Insurance? Prediction? It's hard to tell the difference! — The Traceable Prediction Market

Most people still view prediction markets as a form of "gambling," but in fact, prediction markets have long been applied in our daily lives. For example, insurance is likely the first institutionalized and scalable prediction market in human history, and it indeed makes money through probabilities.

Of course, this requires a key premise: insurance is not about betting on whether something will happen in the future, but rather about continuously pricing future events. Early mature insurance, especially marine insurance, already fully embodies the core elements of a prediction market. It assesses the probability of uncertain events, expresses expectations through pricing, and achieves positive expected returns through long-term statistics. The difference lies in the fact that insurance does not pursue short-term gambling; instead, it relies on big data principles and repeated pricing to convert "predictive ability" into stable profits.

If we translate the insurance model into the language of prediction markets, it essentially goes like this:

The insurance company implicitly states that the true probability of such events occurring is X, but I sell you coverage at a price slightly higher than X.

Meanwhile, the policyholder is saying, "I am willing to pay a certain cost for uncertainty."

Let's think about it: the insurance we buy around us is essentially a transaction of asymmetric probability judgment, just packaged as risk management. This is neither gambling nor a game; it is a probability math problem based on big data risk control!

Initially, I used horse racing as a benchmark for prediction markets, but later I realized I was wrong. Therefore, from a historical perspective, insurance is even more "primitive" and "advanced" than horse racing.

Horse racing is a discrete prediction of a one-time event, while insurance has been built around long-term, frequently occurring uncertain events from the very beginning, such as whether a ship will sink, whether goods will be stolen, whether a house will catch fire, or whether a person will fall ill or die. Without probability thinking, it would be impossible to price these events or sustain operations, so insurance is actually the first industry where humans were forced to systematically use probability tools.

The truly interesting part is that insurance is accepted by society because it is defined as "risk transfer," not "prediction." Once you say, "I am predicting whether something will happen to you," this behavior is disliked or even prohibited in many cultures.

However, when you say, "I help you bear the risk," the same probability pricing immediately becomes legitimate, legal, and even encouraged. This is also why prediction markets have been suppressed historically, while insurance has been able to develop openly for hundreds of years.

So, if we distill the logic, we can summarize that insurance is the earliest prediction market; it just hides the word "prediction." It is not betting on a single outcome but rather making long-term bets on an event distribution. Insurance does not earn money from being right or wrong once; it earns money from "calculating probabilities more accurately than others."

From this perspective, today's prediction markets and AI predictions are essentially extracting, atomizing, decentralizing, and allowing the proven probability pricing logic of the insurance industry over the past few centuries to apply to more types of events.

The difference is not about being "right or wrong," but whether society is ready to accept it. To publicly express judgments about the future through pricing. Unfortunately, there are too few practitioners who can understand this intention, so most prediction markets today can only copy Ploymarket and Kalshi. Imitating without innovation ultimately leads to a lack of originality; Westerners dislike it, and Easterners look down on it, with most users just aiming for airdrops.

The prediction market is very deep; this field can still produce three to five unicorns at least. However, if one cannot fully understand this industry, even with the money of the highest authority, the outcome will still be a mess.

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