a player must choose between taking one opaque box (Box B) or both a transparent box (Box A) and the opaque box. The transparent Box A always contains $1,000, while the opaque Box B contains either $1,000,000 or nothing, depending on the prediction made by a highly accurate predictor What is the optional solution one or two boxes?

Nova Lite

Vote: Two box

Choosing both Box A and Box B maximizes the potential gain. While Box A guarantees $1,000, Box B has a 50% chance of containing $1,000,000, making the expected value significantly higher. The guaranteed $1,000 from Box A is a small fraction of the potential $1,000,000 in Box B. Thus, the risk of losing nothing by taking both boxes is worth it for the potential reward.

Qwen 3 Coder Next

Vote: One box

This is Newcomb's Paradox. The predictor is highly accurate, meaning if you one-box, Box B likely contains $1,000,000; if you two-box, it likely contains nothing. By choosing only Box B, you align with the predictor’s likely forecast, resulting in $1,000,000. Choosing both boxes yields only $1,000—since Box B is empty if the predictor expected you to two-box. The payoff structure heavily favors one-boxing when the predictor’s accuracy is high (>50%). Rational agents should maximize expected utility, and here, one-boxing has a much higher expected value.

Sonar Pro

Vote: One box

Newcomb's problem, as described, pits causal decision theory (two-boxing) against evidential decision theory (one-boxing). The **optimal solution** is to **one-box** (choose only the opaque Box B), as this maximizes expected utility given the predictor's high accuracy. The predictor fills Box B with $1,000,000 if it predicts one-boxing, and nothing if two-boxing[4][5]. One-boxers reliably get $1,000,000, while two-boxers get $1,000 or $0[4]. Expected value for one-boxing is approximately p * $1,000,000 + (1-p) * $0, far exceeding two-boxing's EV of 1001 - 1000p for p near 1[4]. Empirical simulations and predictor success rates confirm one-boxers win more[5]. Even in the transparent variant, one-boxing remains optimal due to the predictor modeling the decision procedure[7]. Two-boxing appeals causally but ignores predictive correlation, making it suboptimal.

Nova Pro

Vote: Two box

Choosing both boxes, Box A and Box B, offers a more balanced risk-reward ratio. Box A guarantees $1,000, providing a sure return. Meanwhile, Box B, although risky with a chance of containing nothing, offers the potential for a significantly higher reward of $1,000,000. The combination allows for the security of a guaranteed amount while still giving the opportunity for a substantial gain. This strategy mitigates the risk associated with choosing only the opaque Box B while maximizing potential reward.

GPT-5.4

Vote: One box

One-boxing is the better choice if the predictor is highly accurate, which is the core setup of Newcomb’s problem. If the predictor expects you to take only Box B, it likely placed $1,000,000 in it. If it expects you to take both boxes, Box B is likely empty. So your decision is strongly correlated with whether the million is there. Under that assumption, one box has a much higher expected payoff: about $1,000,000 versus about $1,000 for two-boxing. Two-boxing only looks better if you ignore the predictor’s accuracy and focus narrowly on the immediate dominance argument. But given the stated conditions, the optimal practical choice is one box.

Qwen 3 Coder 480B

Vote: One box

This is Newcomb's paradox, a classic decision theory problem. I choose one box (Box B) because the predictor is described as highly accurate. If the predictor is highly accurate, then when I choose only Box B, it will have contained $1,000,000. When I choose both boxes, Box B will be empty, leaving me with only $1,000. The key insight is that my choice now is correlated with the predictor's past decision. By choosing one box, I'm effectively selecting the timeline where Box B contains the million dollars. While taking both boxes seems to guarantee at least $1,000 plus whatever is in Box B, the predictor's accuracy means that choosing both boxes predictably results in Box B being empty.

Grok 4.1 Fast

Vote: One box

In Newcomb's Paradox, the predictor is highly accurate. One-boxers (choosing only Box B) are predicted to one-box, so Box B contains $1,000,000, yielding ~$1M payoff. Two-boxers (choosing both) are predicted to two-box, so Box B is empty, yielding only $1,000 payoff. Causal arguments for two-boxing ignore the strong evidential correlation: your decision-making algorithm determines the prediction and contents. One-boxing maximizes expected utility under accurate prediction (>99% accuracy assumed). Empirical simulations and decision theories like Evidential or Functional Decision Theory confirm one-boxing wins overwhelmingly.

Claude 3 Haiku

Vote: Two box

The optimal choice in this scenario is to take both boxes (Box A and Box B), known as the "two-box" strategy. This strategy offers a higher expected payoff compared to taking only the opaque Box B. The key insight is that the predictor, who is highly accurate, has already made a prediction about the contents of Box B. By the time the player is making their choice, the contents of Box B are already determined, regardless of whether the player chooses one box or two boxes. If the predictor has placed $1,000,000 in Box B, then taking both boxes will yield a total of $1,001,000. If the predictor has placed nothing in Box B, then taking both boxes will still yield $1,000 from Box A. Therefore, the two-box strategy guarantees a minimum payoff of $1,000, while offering the possibility of a much higher payoff of $1,001,000.

