One classic story about Newcomb’s problem is that, at least initially, people one-box and two-box in roughly equal numbers (and that everyone is confident in their position). To find out whether this is true or what exact percentage of people would one-box I conducted a meta-survey of existing polls of people’s opinion on Newcomb’s problem.
The surveys I found are listed in the following table:
I deliberately included even surveys with tiny sample sizes to test whether the results from the larger sample size surveys are robust or whether they depend on the specifics of how they obtained the data. For example, the description of Newcomb’s problem in the Guardian survey contained a paragraph on why one should one-box (written by Arif Ahmed, author of Evidence, Decision and Causality) and a paragraph on why one should two-box (by David Edmonds). Perhaps the persuasiveness of these arguments influenced the result of the survey?
Looking at all the polls together, it seems that the picture is at least somewhat consistent. The two largest surveys of non-professionals both give one-boxing almost the same small edge. The other results diverge more, but some can be easily explained. For example, decision theory is a commonly discussed topic on LessWrong with some of the opinion leaders of the community (including founder Eliezer Yudkowsky) endorsing one-boxing. It is therefore not surprising that opinions on LessWrong have converged more than elsewhere. Considering the low sample sizes, the other smaller surveys of non-professionals also seem reasonably consistent with the impression that one-boxing is only slightly more common than two-boxing.
The surveys also show that, as has often been remarked on, there exists a significant difference between opinion among the general population / “amateur philosophers” and professional philosophers / decision theorists (though the consensus among decision theorists is not nearly as strong as on LessWrong).
Acknowledgment: This work was funded by the Foundational Research Institute (now the Center on Long-Term Risk).