These days, discussions about prediction markets are abundant on social media. However, many of these discussions often reduce prediction markets merely to a "casino." Such a view is nothing more than a tourist's observation. A prediction market is not a game of simply guessing outcomes. Beyond passive forecasting, prediction markets can serve as an active governance protocol that aggregates decentralized information through economic incentives and actively shapes real-world outcomes based on that aggregated information.

The reason we can trust the price is simple. Words cost nothing, but trades have a cost. Buying and selling are actions that carry responsibility, and their results are recorded in the price. Under the simple assumption of risk-neutrality, this record is interpreted as the probability of an event. In other words, the price in a prediction market is not gossip but a belief verified by money, and it self-corrects through profit and loss whenever new information arrives. Here, the price is not an utterance but a trace of execution. Each participant submits the piece of information they possess along with money, and the aggregate becomes the price. This structure uses "skin in the game" to make falsehood expensive and information cheap. This article attempts to frame prediction markets, Futarchy, and Hyperstition together, peeling back the common misconception of a "casino" to broaden the scope of the discussion. While it lightly invokes the problematique of the 90s cypherpunk mailing list as a reference, the core is simple: to show how to read price as information and connect that information to decisions using contemporary examples and designs.

The 90s cypherpunk mailing list explicitly discussed this point. They saw digital cash not as an end goal but as a bootstrap, seeking to extract truth and decisions from the market through markets that circumvented regulation. Timothy C. May's Cyphernomicon argued that the combination of cryptography, anonymity, and digital cash could create a structure where information converges into a price, and that price incentivizes action, all without central authority. As Robin Hanson's ideas on information markets and conditional markets, which intersected with the Extropy community around the same time, flowed into the community, the notion of selecting policies through markets became concrete. Extreme examples included Jim Bell’s Assassination Politics, followed by more institutional experiments such as DARPA's Policy Analysis Market. The point was clear: they treated prediction markets not as a spectacle but as a mechanism that aggregates information into a price to regulate reality. It was a method of gathering information with money at stake and converting it into a decision on a censorship-resistant payment rail. In Nick Land's terminology, the prediction market is a device for designing a future (Hyperstition) that realizes itself by binding belief with capital. The prediction market is a cockpit, not an observation deck.

The prediction market creates a price by trading payoffs tied to future events. Since trading is an action with responsibility, the price reflects only judgments for which a cost has been paid. Assuming risk-neutrality for simplicity, this price is read as the probability of the event. Thus, the prediction market is not a lucky bet but a device that compresses scattered private information into a single signal.

The payoff here refers to a payment determined later based on a future outcome, not the present moment. The simplest form is a binary contract that pays $1 if an event occurs and $0 if it does not, followed by scalar contracts that pay in proportion to a continuously varying metric. Futarchy uses conditional contracts that pay based on a metric only if a specific policy is adopted. When the actual outcome is confirmed, an oracle finalizes the value and settles the contract. In this structure, the current market price becomes a summary of the expected payoff submitted by participants along with their money and information. In the case of binary contracts, it is effectively read as the probability of that event occurring. This is where Hayek’s observation comes to life. Knowledge scattered throughout society is summarized in the price, and the prediction market extends this summary into the future, where each person's piece of information is combined through trade, and the result becomes the collective probabilistic judgment.

This covers what is being traded and how its price is read. The remaining question is simple: How is that price continuously created and maintained? If this part falters, aggregation stops.

A design that reduces friction is necessary for smooth aggregation. Ideally, in a decentralized market, anyone should be able to open a market and anyone should be able to provide liquidity. Quotes should continue without the permission of any specific entity, and as the number of participants increases, the price adjusts more closely to real-world information. The price naturally seeks equilibrium, becoming more expensive when betting crowds one side and cheaper when interest wanes on the other.

In reality, we have not yet reached the perfect ideal. Today, various mechanisms-such as AMMs, order books, and incentive programs-are combined to ensure liquidity and prevent trades from breaking down. Ultimately, the criteria are simple: first, the price must be continuously presented; second, the market must slightly adjust its direction with every incoming trade. Regardless of the implementation method, if these two conditions are met, the price quickly absorbs new information without excessive volatility.

Now, let's look at one scenario where this rule actually aids a decision. "Will the next quarter's launch hit the scheduled date?" Insiders, users, supply chain partners, and analysts submit their fragmented information along with money. If signs of a launch delay appear, the price drops; if beta reaction is good, it rises. The team can use this signal to reallocate resources. This is the moment when the aggregated price aids execution.

When moving from aggregation to execution, limitations and ripple effects arise. As the signal strengthens, participant strategies change, those strategies alter the outcome, and the altered outcome, in turn, corrects the price. This loop is both a risk and a force. Three key variables determine market quality: first, illiquidity when trading is thin; second, biased participation skewed toward specific groups and stakeholders propping up their own positions; and third, incorrect settlement due to oracle delay, error, or manipulation.

Futarchy doesn't eliminate these problems but seeks to control them by converting them into costed beliefs. Value metrics are fixed by voting, factual judgments are left to the market, and policies are chosen by the difference in conditional prices. Any attempt at manipulation must incur a cost, and that cost becomes an opportunity for profit for the opposing side. However, if the market is thin, the time window is short, or the settlement metric and oracle judgment rules are fragile, biased participation can leverage Hyperstition to warp reality. Therefore, guardrails are necessary. Fix the settlement schema in advance, avoid hasty evaluation times, symmetrically incentivize participation, and ensure trust through disputable oracles and collateral structures. The loop must be open, but the rails must be robust.

In a market with guardrails, the price is the record left by a costed choice, and buying and selling are public procedures that surface scattered tacit knowledge. When this aggregated signal leads to a decision, the prediction market shifts from a spectacle to an operation.

The perspective that views prediction markets as an information aggregation engine stems directly from the problematique of cypherpunks. The goal is to enable exchange and cooperation to function without the state's license or bureaucratic review. Digital cash is a bootstrap, not a destination; cryptography is a tool to decouple identity and confer reputation on pseudonyms; and public rules enable order to operate without permission. At this point, the market operates as a protocol, not by permission.

Cypherpunk was a loose hacker movement that began in the early 1990s on the US West Coast. Timothy C. May's Crypto Anarchist Manifesto outlined the principles in 1988, and a public subscription mailing list started by Eric Hughes, John Gilmore, and Tim May in 1992 became the forum for discussion. By 1994, it had grown to hundreds of members, exchanging dozens of technical, political, and practical proposals daily. They viewed strong cryptography as a tool for individual sovereignty, treated privacy as a prerequisite for freedom, and prioritized code over arguments.

The blueprints for these concepts were repeatedly submitted to the cypherpunk mailing list. Anonymous remailers decoupled identity, PGP signatures fixed pseudonyms, digital cash handled payments, and escrow and deposits costed breaches of contract. Reputation and Webs of Trust were combined to accumulate trust in a decentralized manner, and timelocks conditioned the release of information. To reduce spam and fraud, hash-based Proof-of-Work priced externalities, and blind-signature electronic cash provided a payment path without tracking. May's proposed BlackNet added information bounties, sealed-bid auctions, and the circulation of data and reports to treat "information itself as a price-attached asset." Around the same time, Adam Back's Hashcash proposal (1997) was posted to the list, suggesting a cost-attached design to "reduce noise by costing speech," and May's Cyphernomicon systematically bundled BlackNet with anonymous payment and information market ideas. Robin Hanson's Idea Futures was actively discussed primarily in the Extropians community, and the exchange of ideas crystallized the design of prediction markets and conditional markets.

