Why are the two current mainstream forecasting market mechanisms unable to lead the future, CLOB+CTF and LSMR?

Author: Voice of DAO

1. Mechanism is not only a tool, but also an expressive of belief

In the prediction market, a system that relies on collective cognition to build prices, the market mechanism of trading not only undertakes the function of order matching, but also carries the judgment of future events.

The quality of a set of mechanisms not only determines whether the transaction is efficient, but also determines whether we can accurately capture and express the outline of "future possibilities".

Because of this, the two mainstream forecasting market mechanisms currently -CLOB+CTF (order book + conditional Token) and LSMR (logarithmic score automatic market making), although they have made achievements in the past few years, they have exposed structural shortcomings in practice.

These problems are not only technical challenges, but also reflect their incomplete understanding of the core issue of "how group beliefs are generated and measured".

2. CLOB+CTF: Market fragmentation and inefficient liquidity brought by

high structure

1. The concept is clear, but the system is overly split

The logic of CLOB+CTF is not complicated:

1.CLOB provides a market interface for limit orders and matching

2.CTF disassembles the multiple-choice event into a series of YES/NO binary contracts , and completes supply and demand adjustment through automatic token casting/destruction .

This model is highly structurally consistent with the traditional financial market, so it has a certain user cognitive foundation.

But once the event options increase, the system will be forced to create multiple submarkets , each with its own pair of tokens and order books, greatly increasing the system complexity and user participation thresholds .

2. The opponent's dependence leads to liquidity imbalance

CLOB is a pending order mechanism and therefore essentially relies on an active bilateral market. In most forecasting markets, due to problems such as unstable user participation enthusiasm, diversified events, and information asymmetry, the depth of orders is often seriously insufficient, forming a "subscription window" or "unilateral squeeze" .

The result is:

1. If the user places an order, there is no rival order, the price will stagnate;

2. The market has fallen into failure under an illiquid state, seriously affecting the user experience.

Although CTF can fill the opponent's gap to a certain extent by minting YES/NO assets, it itself has become an arbitrage tool, making the price signal more susceptible to short-term behavior manipulation rather than long-term expectations .

3. "Arbitrage Driven" overwhelms "cognitive aggregation"

The lateral mechanism of CTF is intended to help market prices converge to the real probability through the system of casting and destroying YES/NO assets. But in practice, this mechanism instead:

1. Encourage users to trade around arbitrage space rather than the event itself;

2. Passively amplify the short-term impact of market irrational behavior on prices;

3. Weak the interpretability and stability of price as a "collective belief function".

In short, although CLOB+CTF is structurally complete, it is difficult to provide a stable and effective price discovery mechanism in the context of low liquidity, large cognitive differences, and non-specialization of users .

3. LSMR: The dilemma from function elegance to liquidation uncertainty

1. The mechanism is concise, but it is out of user comprehensibility

LSMR (Logarithmic Scoring Market with Reservation) takes a logarithmic function as the core and unifies the market price and Token issuance logic in a set of mathematical expressions. It has two significant advantages:

1. There is no need to split the multi-select market, and all options coexist in the same price structure;

2. Theoretically, the sum of all Token prices is always 1, which conforms to probability intuition.

However, the problem of LSMR is also emerging in this mathematical system that is too "black box": users cannot judge the relationship between the actual investment cost and potential benefits when purchasing an option; the process of forming market prices lacks a clear path , and the trading experience is "blind" by the algorithm abstraction; the liquidation logic cannot form the "odds" expected by users and lacks game transparency .

2. The dual dilemma of slippage and parameter out of control

The only control parameter b of LSMR determines the market's price response intensity (slip level). But the design of this parameter itself faces a paradox:

- b The value is too large → the price changes slowly, and the real probability is difficult to express;

- b The value is too small → The price is extremely sensitive and easily manipulated, forming a price bubble.

To make matters worse, b is necessary before the market starts , but it is almost impossible to tell at the beginning how much trading volume and volatility the market will usher in. This makes b a structural risk factor rather than a stable anchor of the system.

4. Core issues are summarized: Three major flaws of the old mechanism

1. It is difficult to express the true probability of the price

In CLOB+CTF, the pending order structure and liquidity supply and demand are prone to price deviation from the real probability of the event;

In LSMR, price is just the result of the function output, and it is difficult for users to map the relationship between subjective probability and odds.

2. The mechanism is easily dominated by arbitrage and it is difficult

to form rational collective signals.

Arbitrage logic is originally a supplement to the mechanism to maintain price rationality, but in these two systems, arbitrage has become the main trading driver, dominating the market structure and interfering with cognitive aggregation.

3. The user participation experience is poor and the transaction lacks

certainty

Whether it is the depth problem of the order book or the slippage caused by the logarithmic function, it is difficult for users to obtain a clear and cost-controllable trading experience when participating. This is a fatal threshold for the prediction market's "open to non-professional users".

Order book depth schematic via investopedia

5. Direction to a new mechanism: The mechanism should express beliefs,

not just carry transactions

The essence of the prediction market is to assetize beliefs, which is a mechanism for integrating social cognition and economic incentives. Therefore, an ideal market mechanism must meet the following three points:

1. The pricing is clear and the probability meaning is clear, and users can understand the meaning behind the price;

2. The price is closed with the liquidation structure to ensure that the expected returns of participants are consistent with the actual returns;

3. It can operate stably without complex splitting or parameter control. The mechanism itself has anti-manipulation and self-balancing capabilities.

These goals are exactly the direction that new mechanisms such as APMM (Automatic Prediction Market Maker) are trying to achieve. They draw on the experience of previous generation mechanisms and respond to the core pain points in real trading behavior.

6. Conclusion: The root of mechanism evolution is not just engineering,

but adaptation of cognitive ecology.

The mechanism problems of CLOB+CTF and LSMR are not because they are not "smart" enough, but because they rely too much on the encapsulation and formal elegance of technology when dealing with the problem of "how humans judge the future", and ignore the user's real behavior, psychological and cognitive paths.

The market mechanism is a civilized tool, and ultimately serves not functions and assets, but people's decisions and expressions in uncertainty.