Handling of propensity models

So, at the height of the 2010 election madness in Australia I used the Centrebet odds for each seat to model the propensity for various outcomes. This model showed a 30% propensity for delivering a hung parliament, slightly less likely than the predicted propensity for a Labor majority of 45%.

But what did these figures show us? How could they help to guide our actions?

To say "Labor will probably win" before election day is, in fact, an unhelpful statement to make. There is only one election, so even if in 99 out of 100 hypothetical universes Labor would win, it is the results in our universe that matter.

If the Coalition had won then the complex system of Australian Politics would have spun off in a completely different direction, and the 2013 election would have looked completely different.

In environments or systems with a continuous range of possible outcomes, aiming for the average and hedging to cater to a certain level of tolerance on either side makes sense. But in a situation where we have propensities for a set of discrete events (ie they either happen or they don't) we need a different approach.

There are four possible scenarios:

  • we control a splittable system and outcomes can be influenced
  • we control a splittable system and outcomes can't be influenced
  • we control a unified system and outcomes can be influenced
  • we control a unified system and outcomes can't be influenced

"Splittable" in this sense means that the system is self-similar enough to allow us to create a sub-system which can be treated separately. For example, if we owned a mining company, we might treat all our WA projects as if a mining tax were to be imposed, but Queensland might continue all projects as if the tax would be voted down. When a system is splittable, this becomes a hedging option that can be taken to minimise risk no matter what outcome occurs.

"Influence" refers to our ability to influence the propensity of outcomes by modifying our system's actions in some way. In general, the best way to increase the propensity for a particular outcome is reduce variability of either our system or its surrounding environment.

This provides us with four main options for action based on known propensities:

  • Hedge - split our system and make changes "as if" a different future outcome was going to occur in each
  • Buffer - reduce the variability of our system so that any external impacts have as little effect as possible
  • Feedback - continuously monitor and take actions that increase the propensity for a desired outcome
  • Ignore & Accept - if we really cannot split our system, and we cannot influence the outcomes in any way, then knowing the propensities is of no use. All we can do is be aware of and accept the range of possible outcomes.

There are a variety of combinations of these strategies that can be employed. Safe-fail experimentation generally uses a combination of all four techniques.

Back to the election for a moment! For average Joes, the variability of the election environment can't be influenced. The media think they can influence the environment more than they probably can. So in most cases, people simply revert to an Ignore & Accept strategy on the basis that any outcome isn't the worst thing in the world. Some choose to Hedge by voting for one party in the Lower House and another in the Upper!

[As an aside -- the Ignore & Accept strategy also explains the appeal of gambling. When Lotto players buy a ticket, they accept the likely loss of money for the ticket in exchange for the possibility of their future reality including a huge cash windfall.]

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