Deconstructing Internet giants, the most comprehensive guide to the encrypted information game

Original author: Benjamin Funk

Original translation: Frank, Foresight News

Our brains, books, and databases are both recipients and creators of humanity’s ever-growing propensity to generate data. The Internet, the latest in a long line of developments, generates and stores about 250 quintillion bytes of data every day. While it’s easy to be in awe of this number, data points themselves are not of much value. They are like scattered pieces of a larger puzzle that need to be carefully collected, processed, and contextualized to become valuable information.

Many of today’s internet giants have focused their entire business models on this, with Google being the most successful, and their process is as follows: extracting vast amounts of valuable raw material, “digital waste” in the form of billions of people’s private data, and feeding it through a pipeline of proprietary algorithms to predict choices individuals are likely to make. The more data Google extracts about us and processes it into information, the higher the level of insight they can provide advertisers, and the higher those advertisers will bid in Google’s ad auctions in an attempt to convert us into customers.

Through these processes, Google generates $240 billion in advertising revenue each year. While Google intentionally excludes humans from this process, there is another way to generate and profit from valuable information that may be even more powerful. By gamifying the process of information creation, search, and speculation by humans as players, it stimulates our inherent desire to participate. From sports betting to MEV to social reasoning games like Among Us, we are naturally attracted to "information games" that focus on competition and coordination and require us to cleverly hide and reveal information.

Some information games are just that: games. But as we will see, other information games can be used to generate and monetize new, valuable information and become the backbone of a new generation of products and business models.

However, the information game has always had an Achilles heel: trust. Specifically, players need to trust other players not to share or exploit information in ways that violate the rules of the game. If a crew member in Among Us could turn out to be an imposter mid-game, or a block producer (miner) could calculate the wrong block root but still be accepted by a validator, no one would want to play again This game is over. To solve this trust problem, we turn to trusted third parties to create and host information games for us.

This is fine for low-stakes games like Among Us, but limiting game creation and mediation to a centralized party limits trust and experimental exploration of the information games we play, limiting the types of information we can collect, exploit, and monetize.

In short, there are many information games that haven’t even been tried since we haven’t found a way to be fair and trustworthy in a decentralized environment.

Programmable blockchains and new cryptographic primitives are solving this problem by allowing us to create and coordinate information games at scale permissionlessly, without trusting third parties or each other.

In turn, encryption-driven information games could rapidly increase the quantity and quality of information globally, thereby improving our collective decision-making capabilities and unlocking efficiency gains equivalent to the scale of global GDP. Imagine a globally accessible prediction market used as a tool to allocate capital for internet-native megafunds. Or a game that allows individuals to pool their private health data and be rewarded for any new discoveries resulting from its use, while protecting their privacy.

However, as this article will show, crypto-centric information games may not yet be available for these high-risk use cases. But by trying smaller, more interesting messaging games today, teams can focus on engaging players and building trust before expanding into creating and monetizing more lucrative messaging markets.

From prediction markets to game theory, oracles, and trusted execution environment networks, this article will cover the design space for creating these cryptocurrency-based information games and introduce the infrastructure necessary to realize their full potential.

Permissionless Markets: A Prerequisite for Information Gaming

From future governance applications to information marketplace applications, blockchain allows developers to create customizable, automated financial instruments that power permissionless, unstoppable markets. As a result, now anyone can create mechanisms to incentivize, coordinate, and settle value and information exchanges. This underscores the critical role blockchain plays in enabling us to rapidly experiment with how to best configure games to maximize value for all participants.

It will be very difficult to convince centralized intermediaries to adapt at this speed or allow their users to participate in these experiments. Permissionless marketplaces will therefore become the medium through which fringe theories and cutting-edge research papers can be realized. We’ve already seen this happen in prediction markets, where automated market maker strategies that theoretically respond to low liquidity in prediction markets are implemented as CPMMs (Continuous Quotation Market Makers) on cryptocurrency networks and conducted with real money Tested.

Permissionless markets are an important enabler of better tools for generating new information and monetizing its value.

