1 INTRODUCTION

Like other branches of philosophy, epistemology faces a methodological challenge: to make its procedures more robust, so that errors are more easily corrected. The problem is not any general unsoundness in its basic methods, but difficulty in recovering from errors once made.

For example, thought experimentation is a legitimate and ordinary way of learning what would have been in counterfactual possibilities, as I have argued elsewhere (Williamson, 2007). Nevertheless, it is fallible, just as sense perception, memory and reasoning are fallible. Although the communal nature of epistemology means that merely idiosyncratic mistakes in designing and conducting a thought experiment tend to be quickly recognized, that is not the only possible kind of error. A particular thought experiment may exploit a glitch in human cognitive psychology to which we are all prone. For instance, the set-up may prompt us unconsciously to apply a common heuristic in conditions beyond its range of reliability. How can we guard against the danger of wrongly dismissing a correct theory as refuted because it conflicts with a heuristic in such a case? The answer is not to stop using all cognitive faculties which can lead us astray, for that would leave us with nothing. We need, not ways of never making mistakes, but ways of recovering from our mistakes once made.

We can do better by using more than one method. When diverse methods lead us to the same conclusion, we have a more robust basis for endorsing it. When they lead to conflicting conclusions, we have a warning sign that something has gone wrong. Each method acts as a potential corrective to the others. For the method of thought experimentation, an appropriate corrective is the method of formal model building—and vice versa (Williamson, 2017). Thus, formal models provide independent confirmation for the morals of Gettier thought experiments (Williamson, 2013).

The issue of robustness also arises within the model-building approach. Some features of a model may result from arbitrary or at least unforced choices; a better model may avoid them. We can have more confidence in features which are somehow persistent: more specifically, features which provably follow from appropriate general constraints on models. This paper exemplifies that approach, applying it to counter-models to the controversial ‘KK’ principle that whenever you know p, you also know that you know p. It is to be hoped that a similar approach can also be applied to other questions in epistemology.

中文翻译：

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KK原理和旋转对称

1 介绍

与哲学的其他分支一样，认识论面临方法论上的挑战：使其程序更加稳健，从而更容易纠正错误。问题不是它的基本方法有任何普遍的不健全，而是一旦发生错误就很难从错误中恢复过来。

例如，正如我在别处论证的那样，思想实验是一种合法和普通的学习方式，可以了解反事实可能性中的内容（威廉姆森，2007）。然而，它是易出错的，就像感官知觉、记忆和推理是易出错的一样。尽管认识论的公共性质意味着在设计和进行思想实验时，人们往往会很快认识到仅仅是特异的错误，但这并不是唯一可能的错误类型。一个特定的思想实验可能会利用我们都容易出现的人类认知心理学中的一个小故障。例如，设置可能会无意识地提示我们在超出其可靠性范围的条件下应用通用启发式方法。在这种情况下，我们如何避免错误地将正确的理论视为被驳斥的危险，因为它与启发式相冲突？答案是不要停止使用所有可能导致我们误入歧途的认知能力，因为那会让我们一无所有。我们需要，而不是永不犯错的方法，

我们可以通过使用不止一种方法做得更好。当不同的方法使我们得出相同的结论时，我们就有了更强有力的支持它的基础。当他们得出相互矛盾的结论时，我们就有了一个警告信号，表明出现了问题。每种方法都可以作为对其他方法的潜在纠正。对于思想实验的方法，适当的修正是形式模型构建的方法——反之亦然（威廉姆森，2017）。因此，形式模型为 Gettier 思想实验的道德提供了独立的确认 (Williamson, 2013 )。

稳健性问题也出现在模型构建方法中。模型的某些特征可能来自任意的或至少是非强制的选择；更好的模型可能会避免它们。我们可以对以某种方式持久的特征更有信心：更具体地说，可以证明是从模型的适当一般约束中遵循的特征。本文举例说明了这种方法，将其应用于有争议的“KK”原则的反模型，即每当您知道p 时，您也知道您知道p。希望类似的方法也可以应用于认识论中的其他问题。