Conditional Markets for Futarchy, Decision Markets & Info Finance

Seer PM
5 min readJan 14, 2025

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The concepts of futarchy, decision markets and info finance have generated a great deal of interest but little in the way of meaningful implementation. Below we’re going to briefly cover the three concepts before explaining how Seer’s conditional markets can be used for futarchy and decision making. We’ll then walk through a real use case of conditional markets in the 2024 South Korean constitutional crisis.

Futarchy

In his seminal manifesto on the “futarchy” 25 years ago, Professor Robin Hanson devised a form of government using prediction markets to set public policy:

In “futarchy,” we would vote on values, but bet on beliefs. Elected representatives would formally define and manage an after-the-fact measurement of national welfare, while market speculators would say which policies they expect to raise national welfare.

[D]emocracy would continue to say what we want, but betting markets would now say how to get it.

Prediction markets have been proven superior to experts in a wide array of fields (politics, finance, economics, public health, meteorology, and more). Companies like Google are using them right now for forecasting performance. Rather than use experts to set policy, futarchy has elected officials set up prediction markets to decide the best course of action. And it makes sense: If prediction markets are more accurate than experts at forecasting, why not use markets to forecast public policy outcomes and automatically enact the best policy into law?

Decision Markets

Less rigid than Futarchy, “Decision Markets” use prediction markets to guide decision making. Nothing is automatically implemented. In “From prediction markets to info finance,“ Vitalik Buterin describes them as:

[Y]ou want to know whether decision A or decision B will produce a better outcome according to some metric M. To achieve this, you set up conditional markets: you ask people to bet on (i) which decision will be chosen, (ii) value of M if decision A is chosen, otherwise zero, (iii) value of M if decision B is chosen, otherwise zero. Given these three variables, you can figure out if the market thinks decision A or decision B is more bullish for the value of M.

Decision markets are used to provide information about competing courses of action. In the diagram above from Buterin, the Board of Directors for a company (“ExampleCorp”) is considering whether to fire its CEO. A parent market is created:

  • Will ExampleCorp fire its current CEO? [Yes/No]

To measure the outcomes of fire or not firing the CEO, two conditional markets are created:

  • Share price if CEO fired? [If parent market “Yes”]
  • Share price if CEO not fired? [If parent market “No”]

In Buterin’s example, the company’s share price will be $32.70 if the CEO is fired, and $17.50 if the CEO is not fired. The markets signal to the board that the optimum decision in terms of share price is firing the CEO.

The CEO example only has one metric under consideration but other metrics could be used simultaneously with share price. Conditional markets could have also been created for revenue growth, earnings per share, profit margin, or any other forecast that would inform the board of the best course of action.

Info Finance

Coined by Buterin this past November, “Info Finance” is any use of finance to “align incentives in order to provide viewers with valuable information” where prediction markets and decision markets are mere subsets of this much broader category.

He describes the process as merely:

(i) start from a fact that you want to know, and then (ii) deliberately design a market to optimally elicit that information from market participants.

Buterin believes info finance can be applied to DAO governance, advertising, peer review and other use cases. AI will eventually “turbocharge” info finance by providing quality information to the markets but for less cost than is needed to motivate humans to trade in these markets.

Conditional Markets on Seer

In each of Futarchy, Decision Markets and Info Finance, there is the need for designing markets conditional on the outcome of other markets. Seer has built the infrastructure for these “conditional markets” using the Gnosis Conditional Token Framework. Now users can create a market for an event they want to know and design conditional markets to measure each outcome.

Let’s say we want information about how the South Korean constitutional crisis is affecting the Korean economy. We’ll design a market about whether South Korean President Yoon survives for one week after his martial law debacle:

The three possible outcomes — Yoon’s status in one week after martial law is lifted — are:

  • Impeached & currently President, but powers suspended
  • Actively serving as President
  • Former President, resigned or removed from office

To measure the economic consequences of what happens to Yoon, we’ll create three conditional markets about the value of the South Korean KOSPI composite index one week after martial law was lifted for each of the three possible outcomes.

One day out from the Yoon market close, traders signaled different estimates for the KOSPI based on the outcome for Yoon:

Traders believe that Yoon leaving office (resigned or removed) would lead to the highest value for the KOSPI (2466) whereas Yoon remaining in office with full powers would lead to the worst economic outcome (2419). South Korean National Assembly members, leaders and citizens, could use these forecasts to inform their own personal decisions on how to proceed with President Yoon.

In Robin Hanson’s futarchy, Yoon’s fate would be tied to the conditional market with the highest KOSPI value. In decision markets and info finance, these prediction markets merely provide information that could be used in decision making.

The KOSPI is just one condition that could be used. There also could have been conditional markets on other metrics like the value of the Won or Samsung’s stock price. Categories other than financial could be measured; for example, what will be South Korea’s rating in The Economist Democracy Index for 2024?

Of interest, the actual value of the KOSPI index for 11 December 2024 was 2443, only one off from the market conditional on impeachment. However, while the impeachment process was in motion, Yoon wasn’t actually impeached until a few days later.

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