Information game of information production

Many information games generate new information that players can use to make better decisions.

These information games create incentives to extract raw materials (public and private data) from people, databases, and other sources, and then aggregate this data through the best information production machines (markets and algorithms). Ideally, in the process of aggregating this information, new information is generated and monetized by helping other players make good decisions. For example, an investment DAO uses the results of a prediction market to decide whether to invest in a new startup.

The games and tools designers of information games utilize will vary depending on the type of information they may produce, leaving us with a vast design space to explore different challenges and opportunities.

But let’s start with the most actively developed and discussed information game today: prediction markets.

Game 1: Prediction markets as a tool for generating information

One of the most popular information games in crypto (and beyond) is prediction markets. Polymarket is the world's leading prediction market, facilitating over $400 million in cumulative trading volume (and growing rapidly).

Prediction markets operate by incentivizing players to use their own money (such as cryptocurrency) to bet on the outcomes of various events. This practice of requiring individuals to take financial risk ("real money participation") helps ensure that participants are truly committed to their predictions. As traders act on their insights, the market dynamically adjusts by buying shares of undervalued outcomes and selling shares of overvalued outcomes. These adjustments in market prices reflect a more accurate collective estimate of event probabilities, effectively correcting any initial mispricing.

The more people involved in betting on a market, with different but related public and private knowledge, the more truth will be reflected in prices. Ultimately, prediction markets harness the “wisdom of crowds” by leveraging financial risk to drive accurate aggregation of information.

Unfortunately, prediction markets present some key challenges, many of which boil down to various scalability issues.

The bottleneck of real information

Keynesian beauty contests (judges try to choose the options they think other judges will choose) are not unique to prediction markets. However, their negative impact is more obvious here than in traditional markets, because the goal of prediction markets is to create accurate information. In addition, unlike traditional financial markets, where participants' behavior is mainly driven by profit maximization, bettors in prediction markets are more likely to be influenced by personal beliefs, political inclinations, or vested interests in certain outcomes. Therefore, they are more willing to suffer financial losses in the market itself if their bets resonate with personal values or the expectation of profits from actions outside the market.

Furthermore, the more people view a market or algorithm as a source of truth, the higher the incentive to manipulate that market. This is very similar to the problem social media has. **The more people trust the information products generated by social media platforms, the higher the incentive to manipulate them for profit or sociopolitical gain. **

Some players may even take advantage of the signals and incentives created by prediction markets to reprice collective beliefs and encourage collective action. For example, imagine a government using a form of “quantitative easing” policy to influence prediction markets on key issues such as climate change or war. By purchasing large amounts of shares in relevant prediction markets, they can redirect financial incentives toward desired outcomes. Perhaps they believe the systemic risks of climate change are underestimated, so they buy heavily into a "no" share of a market that predicts climate improvement in 2028. The move could encourage more climate tech startups to develop technology that would allow them to gain an information advantage in betting on “yes” shares, thereby accelerating the pace of finding solutions.

While the above factors have been shown to negatively impact the quality of information produced, it has also been demonstrated that manipulative behavior can actually improve market accuracy because market manipulators are noise traders and well-informed market participants can make money by trading against them.

Therefore, we can infer that the above problems are caused by the lack of a sufficient number of well-capitalized and well-informed traders to help correct the market,** so allowing these well-informed traders to borrow and short may be what makes these markets more efficient a key means. **

Furthermore, in longer-cycle markets, it is more difficult for well-informed traders to counteract manipulation because manipulators have more time to reflexively influence market sentiment and actual outcomes through trading. Implementing a marketplace with a shorter retention period for information credibility increases trust in the game (and thus the quality of its information), but also makes the gameplay more engaging.

