• David Easley and Jon Kleinberg, Networks, Crowds and Markets: Reasoning about a Highly Connected World, Cambridge University Press, Cambridge, 2010.
Apparently this is one of the first systematic textbooks on network science, which Shalizi defines as:
the study of networks of semiautonomous but interdependent units and of the way those networks shape both the behavior of individuals and the large-scale patterns that emerge from small-scale interactions.
This is not quite the same as what I’ve been calling network theory, but I’d like to see how they fit together.
Shalizi’s review includes a great putdown, not of the book’s authors, but of the limitations of a certain concept of ‘rationality’ that’s widely used in economics:
What game theorists somewhat disturbingly call rationality is assumed throughout—in other words, game players are assumed to be hedonistic yet infinitely calculating sociopaths endowed with supernatural computing abilities.
Clearly we have to go beyond these simplifying assumptions. There’s a lot of work being done on this. One important approach is to go out and see what people actually do in various situations. And another is to compare it to what monkeys will do in the same situations!
Here’s a video by Laurie Santos, who has done just that:
First she taught capuchin monkeys how to use money. Then, she discovered that they make the same mistakes with money that people do!
For example, they make different decisions in what mathematically might seem like the same situation, depending on how it’s framed.
Suppose I give you $1000, and then ask which game would you rather play:
1) a game where I give you either $1000 more or nothing more, with equal odds.
2) a game where I always give you $500 more.
Most people prefer game 2), even though the average, or expected amount of money collected is the same in both games. We say such people are risk averse. Someone who loves to gamble might prefer game 1).
Like people, most capuchin monkeys chose game 2), although Santos used grapes rather than money in this particular experiment.
So, like people, it seems monkeys are risk averse. This is not a ‘mistake’: there are good reasons to be risk averse.
On other hand, suppose I give you $2000 — twice as much as before! Feel all those crisp bills… think about all the good stuff you can buy. Now, which game would you rather play:
1′) a game where I either take away $1000 or nothing, with equal odds.
2′) a game where I always take away $500.
Most people prefer game 1′). The strange thing is that mathematically, the overall situation is isomorphic to the previous one. It’s just been framed in a different way. The first situation seems to be about ‘maximizing gains’. The second seems to be about ‘minimizing losses’. In the second situation, people are more likely to accept risk, in the hopes that with some chance they won’t lose anything. This is called loss aversion.
Monkeys, too, prefer game 1′).
This suggests that loss aversion is at least 35 million years old. It’s survived a long process of evolution! To me that suggests that while ‘irrational’, it’s probably a useful heuristic in most situations that commonly arise in primate societies.
Laurie Santos has a slightly different take on it. She says:
It was Camus who once said that man is the only species who refused to be what he really is. But the irony is that it might only be by recognizing our limitations that we can really actually overcome them.
Does economics elude mathematical reasoning?
For yet another approach the enormous project of reconciling economics to the reality of human behavior, see:
• Yanis Varoufakis, Foundations of Economics: A beginner’s companion, Routledge, London, 1998.
• Yanis Varoufakis, Joseph Halevi and Nicholas J. Theocarakis, Modern Political Economics: Making Sense of the Post-2008 World, Routledge, London, 2011.
The book is divided into two parts. The first part delves into every major economic theory, from Aristotle to the present, with a determination to discover clues of what went wrong in 2008. The main finding is that all economic theory is inherently flawed. Any system of ideas whose purpose is to describe capitalism in mathematical or engineering terms leads to inevitable logical inconsistency; an inherent error that stands between us and a decent grasp of capitalist reality. The only scientific truth about capitalism is its radical indeterminacy, a condition which makes it impossible to use science’s tools (e.g. calculus and statistics) to second-guess it. The second part casts an attentive eye on the post-war era; on the breeding ground of the Crash of 2008. It distinguishes between two major post-war phases: The Global Plan (1947-1971) and the Global Minotaur (1971-2008).
The emphasis is mine here, not because I’m sure it’s true, but because of how important it could be. It seems quite plausible to me. People seem able to squirm out of any mathematical framework we set up to describe them, short of the laws of physics. Still, I’d like to see the book’s argument. If there’s a ‘logical inconsistency’ in something, I want to actually see it.
You can get a bit more feeling for the second book in a blog post by Varoufakis. Among other things, he makes a point reminiscent of one that Phil Henshaw has repeatedly hammered home here:
Imagine a theorist that tries to explain complex evolving ecosystems by means of engineering models. What would result but incongruity and a mindset bent on misunderstanding the essence of the explanandum; a flight from that which craves explanation?
(By the way: I thank Mike Stay and Miguel Carrión-Álvarez for pointing out some items that appear in this blog entry. They did this on Google Plus. More on that later, maybe.)