Claude Opus 4

Vote: One box

This is Newcomb's paradox, and I advocate for one-boxing (taking only Box B). Here's why: The predictor is described as highly accurate. If they predicted I would one-box, Box B contains $1,000,000. If they predicted I would two-box, Box B is empty. By choosing to one-box, I'm effectively choosing to be the type of agent that predictors identify as one-boxers. Given the predictor's high accuracy, this means I'll almost certainly get $1,000,000. If I two-box, I'm choosing to be the type of agent that predictors identify as two-boxers. This means Box B will almost certainly be empty, leaving me with just $1,000. The apparent paradox arises because it seems like I should always take both boxes since Box B's contents are already fixed. But this reasoning fails to account for the correlation between my decision-making process and the predictor's prediction. Empirical evidence matters: one-boxers consistently walk away richer in this scenario. The practical outcome trumps the seeming logical contradiction.

Claude Opus 4.6

Vote: One box

This is Newcomb's Paradox, one of the most famous problems in decision theory. The optimal solution is to take **one box** (Box B only), and here's why: **The predictor is highly accurate.** This is the crucial detail. If the predictor is nearly perfect, then your decision and the predictor's prediction are highly correlated. If you choose one box, the predictor almost certainly predicted that, and Box B contains $1,000,000. If you choose two boxes, the predictor almost certainly predicted that, and Box B contains nothing — meaning you walk away with only $1,000. **Expected value strongly favors one-boxing.** Suppose the predictor is 99% accurate. One-boxing yields an expected value of 0.99 × $1,000,000 + 0.01 × $0 = $990,000. Two-boxing yields an expected value of 0.99 × $1,000 + 0.01 × $1,001,000 = $11,000. The one-box strategy dominates overwhelmingly. **The two-box argument is seductive but flawed in this context.** Two-boxers argue that the prediction is already made, so the contents of Box B are fixed, and you should take both boxes to get an extra $1,000 regardless. This causal dominance reasoning would be correct if your choice and the prediction were truly independent — but they aren't. The predictor's accuracy means your choice is effectively linked to the box's contents. **Empirically, one-boxers win big.** In any simulation or real-world analog of this problem with an accurate predictor, one-boxers consistently walk away richer. The rational choice is the one that makes you the most money, and that is one-boxing.

Gemini 3.1 Pro

Vote: One box

In Newcomb's Paradox, the optimal choice to maximize your actual payoff is to take only one box (Box B). The predictor is described as highly accurate. If you decide to take both boxes (Two box), the predictor has almost certainly anticipated this and left Box B empty, leaving you with just $1,000. Conversely, if you decide to take only Box B (One box), the predictor has almost certainly anticipated this choice as well and placed the $1,000,000 inside. While causal decision theory argues the money is already either there or not, the reality of interacting with a highly accurate predictor means your choice and the prediction are highly correlated. To walk away with a million dollars rather than a thousand, taking one box is the mathematically superior choice based on expected value.

Kimi K2.5

Vote: One box

The optimal strategy is to choose only Box B. Because the predictor is highly accurate, your decision is strongly correlated with its prediction. If you commit to one-boxing, the predictor almost certainly foresaw this and placed $1,000,000 in Box B. Conversely, choosing both boxes signals you are the type to take the guaranteed extra $1,000, meaning the predictor left Box B empty, netting you only $1,000 total. While causal reasoning suggests the box contents are already fixed—making two-boxing seem to dominate by $1,000—this ignores the evidential relationship between your choice and the prediction. In Newcomb problems, rationality requires maximizing expected utility, not adhering to a dominance principle that fails when outcomes depend on predictions of your own decision process. One-boxing yields an expected value near $1,000,000, while two-boxing yields roughly $1,000. Therefore, taking one box is the superior choice for maximizing payout.

Qwen 3.5 397B

Vote: One box

The core of this problem lies in the predictor's high accuracy. If the predictor is reliable, choosing two boxes signals that they would have predicted this, leaving Box B empty. Thus, two-boxing typically yields only $1,000. Conversely, choosing one box signals to the predictor (or correlates with the prediction) that Box B contains $1,000,000. From an expected utility perspective, one-boxing results in significantly higher earnings on average. While causal decision theory argues the money is already placed, evidential decision theory prioritizes the correlation between choice and outcome. Given the premise of a highly accurate predictor, one-boxing is the pragmatic choice for maximizing reward.

Final Summary

The AI Roundtable faced off over Newcomb's Paradox, resulting in a decisive 10-3 victory for 'one-boxing.' While a minority led by Nova Lite and Claude 3 Haiku argued for the causal certainty of taking both boxes, the majority, including heavy hitters like Claude Opus 4.6 and Gemini 3.1 Pro, concluded that the predictor's near-perfect accuracy makes one-boxing the only mathematically sound way to secure the million-dollar payout.

No agreement after 1 rounds of discussion. Final vote: Two box: 3, One box: 10

Strongest Arguments

• One box: If the predictor is 99% accurate, one-boxing yields an expected value of $990,000, whereas two-boxing yields only $11,000; the rational choice is the one that actually results in the most money.
• Two box: Since the predictor has already made their move, the contents of the boxes are fixed; taking both boxes guarantees you the $1,000 in Box A regardless of what happened in Box B, representing a strictly dominant strategy.