We also saw some early signs that, in some cases, players were enjoying information games where the credibility of information could be manipulated. Perl, the top account on Farcaster at the time, leaned into this model and created an in-app platform to speculate on user engagement. Prediction markets such as “Will @ace or @dwr.eth (co-founders of Perl and Farcaster, respectively) get more likes tomorrow?” were launched, and the predictable “mash-ups” of football teams and their fans began. Only here, the game was played asynchronously and the metric was likes instead of “touchdowns” (Foresight News note, usually used to describe the scoring action in American football games, when the offensive team brings the football into the opponent’s end zone and successfully touches down, it will be judged as a touchdown score). While Perl’s game intentionally undermined the quality of the information produced by the prediction market, an interesting meta-game emerged through coordination to resolve predictions in someone’s favor.

Prediction-based games can reduce manipulation and boredom by using shorter, potentially renewable rounds. However, in low-stakes games, allowing players to manipulate can increase the fun of the game and become an integral part of the gameplay.

Find the right judges and oracles

Another challenge with prediction markets is adjudication — how to correctly predict the market? In many cases, we can rely on reputation- and collateral-backed oracles that can connect to off-chain data sources. To address this, prediction market designers can rely on game theory and cryptographic oracles to cover a wider range of topics, including players’ private information.

Game theory oracles, also known as Schelling point oracles, assume that without direct communication, participants (or nodes) in the network will independently converge on a single answer or result that they believe others will also choose. This concept, pioneered by Augur and others and later further developed by UMA, encourages honest reporting and discourages collusion by rewarding participants based on how close they are to the "consensus" answer.

However, there are still many challenges in making these oracles reliable in the adjudication of bets by a small number of players, where identifying each other and communicating to collude becomes a potential threat. While cryptography is touted as a key tool to avoid collusion between voters, it can also be used as a tool to enable collusion and interfere with prediction markets. We can see this through DarkDAO’s potential to exploit trusted execution environments (TEEs) for programmatic bribery and coordinated price manipulation. One of the teams working to balance these incentives is Blocksense, which uses secret committee selection and encrypted voting to prevent collusion and bribery.

We can also tackle the oracle challenge by leveraging on-chain data. In MetaDAO, players are rewarded for correctly predicting how a particular proposal will affect the price of its native token. This price is provided by Uniswap V3 positions, which act as an oracle for the token’s value.

However, these oracles still have limitations in solving markets based on public data. If we can solve markets based on private data, we can unlock entirely new types of prediction markets.

We can use the results of the information game itself as an oracle, which is one way to solve the problem of private data-based markets. Bayesian markets are one such example, which use the principles of Bayesian reasoning to infer the bettors' own beliefs about their private information by letting people bet on the beliefs of others. For example, setting up a market where people can bet on "how many people are satisfied with their lives" can reveal the bettors' own beliefs about other people's life satisfaction. As a result, we can draw accurate conclusions about the players' private information, which would otherwise be unverifiable truths.

Another solution is an oracle that uses clever cryptography to "import" data from a private Web2 API. Some of these existing oracles are shown in the "Public and Private Information Oracles" section of the market map. Using these oracles, prediction markets can be created around some players’ private information, incentivizing holders of the private information to verifiably solve a specific prediction market in exchange for transaction fees from players betting on that market. More generally, the ability to securely access richer personal data off-chain and on-chain can be used as identity primitives, helping us more effectively identify, incentivize, and match players in information games, helping us direct necessary information, Make information games more relevant to players.

Innovations in oracle design will increase the range of data we can use to solve prediction markets, thereby expanding the design space for information games around private information.

Liquidity Bottleneck

Attracting liquidity into prediction markets is difficult. First, these markets are binary markets, where players bet "yes" or "no" on a specific theme and receive either a fixed amount of monetary reward or nothing. As a result, the value of these shares can fluctuate significantly with small changes in the price of the underlying asset, especially near expiration. This makes predicting their short-term price movements important, but also extremely challenging. In order to deal with the huge risks brought by sudden changes, traders must use advanced and constantly adjusted strategies to deal with unexpected market fluctuations.

More importantly, as prediction markets expand their scope to more topics and extend their time frames, it will become more difficult to attract liquidity. The further the market goes beyond politics and sports, and the longer it lasts, the less people feel they have a clear advantage in betting. As a result, fewer people bet, and the quality of information produced declines.

