The Catastrophic Consequences of Agnosticism for Life Searches and a Possible Workaround
```json
{
"titleZh": "对生命探索的怀疑论可能带来的灾难性后果及可能的解决方案",
"summaryZh": "研究表明，在探索生命（包括生物生命和技术生命）时，由于我们无法确定未知的信息，存在产生误判的风险。
采取最谨慎的怀疑论方法，对生命存在的可能性以及潜在的干扰因素都应采用不带信息先验概率（diffuse priors）。
采用这种方法，生命探测所需的样本量将变得巨大，至少需要 ${\sim}10^4$ 个目标，甚至可能高达 ${\sim}10^{13}$ 个，才能获得对生命存在的“强有力证据”。
为了解决这个问题，研究提出了一种新的方案：将样本划分为两组，两组的生命存在率不同，但干扰因素的发生率是全局性的，这样理论上可以显著提高探测生命的可能性。
"
}
```
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Abstract:Planned and ongoing searches for life, both biological and technological, confront an epistemic barrier concerning false positives - namely, that we don't know what we don't know. The most defensible and agnostic approach is to adopt diffuse (uninformative) priors, not only for the prevalence of life, but also for the prevalence of confounders. We evaluate the resulting Bayes factors between the null and life hypotheses for an idealized experiment with $N_{pos}$ positive labels (biosignature detections) among $N_{tot}$ targets with various priors. Using diffuse priors, the consequences are catastrophic for life detection, requiring at least ${\sim}10^4$ (for some priors ${\sim}10^{13}$) surveyed targets to ever obtain "strong evidence" for life. Accordingly, an HWO-scale survey with $N_{tot}{\sim}25$ would have no prospect of achieving this goal. A previously suggested workaround is to forgo the agnostic confounder prior, by asserting some upper limit on it for example, but we find that the results can be highly sensitive to this choice - as well as difficult to justify. Instead, we suggest a novel solution that retains agnosticism: by dividing the sample into two groups for which the prevalence of life differs, but the confounder rate is global. We show that a $N_{tot}=24$ survey could expect 24% of possible outcomes to produce strong life detections with this strategy, rising to $\geq50$% for $N_{tot}\geq76$. However, AB-testing introduces its own unique challenges to survey design, requiring two groups with differing life prevalence rates (ideally greatly so) but a global confounder rate.
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