I’m a skeptic of forecasts in general. But if we are going to forecast, we need more than just an understanding of past performance.
We need to understand how the various actors in "Muniworld" are likely to react to changing circumstances and to each other.
What won’t work is a simple model in which “issuers” always act according to a version of rationality approved by financial experts.
Muni issuers are not individuals—they are collective bodies subject to numerous influences. They do not act “rationally”—they take actions under the influence of special interests, which are acting in coordination and clashing with each other.
In short, we need to abandon the naïve view of politics that often informs the muni debate, taking instead something like NYU Professor Bruce Bueno de Mesquita’s statistical model for politics. Here’s how Clive Thompson described this in The New York Times magazine a while back:
It is based loosely on Black’s voter theory, and it works like this:
To predict how leaders will behave in a conflict, Bueno de Mesquita starts with a specific prediction he wants to make, then interviews four or five experts who know the situation well. He identifies the stakeholders who will exert pressure on the outcome (typically 20 or 30 players) and gets the experts to assign values to the stakeholders in four categories: What outcome do the players want? How hard will they work to get it? How much clout can they exert on others? How firm is their resolve? Each value is expressed as a number on its own arbitrary scale, like 0 to 200. (Sometimes Bueno de Mesquita skips the experts, simply reads newspaper and journal articles and generates his own list of players and numbers.) For example, in the case of Iran’s bomb, Bueno de Mesquita set Ahmadinejad’s preferred outcome at 180 and, on a scale of 0 to 100, his desire to get it at 90, his power at 5 and his resolve at 90.
Then the math begins, some of which is surprisingly simple. If you merely sort the players according to how badly they want a bomb and how much support they have among others, you will end up with a reasonably good prediction. But the other variables enable the computer model to perform much more complicated assessments. In essence, it looks for possible groupings of players who would be willing to shift their positions toward one another if they thought that doing so would be to their advantage. The model begins by working out the average position of all the players — the “middle ground” that exerts a gravitational force on the whole negotiation. Then it compares each player with every other player, estimating whether one will be able to persuade or coerce the others to move toward its position, based on the power, resolve and positioning of everyone else. (Power isn’t everything. If the most powerful player is on the fringe of an issue, and a cluster of less-powerful players are closer to the middle, they might exert greater influence.) After estimating how much or how little each player might budge, the software recalculates the middle ground, which shifts as the players move. A “round” is over; the software repeats the process, round after round.
The game ends when players no longer move very much from round to round — this indicates they have compromised as much as they ever will. At that point, assuming no player with veto power had refused to compromise, the final average middle-ground position of all the players is the result — the official prediction of how the issue will resolve itself.
For municipal debt, we’d have to include office holding politicians, candidates, public unions, pension funds, taxpayer groups, transfer payment clients, bureaucrats and bond holders. Someone should start building this right away.
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