The core is building markets that circumvent state licensing and bureaucratic review. Instead of licenses and permits, pseudonyms, deposits, reputation, and settlement logs guarantee contracts. Administrative delays are replaced by fixed evaluation times and oracle dispute procedures, and the boundaries of borders and jurisdictions are blurred by censorship-resistant payments and network-based arbitration. Bureaucratic judgment is replaced not by reports but by cost structures like escrow and slashing, and the interpretation of regulations is fixed by code, not legal text. In this order, the rule is implementation, not permission, and authority is proven by settlement, not declaration.

This trajectory also aligns with the intellectual landscape of the Bay Area during the same period. Hayek's insight that "scattered knowledge is summarized in the price" led to viewing the market as an information processing device, and Robin Hanson's 'Idea Futures' and conditional market designs, presented in the 1990s, provided concrete mechanisms connecting this perspective to decision-making. This trajectory was later summarized in the Futarchy slogan: "Vote on values, bet on beliefs."

Prediction markets stand at the center of this concept. They transform facts and forecasts into contracts, and by making those contracts tradable claims, they establish a price. Instead of reports and meeting minutes, the price moves first, and that signal realigns the actions of capital and organizations. The market is not where truth is announced, but where truth is bought, and governance is settlement, not debate. I affirm this transition. Here, hyper-financialization is an engine of alignment, not chaos. Hyper-financialization connects more domains to price and settlement, making signals denser and decision-making converge faster. Price makes falsehood expensive and truth cheap. Liquidity is not bait to attract attention but fuel to rapidly converge decisions. Hyperstition is not accidental self-realization but a circuit that binds belief with capital to generate action, and we can intentionally design that circuit.

When people hear hyper-financialization, they usually think of simple speculation. However, the core of hyper-financialization is not the expansion of speculation but the expansion of measurement. Translating more domains into the language of price and settlement means fixing promises in numbers and converting responsibility into positions. Words are light, positions are heavy. The price is a cost-attached assertion, and settlement is the judgment on that assertion.

The liberation sought by cypherpunks was not the revocation of permission but the irrelevance of permission. Instead of bureaucratic approval and licenses, public rules, deposits, reputation, and slashing guarantee the contract. Hyper-financialization extends this principle to decision-making across the board. If policy, product, and community choices are connected to price signals, debate leads to information submission, information submission leads to positions, and positions lead to settlement. Procedures are shortened, speed increases, and ex-post excuses are replaced by ex-ante betting.

We connect more domains to price and settlement, accelerating responsibility. The purpose is not the worship of capital but the protocolization of responsibility. Anyone can participate with small amounts, execution and price are transparently recorded, but participant identity and position size follow the minimum disclosure principle. What to monetize is also a design choice. Non-profit values such as public goods metrics, safety, quality, and delay rates can be placed on the settlement metric to create a signal. Price is not a device to fractionalize ownership but an interface to measure judgment and translate it into execution.

The criticism of speculation misses one fact: speculation is exploration, not randomness. Liquidity is a subsidy for information submission, and the spread is the price of uncertainty. In a well-designed market, alignment, not overheating, occurs. The settlement metric is fixed in advance, the oracle is disputable, and liquidity provision maintains symmetry. On these rails, falsehood becomes expensive, and truth becomes cheap. The price becomes not a mere reaction but a command that induces action, and the command, in turn, corrects the outcome. Prediction shifts to formation.

The prediction market is the experimental apparatus for verifying this thesis. It turns events and policies into contracts and extracts a signal by allowing those contracts to be bought and sold. When the price moves, resource allocation is realigned, and that realignment, in turn, corrects the probability of the outcome. This loop is both a risk and a force. The risk is reduced by guardrails, and the force is amplified by leverage. A well-designed prediction market turns hyper-financialization into an engine of alignment, not chaos.

I support this direction. The more domains are connected to price and settlement, the denser the signals become, and the faster decision-making converges. Society gains judgment faster than words, and organizations prove themselves with ex-ante positions, not ex-post excuses. More price, faster settlement, less permission. And less falsehood.

Hyperstition is a concept introduced by Nick Land, referring to a claim that makes itself real. Land's explanation is clear: representations do not follow reality; instead, narrative and symbols precede and call forth reality. It is a device where stories, symbols, mathematics, and code combine to pull future possibility into present action. Claims create narratives, narratives call forth positions, and positions realign physical outcomes and institutional arrangements. When this self-amplifying circuit, where narrative and outcome reinforce each other, is opened, a proposition that was fictional, at the level Land spoke of, gains real-world effect through narrative and practice. Translating this into market design language, the price signal and settlement procedure fix the narrative as an outcome. This perspective aligns with Land's concept of Teleoplexy.

Teleoplexy views capital as a machine that compresses information and draws the future forward. Price is a command signal that bundles decentralized signals into one line, credit preempts structures by staking present choices on the future, and speculation is an engine that explores unknown possibilities to reconfigure arrangements. When these three mechanisms interlock, capital acquires the power of command to coordinate market and social processes.

Based on the discussions of the cypherpunk mailing list, Hyperstition starts as a concept and a phenomenon but can be realized as a protocol through market design. In the hyper-financialization thesis, the prediction market operates not as a 'predictor' but as a device that forms and executes consensus through price and settlement. That consensus, in turn, calls forth action and executes Hyperstition. The core is that the price precedes the action. The preceding price is not merely a prediction but a signal that calls forth action. Once the price is established, organizations reallocate budgets and priorities, participants adjust their positions, and media and communities adjust their narratives. This realignment and narrative actually move the real probability, and the changed probability corrects the price again. The market becomes a control device that changes the trajectory, not an observation device.

For this 'price preceding' to work stably, rails are necessary. The design points described below start from the principles presented by cypherpunks in the 1990s-cost-attachment, pseudonymity, and permissionless markets-and are organized into implementation directions suited for today's prediction market and on-chain governance environment. They are not yet standard and may require adjustment depending on the context. First, clearly define the goal of the signal by fixing the settlement metric and oracle judgment rules. Next, specify the evaluation time, data source, missing data handling, and dispute resolution channel with the settlement schema. Where possible, conditional markets are opened in parallel to show the prices for both policy adoption and non-adoption. This ensures that those who attempt manipulation incur a cost, and that cost accrues as compensation for the opposing position. The loop is kept open, but the direction is led by the price.

Specifically, the loop operates as follows: First, a proposition is presented (e.g., The launch schedule will meet the target date). Second, the proposition is fixed as a price signal. Third, stakeholders observing the price reallocate resources and adjust strategy. Fourth, this change in behavior actually moves the probability of the outcome. Fifth, as the oracle settles, the quality of the signal is verified, with incorrect narratives resulting in cost, and correct narratives resulting in reward. As this circuit repeats, the narrative aligns, the signal becomes faster, and noise is reduced.

However, there are cases where the signal over-amplifies itself. This is a state of signal-to-noise collapse. The price excessively fluctuates with little new information, and that fluctuation itself stimulates people's words and actions, moving the price even further. For example, if a large order in a thin market pushes the price up, and that rise is noticed, the community and media amplify it as a story, and late buyers rush in after seeing the story, pushing the price up further-this is the 'story created by the price' driving the price, not information. Such signal-to-noise degradation can occur when the spread widens, capital is heavily skewed to one side, or when trading volume and volatility surge without clear news.

