Politicians vs. Bloggers

After reading Jonathan Chait's posts on Howard Dean I was thinking an interesting post would be how the kinds of things that tend to make someone a successful politician differ from the kinds of things that tend to make someone a successful blogger, like Chait -- and the very different kinds of people that tend to go into each.

One really common way to increase your odds of success as a politician is to be kind of a weasel, or a huge weasel. There are exceptions; you can be very successful through great charisma and competence (Elizabeth Warren), but few people have that kind of charisma and competence.

Note, however, what true weaselness is: Sometimes looking like a weasel is actually doing what you think is best for the country – You support, or go along with, something bad in exchange for helping some greater good get done. Any good politician should do this sometimes, and to some extent. It's unavoidable if you want to optimize the good of 300 million Americans and 7 billion human beings. But the weasels will do this not for the greater good, but for more personal power, prestige, and/or money.

And politicians tend to be people who will go to incredible lengths, often unethical, often that they know really hurt the country and the world, for power and prestige (although there's an argument that women politicians are much less like this [1]). John McCain is a prime example; it's all about him and what he wants, and his personal vendettas, rather than 300 million Americans and 7 billion human beings.

As a blogger, however, there's very rarely big bucks and prestige among the general public. You do it largely because you care, rather than a super drive to win and have power. And you better be smart and do your homework, because your derp, or mistakes, are often immediately attacked by people who largely determine how respected and influential you are. And your audience is way more informed than the average, so you just can't get away with politicians tricks nearly as well.

Of course, I'm not speaking of conservative bloggers, who primarily speak to a bizarro tribal world, and say what their super-rich patrons want them to say.


[1] From the New York Times, 6/11/11:

Research points to a substantial gender gap in the way women and men approach running for office. Women have different reasons for running, are more reluctant to do so and, because there are so few of them in politics, are acutely aware of the scrutiny they draw — all of which seems to lead to differences in the way they handle their jobs once elected.

“The shorthand of it is that women run for office to do something, and men run for office to be somebody,” said Debbie Walsh, director of the Center for American Women and Politics at Rutgers University. “Women run because there is some public issue that they care about, some change they want to make, some issue that is a priority for them, and men tend to run for office because they see this as a career path.”

What about Expenditure Cascades?

Paul Krugman has an interesting post on the question of the causes of the explosion in household debt that was an important factor in our financial and economic crisis:

The president came down pretty much for what we might call a Stiglitzian view (although it’s widely held): debt was driven by rising inequality. The rich were taking an ever-larger share of the pie, but not spending to match, while working Americans took on ever more debt to make ends meet.

What’s the alternative? Minsky: debt exploded because the Great Depression was receding into the mists of forgetfulness, and both lenders and borrowers — enabled and encouraged by financial deregulation — forgot the dangers of leverage.

I'd add to this, importantly, expenditure cascades, from Cornell economist Robert H. Frank.

In an expenditure cascade, the tip-top pulls away in income, and thus spending, so level two notches up their spending to not lose their position and feel bad and embarrassed, and this involves more borrowing/less saving. When level two notches it up, this induces level three to do the same, and so on.

How much pressure do middle class people now feel to put granite, wood, and stainless steel in their perfectly functioning kitchens so they don't feel and look crappy and poor?

The 1/10th of 1% used to drive $120,000 Mercedes, and the 1% used to drive $70,000 Mercedes. Now the 1/10th of 1% are buying $400,000 Rolls Royces, making the 1% feel like their $70,000 Mercedes are crappy and embarrassing, so they save less/borrow more and buy $200,000 Bentleys. Now that the 1% went from $70,000 Mercedes to $200,000 Bentleys, the 5% feel like their $45,000 Mercedes are crappy and embarrassing, so they save less/borrow more and buy $80,000 Mercedes, and so on down the line.

I think expenditure cascades are important to add, especially since positional externalities are the pink elephant of economics.

How about a model just as a simulator/tester, free of the constraints from having to solve for the global optimum?

