The Failure of Closed Market Creation Models

Why gated market creation caps information, liquidity, and relevance.

Most prediction market platforms fail not because their mechanics are wrong, but because their market creation model is structurally constrained.

In closed systems, markets are curated by a central team. Users may be allowed to trade, but they are not allowed to define what is traded. At best, they can suggest market ideas and wait for approval. This model introduces a hard bottleneck at the most critical layer of the system: question formation.

From an information-theoretic perspective, this is fatal.

Prediction markets derive their power from the diversity and specificity of questions they can encode. Closed creation models necessarily bias toward:

• Broad, headline-driven events

• Politically or socially salient outcomes

• Questions that are legible to a general audience

What they exclude is the long tail: niche domains, specialized expertise, localized knowledge, community-specific questions, and fast-moving contexts where central curation cannot keep up. The result is an artificially narrow information surface.

There is also an economic failure. Liquidity follows relevance. Traders are most active where they possess informational edge. When users cannot create markets around the questions they understand best, they disengage or treat markets as entertainment rather than signal. Closed platforms concentrate liquidity into a small number of marquee markets, while the rest of the potential forecasting economy remains unrealized.

Operationally, centralized creation does not scale. Every new market requires review, parameterization, legal consideration, and operational overhead. This slows iteration and introduces conservative bias. Platforms avoid ambiguous or unconventional questions, even when those questions are precisely where markets are most informative.

There is a deeper structural issue as well: misaligned incentives. In closed systems, the platform captures most of the upside from market creation, while users bear the informational and trading risk. Users are price takers not only in trades, but in agenda-setting. This discourages serious contributors from investing time and capital into forecasting.

Closed creation models treat markets as content. Foremarket treats markets as infrastructure.

If prediction markets are to function as a generalized forecasting layer, they cannot depend on a small group deciding what is worth predicting. That decision must be decentralized to the edges, where information originates. Otherwise, the system will always lag reality.

The failure of closed market creation is not philosophical. It is mechanical. Bottlenecks reduce surface area. Reduced surface area limits information. Limited information produces weak signals.

Foremarket is designed to remove this bottleneck entirely.