Three design points mitigate signal-to-noise degradation: First, designate a handle that connects directly to action. Execution levers like budget allocation, release schedules, or parameter settings must be connected to the price. Second, manage the time window and liquidity symmetry. If settlement is too close or capital is too heavily skewed, the narrative amplifies abnormally. Third, cost the narrative. Binding public announcements and promises to positions and deposits makes words light and actions heavy.

A noise filter is added here. This stems from the signal-to-noise (S/N) discussion repeated in the 1990s cypherpunk mailing list and the principle of costed speech suggested by Hashcash. It has been proposed in the subsequent on-chain environment, reviewed depending on the context, or experimented with in prototype stages. Small costs are attached to speech, sufficient time is attached to judgment, and multiple paths are attached to data. For example, commit-reveal is used to reduce herd following, minimum stakes and deposits curb spam orders, and making snapshot times difficult to predict dulls short-term manipulation. Oracles receive multiple quotes from independent sources and settle on the median, and disputes are submitted with collateral to make frivolous challenges expensive. Execution and price are transparently recorded, but participant identity and position size are protected by the minimum disclosure principle to reduce the noise of retaliation and cartels. The core is to increase the cost of noise and reduce the friction of the signal.

With these rails and filters in place, the prediction market converts this traction process into a public procedure, requiring participants to submit what they know through trade. Once settlement is complete, the narrative is updated along with the outcome, and the price of the next round converges faster. Truth becomes clearer not in the debate forum but in the settlement court.

In conclusion, Hyperstition is not a mystery. The settlement metric and oracle judgment rules provide the rails, conditional markets designate the direction, and public rules and deposits control the speed. The price forms first, and that price calls forth action, causing reality to follow. Here, prediction turns into formation, observation into operation, and words into settlement.

Assassination Politics (AP), a proposal serialized in essays by Jim Bell between 1995 and 1997, was designed so that compensation would only go to the person who accurately predicted the exact date of a specific person's death using strong anonymous electronic cash and public-key cryptography. Bell called this "prediction," but a structural analysis reveals it is not simple fortune-telling but a circuit where the price signal drives action and outcomes. The starting point is simple. Digital cash conceals who paid the money, and public-key cryptography proves what was paid. Linking the two creates a system where information is proven but identity is concealed.

Let's break down the operating flow according to the original text. Anyone can anonymously contribute small amounts to a public, subject-specific fund pool. The cumulative prize for the subject is immediately reflected on a public board, serving as a deterrent signal in itself. Separately, participants submit and disclose a prediction encrypted with a key known only to themselves. The content is in the form of "Subject X will die on YYYY-MM-DD." After the event occurs, the predictor discloses the key, self-attesting that the pre-submitted ciphertext was that exact prediction. The operating entity can send the prize only after the event without knowing the recipient's real identity, and is designed not to know the concrete details of the prediction (subject and date) until payment. Compensation is paid only for 'accurate date hitting' (based on the original plan), and to prevent random guessing, a meaningful electronic cash fee is enclosed with the prediction submission, making probabilistic spam uneconomical.

The economics of this design arise from its ex-post payment structure. Traditional deterrence systems require massive fixed costs (military, police, surveillance, judiciary), but AP only incurs costs when an event is confirmed. Contributors anonymously invest small amounts with limited risk, and the fund accumulates over time. At a certain point, when the expected reward crosses a threshold, the price signal itself is read as an incentive for action. From a deterrence perspective, it is a way to rationalize resource use by replacing the fixed costs of widespread surveillance and mobilization with event-unit variable costs. Furthermore, since the cost is concentrated on specific power executors, the precision of responsibility attribution increases. Bell hypothesized that the deterrent effect would be even greater if the target range was expanded beyond government power executors (those responsible for rights infringement incidents) to include foreign leaders who incite war or habitual criminals.

Here, the price goes beyond a summary of forecasts and becomes a signal close to execution. As the fund builds up and the distribution is revealed, stakeholders adjust their strategies. The calculus for security, exposure, and decision-making changes, and that change shifts the actual probability of the event. When the event occurs, settlement is made, and the quality of the signal is judged by cost and reward. This loop is a self-driving cycle of Price → Action → Outcome → Price. From this perspective, AP blatantly demonstrates the essence of a "prediction market." The focus is not on prophecy but on a device that translates consensus into action. The price is established first, and reality follows that price.

Transporting the same problematique to today's governance opens up different paths. Bell's concept was an idea of its time, but in today's language, it aligns with Futarchy's fundamental idea and clearly exemplifies the functional possibility of Hyperstition. The core is the flow that uses the price signal as the entry point for decision-making, translating consensus into execution. This flow is directly applicable to scenarios like budget allocation, policy adoption, and priority adjustment. For example, if a difficult-to-manipulate public value metric is prioritized, and money converges toward the choice that will best elevate that metric, that converged money becomes the signal that calls for execution.

AP most boldly illustrated this flow. When decentralized small amounts create an expected reward, the calculus of power changes, and the change in that calculus shifts the actual decision. Therefore, AP is a compelling example explaining how the slogan 'Vote on values, bet on beliefs' organizes real-world choices.

Furthermore, this family of ideas has the potential to converge into a single protocol from a Teleoplexy perspective. If we call the cycle of Narrative–Price–Action–Outcome–Narrative the Teleoplexy Protocol, the tasks are two: First, to establish the minimum discipline to stably maintain the price-preceding loop. Second, to clearly define the criteria for translating consensus into execution. In this context, the market transcends an observation device and becomes a self-pulling device for the future.

The Policy Analysis Market (PAM) was an information market concept promoted around 2001 by the Information Awareness Office (IAO) under DARPA as part of the FutureMAP program, designed to assist policy decision-making. Starting from a proposal by a private vendor (Net Exchange), it sought to create contracts for conditional scenarios based on geopolitical developments and policy choices in the Middle East, allowing participants to buy and sell these contracts, thus using the price as a policy signal. The Iowa Electronic Market (IEM), known for its high accuracy at the time, was cited as a reference, and potential operating partners were lined up. The core was not a gamble on specific terrorist details but estimating the difference in outcomes based on policy choices via price, providing real-time signals to decision-makers.

In brief, the timeline was: Development started in 2001 → Planned to open pilot trading in September 2003 with $100 allocated to 100 preliminary testers → Public trading (up to 1,000 participants) aimed for January 2004 launch. The IAO was already under political pressure due to its 'TIA (Total Information Awareness)' program during the same period, and PAM shared its fate with this budget line. When sensitive event examples (assassinations, coups, etc.) were exposed in the sample screen of the project website, public criticism from the Senate was triggered in late July 2003, and the Department of Defense announced the cancellation less than a day later. The IAO director's subsequent resignation followed, and PAM was halted due to political appearance issues.

