The Economy of Truth: How Resources Shape What can be Known

I propose a resource-sensitive theory of truth grounded in the structure of linear logic. On this view, a proposition counts as true for an agent only when it can be derived from the finite informational resources the agent currently possesses such as data, concepts, tools or premises, through a form of inference that tracks how those resources are used. Rather than treating truth as a static or purely metaphysical attribute, my account emphasizes that truth emerges within the constraints of actual reasoning. A key distinction is drawn between local derivability, which concerns what an agent can establish with the resources immediately available and global derivability, which reflects what could be established under idealized conditions. This allows my approach to preserve objectivity while acknowledging that access to truth is often limited and uneven. The framework yields a new perspective on classical epistemological issues, including the nature of justification and the structure of rational inference, while clarifying how inferential breakdowns can occur when the resources supporting a reasoning process are incomplete or unreliable. It also provides a natural lens for examining truth in science, legal reasoning and artificial intelligence, domains in which information is finite, traceable and central to responsible decision-making.