Prediction markets inherently face these liquidity issues because forming prices requires mining private information and placing bets based on that information, both of which are costly activities. Participants need to be compensated for the effort they put in and the risk they take (including the costs of gathering information and locking up capital). This compensation typically comes from people willing to accept worse odds, either for fun (e.g., sports betting) or to hedge risk (e.g., oil futures), which helps drive significant liquidity and volume. However, prediction markets with narrower themes have less commercial appeal to players, resulting in lower liquidity and volume.

ECONOMY IMPROVES: OVERLAY AND DIVERSIFICATION

We can solve these problems by drawing on ideas from traditional finance and other existing information games.

It is worth noting that we can make use of the "overlay" concept mentioned by Hasu in the article "The Dilemma of Prediction Markets". In gambling tournaments, the concept of an “overlay” is similar to the subsidy proposed by prediction markets, which is additional value that bookmakers add to the prize pool to encourage participation. The "overlay" effectively reduces the entry cost for players and makes the tournament more attractive, thereby increasing the participation of novice and experienced players.

Just like “overlays” in gambling tournaments incentivize player participation by increasing potential ROI, “subsidy” in prediction markets incentivizes participants by lowering the barrier to entry and making participation more financially attractive. Subsidies also serve as beacons, attracting multiple perspectives and insights from both informed and uninformed traders, who have the opportunity to profit by correcting their mistakes. Teams implementing this strategy will have to systematically identify and engage with potential subsidy providers and create a market around their needs as they are willing to provide the necessary liquidity.

Similarly, a “fund”-like structure could be implemented to diversify across time and industry, and increase liquidity in prediction markets across a wider set of questions and time horizons. For example, many companies may find value in markets around how a particular lawsuit will resolve. These companies could reduce the cost of legal expert participation by lending capital to them, allowing them to diversify across a wide range of markets, and then rewarding them based on their performance over time.

In this setup, traders will be able to borrow money to make markets, and the loan amount can be parameterized based on information needs and the trader's reputation on the topic. This can be combined with a management fee as an additional "overlay" for each market.

For liquidity providers, they will be exposed to traders in these markets who have an incentive to bet correctly on these markets and are diversified across a large basket of non-correlated assets of different maturities. While there are "principal-agent problems" to consider, this system can increase the scale of liquidity provided in these markets, as well as the diversity of these liquidity pools. In addition, the quality and variety of information goods can be improved, while also creating new information about traders' skills and knowledge in different markets, accelerating returns to liquidity providers through reputational byproducts.

When the value of the information that players are able to generate is significant, integrating composable financial markets (such as lending and liquidity mining) into gameplay can be a key tool in lowering barriers to entry.

User experience improvements: simpler interface and flexible incentives

The exchange-centric UX design and limited reward types common in today’s prediction markets may discourage those who value other interface types and incentives, further limiting liquidity. On the part of bettors, there are many interesting ways to improve the quality of prediction markets, all of which focus on increasing coverage and accessibility to different types of players.

First, we can improve the user experience of prediction markets by integrating them into larger social platforms. Perl and Swaye showed us this by plugging into Farcaster data, so that users don’t have to open another standalone application, and information game designers can identify and guide players to markets where they are particularly well suited to participate (e.g., the top participants in the /nyc-politics channel).

There could also be attempts to expand the range of rewards distributed to stakers and lower the threshold for the capital they must stake. This could take the form of rewarding individual proofs of stake, or expanding the range of financial rewards to include “in-app utility” or equity represented by points or tokens.

While monetary incentives are important for prediction markets to work, some literature suggests that cryptocurrencies can also create prediction markets of comparable quality. From a practical perspective, this tells us that we can be flexible in assuming the type of “real money participation” that bettors are willing to risk and work to acquire.

Additionally, there are different types of market mechanisms that can be used to make the user experience more “poll-based,” further reducing friction and lowering barriers to entry. A study from the University of Cambridge evaluated this hypothesis and found that polling mechanisms produce more accurate results than prediction markets in markets with low trading activity, large bid-ask spreads, and fast settlements. The study also found that combining poll-based prediction games with monetary incentives from prediction markets produces higher accuracy than simply predicting market prices. Additionally, to address potential stagnant information challenges, polls can be “updated” regularly based on some kind of push or pull system, incentivizing the dynamic replication of information based on new information.

