Article by: Vaidik Mandloi
Compiled by Chopper, Foresight News
Currently, all prediction markets, whether Polymarket or Kalshi, output only one result: probability. Probability is a number representing the collective market judgment of the likelihood of an event occurring.
Over the past two years, this underlying tool has proven that the market can aggregate fragmented information from various sources to predict future events more accurately than polls, expert reviews, and all previous human forecasting methods.
However, the information of "probability" alone is incomplete in finance. If you hold Bitcoin and see a market prediction that there’s a 65% chance the Fed will cut rates by 75 basis points next month, you know the event is likely to occur—but you have no idea how it will impact your holdings. You understand the likelihood of rate cuts is high, but you still don’t know how Bitcoin’s price will specifically move. If the rate cut happens, will Bitcoin rise to $110,000, $95,000, or $140,000? Traditional prediction markets cannot answer this question.
The event itself and the extent to which it impacts businesses and assets are two entirely different things. For a long time, financial markets have conflated these two. Distinguishing between them is the direction for the next generation of financial products.
The industry is currently developing two distinctly different types of products. The first stems from insights derived from prediction markets: if markets are so effective at aggregating information, why should organizational governance rely on voting? These products are called decision markets. The second is entirely new: it directly tells you what the price of Bitcoin would be if the Federal Reserve cut interest rates by 75 basis points. It doesn’t just predict whether the rate cut will occur—it calculates the corresponding USD price of Bitcoin under that hypothetical scenario. These markets are called impact markets, and their value is currently severely underestimated. Both types are categorized as conditional markets.
We’ll discuss market influences in detail later. To quickly understand why it’s important, first familiarize yourself with the limitations of prediction markets.
What happens when governance becomes a market?
Decision markets were first used in governance scenarios because DAOs and token voting inherently have flaws: those with the most tokens may possess the least information, while those with critical information often hold too few tokens to influence voting outcomes. Decision markets flip this model: anyone can express their view simply by putting money at stake, regardless of how many tokens they hold. Markets can price information in real time.
Robin Hanson proposed the concept of "prediction governance" as early as 2000, and the core idea is simple. Instead of using token voting to decide whether to reduce staking rewards, traders would price the protocol’s token on two futures contracts: one tied to "reward reduction" and another to "maintaining current rewards." The scenario with the higher token price would be implemented. If you trade blindly without understanding the protocol’s economic logic, knowledgeable participants will profit at your expense—and your losses will directly reduce your voting weight on all future governance proposals.
This system becomes smarter over time: uninformed capital continuously loses money and exits, while informed, professional capital steadily accumulates. Token voting works the opposite way—even if you make incorrect choices on every proposal for an entire year, your voting power remains unchanged. Oracle governance imposes real consequences for ignorance, something no previous governance system has achieved.
MetaDAO is the first team to implement this mechanism. In late 2023, it launched on Solana using a binary model: each proposal has only two possible outcomes—pass or fail—determined by a conditional market, eliminating token voting entirely; the market's conclusion is binding.
However, this binary model introduces new problems. The market can only answer “yes” or “no,” so all parameters of the proposal—such as budget, timeline, and technical changes—must be negotiated in advance on the forum before the market opens. This contradicts the original goal of decision markets, as proposal details still rely on the old informal consensus process, with large holders continuing to dominate the discussion. You’ve merely delegated the final binary choice to the market—a partial improvement—but the proposal design phase remains trapped within the very system you sought to replace.
In September this year, Umia upgraded the MetaDAO system to a multi-outcome version. Instead of a simple pass/fail vote, multiple parameter proposals for the same initiative can now compete simultaneously. The market automatically eliminates unreasonable parameters through trading, allowing the optimal proposal to emerge directly from market dynamics rather than being predetermined in forums. If the core intention of this mechanism is price discovery, this is exactly how it should be.
Currently, 22 DAOs are using this mechanism, and 96 proposals have been approved through market governance. The total treasury funds under market management amount to $50.5 million, with MetaDAO accounting for $39.3 million. This demonstrates significant potential for scaling this model. However, even if the treasury size were to increase a hundredfold, the decision market would still face an upper limit—a bottleneck unrelated to funds, user base size, or development team capability.
In economics, there is Goodhart’s Law: when a measure becomes a target, it ceases to be a good measure. Credit ratings were originally intended to assess default risk; once banks began relying on ratings to select mortgage products, investment banks deliberately structured securities to meet rating criteria rather than screening for high-quality loans. Underlying mortgage assets deteriorated steadily, yet the securities continued to receive the highest AAA rating.
Decision markets inherently have this flaw. When a proposal is approved, the token price rises partly because the proposal itself creates value, and partly because its passage signals strong team confidence. Even if a high-quality project proposes using treasury funds to cast a bronze bull statue, the token price would still rise upon approval—traders are betting on confidence in governance, not on the statue itself. A similar phenomenon occurs when Apple, despite being highly valued, announces a share repurchase.
