I assign weights to terminal and instrumental value differently, with instrumental value growing higher for steps that are less removed from producing terminal value and/or for steps that won’t easily backslide/revert without maintenance.
As far as uncertainty goes, my general formula is to focus upon keeping plans composed of “sure bet” steps if the risk of failure is high, but I’ll allow less surefire steps to be attempted if there is more wiggle room in play. This sometimes results in plans that are overly circuitous, but resistant to common points of failure. The success rate of a step is calculated from my relevant experience and practice levels, as well as awareness of any relevant environmental factors. The actual weights were developed through iteration, and are likely specific to my framework.
Here’s a real example of a decision calculation, as requested:
Scenario: I’m driving home from work, and need to pick which restaurant to get dinner from.
Value Categories (a sampling):
Existing Desires: Is there anything I’m already in the mood for, or conversely something I’m not in the mood for?
Diminishing Returns: Have I chosen one or more of the options too recently, or has it been a while since I chose one of the options?
Travel Distance: Is it a short or long diversion from my route home to reach the restaurant(s)?
Price Tag: How pricey or cheap are the food options?
I don’t enjoy driving much, so Travel Distance is usually the highest-ranked Value Category, thoroughly eliminating food options that are too much of a deviation from my route. Next is Existing Desires, then Diminishing Returns, which let me pursue my desires and avoid getting overexposed to things. My finances are generally in a state where Price Tag doesn’t make much difference on location selection, but it will play a more noticeable role when it comes time to figure out my order.
I assign weights to terminal and instrumental value differently, with instrumental value growing higher for steps that are less removed from producing terminal value and/or for steps that won’t easily backslide/revert without maintenance.
As far as uncertainty goes, my general formula is to focus upon keeping plans composed of “sure bet” steps if the risk of failure is high, but I’ll allow less surefire steps to be attempted if there is more wiggle room in play. This sometimes results in plans that are overly circuitous, but resistant to common points of failure. The success rate of a step is calculated from my relevant experience and practice levels, as well as awareness of any relevant environmental factors. The actual weights were developed through iteration, and are likely specific to my framework.
Here’s a real example of a decision calculation, as requested:
Scenario: I’m driving home from work, and need to pick which restaurant to get dinner from.
Value Categories (a sampling):
Existing Desires: Is there anything I’m already in the mood for, or conversely something I’m not in the mood for?
Diminishing Returns: Have I chosen one or more of the options too recently, or has it been a while since I chose one of the options?
Travel Distance: Is it a short or long diversion from my route home to reach the restaurant(s)?
Price Tag: How pricey or cheap are the food options?
I don’t enjoy driving much, so Travel Distance is usually the highest-ranked Value Category, thoroughly eliminating food options that are too much of a deviation from my route. Next is Existing Desires, then Diminishing Returns, which let me pursue my desires and avoid getting overexposed to things. My finances are generally in a state where Price Tag doesn’t make much difference on location selection, but it will play a more noticeable role when it comes time to figure out my order.