Reconstructing the design philosophy, the core was fourfold. First, the scope focused not on high-volatility items like single terrorist incidents but on macro variables that are measurable with public data and difficult to manipulate, such as regime stability, inflation, bond spreads, exchange rates, unemployment, and frequency of armed conflicts. Second, conditional contracts were opened in parallel. For example, when strengthening and relaxing sanctions compete, the expected values of settlement metrics-such as the 6-month average CDS, the number of violent incidents, or growth rate-were priced separately under each condition, and the difference between the two prices was used as a policy input. This difference was interpreted not as a simple forecast but as a signal about the policy's causal direction. Third, continuous indicator-type contracts were mixed to reduce distortion from single events. Using time-aggregated metrics like 90-day moving averages, quarterly totals, or the number of days exceeding a threshold within a period reduced noise and smoothed the signal. Fourth, accountability of the price was secured with public rules and auditable records. Contract specifications, settlement metric definitions and data sources, cutoffs, and dispute resolution channels were publicly announced, and execution logs, quote history, and settlement reports were preserved for ex-post verification. The price difference obtained this way became a fearless signal replacing reports and briefings. Position preceded words, and costed information surfaced instead of subjective narratives.

Why did it fail? The criticism successfully relied on the moral rhetoric that "the federal government encourages betting on atrocities," framing PAM as a machine for moral hazard. Furthermore, geopolitical derivatives were likely to clash with CFTC regulations, and the political risk of the IAO budget resulted in PAM becoming a target of attack. Consequently, PAM failed to defend the economics of policy prediction markets in the language of public politics.

However, the idea did not disappear. Even after the public launch was thwarted, subsequent experiments like ICPM (Intelligence Community Prediction Market) and ACE (Aggregated Contingent Estimates) were successfully operated within the Intelligence Community (IC) in the 2010s. That is, the mechanism works if the political facade is removed and participants, contracts, and transparency are controlled. Private variations like Intrade also provided geopolitical contracts, confirming demand (though they were later halted due to financial and regulatory risks).

The key is the interface, not the norm. Policy is priced when presented not as text but as a settlable metric, and when priced, the choice is not delayed. The price gap between the conditional prices of Policy A and B is read as a hint of causality, not a simple forecast, and public topic boundaries, settlement metrics, evaluation windows, and dispute resolution fix that hint as responsibility. In contexts where an open market is politically impossible, the same mechanism can be preserved by moving it to an internal/expert market. The conclusion is clear. Decision-making is aligned faster by a cost-attached signal than by the heat of debate. In that sense, PAM was a preceding attempt to verify that conditional prediction markets could function as input signals for policy choices within an institutional context, and an example of testing a public version of the Hyperstition circuit where the price is established first and reality follows.

More importantly, the potential seen by the parties involved at the time: First, they believed that parallel comparison of conditional prices provided a causal hint for policy, not just a simple forecast, making the responsibility of choice clearer. Second, the price record became a faster, audit-resistant feedback channel than reports, providing behavioral guidance that circumvented bureaucratic narratives. Third, they judged that the same function could be maintained through internal, exclusive markets even if the open type was politically blocked, and subsequent experiments supported that assumption. Fourth, the market interface could be a control stick for decisions, not a memo or briefing book for the Secretary's office, and they confirmed that organizational budget and priorities realign with the price signal when the price precedes. The implication left by PAM is clear. Policy is not aligned only by discourse. When granted the control surface of price, the state can correct itself more quickly.

The conclusion is that prediction markets converge to this circuit. A Narrative (N) is fixed by a Price (P), that price induces an Action (A), and the action creates an Outcome (O) that updates the Narrative again. That is, a closed loop of N (Narrative) → P (Price) → A (Action) → O (Outcome) → N (Narrative). The prediction market is the machine that opens the P phase of this loop, and hyper-financialization connects N, P, and A more densely, increasing the speed of the loop. Future markets will not be bulletin boards showing probabilities but operational interfaces where the price is established first, and organizations use that price as a default input to automate execution. The structure is the same at the community level. Price is responsible for the signal, settlement for verification, and logs for responsibility.

The minimum principles required to become the standard are simple.

• Signal Discipline: Prioritize settlable metrics and judgment rules to clarify the price's goal.

• Execution Handle: Pre-determine execution levers connected to the price to prevent the signal from being consumed in the air.

• Responsibility Ledger: Preserve execution logs and settlement reports to track the path where the signal was translated into action.

• Noise Filter: Mitigate overheating with costed speech elements like time windows, liquidity symmetry, commit-reveal, and deposits.

• Symmetric Incentive: Design the system so that the reward for the opposite side automatically increases when skew toward one side becomes severe, maintaining equilibrium.

With these rails in place, the loop accelerates itself. Narrative creates price, price calls forth action, and outcome corrects narrative. Prediction changes to formation, observation to operation, and words to settlement. The prediction market will ultimately adopt this protocol as the default, and the price will become a command, not a report.

Furthermore, the loop does not end after one cycle. The N-P-A-O-N loop, as it repeats, updates the baseline of the narrative, and the price formation of the next round uses this updated narrative as the initial condition. Repetition works in two directions: First, Convergence Mode: If settlement consistently sends the same signal, Narrative-Price-Action gains coherence, and volatility decreases. Second, Amplification Mode: If the signal is unstable or noise is high, the narrative is overcorrected, and the price amplifies this again, creating a path-dependent divergence. Therefore, the minimum principles (Signal Discipline/Execution Handle/Responsibility Ledger/Noise Filter/Symmetric Incentive) are not just a checklist for the first execution but stability conditions for the repeated loop. Understanding this repeatability means 'prediction' leads to narrative update in every round, and the narrative becomes the price initialization for the next round.

When the prediction market becomes an operational interface, implementation will converge along the following path:

1. Near Stage: Elevate internal prediction markets to the operational layer. Fix settlement metrics and document the Price Threshold → Execution Lever rules. Reports are condensed into price dashboards.

2. Next Stage: Compare policy and product choices using the price gap of conditional markets. Pre-agree on automatic submission or immediate execution procedures upon exceeding a threshold.

3. Expansion Stage: Institutionalize discipline rails like oracle standards, public audit logs, and deposit-based dispute resolution, and open composable APIs that link price signals to external systems.

4. Stabilization Stage: Continuously operate noise filters and symmetric incentives to prevent overheating of the N-P-A-O-N loop and manage the speed of convergence.

Following this path, the market becomes a control device that changes the trajectory, not an observation device. Price becomes an execution signal, not an object of interpretation, and the loop becomes faster with each repetition.

Even today, many builders approach prediction markets with the 'casino' frame. However, cypherpunks had already built a long history of productive discussions in the 1990s—costed belief, pseudonymity, costed speech, conditional markets, and settlement rules. That accumulation has continued through early cypherpunk proposals and internal prediction market experiments. The more the rails are laid, the more the market matures from a game to an operation. This is the purpose of this article: to reconnect that discussion in today's language and broaden the conversation so that builders design prediction markets as information aggregation and execution interfaces. The maturation has already begun.

The maturation referred to here is not the simple expansion of speculation. Hyper-financialization is the flow of measuring more domains with the language of price and settlement, accelerating responsibility. Hyperstition is the phenomenon where narrative and capital combine to precede price, and that price calls forth action to realign reality. The prediction market is the interface that translates this process into the execution phase, fixing the narrative as reality through the preceding price. This N-P-A-O-N operational loop closes that process. When these three axes overlap, the prediction market transcends an observation tool and becomes an interface that translates consensus into execution.

One question remains: How far will we extend this signaling system?

Hyperstition pulls narrative into reality. Hyper-financialization pulls responsibility forward with price and settlement, removing the shadow of nihilistic finance. The prediction market is the interface for this flow. Its speed will soon increase. Accelerate.