The entire mechanism would break down, as the market’s outcome would feed back and influence the very asset it aims to evaluate. Without an incentive for traders to participate, this issue cannot be fixed. However, if the governance component is removed and this conditional mechanism is instead used to analyze external events—events outside the protocol whose outcomes cannot be altered by trading—the feedback loop disappears, making it much harder to manipulate prices. This is how it influences the market.
What can influence the market?
Currently, hedging tools in the crypto industry are very crude. If you hold Bitcoin and are concerned that a specific regulatory decision might affect your position, you have only two choices: either sell outright and forfeit all potential upside gains under favorable scenarios, or buy volatility products—but these track overall market volatility and have nothing to do with the specific event you’re worried about. In the past, no financial product existed that could isolate a single event and allow traders to bet on how much that event would impact an asset’s price. But ImpactMarket can do that.
For example, the U.S. SEC is about to determine whether a certain category of DeFi protocols qualifies as securities, and you hold a large amount of ETH. You believe that if the ruling is negative for DeFi, ETH will drop to $1,800. However, the current market price of ETH at $2,300 reflects the collective market expectation that, should the ruling be issued, ETH’s price will settle at $2,300.
You sold this contract at $2,300. Subsequently, a bearish ruling was implemented, causing ETH to drop to $1,800. Because your judgment was more accurate than the market’s, you earned $500 per token. Even if you were wrong and ETH only fell to $2,200, you would still profit $100. Before such market-moving events occurred, this type of trade did not exist. No product previously allowed traders to independently bet on the price movement of a specific asset triggered by a single event, with pricing determined in real time by the market.
The principle is that the current price we see for an asset is essentially a single number compressing the average of all possible future outcomes. Influencing the market allows us to penetrate this surface layer and trade individual future scenarios separately.
To hedge against specific risks, most people currently diversify across multiple assets, essentially making rough guesses about how different assets move in relation to each other. Impact Market has completely changed this approach: you can hold a large position in Bitcoin and buy targeted risk protection only for the regulatory or macro events you’re concerned about—without selling your holdings or paying for volatility insurance for thousands of irrelevant scenarios.
Instead of hedging by diversifying assets or betting on price correlations, directly hedge against risks in specific scenarios. This is a new approach to risk management, predicated on products offering conditional prices rather than event probabilities.
I believe the market's influence will far exceed that of prediction markets. Prediction markets settle only into two outcomes—“occurred” or “did not occur”—which, while useful, have limited applications. Probabilities themselves cannot be directly traded and cannot serve as the foundation for complex financial instruments.
What drives market output is the dollar price of assets under specific hypothetical scenarios. You can use these to hedge risk and further develop derivatives. Such instruments could not have been created before reliable conditional prices existed. The various derivatives built on reliable conditional prices will ultimately far exceed the size of the conditional market itself—just as options and derivatives markets far surpass the underlying stock market.
The market space for "Will the event occur?" is substantial but inherently limited, as probabilistic approaches have inherent constraints. In contrast, the scenario of "How much will this event change asset prices?" has infinite applications, with market depth equivalent to the entire financial industry.
The first products have already launched, and expansion is accelerating rapidly. Protocols like Branchpoint have even developed Branchmap, a universal naming standard across projects that uniformly defines various conditional scenarios. Standardization is critical: only when the meaning of "ETH under an SEC negative ruling scenario" is identical across all trading platforms can market impact become a true financial foundation. Without a unified standard, liquidity becomes fragmented, and conditional prices across different platforms cannot be compared.
Branchpoint calls this system Multiverse Finance. Each conditional market is like a window, corresponding to a possible future or a branch of reality, representing the world that unfolds after an event occurs or does not occur. As these markets mature and liquidity increases, they no longer just provide conditional prices for single events, but fully reveal an asset’s valuation across countless parallel futures and diverse scenarios.
For decades, the financial industry has sought to separate the underlying drivers of asset prices. The entire discipline of event studies relies on historical data to estimate how much a specific event contributed to price movements, with the remainder attributed to noise. However, in the past, people could only make rough estimates after the fact.
And the market can perform this split in advance or in real time. Once such data becomes available, the fundamental logic of risk pricing will be rewritten.
In the next phase, the conditional price will be integrated with risk models and portfolio systems, which currently rely solely on spot prices and implied volatility. Once connected, the frameworks for capital hedging and risk exposure management in the crypto space will be completely restructured.
This mechanism is not limited to the crypto industry. Once this price is established, the way capital is allocated undergoes a cascading transformation. Insurance companies can transfer niche risks they can’t afford to underwrite to conditional markets, enabling them to accept policies they previously had to decline. Venture capital funds can separately hedge industry risk from individual company risk, allowing them to confidently make large bets on founders they believe in, rather than spreading investments across 30 projects to compensate for lack of confidence. When governments consider raising the minimum wage, they can use conditional markets to see the real-time price of employment impact determined by capital博弈, instead of debating among conflicting economic models.
In the 1970s, options brought the same transformation to the stock market, giving rise to entirely new industries that were previously unimaginable. Conditional markets, like options, are among the most fundamental foundational tools in finance. The difference lies in the fact that they split risk based on various events rather than on the assets themselves; the number of potential events in the world is unlimited.