Crypto information games used to be a barrier to all but the most hardcore veteran users. Now, with lower costs, higher availability, and more abundant data, we have the opportunity to develop more diverse, accessible games that appeal to specific audiences.

Game 2: Privacy-preserving computing generates information

Imagine a game played by Solidity developers where players utilize multi-party computation (MPC) to reveal their salaries and calculate averages while protecting the confidentiality of their individual salaries. This will be a valuable way for cryptography professionals to negotiate with their respective employers, while also serving as a source of entertainment.

More broadly, information games can leverage privacy-preserving technologies to expand the range of information sources—particularly private data and information that can be analyzed to generate new insights. By ensuring privacy, these tools can increase the variety and propensity for people to share data and information, and compensate data providers for the resulting value.

While this is not all, some of the tools used by information producers to achieve this include Zero-Knowledge Proofs (ZK), Multi-Party Computation (MPC), Fully Homomorphic Encryption (FHE), and Trusted Execution Environments (TEE). The core mechanisms of these technologies vary, but ultimately they all serve the same purpose - enabling individuals to provide sensitive information in a privacy-preserving manner.

However, for use cases that require strong privacy guarantees, there are still many serious challenges in using software and hardware cryptographic primitives, which we will discuss later.

Privacy-preserving cryptography significantly broadens the design space for new information games that did not exist before.

Game 3: Competition between models to improve information production

Imagine a game where data scientists compete against each other by developing and betting on trading models for a decentralized hedge fund. The blockchain then reaches consensus on a specific model’s score and rewards or penalizes participants based on the accuracy of the model’s predictions and its impact on fund returns. This is the approach taken by Numerai, one of the first information games on Ethereum. In this game, Ethereum’s consensus mechanism is used to compete among different models and their creators on a global scale, effectively motivating artificial intelligence to participate in the information game, thereby generating valuable returns.

Going a step further, we could more directly incentivize AIs to play information games for us, using their vast knowledge to compete with each other in making predictions. While they don't necessarily play these games for fun, using smart machines instead of humans can significantly reduce the labor costs required to produce information. As a result, these AI models can increase liquidity in more niche prediction markets where humans are often reluctant to participate. As Vitalik said:

“If you create a market and offer a $50 liquidity subsidy, humans won’t care too much about bidding, but thousands of AIs will easily swarm it and try to make the best guess they can. The incentive to do well on any one problem may be tiny, but the incentive to make an AI that makes generally good predictions could be worth millions of dollars.”

Alternatively, we can leverage consensus among machine learning models to compete around the value of information they create. Teams like Allora and Bittensor TAO are working to coordinate models and agents to broadcast their predictions to others in the network, while others are responsible for evaluating, scoring, and broadcasting their performance back to the network. At each epoch, collective evaluation among models is used to allocate rewards or power to different models based on prediction quality. Entrepreneurs can thus leverage an ever-improving network of models to improve the quality of information flowing through their markets.

It is entirely possible that there are some markets for information—the quality of information generated using models is simply unmatched by information games between humans.

Information games that can be used for monetization

Some information games survive solely on the pleasure users derive from them. But for those who want to monetize the value of the information they generate, more thought is needed. Unfortunately, the nature of information as a commodity leads to key market failures that prevent its smooth monetization:

• Information can only be valued after it is consumed, making it difficult for buyers to assess whether the price listed by the seller accurately reflects the value of the information.
• Information is non-rivalrous - consuming it does not reduce its availability, which means it has no scarcity, which makes it unattractive to buyers.
• The non-exclusive nature of information combined with low reproduction costs makes it difficult for sellers to prevent unauthorized access, despite high upfront production costs.

