A Calculus of Discernment: Decision-Relevant Insight, Sequence Value, and Forgetting as Higher-Order Learning
In a world of generative AI, candidate insights are abundant; what is scarce is the capacity to discern which matter, to act on them in the right amount and order, and to forget the rest so the system can adapt. We argue these scarcities are governed by one object and build a framework around it. We define an insight strictly as a lever with an identified, measurable effect on an objective, and rank candidates by decision-relevance via the expected value of information rather than novelty. We show action carries an order, not only a size: under realistic belief dynamics, content "touches" are non-commuting operators, so a fixed plan delivered in different orders yields different outcomes,...
| Authors | Suyash Mishra |
|---|---|
| Primary category | cs.GT |
| Published | 17 June 2026 |
| Last updated at source | 17 June 2026 |
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