Shadow market ideas
Shadow Markets
Shadow markets are prediction markets where traders forecast how different decisions will impact a chosen metric, while an external party retains the authority to make the final choice. Their primary advantage over futarchy-style decision markets is that they allow decision makers to gauge market accuracy and behavior before delegating actual decisions to market prices. Shadow markets also enable outsiders without decision-making authority to create markets that persuade decision makers by demonstrating that their proposals will likely lead to better outcomes, thereby justifying further investigation and evaluation.
These markets provide a credibly neutral signal of a proposal’s expected benefits. Market signals carry credibility because any trader can correct prices they believe to be inaccurate, creating a self-correcting mechanism that lends legitimacy to the information conveyed.
Applications of Shadow Markets
Shadow markets can evaluate large-scale DAO capital allocation proposals and proposals for new major products, strategies, initiatives, or decisions. They can predict the future success of competing projects according to key performance indicators.
Protocol-level decisions offer numerous applications. Markets can assess the impact of enabling, disabling, or adjusting protocol fee switches, deploying to new chains, and integrating new protocols or assets. Token-related decisions include predicting token prices conditional on launch parameters and evaluating various airdrop distribution approaches.
Budget and resource allocation decisions represent another category. Markets can evaluate the impact of de-funding contentious DAO programs that currently consume large budgets, de-funding potentially parasitic actors such as the protocol’s foundation or labs organization and other potentially misaligned entities in the ecosystem, and ending or deploying incentive campaigns. These markets can identify both opportunities and instances of waste or spending misaligned with token holder interests.
Corporate governance applications include evaluating executive or CEO equity compensation proposals. Markets can also assess the impact of adopting features or changes recently and successfully adopted by competing protocols or projects.
Beyond crypto-specific contexts, shadow markets can evaluate the impact of election outcomes on national currencies or stock markets for countries like Argentina with volatile political landscapes. They can aid in selecting suppliers or service providers (https://www.overcomingbias.com/p/futarchy-for-ad-supplier-choice) and evaluating proposed capital raises or token supply adjustments (https://www.overcomingbias.com/p/futarchy-for-fundraising).
Finally, markets can predict the results of future retrospective evaluations. For example, they might predict the result of a temp-check polling the DAO one year later on whether an initiative was overall worthwhile. Authors of large proposals can use such markets to increase their proposal’s credibility by demonstrating that the market predicts it will be evaluated positively in the future.
Criteria for When Shadow Markets Are Most Useful
Not all of these criteria are necessary, but each enhances the effectiveness of shadow markets.
Material impact on the chosen metric. Decisions must influence a substantial amount of resources or capital, or achieve impact through other means, so that effects are visible when comparing conditional market prices rather than being obscured by market noise.
Timely impact on the metric. Decisions should materially affect the metric within several months of enactment. This requirement avoids the capital-efficiency downsides of extremely long-lived metric markets. Financial asset price metrics offer a significant advantage here because they quickly incorporate the expected effects of decisions once enacted, enabling fast resolution.
Non-trivial probability for all options. All options under consideration must have a meaningful chance of being chosen. Traders have no incentive to trade in conditional markets for outcomes guaranteed not to occur, yet trading must occur for all outcomes that require evaluation.
Manipulation-resistant metrics. Metrics must resist manipulation sufficiently that decision makers can trust the market’s reliability. Without this property, decision makers may worry that interested parties could manipulate the metric and by extension the market. Asset price metrics generally perform well on this dimension.
Contention and disagreement. High information dispersion regarding which approach will yield the best outcome increases trader engagement and market informativeness.
Clear definitions and methodology. Both the metric calculation methodology and decision definition must be unambiguous. Clarity ensures that traders can confidently participate knowing how markets will resolve, and decision makers can trust the market’s accuracy.
Subsidized liquidity from interested parties. When certain parties have stakes in particular outcomes, they may subsidize conditional market liquidity to improve accuracy and increase the weight decision makers place on market predictions.
Decision maker receptivity. Decision makers should be willing to further investigate or consider proposals that conditional markets identify as likely beneficial. This receptivity means that even if a proposal initially seems unlikely to be accepted, the market’s endorsement can increase its chances, thereby incentivizing trading in conditional markets for promising but underappreciated proposals.
Informed participation and timely decisions. Decision makers and other informed parties should be able to trade in the market, and decisions should occur relatively soon. These conditions help avoid decision selection bias.
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futonomous mechanism design. cryptoecon. austrian econ. epistemology. liberty.