Snakes

Simulations in R to explore the evolutionary advantage of an ambiguity processor distinct from and often competing with a risk processor based on frequencies.

I was thinking about the snake

simulation. Right now, if I correctly understand the simulations, the

agents (the foragers) face snakes with fixed nutritional value and

variable (binary) status (poisonous vs. not). The agents then

probabilistically choose to eat feed or flee. In the simulations you

sent me that's about all there is, but I recall Jack discussing a

version where some agents included a probabilistic representation of

their uncertainty about the poison/not state of snakes a other agents

worst-case representation. However, my understanding was that the

agents uncertainty was always about this boolean poisonous/not

proposition. (Again, this is all pieced together from very old

discussions, so I may be off).

I was thinking about the case in which the uncertainty was instead

about a continuous variable (e.g., how poisonous a snake is) and in

which actions (feed or flee) was a function of expectations of those

actions (e.g., expectation about how poisonous snakes are and the

agent's current level of fitness). Would that "fix" the time scale

issue? Agents with probabilistic uncertainty would need full PDFs

rather than single probabilities. When encountering extreme but rare

values (the one super-poisonous snake), the probabilistic agents would

note such extreme values as possible but unlikely whereas the

worst-case agents would essentially "forget" all less-poisonous

snakes. If the world suddenly and unexpectedly got much, much worse,

the worst-case agents would have a chance to cope whereas the

probabilistic agents would try to learn about the new, shifting

distribution of poison level, but presumably die in the process.

The basic difference picks up on something that we have run into in

other contexts before: single observations of a discrete/binary

variable provides very little information about the probabilistic

distribution over those discrete/binary values (particularly about the

tails). For example, observing 1 poisonous snake tells you that

P(poisonous) cannot be 0, but not a whole lot more than that. In

contrast, observing a highly poisonous snake tells you that such

things exist and, if that's all you care about, that may be enough.

Like the loss aversion story, as long as things are going well, such

overly conservative attitudes may be beneficial.

Anyway, I'm not sure that makes a lot of sense. It may be the kind of

thing that is more easily explained in person, but I thought I would

write it down while I still remembered it. If I have some time

(unlikely), I may try to code up a little simulation to at least

formalize things.

C

Maybe the snake business could be seen as a matter of the advantages of instinct vs learning in different situations. There must be some biological theory about when it is better to decide on instinct vs decide on experience. Maybe I will try to see if there is anything relevant.

Jack