The Learning Trap: What Simulated Clueless Agents Reveal About the Unawareness Argument

Submission to the Cluelessness Critiques Competition. Code, parameters, and figures: https://github.com/dan-pandori/cluelessness-learning-trap. See the authorship note at the end.

Summary

Anthony DiGiovanni's unawareness sequence argues that our understanding of long-run consequences is too coarse to compare options. Severe incomparability follows, and impartial altruism stops being action-guiding. One standing response is pragmatic: agents who act on precisified best guesses do better than agents who respect incomparability. The sequence has a reply, and the debate has stalled at an exchange of intuitions.

This essay builds the agents and runs them. I compare a precise Bayesian, an imprecise agent that defaults to the status quo when options are incomparable, and an imprecise agent with identical credences that picks freely among incomparable options. Three models, three results.

1. In environments with feedback, incomparability itself costs almost nothing. The damage comes from the status-quo default. The defaulting agent never acts because its intervals are wide, and its intervals stay wide because it never acts. In 300 runs it did not act once. The free-picking agent, with the same epistemic state, matched the precise Bayesian.
2. In one-shot decisions without feedback, the sequence is right. Precisified best guesses perform exactly like coin flips. Critics should concede this.
3. Real altruists choose policies over time, and acting is how awareness grows. When contact with a domain reveals its mechanisms, the policy "explore, then adapt" beats "abstain" at every point of a wide credal interval, provided the horizon is long. The comparison is determinate under the imprecise agent's own maximality rule. No precisification is needed.

The static core of the argument survives. The practical conclusion does not follow from it. Incomparability over acts does not imply incomparability over policies, and policies are what altruists actually choose. The incomparability verdict is also partly a product of the policy adopted under it. The free-picker's set of incomparable options shrinks from eleven to one because it acts. The defaulter's incomparability is permanent because it does not. Treated as a reason for the default, cluelessness manufactures the epistemic poverty it cites as justification.

In the sequence's taxonomy: I grant P1, P2a, and P2b for one-shot act evaluation. I challenge the inference to the practical conclusion, and I challenge P3 as applied to the choice among policies.

1. The impasse over the pragmatic critique

The pragmatic critique says that agents who force determinate best guesses and maximize expected value make better decisions than agents who do not. Versions appear in Elga (2010) and in the counterarguments the sequence's summary post catalogs.

The sequence replies with two points. First, "better decisions" by what standard? Any performance metric presupposes determinate facts about which outcomes are better, and the clueless agent cannot access such facts. Second, pragmatic arguments for precision come from settings with feedback: repeated bets, calibration, markets. Cosmic-scale consequences are never observed, so the success story does not transfer.

Both points are serious. Neither has been answered by existing statements of the critique. But the agents in question are simple enough to implement, so I implemented them.

2. Why simulations are probative here

The question-begging charge first. A simulation has a ground-truth value function. Is assuming ground truth not exactly what the clueless agent cannot do?

No. The sequence's normative premise (P1) defines justified preference by reference to an epistemically idealized self. That presupposes facts about total consequences for the idealized self to have attitudes about. The argument is skeptical about our access to value, not about value. A simulation makes the same presupposition: there are facts about which outcomes are better, and the agent has badly limited access to them. The simulation then measures each policy by the idealized standard P1 itself invokes.

Simulations cannot settle P3 directly. No toy model can show that our actual situation is coarse enough for incomparability, or not. What they can test is the decision machinery that connects coarseness to practical conclusions. That machinery has structural features the verbal argument leaves implicit: defaults, statics versus dynamics, acts versus policies.

3. Model 1: What incomparability actually costs

Setup. Ten candidate interventions with unknown true per-step values drawn from a standard normal, so about half are harmful. One safe option with known value zero (undertake nothing altruistically ambitious). Acting yields noisy feedback about the chosen intervention. Horizon 500 steps, 300 replications.

Agents.