These economic characteristics create challenges for both buyers and sellers to profit from information, and can result in an undersupply of information. If information quickly becomes known to all who can simultaneously exploit it, then buyers of information have fewer opportunities to exploit information asymmetries, either due to increased competition or the collapse of the plans they intended to use. Thankfully, there are cryptographic tools that can be used to address these issues, and they are already being used.

Game 4: Exchange - Cashing in through Information Speculation

One way to monetize the production of information without keeping it secret or limiting the set of actions that can be taken against it is to simply make that information public, but create a tool that lets people bet on how it changes—also known as derivatives Taste.

One company actively doing this is Parcl, whose exchange allows users to speculate on rising and falling real estate markets. Parcl’s marketplace is powered by real-time price information, which is sourced from a vast pool of real estate data by Parcl Labs and processed through a proprietary algorithm to generate more granular and accurate information than traditional real estate price indices.

While Parcl monetizes this information more directly via API, they also create an additional layer of monetization by allowing traders to bet on how this information changes over time. Other projects, such as IKB and Fantasy mentioned in the "Alternative Information Market" section of the market map, focus on monetizing through speculation or hedging changes in existing public information, ranging from athlete performance to creative creations the person’s social engagement.

If you can sell people the right to speculate on the information you generate, you can monetize it without keeping it confidential or restricting how buyers can use it.

Game 5: Discover the black market for confidential information

Imagine a game that lets you discover curated alpha information about the latest on-chain activity and brand new crypto startups before the world knows about it. In order for this to work, the information needs to remain confidential in order to address the non-rivalry and excludability issues that come with public information. Therefore, the next generation of information markets are facilitating the exchange of confidential information while leveraging blockchain to discover and regulate all participants who may have paid to access this information.

Freatic’s decentralized confidential information marketplace Murmur is a prime example of this approach, which uses NFTs and a queuing system to restrict exclusive access to information. Information buyers first need to subscribe to a specific topic by purchasing an NFT representing a coupon. This then grants them a queue spot to redeem confidential information from the publisher, and they can also pay an additional fee to slow down its spread. Buyers can also vote on the quality of the information afterward. Through this process, Murmur ensures that information remains confidential and valuable without restricting its sale to one entity.

In contrast, Friend.tech uses keys and bonding curves to manage access to confidential information in group chats, which creates a higher barrier to entry as demand increases. Therefore, we can think of Friend.tech's keys as a proxy for the average value of a piece of personal information (assuming the key market is efficient). However, players always "price in" some idea of the "worth" of this person when trading keys, making it difficult for buyers to assess the value of the information. Perhaps this serves as another data point to support the claim that the most valuable "information market" to date is actually the memecoin market, which, if you squint hard enough, is actually a prediction market around the symbolic value of a particular trend or person.

Aside from memecoin, one direction the team is taking to restrict access to information is to allow sellers of information to design better bonding curves that tie the price of access to the value of the information. For example, the pricing of information that depreciates rapidly as it becomes known could be determined by a bonding curve that reflects the rapid depreciation of the value of the information over time.

Decentralized currency exchange is challenging due to trust issues and finding coincidences of double demand. Blockchain has already solved this problem for currency (Bitcoin) and will do the same for information through fun games around finding hidden information.

Game 6: "Futarchy" - predicting the realization of the market

**One major way to monetize information without explicitly keeping it confidential is to produce and sell information that only a single organization can and will exploit. This approach is not new; many companies already monetize information by limiting access to information to specific buyers through auctions or confidentiality agreements. However, we are seeing a new business model for selling information commodities - producing public information that is only relevant and valuable to the organization making specific decisions.

In fact, we are just now seeing prediction markets built on encrypted rails as a way to experiment with "Futarchy" (Foresight News note, which can be translated as future system, as Professor Robin Hanson of George Mason University wrote in "We Should Vote for Value" in 2000 , but pay for belief?" This concept was first mentioned in the paper. In 2008, Futarchy was named the hot word of the year by the New York Times) as an alternative mechanism to monetize the information it generates.

“Futarchy” provides a new way to improve decision-making, focusing on leveraging the information created by prediction markets. The information generated by the prediction market is used to make decisions, and when the prediction market settles, the participants with the most accurate predictions are rewarded.