Please bear with me and read this long quote from a paper in the lifecycle portfolio strategy literature:

The original literature on dynamic portfolio choice, pioneered by Merton (1969,1971) and Samuelson (1969) in continuous time and by Fama (1970) in discrete time, produced many important insights into the properties of optimal portfolio policies. Unfortunately, closed-form solutions are available only for a few special parameterizations of the investor's preferences and return dynamics, as exemplified by Kim and Omberg (1996), Liu (1999a), and Wachter (2002).

The recent literature therefore uses a variety of numerical and approximate solution methods to incorporate realistic features into the dynamic portfolio problem. For example, Brennan, Schwartz, and Lagnado (1997) solve numerically the PDE characterizing the solution to the dynamic optimization. Campbell and Viceira (1999) log-linearize the frst-order conditions and budget constraint to obtain approximate closed-form solutions. Das and Sundaram (2000) and Kogan and Uppal (2001) perform different expansions of the value function for which the problem can be solved analytically. By far the most popular approach involves discretizing the state space, which is done by Balduzzi and Lynch (1999), Brandt (1999), Barberis (2000), and Dammon, Spatt, and Zhang (2001), among many others. Once the state space is discretized, the value function can be evaluated by a choice of quadrature integration (Balduzzi and Lynch), simulations (Barberis), binomial discretizations (Dammon, Spatt, and Zhang), or nonparametric regressions (Brandt), and then the dynamic optimization can be solved by backward recursion.

These numerical and approximate solution methods share some important limitations. Except for the nonparametric approach of Brandt (1999), they assume unrealistically simple return distributions. All of the methods rely on CRRA preferences, or its extension by Epstein and Zin (1989), to eliminate the dependence of the portfolio policies on wealth and thereby make the problem path-independent. Most importantly, the methods cannot handle the large number of state variables with complicated dynamics which arise in many realistic portfolio choice problems. A partial exception is Campbell, Chan, and Viceira (2003), who use log-linearization to solve a problem with many state variables but linear dynamics…

From: Brandt, Michael W., Amit Goyal, Pedro Santa-Clara, and Jonathan R. Stroud, 2005, A simulation approach to dynamic portfolio choice with an application to learning about return predictability, Review of Financial Studies 18, 831–873.

The authors go on to present their new numerical solution method which overcomes a lot of the limitations of old solution methods, and allows more realistic modeling – IF you feel like you can only create a model if you are able to find the global optimal solution to it.

But this is my point. In all of those papers cited on lifetime portfolio choice strategy, the authors put in severe unrealism for one reason, so that they would be able to solve for the perfect globally optimal behavior of the people in the model.

What if, at least for just one paper, let alone a branch of the literature, they said, I’m not going to be severely constrained by the need to calculate the exact utility optimizing behavior. I’m going to create a really really realistic model, say of lifetime portfolio strategy, and use it as a simulator tester. I’ll try various popular and/or intuitive strategies, plug them into the model on my computer and see what expected utility score comes out.

Wouldn’t this be a great way to prove and see which strategies are better than others, with a really realistic model, that could have really complicated realistic utility and other functions – functions which wouldn’t even have to be analytical; they could be highly complicated and realistic empirical functions; functions written with multiple lines of computer code, instead of being limited to one neat compact simple equation.

Sure, such a model might be so complicated and irregular that you’ll never solve for the global optimum with a high degree of confidence, even with numerical methods and supercomputers, but you could test really well how one important and/or popular strategy compares to others in expected utils. You could use your intuition and new understandings in the field to come up with ever better strategies that test higher and higher in utils in the super realistic simulator. You could quickly test hunches in the simulator. Heck, you could even put out a prize for the first person to come out with a strategy that exceeds X expected utils in the simulator.

Why does no one do this in economics or finance? It seems in the physical sciences that they always use simulators to test things, making them as realistic as possible, instead of making them way less realistic in very important ways in order to be able to exactly calculate the perfect global optimum.