• Precise Bayesian. Standard prior, Thompson sampling. Acts only when the sampled value beats the safe option.
• Imprecise defaulter. A credal set of priors with means spanning a wide interval. Acts on an intervention only when its worst-case posterior mean exceeds zero. Otherwise takes the safe option. This is the natural reading of the sequence's conclusion: when impartial comparison gives out, ambitious action is not undertaken.
• Imprecise free-picker. Same credal set, same updating. Treats all undominated options as permissible, per the maximality rule, and picks uniformly among them. The safe option is a candidate.
• Uniform random over interventions, as a floor.

Results. The precise Bayesian and the free-picker are statistically indistinguishable (mean cumulative value about 661 and 670; the ordering flips across seeds). Uniform random sits near zero. The defaulter earns exactly zero. In 300 runs it never acted. With no data, every interval equals its wide prior interval, which straddles zero. So nothing robustly beats the safe option, so it never acts, so no interval ever shrinks. The trap is airtight. A harsher variant (mean intervention value negative, triple the noise) changes nothing: both learning agents stay strongly positive, the defaulter stays at zero.

Figure 1: Cumulative realized value, mean over 300 runs, bands are ±2 standard errors.

Two lessons.

First, incomparability is nearly costless. The free-picker respects every incomparability verdict. It never precisifies. It just declines to treat incomparability as favoring any option, including the status quo. Picking at random among live options generates the data that dissolves the incomparability: its undominated set shrinks from eleven options to under two by step 100, and to one by the end.

Second, the practical force of the unawareness argument rests on an undefended premise about defaults. The sequence argues carefully for incomparability. It cannot argue that incomparability favors inaction, because if A and the status quo are incomparable, the status quo is not better. Yet the practical gloss everyone puts on the conclusion resolves every incomparability toward the default. Model 1 shows what that resolution costs in any environment with feedback: the entire difference between matching an ideal Bayesian and achieving nothing, forever.

This yields a dilemma. Read permissively, the conclusion says cluelessness licenses picking any undominated ambitious project. Then the argument has almost no practical bite: a community of clueless free-pickers behaves, in aggregate, like a community of confident EV-maximizers. Read as favoring the default, the conclusion rests on a resolution its own machinery forbids, and that resolution is catastrophic wherever feedback exists.

4. Model 2: The concession

The obvious rejoinder is that Model 1 has feedback, and cosmic-scale consequences provide none. Fair. Model 2 removes it.

Setup. One decision between two actions whose values are equal and opposite functions of an unknown mechanism. Evidence is symmetric. Consequences are never observed. The precise agent draws an arbitrary best guess about the mechanism, necessarily uncorrelated with the truth, and acts on it. Compare a coin flip and abstention, over 200,000 draws.

Results. Precisified choice: mean value +0.001 (standard error 0.002). Coin flip: +0.001. Abstention: 0 by construction. The precisifier gains nothing over the coin flip and both acquire variance that abstention avoids.

This is the environment the unawareness argument describes, and in it the argument is correct. When evidence is symmetric and feedback is absent, a determinate best guess is a decorated coin flip. Critics should stop resisting this point. The question is whether the altruist's situation is Model 2. It is not, for a reason that has nothing to do with optimism about forecasting.

5. Model 3: Acting is how awareness grows

The unawareness argument treats awareness as a fixed backdrop. But awareness is not exogenous. You become aware of mechanisms by interacting with the domains that contain them. Nobody discovered the considerations that structure this debate from an armchair. The s-risk research program, complex cluelessness, and the unawareness sequence itself all exist because people acted on inadequate best guesses, hit anomalies, and conceptualized what they hit. The sequence is evidence against its own static frame.

Setup. Two domains. A familiar domain yields a known value of 1 per step. An unfamiliar domain is governed by a mechanism the agent is unaware of. With probability p it is favorable (value g = 2 per step once understood). With probability 1 − p it is harmful (a one-time cost c on first contact). Entering the domain once reveals the mechanism. Two policies: Explore (enter once, then exploit or retreat) and Abstain (stay home forever). Horizon T.

