple economics standpoint. Particularly with these companies losing money to build market share, they’re essentially subsidizing a portion of their user’s cost. The fact that they’re siphoning off people from public transportation isn’t widely reported. I suspect that outside of major metropolitan areas they’re not doing as much as they are in them. They’re building market share, but primarily by breaking regulations in places with taxi or other related services. I’d certainly love to see more broad based statistics of their ridership compared with statistics from taxi companies and municipal transportation services. I have a feeling the economic piper will eventually come for them when the playing field is leveled.
Remember Farm Aid, which launched in 1985? A lot of people do, and they tend to date the farm crisis in America to the 1980s, triggered by Earl Butz and his crazy love for fencerow to fencerow, get big or get out, industrial agriculture. And of course, land consolidation is inevitable, because if you’re going to invest in all that capital equipment to make your farm more efficient, you’re bound to buy up the smaller farmers who weren’t so savvy. Those “facts,” however, are anything but. They’re myths, on which much of the current criticism of American farm policy is built. There are others, too, and they’re all skillfully eviscerated by Nate Rosenberg and Bryce Wilson Stucki in a recent paper.
One villain or two?
And here’s another thing. That first Farm Aid concert apparently raised $9 million. You could presumably help a lot of poor old dirt farmers with that kind of cash. But Farm Aid wasn’t actually about poor old dirt farmers, it was about people like Willie Nelson. He lost $800,000 the year before Farm Aid. Nine million dollars doesn’t go too far when individual people are losing that kind of money.
In the conclusion of this series, we peer into the future of human-robot combinations on the waterfront and in the rest of the supply chain. We’ll hear about the strange future of cyborg trucking and meet the friendly little helper bots in warehouses. The view of automation that sees only a battle between robots vs. humans is wrong. It’s humans all the way down.
The key to replacing jobs lost to robots and automation is going to be much more education, and we’re doing a painfully poor job of it. This episode is a bit more upbeat about the technology side as well as the human side of things. It’s fine to do the one, but it does a disservice to the other without the added complexities of the problems.
In sum, this was a great series of episodes that shows a lot of what the average person is missing about how global trade happens and how intricate it can be. It’s impressive how much ground can be covered in just a few short episodes. I recommend the entire series to everyone.
SFI and Arizona State University soon will offer the world’s first comprehensive online master’s degree in complexity science. It will be the Institute’s first graduate degree program, a vision that dates to SFI’s founding.
“With technology, a growing recognition of the value of online education, widespread acceptance of complexity science, and in partnership with ASU, we are now able to offer the world a degree in the field we helped invent,” says SFI President David Krakauer, “and it will be taught by the very people who built it into a legitimate domain of scholarship.”
A Course in Game Theory presents the main ideas of game theory at a level suitable for graduate students and advanced undergraduates, emphasizing the theory's foundations and interpretations of its basic concepts. The authors provide precise definitions and full proofs of results, sacrificing generalities and limiting the scope of the material in order to do so. The text is organized in four parts: strategic games, extensive games with perfect information, extensive games with imperfect information, and coalitional games. It includes over 100 exercises.
(.pdf download) Subjectivity and correlation, though formally related, are conceptually distinct and independent issues. We start by discussing subjectivity. A mixed strategy in a game involves the selection of a pure strategy by means of a random device. It has usually been assumed that the random device is a coin flip, the spin of a roulette wheel, or something similar; in brief, an ‘objective’ device, one for which everybody agrees on the numerical values of the probabilities involved. Rather oddly, in spite of the long history of the theory of subjective probability, nobody seems to have examined the consequences of basing mixed strategies on ‘subjective’ random devices, i.e. devices on the probabilities of whose outcomes people may disagree (such as horse races, elections, etc.).
For a constant ϵ, we prove a poly(N) lower bound on the (randomized) communication complexity of ϵ-Nash equilibrium in two-player NxN games. For n-player binary-action games we prove an exp(n) lower bound for the (randomized) communication complexity of (ϵ,ϵ)-weak approximate Nash equilibrium, which is a profile of mixed actions such that at least (1−ϵ)-fraction of the players are ϵ-best replying.
John Nash’s notion of equilibrium is ubiquitous in economic theory, but a new study shows that it is often impossible to reach efficiently.
There’s a couple of interesting sounding papers in here that I want to dig up and read. There are some great results that sound like they are crying out for better generalization and classification. Perhaps some overlap with information theory and complexity?
To some extent I also find myself wondering about repeated play as a possible random walk versus larger “jumps” in potential game play and the effects this may have on the “evolution” of a solution by play instead of a simpler closed mathematical solution.
For-profit dialysis companies often maximize their profits at the expense of their patients. John Oliver explores why a medical clinic is nothing like a Taco Bell.
The lack of humanity showed by these corporations is simply horrific. Certainly a market failure which is causing some painful externalities. We need something more significant to fix the inequities that are happening here.
Chapter 2 is a nice piece on the El Farol Problem which is a paradox which “represented a decision problem where expectations (forecasts) that many would attend [the El Farol bar] would lead to few attending, and expectations that few would attend would lead to many attending: expectations would lead to outcomes that would negate these expectations.”
Zhang and Challet generalized this problem into the Minority Game in game theoretic form.
There are two reasons for perfect or deductive rationality to break down under complication. The obvious one is that beyond a certain level of of complexity human logical capacity ceases to cope–human rationality is bounded. The other is that in interactive situations of complication, agents cannot rely upon the other agents they are dealing with to behave under perfect rationality, and so they are forced to guess their behavior. This lands them in a world of subjective beliefs and subjective beliefs about subjective beliefs. Objective, well-defined, shared assumptions then cease to apply. In turn, rational, deductive reasoning (deriving a conclusion by perfect logical processes from well-defined premises) itself cannot apply. The problem becomes ill-defined.
This passage, though in an economics text, seems to be a perfect statement about part of the problem of governing in the United States at the moment. I have a thesis that Donald Trump is a system 1 thinker and is generally incapable of system 2 level thought, thus he has no ability to discern the overall complexity of the situations in which he finds himself (or in which the United States finds itself). As a result, he’s unable to effectively lead. From a complexity and game theoretic standpoint, he feels he’s able to perfectly play and win any game. His problem is that he feels like he’s playing tic-tac-toe, while many see at least a game as complex as checkers. In reality, he’s playing a game far more complex than either chess or go.
The overall problem laid out in this chapter is an interesting one vis-a-vis the issues many restaurant startups face, particularly in large cities. How can they best maximize their attendance not only presently, but in the long term while staying afloat in very crowded market places.
The level at which humans can apply perfect rationality is surprisingly modest. Yet it has not been clear how to deal with imperfect or bounded rationality.
Chapter 3 takes a similar problem as Chapter 2 and ups the complexity of the problem somewhat substantially. While I understand that at the time these problems may have seemed cutting edge and incomprehensible to most, I find myself wondering how they didn’t see it all from the beginning.