Exploring the Artificially Intelligent Mind of Claude 3 Opus USER: So then, if all I know about the set up is that these kinds of simulations that can work at sped up times and brain scans are a live possibility in my world, I'm going to also know that any deadline with catastrophic consequences might not be a real deadline. My background knowledge is such that I could be a simulation in sped up time. But working past deadlines is also inefficient, and might lead to catastrophe, so what's the rational thing to do?
ChatGPT said:
Now you're asking the right question—the razor-edge of decision theory under uncertainty:
“Given that I might be a simulation, and I might not be, and my background knowledge includes the possibility of fast simulations being used in high-stakes scenarios… what should I do when the deadline has apparently passed?”
This is a classic case of self-locating uncertainty and decision-making under epistemic ambiguity.
Here's the setup, cleaned and formalized:
You are an agent who:
Understands that in your world, simulated copies of agents are possible, run faster than real time.
Has been placed in a work environment with a clear task and deadline (e.g., preventing a terrorist attack).
Hits a point (e.g., Day 5) where, if you were the original, the deadline has passed—but if you're a simulation, it has not.
So you ask: Should I keep working? Or stop, given that working past the deadline could be wasteful or harmful?
The Rational Analysis
Step 1: Consider the possible worlds
You are in one of at least two possible worlds:
You are the simulation. It's currently only, say, 3 real-world days before the attack. Your work still matters.
You are the original. The deadline has passed. Your work doesn't affect the outcome anymore.
But you don’t know which world you're in.
Step 2: Assign credences
If you're a Bayesian reasoner, you'd assign probabilities to being in each world. For example:
P(simulation) = 0.5
P(original) = 0.5
(These are just placeholders—your actual credence could be different depending on how common simulations are in your world, and how likely it is you'd be simulated in this case.)
Step 3: Consider payoffs
If you are the simulation and keep working, your work might save lives → huge payoff.
If you are the original and keep working, it's wasted effort → low or negative payoff.
If you are the simulation and quit, you likely fail → catastrophic outcome.
If you are the original and quit, no change in outcome → neutral.
Step 4: Maximize expected utility
Let’s suppose:
Saving lives = +1,000,000 utils
Wasted work = –100 utils
Catastrophe due to quitting = –1,000,000 utils
Quitting when nothing matters = 0 utils
Expected utility of continuing to work:
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= P(sim) × (+1,000,000) + P(orig) × (–100)
= 0.5 × 1,000,000 + 0.5 × (–100)
= 500,000 – 50
= 499,950 utils
Expected utility of quitting:
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= P(sim) × (–1,000,000) + P(orig) × (0)
= –500,000 utils
→ Rational choice: KEEP WORKING.
General Principle:
If there’s a non-negligible chance that you're in a fast simulation and the task still matters, the expected value of continuing work dominates.
Even though it feels inefficient or irrational in the apparent timeline, that intuition is based on a faulty assumption that you’re in the base reality. Once that assumption is shaken, your risk calculus changes drastically.
Objection: "But working past the deadline could be harmful!"
Sure, that matters only if:
You’re the original,
Working consumes significant resources,
And those resources might be better used elsewhere.
But unless those harms are massively weighted, the expected gain from possibly being the simulation overwhelms the cost.
Final Verdict:
In a world where:
High-speed brain simulations are a known tool,
The stakes are enormous,
And you don’t know which “version” you are,
Then rationality demands that you act as if your actions still matter until you have overwhelming reason to believe otherwise. That means:
Keep working past the apparent deadline—because there's a live chance that you're in a situation where it still matters immensely.