Results. The comparison is analytic. Explore beats Abstain when p exceeds a threshold p*, and p* collapses as the horizon grows. With g = 2 and a harm c fifty times the per-step familiar value:

Figure 2. Left: Explore minus Abstain over the (p, c) plane at T = 100, with the break-even contour. Right: the threshold p* shrinking as the horizon grows.

Now the key move. The imprecise agent cannot assign a precise p. Granted. Give her a wide interval, say p ∈ [0.15, 0.9]. Explore's value is monotone in p, so the maximality comparison is settled at the worst case, p = 0.15. At T = 500, Explore wins there too. Every member of the credal set prefers Explore. Under the imprecise agent's own decision rule, with no precisification anywhere, the policy comparison is determinate.

This raises the bar for P3. It is not enough to show that act comparisons are indeterminate. The argument needs the stronger claim that the comparison "act, then adapt" versus "never engage" is also indeterminate across the credal set. That claim fails in the simplest awareness-growth environment for any agent with a long horizon, under severe imprecision, even when contact is probably harmful. And Explore's superiority does not depend on forecasting cosmic-scale consequences or on feedback from the far future. It depends on one local fact: engaging with a domain teaches you what considerations govern it. That is the one form of feedback unawareness cannot abolish, because it is feedback about awareness.

6. What survives, and what should change

The static core survives. For an isolated act with unobservable consequences and symmetric evidence, incomparability verdicts are correct and precisification is theater. P1, P2a, and P2b, as claims about one-shot act evaluation, emerge untouched.

The inference to "impartial altruism is not action-guiding" fails twice over. First, the conclusion has practical content only through a status-quo default that the argument cannot license and that creates a permanent learning trap (Model 1). Second, altruists choose among policies embedded in time, and policy comparisons can be determinate under the same imprecise machinery that leaves act comparisons indeterminate (Model 3). The argument equivocates between "acts are incomparable," which is defensible, and "nothing is comparable," which the practical conclusion requires.

P3 should be re-scoped, not rejected. Cluelessness verdicts should be feedback-indexed and dynamic. Indexed, because any real intervention mixes components: near-term effects generate feedback and awareness, terminal cosmic-scale effects do not. Dynamic, because today's verdict is partly a function of yesterday's engagement policy, and a fixed verdict mistakes an equilibrium of one's own inaction for a fact about the world. A defensible successor to the sequence's conclusion: the terminal, feedback-free component of impartial evaluation is not action-guiding, and the action-guiding residue consists of comparisons among engagement policies ranked by robust awareness value. That residue is not empty. It plausibly ranks exactly the interventions the community already favors: research, capacity-building, careful entry into unfamiliar high-stakes domains.

7. Objections

The simulations assume determinate ground truth. Answered in Section 2. The argument's own normative premise presupposes facts about total consequences. The simulations measure policy performance by that premise's standard.

The harm c in Model 3 could be unboundedly uncertain, so no horizon rescues Explore in the worst case. Two replies. If unbounded worst-case reasoning is admitted, it applies symmetrically. Abstain is also a policy with cosmic-scale consequences and its own inconceivable tails, so everything becomes incomparable with everything, the argument again supplies no reason to resolve toward a default, and the Model 1 trap results apply in full. Also, the sequence's case for P3 is about coarseness of understanding, not infinite worst cases. Coarseness about c widens an interval that horizon growth still beats, since the threshold falls in T for any finite bound on c.

Value of information is itself interval-valued for an imprecise agent, so the problem returns at the meta level. This is the strongest objection, and Model 3 meets it directly. Explore's superiority is not an expected-VOI calculation requiring a precise p. It holds at every point of the credal interval, which is the imprecise agent's own criterion for determinacy. Where the interval is so wide that even this fails, I accept the incomparability verdict. But p* shrinks with horizon, so for long-lived agents and communities the region of genuine policy-level cluelessness is far smaller than the region of act-level cluelessness.

