In conventional markets with many buyers and sellers, information providers can profit from participants who have inherent reasons to trade—such as steady demand for food or energy, or the passion of traders for certain teams or candidates (as seen in sports or election markets).
In decision markets (or CFMs), however, these inherent incentives often do not exist, so trading volume can remain low unless holders of relevant private information see a clear reward for participating. When informed participants are not incentivized to trade, market liquidity stays thin and prices become less accurate.
Conditional Funding Markets mitigate this by providing subsidies to draw liquidity providers into conditional token markets, encouraging informed traders to reveal their private information.
Another potential issue is manipulation. Large capital holders can place big bets to skew prices, creating misleading signals about certain outcomes. This risk grows if the participant pool shares similar biases, as Google discovered in its own decision markets—employees tended to overestimate Google’s prospects.
To increase the cost of manipulation, the decision rule (e.g., top-n or budget-based) can be modified to rely on time-averaged prices or similar mechanisms rather than a single snapshot. Importantly, attempts at manipulation can actually improve overall accuracy by attracting more informed traders who profit by correcting artificially distorted prices. Manipulative orders effectively act as noise trades and subsidize those who spot mispricings.
For decision markets to function effectively, the event or metric on which decisions are based must be well-defined and clearly specified. The future can be uncertain in ways that are difficult to predict, so details like the exact time (and time zone) of an event are crucial. If a decision depends on a social event being reported by a specific outlet, the venue must be stated, and a backup option should be included in case the original source is unavailable.
In CFMs, clarity about oracle parameters, resolution dates, and fallback data sources is essential. When markets lack well-structured contract terms, settlements can be disputed, undermining the reliability of the entire mechanism.
Finally, confounding arises when traders price tokens based on factors unrelated to the decision’s true impact. These extraneous influences dilute market signals with irrelevant information. One straightforward way to limit confounding is to design the market so that it directly executes the chosen outcome, minimizing the effect of any external process.
By ensuring the mechanism itself enacts the event it selects, traders focus on pricing the genuine consequences of that action, improving the reliability of the final price signals.