Christopher Choi
X : @ChrisinaShell

These days, discussions about prediction markets are abundant on social media. However, many of these discussions often reduce prediction markets merely to a "casino." Such a view is nothing more than a tourist's observation. A prediction market is not a game of simply guessing outcomes. Beyond passive forecasting, prediction markets can serve as an active governance protocol that aggregates decentralized information through economic incentives and actively shapes real-world outcomes based on that aggregated information.

The reason we can trust the price is simple. Words cost nothing, but trades have a cost. Buying and selling are actions that carry responsibility, and their results are recorded in the price. Under the simple assumption of risk-neutrality, this record is interpreted as the probability of an event. In other words, the price in a prediction market is not gossip but a belief verified by money, and it self-corrects through profit and loss whenever new information arrives. Here, the price is not an utterance but a trace of execution. Each participant submits the piece of information they possess along with money, and the aggregate becomes the price. This structure uses "skin in the game" to make falsehood expensive and information cheap. This article attempts to frame prediction markets, Futarchy, and Hyperstition together, peeling back the common misconception of a "casino" to broaden the scope of the discussion. While it lightly invokes the problematique of the 90s cypherpunk mailing list as a reference, the core is simple: to show how to read price as information and connect that information to decisions using contemporary examples and designs.

The 90s cypherpunk mailing list explicitly discussed this point. They saw digital cash not as an end goal but as a bootstrap, seeking to extract truth and decisions from the market through markets that circumvented regulation. Timothy C. May's Cyphernomicon argued that the combination of cryptography, anonymity, and digital cash could create a structure where information converges into a price, and that price incentivizes action, all without central authority. As Robin Hanson's ideas on information markets and conditional markets, which intersected with the Extropy community around the same time, flowed into the community, the notion of selecting policies through markets became concrete. Extreme examples included Jim Bell’s Assassination Politics, followed by more institutional experiments such as DARPA's Policy Analysis Market. The point was clear: they treated prediction markets not as a spectacle but as a mechanism that aggregates information into a price to regulate reality. It was a method of gathering information with money at stake and converting it into a decision on a censorship-resistant payment rail. In Nick Land's terminology, the prediction market is a device for designing a future (Hyperstition) that realizes itself by binding belief with capital. The prediction market is a cockpit, not an observation deck.

The prediction market creates a price by trading payoffs tied to future events. Since trading is an action with responsibility, the price reflects only judgments for which a cost has been paid. Assuming risk-neutrality for simplicity, this price is read as the probability of the event. Thus, the prediction market is not a lucky bet but a device that compresses scattered private information into a single signal.

The payoff here refers to a payment determined later based on a future outcome, not the present moment. The simplest form is a binary contract that pays $1 if an event occurs and $0 if it does not, followed by scalar contracts that pay in proportion to a continuously varying metric. Futarchy uses conditional contracts that pay based on a metric only if a specific policy is adopted. When the actual outcome is confirmed, an oracle finalizes the value and settles the contract. In this structure, the current market price becomes a summary of the expected payoff submitted by participants along with their money and information. In the case of binary contracts, it is effectively read as the probability of that event occurring. This is where Hayek’s observation comes to life. Knowledge scattered throughout society is summarized in the price, and the prediction market extends this summary into the future, where each person's piece of information is combined through trade, and the result becomes the collective probabilistic judgment.

This covers what is being traded and how its price is read. The remaining question is simple: How is that price continuously created and maintained? If this part falters, aggregation stops.

A design that reduces friction is necessary for smooth aggregation. Ideally, in a decentralized market, anyone should be able to open a market and anyone should be able to provide liquidity. Quotes should continue without the permission of any specific entity, and as the number of participants increases, the price adjusts more closely to real-world information. The price naturally seeks equilibrium, becoming more expensive when betting crowds one side and cheaper when interest wanes on the other.

In reality, we have not yet reached the perfect ideal. Today, various mechanisms-such as AMMs, order books, and incentive programs-are combined to ensure liquidity and prevent trades from breaking down. Ultimately, the criteria are simple: first, the price must be continuously presented; second, the market must slightly adjust its direction with every incoming trade. Regardless of the implementation method, if these two conditions are met, the price quickly absorbs new information without excessive volatility.

Now, let's look at one scenario where this rule actually aids a decision. "Will the next quarter's launch hit the scheduled date?" Insiders, users, supply chain partners, and analysts submit their fragmented information along with money. If signs of a launch delay appear, the price drops; if beta reaction is good, it rises. The team can use this signal to reallocate resources. This is the moment when the aggregated price aids execution.

When moving from aggregation to execution, limitations and ripple effects arise. As the signal strengthens, participant strategies change, those strategies alter the outcome, and the altered outcome, in turn, corrects the price. This loop is both a risk and a force. Three key variables determine market quality: first, illiquidity when trading is thin; second, biased participation skewed toward specific groups and stakeholders propping up their own positions; and third, incorrect settlement due to oracle delay, error, or manipulation.

Futarchy doesn't eliminate these problems but seeks to control them by converting them into costed beliefs. Value metrics are fixed by voting, factual judgments are left to the market, and policies are chosen by the difference in conditional prices. Any attempt at manipulation must incur a cost, and that cost becomes an opportunity for profit for the opposing side. However, if the market is thin, the time window is short, or the settlement metric and oracle judgment rules are fragile, biased participation can leverage Hyperstition to warp reality. Therefore, guardrails are necessary. Fix the settlement schema in advance, avoid hasty evaluation times, symmetrically incentivize participation, and ensure trust through disputable oracles and collateral structures. The loop must be open, but the rails must be robust.

In a market with guardrails, the price is the record left by a costed choice, and buying and selling are public procedures that surface scattered tacit knowledge. When this aggregated signal leads to a decision, the prediction market shifts from a spectacle to an operation.

The perspective that views prediction markets as an information aggregation engine stems directly from the problematique of cypherpunks. The goal is to enable exchange and cooperation to function without the state's license or bureaucratic review. Digital cash is a bootstrap, not a destination; cryptography is a tool to decouple identity and confer reputation on pseudonyms; and public rules enable order to operate without permission. At this point, the market operates as a protocol, not by permission.

Cypherpunk was a loose hacker movement that began in the early 1990s on the US West Coast. Timothy C. May's Crypto Anarchist Manifesto outlined the principles in 1988, and a public subscription mailing list started by Eric Hughes, John Gilmore, and Tim May in 1992 became the forum for discussion. By 1994, it had grown to hundreds of members, exchanging dozens of technical, political, and practical proposals daily. They viewed strong cryptography as a tool for individual sovereignty, treated privacy as a prerequisite for freedom, and prioritized code over arguments.

The blueprints for these concepts were repeatedly submitted to the cypherpunk mailing list. Anonymous remailers decoupled identity, PGP signatures fixed pseudonyms, digital cash handled payments, and escrow and deposits costed breaches of contract. Reputation and Webs of Trust were combined to accumulate trust in a decentralized manner, and timelocks conditioned the release of information. To reduce spam and fraud, hash-based Proof-of-Work priced externalities, and blind-signature electronic cash provided a payment path without tracking. May's proposed BlackNet added information bounties, sealed-bid auctions, and the circulation of data and reports to treat "information itself as a price-attached asset." Around the same time, Adam Back's Hashcash proposal (1997) was posted to the list, suggesting a cost-attached design to "reduce noise by costing speech," and May's Cyphernomicon systematically bundled BlackNet with anonymous payment and information market ideas. Robin Hanson's Idea Futures was actively discussed primarily in the Extropians community, and the exchange of ideas crystallized the design of prediction markets and conditional markets.

