Problem Definition
The core limitation addressed by Preda is the absence of primitives for modeling belief dynamics over time.
Current prediction systems face several structural constraints:
Terminal Resolution Bias Markets resolve at a single endpoint, compressing complex belief trajectories into binary or scalar outcomes.
Lack of Temporal Uncertainty Modeling Participants cannot express uncertainty about when beliefs may change, only about final states.
Opaque Belief Formation Changes in sentiment, narrative dominance, or model consensus are not treated as resolvable events.
Limited Reflexivity Analysis Feedback loops between belief, market pricing, and information dissemination are largely unobservable within the market structure.
Inadequate Handling of Continuous Signals Social sentiment, probabilistic forecasts, and AI model outputs evolve continuously but are forced into discrete resolution frameworks.
As a result, existing markets fail to capture important transitions such as expectation reversals, consensus acceleration, or belief fragmentation. These transitions often carry more actionable information than final outcomes, particularly in fast-moving or information-dense environments.
Preda addresses this gap by reframing belief change itself as the object of prediction.
Last updated