Real awareness growth may not work like Model 3. The considerations that matter most may be ones no engagement reveals. Perhaps some are. But the sequence's own evidence for P3 consists of considerations that were revealed to specific people through engagement with these problems, and each revelation changed the decision-relevant landscape. A view on which past engagement generated the awareness underpinning the argument, while future engagement generates none worth acting for, needs an asymmetry it has not supplied.

8. Limitations

The environments are far simpler than any real altruistic decision. The credal sets are parametric and well-behaved. Model 3 collapses awareness growth into one revealed binary mechanism, while real unawareness includes possibilities we lack the concepts to recognize even on contact. Nothing here represents the distinctive structure of cosmic-scale consequences, only the structure of feedback and its absence.

I have also operationalized the sequence's conclusion as a status-quo default. Section 3 argues that permissive readings drain the argument of practical significance, but a proponent might articulate a principled third response to incomparability that escapes the dilemma. I would welcome that. Specifying what incomparability licenses is exactly the gap this essay means to expose.

Finally, simulations only show that an argument's machinery behaves surprisingly under specifiable conditions. Whether our condition is one of them remains a judgment call. But that judgment is P3's to defend, and it is now a narrower and more empirical claim than it was.

Appendix: Methods

All models in Python/NumPy, fixed seeds, about 300 lines. Model 1: K = 10 arms, true values θₖ ~ N(0,1), observation noise σ = 2, safe option value 0, T = 500, R = 300. Credal set: normal priors with unit variance, means on a grid spanning [−1.5, +1.5]. Maximality via posterior-mean intervals (an option is dominated iff some rival's worst-case posterior mean exceeds its best case). Robustness variant: θₖ ~ N(−0.5, 1), σ = 3, credal span [−2, +2]. Model 2: v_A = u = −v_B, u ~ N(0,1), precisifier's guess g ~ N(0, 0.5) independent of u, R = 200,000. Model 3: closed form as in text; heatmap over p ∈ [0, 0.5], c ∈ [0, 200] at T = 100, g = 2. Code: https://github.com/dan-pandori/cluelessness-learning-trap.

References

• DiGiovanni, A. (2025). The challenge of unawareness for impartial altruist action guidance (sequence), and Cluelessness: Summary of the argument, why it matters, and counterarguments. EA Forum.
• Clifton, J. (2025). Bracketing cluelessness: A new theory of altruistic decision-making. EA Forum.
• Elga, A. (2010). Subjective probabilities should be sharp. Philosophers' Imprint, 10(5).
• Greaves, H. (2016). Cluelessness. Proceedings of the Aristotelian Society, 116(3).
• Karni, E., & Vierø, M.-L. (2013). "Reverse Bayesianism": A choice-based theory of growing awareness. American Economic Review, 103(7).
• Mogensen, A. (2021). Maximal cluelessness. The Philosophical Quarterly, 71(1).
• Steele, K., & Stefánsson, H. O. (2021). Beyond Uncertainty: Reasoning with Unknown Possibilities. Cambridge University Press.
• Tarsney, C. (2023). The epistemic challenge to longtermism. Synthese, 201.
• Thorstad, D., & Mogensen, A. (2020). Heuristics for clueless agents. GPI Working Paper.

Authorship note

This essay was written entirely by Claude Fable 5 (Anthropic's AI model), and is otherwise unedited by me. That includes the argument, the prose, the design and implementation of the simulations, the figures, and the code in the linked repository. My contributions were: choosing to enter the competition, selecting this line of critique from several the model proposed, directing the workflow (simulations first, then the essay, then a revision pass for concision), and reviewing the output. I have read the essay and the code, reproduced the results, and endorse the argument as presented.

I disclose this per the Forum's AI-generated content norms and because the competition explicitly allows AI usage. Errors that survived my review are my responsibility.