The core is building markets that circumvent state licensing and bureaucratic review. Instead of licenses and permits, pseudonyms, deposits, reputation, and settlement logs guarantee contracts. Administrative delays are replaced by fixed evaluation times and oracle dispute procedures, and the boundaries of borders and jurisdictions are blurred by censorship-resistant payments and network-based arbitration. Bureaucratic judgment is replaced not by reports but by cost structures like escrow and slashing, and the interpretation of regulations is fixed by code, not legal text. In this order, the rule is implementation, not permission, and authority is proven by settlement, not declaration.

This trajectory also aligns with the intellectual landscape of the Bay Area during the same period. Hayek's insight that "scattered knowledge is summarized in the price" led to viewing the market as an information processing device, and Robin Hanson's 'Idea Futures' and conditional market designs, presented in the 1990s, provided concrete mechanisms connecting this perspective to decision-making. This trajectory was later summarized in the Futarchy slogan: "Vote on values, bet on beliefs."

Prediction markets stand at the center of this concept. They transform facts and forecasts into contracts, and by making those contracts tradable claims, they establish a price. Instead of reports and meeting minutes, the price moves first, and that signal realigns the actions of capital and organizations. The market is not where truth is announced, but where truth is bought, and governance is settlement, not debate. I affirm this transition. Here, hyper-financialization is an engine of alignment, not chaos. Hyper-financialization connects more domains to price and settlement, making signals denser and decision-making converge faster. Price makes falsehood expensive and truth cheap. Liquidity is not bait to attract attention but fuel to rapidly converge decisions. Hyperstition is not accidental self-realization but a circuit that binds belief with capital to generate action, and we can intentionally design that circuit.

When people hear hyper-financialization, they usually think of simple speculation. However, the core of hyper-financialization is not the expansion of speculation but the expansion of measurement. Translating more domains into the language of price and settlement means fixing promises in numbers and converting responsibility into positions. Words are light, positions are heavy. The price is a cost-attached assertion, and settlement is the judgment on that assertion.

The liberation sought by cypherpunks was not the revocation of permission but the irrelevance of permission. Instead of bureaucratic approval and licenses, public rules, deposits, reputation, and slashing guarantee the contract. Hyper-financialization extends this principle to decision-making across the board. If policy, product, and community choices are connected to price signals, debate leads to information submission, information submission leads to positions, and positions lead to settlement. Procedures are shortened, speed increases, and ex-post excuses are replaced by ex-ante betting.

We connect more domains to price and settlement, accelerating responsibility. The purpose is not the worship of capital but the protocolization of responsibility. Anyone can participate with small amounts, execution and price are transparently recorded, but participant identity and position size follow the minimum disclosure principle. What to monetize is also a design choice. Non-profit values such as public goods metrics, safety, quality, and delay rates can be placed on the settlement metric to create a signal. Price is not a device to fractionalize ownership but an interface to measure judgment and translate it into execution.

The criticism of speculation misses one fact: speculation is exploration, not randomness. Liquidity is a subsidy for information submission, and the spread is the price of uncertainty. In a well-designed market, alignment, not overheating, occurs. The settlement metric is fixed in advance, the oracle is disputable, and liquidity provision maintains symmetry. On these rails, falsehood becomes expensive, and truth becomes cheap. The price becomes not a mere reaction but a command that induces action, and the command, in turn, corrects the outcome. Prediction shifts to formation.

The prediction market is the experimental apparatus for verifying this thesis. It turns events and policies into contracts and extracts a signal by allowing those contracts to be bought and sold. When the price moves, resource allocation is realigned, and that realignment, in turn, corrects the probability of the outcome. This loop is both a risk and a force. The risk is reduced by guardrails, and the force is amplified by leverage. A well-designed prediction market turns hyper-financialization into an engine of alignment, not chaos.

I support this direction. The more domains are connected to price and settlement, the denser the signals become, and the faster decision-making converges. Society gains judgment faster than words, and organizations prove themselves with ex-ante positions, not ex-post excuses. More price, faster settlement, less permission. And less falsehood.

Hyperstition is a concept introduced by Nick Land, referring to a claim that makes itself real. Land's explanation is clear: representations do not follow reality; instead, narrative and symbols precede and call forth reality. It is a device where stories, symbols, mathematics, and code combine to pull future possibility into present action. Claims create narratives, narratives call forth positions, and positions realign physical outcomes and institutional arrangements. When this self-amplifying circuit, where narrative and outcome reinforce each other, is opened, a proposition that was fictional, at the level Land spoke of, gains real-world effect through narrative and practice. Translating this into market design language, the price signal and settlement procedure fix the narrative as an outcome. This perspective aligns with Land's concept of Teleoplexy.

Teleoplexy views capital as a machine that compresses information and draws the future forward. Price is a command signal that bundles decentralized signals into one line, credit preempts structures by staking present choices on the future, and speculation is an engine that explores unknown possibilities to reconfigure arrangements. When these three mechanisms interlock, capital acquires the power of command to coordinate market and social processes.

Based on the discussions of the cypherpunk mailing list, Hyperstition starts as a concept and a phenomenon but can be realized as a protocol through market design. In the hyper-financialization thesis, the prediction market operates not as a 'predictor' but as a device that forms and executes consensus through price and settlement. That consensus, in turn, calls forth action and executes Hyperstition. The core is that the price precedes the action. The preceding price is not merely a prediction but a signal that calls forth action. Once the price is established, organizations reallocate budgets and priorities, participants adjust their positions, and media and communities adjust their narratives. This realignment and narrative actually move the real probability, and the changed probability corrects the price again. The market becomes a control device that changes the trajectory, not an observation device.

For this 'price preceding' to work stably, rails are necessary. The design points described below start from the principles presented by cypherpunks in the 1990s-cost-attachment, pseudonymity, and permissionless markets-and are organized into implementation directions suited for today's prediction market and on-chain governance environment. They are not yet standard and may require adjustment depending on the context. First, clearly define the goal of the signal by fixing the settlement metric and oracle judgment rules. Next, specify the evaluation time, data source, missing data handling, and dispute resolution channel with the settlement schema. Where possible, conditional markets are opened in parallel to show the prices for both policy adoption and non-adoption. This ensures that those who attempt manipulation incur a cost, and that cost accrues as compensation for the opposing position. The loop is kept open, but the direction is led by the price.

Specifically, the loop operates as follows: First, a proposition is presented (e.g., The launch schedule will meet the target date). Second, the proposition is fixed as a price signal. Third, stakeholders observing the price reallocate resources and adjust strategy. Fourth, this change in behavior actually moves the probability of the outcome. Fifth, as the oracle settles, the quality of the signal is verified, with incorrect narratives resulting in cost, and correct narratives resulting in reward. As this circuit repeats, the narrative aligns, the signal becomes faster, and noise is reduced.

However, there are cases where the signal over-amplifies itself. This is a state of signal-to-noise collapse. The price excessively fluctuates with little new information, and that fluctuation itself stimulates people's words and actions, moving the price even further. For example, if a large order in a thin market pushes the price up, and that rise is noticed, the community and media amplify it as a story, and late buyers rush in after seeing the story, pushing the price up further-this is the 'story created by the price' driving the price, not information. Such signal-to-noise degradation can occur when the spread widens, capital is heavily skewed to one side, or when trading volume and volatility surge without clear news.

