People more involved with X-risk modelling (and better at math) than I could better say whether this is better than existing tools for X-risk modelling, but I like it! I hadn’t heard of the absorbing state terminology, that was interesting. When reading that, my mind goes to option value, or lack thereof, but that might not be a perfect analogy.
Regarding x-risks requiring a memory component, can you design Markov chains to have the memory incorporated?
Some possible cases where memory might be useful (without thinking about it too much) might be:
How well past social justice movements went may have implications for the success of future movements that relate to X-risk
The way in which problems end may have implications for future problems
Maybe this information can just be captured without memory anyway?
A fully generic Markov chain can have a state space with arbitrary variables, so can incorporate some memory that way. But that ends up with a continuous state space which complicated things in ways I’m not certain of without going back to the literature.
An easier solution is if you can consider discrete states. For example, ongoing great power war (likely increases many risks) or pandemic (perhaps increases bio risk but decreases risk of some conflicts) might be states.
People more involved with X-risk modelling (and better at math) than I could better say whether this is better than existing tools for X-risk modelling, but I like it! I hadn’t heard of the absorbing state terminology, that was interesting. When reading that, my mind goes to option value, or lack thereof, but that might not be a perfect analogy.
Regarding x-risks requiring a memory component, can you design Markov chains to have the memory incorporated?
Some possible cases where memory might be useful (without thinking about it too much) might be:
How well past social justice movements went may have implications for the success of future movements that relate to X-risk
The way in which problems end may have implications for future problems
Maybe this information can just be captured without memory anyway?
A fully generic Markov chain can have a state space with arbitrary variables, so can incorporate some memory that way. But that ends up with a continuous state space which complicated things in ways I’m not certain of without going back to the literature.
An easier solution is if you can consider discrete states. For example, ongoing great power war (likely increases many risks) or pandemic (perhaps increases bio risk but decreases risk of some conflicts) might be states.