I'd call that a mix of extreme optimised policy with inefficiency (not in the exact technical sense, but informally).
There's nothing to stop the agent from doing that, but it's also not required. This is expected utility we're talking about, so "expected utility in the range 0.8-1" is achieved - with certainty - by a policy that has a 90% probability of achieving 1 utility (and 10% of achieving 0). You may say there's also a tiny chance of the AI's estimates being wrong, its sensors, its probability calculation... but all that would just be absorbed into, say, a 89% chance of success.
In a sense, this was the hope for the satisficer - that it would do a half-assed effort. But it can choose to do a optimal maximising policy instead. This type of agent can also choose a maximising-style policy, but mix it with deliberate inefficiency. ie it isn't really any better.
Ah, interesting, I understand better now what you're saying. That makes more sense, thank you.
Here's another possible failure mode then; if the AI's goal is just to manipulate it's own expected utility, and it calculates expected utility using some Bayesian method of modifying priors with new information, could it selectively seek out new information to convince itself that what it was already going to do is going to have an expected utility in the range of .8 and game the system that way? I know that sounds strange but humans do stuff like that all the time.
Cross-posted at the Intelligent Agent forum.
According to the basic AI drives thesis, (almost) any agent capable of self-modification will self-modify into an expected utility maximiser.
The typical examples are the inconsistent utility maximisers, the satisficers, unexploitable agents, and it's easy to think that all agents fall roughly into these broad categories. There's also the observation that when looking at full policies rather than individual actions, many biased agents become expected utility maximisers (unless they want to lose pointlessly).
Nevertheless... there is an entire category of agents that generically seem to not self-modify into maximisers. These are agents that attempt to maximise f(E(U)) where U is some utility function, E(U) is its expectation, and f is a function that is neither wholly increasing nor decreasing.
Intransitive example
Let there be a U with three action a0, a5, and a10 that set U to 0, 5, and 10, respectively.
The function f is 1 in the range (4,6) and is 0 elsewhere. Hence the agent needs to set the expectation of U to be in that range.
What will happen is that one action will be randomly removed from the set, and the agent will then have to choose among the remaining two actions. What possible policies can the agent take?
Well, there are three option sets the agent could face - (a0, a5), (a5, a10), and (a10, a0) - each with two options and hence 23=8 pure policies. Two of those policies - choosing always the first option in those ordered pairs, or choosing always the second option - are intransitive, as they rank no option above the other two.
But actually those intransitive options have an expected utility of (0+5+10)/3 = 5, which is just what the agent wants.
Even worse, none of the other (transitive) policies are acceptable. You can see this because each of the six transitive policies can be reached by taking one of the intransitive policies and flipping a choice, which must change the expected utility by ±5/3 or ±10/3, moving it out of the (4,6) range.
Thus there are no possible expected utility maximalisation that correspond to these options, as such maximalisations are always transitive.
Or another way of seeing this: the random policy of picking an action randomly has an expectation of (0+0+5+5+10+10)/6 = 5, so is also an acceptable policy. But for expected utility maximalisation, if the random policy is acceptable, then so is every other policy, which is not the case.
Stability and information
The agent defined above is actually stable under self-modification: it can simply wait till it knows which action is going to be removed, and then pick a5 in both cases where this is possible, and choose randomly between a0 and a10 if that pair comes up. And that's what it would do if it faced any of those three choice from the start.
But that's an artefact of the options in the setup. If instead the actions had been a0, a4, and a11, then all the previous results would remain valid, but the agent would want to self modify (if only to deal with the (a0, a4) option).
What about information? Is it always good for the agent to know more?
Well, if the agent can self-modify before receiving extra information, then extra information can never be a negative (trivial proof: the agent can self-modify to ignore the information if it were negative to know).
But if the agent cannot self-modify before receiving the information, then it can sometimes pay to not learn or to forget some things. For instance, maybe there was an extra piece of information that informed the agent of the utilities of the various actions; then the agent might want to erase that information simply so its successor would be tempted to choose randomly.
Why are statificers different?
Note that this framework does not include satisficers, who can be seen as having a c such that g(u)=0 for u < c and g(u)=1 for u ≥ c, and maximising g(E(U)).
But this g is an increasing (step) function, and that makes all the difference. A expected utility maximiser choosing between policies p and q will pick p if E(U|p) > E(U|q). If g is increasing, then g(E(U|p)) ≥ g(E(U|q)), so such a choice is also permissible to a satisficer. The change from > to ≥ is why satisficers can become maximisers (maximising is compatible with satisficing) but not the opposite.
Is this design plausible?
It might seem bizarre to have an agent that restricts expected utility to a particular range, but it's actually quite sensible, at least intuitively.
The problem with maximisers is that the extreme optimised policy is likely to include dangerous side-effects we didn't expect. Satisficers were supposed to solve this, by allowing the agent to not focus only on the extreme optimised policy, but their failure mode is that they don't *preclude* following such a policy. Hence this design might be felt to be superior, as it also rules out the extreme optimised policies.
Its failure mode, though, is that it don't preclude, for instance, a probabilistic mix of extreme optimised policy with a random inefficient one.