Speculative design uses provocative "what if?" scenarios to explore possible sociotechnical futures, yet lacks rigorous criteria for assessing the quality of speculation. We address this gap by reframing speculative design through an information-theoretic lens as a resource-bounded knowledge generation process that uses provotypes to strategically embrace surprise. However, not all surprises are equally informative-some yield genuine insight while others remain aesthetic shock. Drawing on epiplexity-structured, learnable information extractable by bounded observers-we propose decomposing the knowledge generated by speculative artifacts into structured epistemic information (transferable implications about futures) and entropic noise (narrative, aesthetics, and surface-level surprise). We conclude by introducing a practical audit framework with a self-assessment questionnaire that enables designers to evaluate whether their speculations yield rich, high-epiplexity insights or remain at a superficial level. We discuss implications for peer review, design pedagogy, and policy-oriented futuring.

翻译：推测性设计通过提出具有挑衅性的“如果……会怎样？”情景来探索可能的社会技术未来，但缺乏评估推测质量的严格标准。我们通过信息论视角重新审视推测性设计，将其定义为一种资源受限的知识生成过程，该过程利用原型化设计策略性地接纳意外发现。然而，并非所有意外发现都具有同等的信息价值——有些能产生真正的洞见，而另一些仅停留在美学冲击层面。借鉴可学习性（即有限观察者可提取的结构化、可学习信息）的概念，我们提出将推测性制品生成的知识分解为结构化认知信息（关于未来的可迁移启示）与熵化噪声（叙事性、美学性及表层意外）。最后，我们引入包含自评估问卷的实践审计框架，使设计者能够评估其推测是否产生丰富的高可学习性洞见，或仍停留在浅层水平。本文进一步探讨了该方法对同行评审、设计教学及政策导向未来研究的启示。