This article was compiled and organized by BlockWeeks
Over the past year and a half, Galaxy Research has continuously tracked prediction markets and their derivative concepts—decision markets and impact markets. From the applicability of decision markets in DAO governance and their potential as a cold-start mechanism for early crypto projects, to treating prediction markets and impact markets as hedging tools, and on to Hyperliquid's HIP-4 outcome markets, this thread has run throughout: prediction markets are merely the "version zero" of event-driven markets and hedging tools, while experiments around broader concepts are quietly blossoming behind the scenes.
At the time, this sector mostly remained confined to X posts and white papers, with only a few exceptions such as MetaDAO. Now the situation has changed: a batch of projects has entered the testing phase, and a new generation of experiments is beginning to land.
From Prediction Markets to "Conditional Markets"
Previously, decision markets and impact markets were treated as two separate concepts. Now the more reasonable approach is to view them as two subsets of the same larger category—conditional markets. The two are completely identical in market mechanism: conditionalized trading is placed into a market and only settles if a specific outcome occurs. The real difference is that decision markets also settle an outcome with clear binding force, for example, "if the conditional market price deviates from spot by more than 5%, the management team must add a certain new feature to the protocol." Since the mechanisms are shared, and the variety of event-driven markets and hedging tools is rapidly expanding, grouping them into the same category is the most logical approach.
Three Differentiating Characteristics
- Going beyond the "all-or-nothing" binary nature of prediction markets;
- Letting participants trade real assets and their prices, rather than probability-based synthetics;
- Outputting the impact of future events on asset value as an information byproduct, rather than outputting the probability of "whether an event will occur."
These characteristics open up new possibilities that prediction markets simply cannot provide, across three dimensions: hedging, payout structures, and market signals.
Decision Markets: From Binary to Multi-Outcome
The launch of Umia in September broadened the design space of decision markets. This is an ownership-token-style launchpad for EVM-compatible chains. Before this, decision markets were basically confined to Solana, dominated by MetaDAO—which pioneered the concept and was the first to realize it at scale.
Until now, decision markets have all been binary: the decision being traded either passes or fails. Proposal drafters first solicit feedback from the community, refine the proposal, and then launch the market. This approach does make decisions tradable by the market, but the social process in the drafting phase, as well as fixing all proposal variables (for example, raising monthly burn from $50,000 to $65,000, or converting 50% of the DAO treasury into yield-bearing stablecoins), both introduce friction.
Traders can only make decisions based on fixed parameters, even if they agree on the direction. Some may believe monthly spending should indeed increase, but only to $60,000; others may agree the treasury should be put to work, but not by more than 30%. This forces the "optimal outcome" to go through a social filter—it may require repeated proposals, or it may never be priced and traded at all.
Umia brings multi-outcome decision markets: traders can choose among specific parameters of a proposal, for example, "should spending increase from $50,000 to $55,000, $60,000, or higher?". In this way, traders can first agree on the direction of the proposal, and then express their views on a single detail; what gets priced is the proposal's parameters, while the overall scope of the proposal can remain broader.
Letting traders price individual parameters, rather than vote on a fixed proposal, can reduce the social process that binary pass/reject markets rely on, thereby converging on the "optimal" outcome before launch. Rather than relying on drafters to perform social filtering before a proposal becomes a market, multi-outcome markets delegate the filtering to price discovery. The practical effect is: decisions can be priced more accurately and efficiently, and the market will directly surface the specific set of parameters that the community truly supports, rather than forcing people to make a black-and-white judgment on a particular version.
Infrastructure Layer: Events Become a Public Namespace
Beyond decision markets, impact markets and the infrastructure supporting them are also advancing, and beginning to move toward mainnet.
Branchmap: Structuring Events into a Namespace
Branchmap's idea is to make "events" themselves into a reusable namespace. It provides an event context (for example, a Fed interest rate decision), which contains a predicate (for example, "will the target rate be raised?"), and the predicate then generates a named outcome entry for each possible outcome. Selecting a combination of outcomes across one or more predicates defines a "verse," the financial world in which conditional assets live—although the verse itself is not stored as an entry in the namespace.
The core insight is: prediction markets themselves already serve as the numeraire for a certain outcome. A binary contract that pays $1 if an event resolves in a certain way is economically equivalent to "holding $1 in the universe where that outcome occurs." Branchmap goes a step further: any asset can be split into conditionalized versions corresponding to each possible outcome of an event, and then recombined after the event resolves.
The Splitting Mechanism and "Branch Prices"
Specifically, a root asset is split according to each outcome of the predicate into verse-conditionalized copies; once the predicate resolves, the surviving branch is recombined back into an ordinary, unconditional asset.
This splitting mechanism establishes a direct link between the outcome probability given by prediction markets and the asset's price under that outcome—which is exactly the decomposition that impact markets aim to price. Branchpoint explicitly treats this link as one of its applications: the price of a claim on an asset in a given verse (the "branch price") can be decomposed into the state price (provided by prediction markets) and the in-verse value (the market-implied value of the asset within that verse). The state price is simply the prediction market's price for the outcome token, approximately equal to the market-implied probability.
As a result, the same event can be shared by multiple protocols: a prediction market prices its probability, a DEX lists a BTC trading pair conditional on that outcome, a lending protocol accepts this conditionalized BTC as collateral—and none of these protocols needs to define the event or settlement rules itself.
What This Means
The significance of conditional markets is not just to make prediction markets "more fun." It strings together the probability of an event, the event's impact on asset prices, and financial contracts based on that impact into a continuous pricing chain: probability is provided by prediction markets, asset prices are given by impact markets, and the infrastructure lets both share the same event semantics. As multi-outcome designs reduce governance friction and the namespace makes events reusable, event-driven finance is moving from "betting on outcomes" to "building assets around outcomes."
