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基于贝叶斯主义科学哲学的案件分析
Case Analysis Based on Bayesian Scientific Philosophy
1. 假设与先验
Hypotheses and Priors
中文：在贝叶斯框架下，我们首先需要明确假设：
(H_0)：被告没有犯罪意图，也没有造成严重公共混乱。
(H_1)：被告明知传播虚假信息，并造成严重公共混乱。
法律中的“无罪推定”对应于对 (H_0) 的强先验，即在没有充分证据之前，应假设无罪。
English: In the Bayesian framework, we first define hypotheses:
(H_0): The defendant had no criminal intent and caused no serious public disorder.
(H_1): The defendant knowingly spread false information and caused serious public disorder.
The legal principle of “presumption of innocence” corresponds to a strong prior on (H_0), meaning guilt should not be assumed without sufficient evidence.
2. 证据与似然
Evidence and Likelihoods
中文：检方必须提供证据来提升 (H_1) 的后验概率。然而在本案中，账号粉丝极少、互动几乎为零，缺乏任何客观数据证明“严重混乱”。因此，似然比（Bayes factor）接近于 1，无法支持有罪结论。
English: The prosecution must provide evidence to increase the posterior probability of (H_1). In this case, the account had very few followers and almost zero engagement, with no objective data showing “serious disorder.” Thus, the Bayes factor is close to 1, offering no support for a guilty conclusion.
3. 高学历悖论
The High-Education Fallacy
中文：法院以被告的博士身份为由，推定其“必然知道信息是虚假”。这实际上是将身份当作先验概率的提升，而不是基于证据的似然更新。这种做法违背了贝叶斯主义的核心精神：信念更新必须依赖数据，而非身份假设。
English: The court presumed that because the defendant had a PhD, he “must have known” the information was false. This effectively inflates the prior probability of guilt based on identity rather than updating likelihoods with evidence. Such reasoning violates the Bayesian principle that belief updates must rely on data, not identity assumptions.
4. 类别错误与假设空间
Category Errors and Hypothesis Space
中文：艺术作品、情感表达、学术观点与事实陈述在逻辑上属于不同类别。将它们全部归为“虚假信息”是错误的假设空间设定。贝叶斯分析要求对不同类别分别建模，否则似然函数无法正确比较。
English: Artistic works, emotional expressions, academic viewpoints, and factual statements belong to different logical categories. Lumping them all together as “false information” mis-specifies the hypothesis space. Bayesian analysis requires separate modeling of categories; otherwise, likelihood functions cannot be properly compared.
5. 决策理论与损失函数
Decision Theory and Loss Functions
中文：在贝叶斯决策理论中，错误定罪（假阳性）的社会成本远高于错误无罪（假阴性）。因此，在证据不足时，理性决策应选择无罪。
English: In Bayesian decision theory, the social cost of false positives (wrongful convictions) is far greater than false negatives (letting someone go free). Therefore, when evidence is insufficient, rational decision-making should favor acquittal.
6. 结论
Conclusion
中文：本案在贝叶斯框架下存在三大问题：
先验被身份不当抬高；
似然证据缺失或与事实矛盾；
假设空间错误地混合了不同类别。
因此，后验概率应仍然倾向于无罪，判决缺乏理性与科学依据。
English: Under the Bayesian framework, this case suffers from three major flaws:
Priors were improperly inflated by identity;
Likelihood evidence was missing or contradicted by facts;
The hypothesis space was mis-specified by conflating categories.
Therefore, the posterior probability should remain in favor of innocence, and the conviction lacks rational and scientific justification.