Compare Refuge, Resort
Wednesday I gave a brief talk (audio, slides) at the annual meeting of the Society for Risk Analysis. It seems many risk analysts are like futurists in disliking numerical/probability estimates, preferring to qualitatively discuss “scenarios.” They note one can’t think of all possible relevant events, and point to past numerical estimates that now seem way off.
My talk was on a concrete way to get numerical estimates on extreme risks: refuge futures. I’ve given the subject a bit more thought since I talked on it a few years ago; here is my current concept.
Create a set of underground refuges against disaster, some near major transport access points. For example, a $2 Million shelter can hold 36 people with air, water, food, power for 4 years, at less than $14K per person-year. Near each refuge create a matching resort, which supports a comparably utilitarian lifestyle, but does not protect much against disaster. For example, imagine a cheap hotel near an airport, with a refuge dug below it.
Create and sell transferable tickets representing the right of qualified amateurs to stay in those refuges or resorts on particular future dates. Refuges maintain a multi-year supply of food and power, and are staffed by experts who decide when a disaster justifies sealing it. Qualified folks can use their tickets for a date by showing up at the matching resort; they’ll then be escorted to its matching refuge. Those who are in a refuge when it is sealed remain there until its experts decide to unseal it.
The price of a ticket to a particular refuge on a particular date should vary with the estimated chance of a serious disaster near that date and location. But that price should also vary with other factors, such as interest rates, general wealth levels, the local economy, the total supply of related refuge slots, the risk a ticket holder might fail to arrive in time to use a ticket, and the risk that refuge administrators might not honor valid tickets. How can we disentangle these effects?
Regarding variations in interest rates, general wealth, and local growth, such factors could be roughly corrected for via comparing refuge and resort ticket prices. That is, subsidize a market maker who trades of refuge for resort tickets in some ratio. (Ticket fractions could be a random chance of getting a ticket.) The number of resort tickets required to buy a single refuge ticket could be our key disaster indicator.
While an estimate of how disaster risks vary across space and time would be interesting, it would be far more useful to know how disaster risks vary with events, especially relevant decisions. For example, imagine policy-makers were considering a new geo-engineering program. We could then create conditional tickets, such as tickets to a refuge valid on a date only if this new program was begun by some specified prior date. This would allow folks to trade conditional refuge tickets for conditional resort tickets.
The number of conditional resort tickets required to buy a conditional refuge ticket would be a disaster indicator for that condition. If the disaster indicator was lower given the adoption of a geo-engineering policy than given not adopting it, this would suggest that the geo-engineering policy reduces the chance of serious disaster. The possibility of obtaining such valuable policy info would be a major reason to created this whole refuge-resort ticket system.
Regarding the risk of failing to show up to use a refuge ticket, for each slot available we could sell several tickets at different priority levels. If not all first priority tickets holders showed up, the refuge could randomly allocate slots among those who showed up with second priority tickets. If any slots remained, they’d continue with third priority tickets, etc. We could focus on the total price of all refuge priority level tickets for a date, as that should vary less with variations in the chance folks can’t show up to use tickets.
I’m not sure how best to correct for variations in the local supply of refuge or resort slots. I’m also not sure how best to aggregate trades and prices across diverse resort-refuge pairs.
Added 10p: Regarding the risk that refuge administrators might not honor valid tickets, to get useful prices we only need a substantial chance that tickets will be honored. In order to distort our disaster indicator policy advice, ticket speculators need to expect that the chance of valid tickets not being honored is substantially correlated with chosen disaster policy. What policies could plausibly create such an expected correlation?
Added 12Dec: I should add that as futures markets in concrete physical services, refuge and resort futures and their derivatives would seem to avoid anti-gambling laws.