Three design points mitigate signal-to-noise degradation: First, designate a handle that connects directly to action. Execution levers like budget allocation, release schedules, or parameter settings must be connected to the price. Second, manage the time window and liquidity symmetry. If settlement is too close or capital is too heavily skewed, the narrative amplifies abnormally. Third, cost the narrative. Binding public announcements and promises to positions and deposits makes words light and actions heavy.

A noise filter is added here. This stems from the signal-to-noise (S/N) discussion repeated in the 1990s cypherpunk mailing list and the principle of costed speech suggested by Hashcash. It has been proposed in the subsequent on-chain environment, reviewed depending on the context, or experimented with in prototype stages. Small costs are attached to speech, sufficient time is attached to judgment, and multiple paths are attached to data. For example, commit-reveal is used to reduce herd following, minimum stakes and deposits curb spam orders, and making snapshot times difficult to predict dulls short-term manipulation. Oracles receive multiple quotes from independent sources and settle on the median, and disputes are submitted with collateral to make frivolous challenges expensive. Execution and price are transparently recorded, but participant identity and position size are protected by the minimum disclosure principle to reduce the noise of retaliation and cartels. The core is to increase the cost of noise and reduce the friction of the signal.

With these rails and filters in place, the prediction market converts this traction process into a public procedure, requiring participants to submit what they know through trade. Once settlement is complete, the narrative is updated along with the outcome, and the price of the next round converges faster. Truth becomes clearer not in the debate forum but in the settlement court.

In conclusion, Hyperstition is not a mystery. The settlement metric and oracle judgment rules provide the rails, conditional markets designate the direction, and public rules and deposits control the speed. The price forms first, and that price calls forth action, causing reality to follow. Here, prediction turns into formation, observation into operation, and words into settlement.

Assassination Politics (AP), a proposal serialized in essays by Jim Bell between 1995 and 1997, was designed so that compensation would only go to the person who accurately predicted the exact date of a specific person's death using strong anonymous electronic cash and public-key cryptography. Bell called this "prediction," but a structural analysis reveals it is not simple fortune-telling but a circuit where the price signal drives action and outcomes. The starting point is simple. Digital cash conceals who paid the money, and public-key cryptography proves what was paid. Linking the two creates a system where information is proven but identity is concealed.

Let's break down the operating flow according to the original text. Anyone can anonymously contribute small amounts to a public, subject-specific fund pool. The cumulative prize for the subject is immediately reflected on a public board, serving as a deterrent signal in itself. Separately, participants submit and disclose a prediction encrypted with a key known only to themselves. The content is in the form of "Subject X will die on YYYY-MM-DD." After the event occurs, the predictor discloses the key, self-attesting that the pre-submitted ciphertext was that exact prediction. The operating entity can send the prize only after the event without knowing the recipient's real identity, and is designed not to know the concrete details of the prediction (subject and date) until payment. Compensation is paid only for 'accurate date hitting' (based on the original plan), and to prevent random guessing, a meaningful electronic cash fee is enclosed with the prediction submission, making probabilistic spam uneconomical.

The economics of this design arise from its ex-post payment structure. Traditional deterrence systems require massive fixed costs (military, police, surveillance, judiciary), but AP only incurs costs when an event is confirmed. Contributors anonymously invest small amounts with limited risk, and the fund accumulates over time. At a certain point, when the expected reward crosses a threshold, the price signal itself is read as an incentive for action. From a deterrence perspective, it is a way to rationalize resource use by replacing the fixed costs of widespread surveillance and mobilization with event-unit variable costs. Furthermore, since the cost is concentrated on specific power executors, the precision of responsibility attribution increases. Bell hypothesized that the deterrent effect would be even greater if the target range was expanded beyond government power executors (those responsible for rights infringement incidents) to include foreign leaders who incite war or habitual criminals.

Here, the price goes beyond a summary of forecasts and becomes a signal close to execution. As the fund builds up and the distribution is revealed, stakeholders adjust their strategies. The calculus for security, exposure, and decision-making changes, and that change shifts the actual probability of the event. When the event occurs, settlement is made, and the quality of the signal is judged by cost and reward. This loop is a self-driving cycle of Price → Action → Outcome → Price. From this perspective, AP blatantly demonstrates the essence of a "prediction market." The focus is not on prophecy but on a device that translates consensus into action. The price is established first, and reality follows that price.

Transporting the same problematique to today's governance opens up different paths. Bell's concept was an idea of its time, but in today's language, it aligns with Futarchy's fundamental idea and clearly exemplifies the functional possibility of Hyperstition. The core is the flow that uses the price signal as the entry point for decision-making, translating consensus into execution. This flow is directly applicable to scenarios like budget allocation, policy adoption, and priority adjustment. For example, if a difficult-to-manipulate public value metric is prioritized, and money converges toward the choice that will best elevate that metric, that converged money becomes the signal that calls for execution.

AP most boldly illustrated this flow. When decentralized small amounts create an expected reward, the calculus of power changes, and the change in that calculus shifts the actual decision. Therefore, AP is a compelling example explaining how the slogan 'Vote on values, bet on beliefs' organizes real-world choices.

Furthermore, this family of ideas has the potential to converge into a single protocol from a Teleoplexy perspective. If we call the cycle of Narrative–Price–Action–Outcome–Narrative the Teleoplexy Protocol, the tasks are two: First, to establish the minimum discipline to stably maintain the price-preceding loop. Second, to clearly define the criteria for translating consensus into execution. In this context, the market transcends an observation device and becomes a self-pulling device for the future.

The Policy Analysis Market (PAM) was an information market concept promoted around 2001 by the Information Awareness Office (IAO) under DARPA as part of the FutureMAP program, designed to assist policy decision-making. Starting from a proposal by a private vendor (Net Exchange), it sought to create contracts for conditional scenarios based on geopolitical developments and policy choices in the Middle East, allowing participants to buy and sell these contracts, thus using the price as a policy signal. The Iowa Electronic Market (IEM), known for its high accuracy at the time, was cited as a reference, and potential operating partners were lined up. The core was not a gamble on specific terrorist details but estimating the difference in outcomes based on policy choices via price, providing real-time signals to decision-makers.

In brief, the timeline was: Development started in 2001 → Planned to open pilot trading in September 2003 with $100 allocated to 100 preliminary testers → Public trading (up to 1,000 participants) aimed for January 2004 launch. The IAO was already under political pressure due to its 'TIA (Total Information Awareness)' program during the same period, and PAM shared its fate with this budget line. When sensitive event examples (assassinations, coups, etc.) were exposed in the sample screen of the project website, public criticism from the Senate was triggered in late July 2003, and the Department of Defense announced the cancellation less than a day later. The IAO director's subsequent resignation followed, and PAM was halted due to political appearance issues.

Reconstructing the design philosophy, the core was fourfold. First, the scope focused not on high-volatility items like single terrorist incidents but on macro variables that are measurable with public data and difficult to manipulate, such as regime stability, inflation, bond spreads, exchange rates, unemployment, and frequency of armed conflicts. Second, conditional contracts were opened in parallel. For example, when strengthening and relaxing sanctions compete, the expected values of settlement metrics-such as the 6-month average CDS, the number of violent incidents, or growth rate-were priced separately under each condition, and the difference between the two prices was used as a policy input. This difference was interpreted not as a simple forecast but as a signal about the policy's causal direction. Third, continuous indicator-type contracts were mixed to reduce distortion from single events. Using time-aggregated metrics like 90-day moving averages, quarterly totals, or the number of days exceeding a threshold within a period reduced noise and smoothed the signal. Fourth, accountability of the price was secured with public rules and auditable records. Contract specifications, settlement metric definitions and data sources, cutoffs, and dispute resolution channels were publicly announced, and execution logs, quote history, and settlement reports were preserved for ex-post verification. The price difference obtained this way became a fearless signal replacing reports and briefings. Position preceded words, and costed information surfaced instead of subjective narratives.

Why did it fail? The criticism successfully relied on the moral rhetoric that "the federal government encourages betting on atrocities," framing PAM as a machine for moral hazard. Furthermore, geopolitical derivatives were likely to clash with CFTC regulations, and the political risk of the IAO budget resulted in PAM becoming a target of attack. Consequently, PAM failed to defend the economics of policy prediction markets in the language of public politics.

However, the idea did not disappear. Even after the public launch was thwarted, subsequent experiments like ICPM (Intelligence Community Prediction Market) and ACE (Aggregated Contingent Estimates) were successfully operated within the Intelligence Community (IC) in the 2010s. That is, the mechanism works if the political facade is removed and participants, contracts, and transparency are controlled. Private variations like Intrade also provided geopolitical contracts, confirming demand (though they were later halted due to financial and regulatory risks).

The key is the interface, not the norm. Policy is priced when presented not as text but as a settlable metric, and when priced, the choice is not delayed. The price gap between the conditional prices of Policy A and B is read as a hint of causality, not a simple forecast, and public topic boundaries, settlement metrics, evaluation windows, and dispute resolution fix that hint as responsibility. In contexts where an open market is politically impossible, the same mechanism can be preserved by moving it to an internal/expert market. The conclusion is clear. Decision-making is aligned faster by a cost-attached signal than by the heat of debate. In that sense, PAM was a preceding attempt to verify that conditional prediction markets could function as input signals for policy choices within an institutional context, and an example of testing a public version of the Hyperstition circuit where the price is established first and reality follows.

More importantly, the potential seen by the parties involved at the time: First, they believed that parallel comparison of conditional prices provided a causal hint for policy, not just a simple forecast, making the responsibility of choice clearer. Second, the price record became a faster, audit-resistant feedback channel than reports, providing behavioral guidance that circumvented bureaucratic narratives. Third, they judged that the same function could be maintained through internal, exclusive markets even if the open type was politically blocked, and subsequent experiments supported that assumption. Fourth, the market interface could be a control stick for decisions, not a memo or briefing book for the Secretary's office, and they confirmed that organizational budget and priorities realign with the price signal when the price precedes. The implication left by PAM is clear. Policy is not aligned only by discourse. When granted the control surface of price, the state can correct itself more quickly.

The conclusion is that prediction markets converge to this circuit. A Narrative (N) is fixed by a Price (P), that price induces an Action (A), and the action creates an Outcome (O) that updates the Narrative again. That is, a closed loop of N (Narrative) → P (Price) → A (Action) → O (Outcome) → N (Narrative). The prediction market is the machine that opens the P phase of this loop, and hyper-financialization connects N, P, and A more densely, increasing the speed of the loop. Future markets will not be bulletin boards showing probabilities but operational interfaces where the price is established first, and organizations use that price as a default input to automate execution. The structure is the same at the community level. Price is responsible for the signal, settlement for verification, and logs for responsibility.

The minimum principles required to become the standard are simple.

• Signal Discipline: Prioritize settlable metrics and judgment rules to clarify the price's goal.

• Execution Handle: Pre-determine execution levers connected to the price to prevent the signal from being consumed in the air.

• Responsibility Ledger: Preserve execution logs and settlement reports to track the path where the signal was translated into action.

• Noise Filter: Mitigate overheating with costed speech elements like time windows, liquidity symmetry, commit-reveal, and deposits.

• Symmetric Incentive: Design the system so that the reward for the opposite side automatically increases when skew toward one side becomes severe, maintaining equilibrium.

With these rails in place, the loop accelerates itself. Narrative creates price, price calls forth action, and outcome corrects narrative. Prediction changes to formation, observation to operation, and words to settlement. The prediction market will ultimately adopt this protocol as the default, and the price will become a command, not a report.

Furthermore, the loop does not end after one cycle. The N-P-A-O-N loop, as it repeats, updates the baseline of the narrative, and the price formation of the next round uses this updated narrative as the initial condition. Repetition works in two directions: First, Convergence Mode: If settlement consistently sends the same signal, Narrative-Price-Action gains coherence, and volatility decreases. Second, Amplification Mode: If the signal is unstable or noise is high, the narrative is overcorrected, and the price amplifies this again, creating a path-dependent divergence. Therefore, the minimum principles (Signal Discipline/Execution Handle/Responsibility Ledger/Noise Filter/Symmetric Incentive) are not just a checklist for the first execution but stability conditions for the repeated loop. Understanding this repeatability means 'prediction' leads to narrative update in every round, and the narrative becomes the price initialization for the next round.

When the prediction market becomes an operational interface, implementation will converge along the following path:

1. Near Stage: Elevate internal prediction markets to the operational layer. Fix settlement metrics and document the Price Threshold → Execution Lever rules. Reports are condensed into price dashboards.

2. Next Stage: Compare policy and product choices using the price gap of conditional markets. Pre-agree on automatic submission or immediate execution procedures upon exceeding a threshold.

3. Expansion Stage: Institutionalize discipline rails like oracle standards, public audit logs, and deposit-based dispute resolution, and open composable APIs that link price signals to external systems.

4. Stabilization Stage: Continuously operate noise filters and symmetric incentives to prevent overheating of the N-P-A-O-N loop and manage the speed of convergence.

Following this path, the market becomes a control device that changes the trajectory, not an observation device. Price becomes an execution signal, not an object of interpretation, and the loop becomes faster with each repetition.

Even today, many builders approach prediction markets with the 'casino' frame. However, cypherpunks had already built a long history of productive discussions in the 1990s—costed belief, pseudonymity, costed speech, conditional markets, and settlement rules. That accumulation has continued through early cypherpunk proposals and internal prediction market experiments. The more the rails are laid, the more the market matures from a game to an operation. This is the purpose of this article: to reconnect that discussion in today's language and broaden the conversation so that builders design prediction markets as information aggregation and execution interfaces. The maturation has already begun.

The maturation referred to here is not the simple expansion of speculation. Hyper-financialization is the flow of measuring more domains with the language of price and settlement, accelerating responsibility. Hyperstition is the phenomenon where narrative and capital combine to precede price, and that price calls forth action to realign reality. The prediction market is the interface that translates this process into the execution phase, fixing the narrative as reality through the preceding price. This N-P-A-O-N operational loop closes that process. When these three axes overlap, the prediction market transcends an observation tool and becomes an interface that translates consensus into execution.

One question remains: How far will we extend this signaling system?

Hyperstition pulls narrative into reality. Hyper-financialization pulls responsibility forward with price and settlement, removing the shadow of nihilistic finance. The prediction market is the interface for this flow. Its speed will soon increase. Accelerate.

Christopher Choi
X : @ChrisinaShell