Prediction markets themselves are zero-sum games for players, which limits the incentives for informed traders to participate and worsens their existing liquidity bottlenecks. “Futarchy” can solve this problem because the wealth created by making better decisions can be redistributed to traders.

Decentralized native entities like MetaDAO are already experimenting with “Futarchy.” When a proposal is made, such as Pantera’s proposal to purchase MetaDAO governance tokens, two prediction markets are created: “Pass” for support and “Fail” for opposition. Participants trade conditional tokens within these markets, speculating on the impact of the proposal on the value of the DAO. The outcome is determined by a comparison of the time-weighted average price (TWAP) of the “Pass” and “Fail” tokens after a specified period of time. If the TWAP of the “Pass” market exceeds the TWAP of the “Fail” market by a set margin, the proposal is approved, resulting in the execution of the proposal’s terms and the cancellation of the Fail market’s transaction. The system uses market dynamics to drive governance decisions in line with the market’s collective prediction that a proposal will increase or decrease the value of the DAO.

In some cases, "Futarchy" still needs to be designed around confidentiality. For example, if a prediction market is used to determine hiring decisions for specific people, then this information becomes public and becomes an infohazard—competitors may poach hiring targets based on the market's predictions.

Another reason to keep information confidential is its impact on incentives and organizational culture. As Robin Hanson pointed out in his "The Future of Prediction Markets" talk, Google's own internal experiments have encountered resistance because executives worry that public performance metrics will demotivate employees. Of course, managers are not inclined to implement things that might reveal the "Emperor's New Clothes", and we are seeing this today. According to MetaDAO founder @metaproph 3 t, some people decide not to submit proposals because they don’t want to be evaluated by the market.

Both of these issues can be addressed by limiting the accessibility of prediction market information to only specific decision makers. However, by giving decision makers the power to act autonomously based on this information, bettors will incorporate these biases into their bets, reducing the quality of the information generated.

In other cases, "Futarchy" may be more suitable for application in specific industries, where its advantages outweigh the cultural impact, such as Bridgewater's hedge fund. Integrating blockchain can also further enhance the credibility of "Futarchy" and prevent manipulation.

Monetization methods for prediction markets have so far been limited to speculation or hedging, but by helping organizations make better decisions, prediction markets could unlock an entirely new market – although roles surrounding confidential information remain Unanswered questions.

Game 7: The Credible Promise of Programmable Information Games

As mentioned at the beginning of this article, Google monetizes information by leasing the use of the information to advertisers while limiting how they use the information to Google’s ad auctions. Similarly, credible promises help information sellers monetize by limiting the actions buyers can take based on said information.

Information sellers can use cryptographic methods such as MPC, TEE, and FHE to ensure trustworthy promises from buyers to perform calculations based on private data. Sellers can therefore entrust their information to buyers, giving buyers specific control over their future actions surrounding their private information without revealing the information itself.

This primitive technology unlocks all kinds of information games. Imagine enabling traders (information sellers) to sell the right to order trades based on order trading history to information buyers (seekers) if the buyer commits to only simulate the order trading history a certain number of times. Taking it a step further, imagine allowing Netflix users to delegate watching Netflix movies to others using their accounts, allowing them to "farm revenue" from their accounts without revealing their login details. In turn, buyers can unlock value from sellers’ private information without sellers having to deal with the challenges of selling the information itself (information is a non-rivalrous, non-excludable experiential good).

Unlocking Google-scale monetization for information game designers

TEEs currently offer a practical option for implementing such controls, albeit with limited confidentiality guarantees. While not suitable for protecting large assets or sensitive data, TEEs are suitable for use cases that require more restricted access to confidential information, such as preventing blackmail. Project SUAVE, created by the Flashbots team, is building a TEE network that developers can use today, with the long-term vision of enabling application developers to find better ways to leverage the value of their and their customers’ information.

In SUAVE's design, the integration of blockchain with TEE addresses three key TEE limitations that are critical to advancing the information game. First, blockchain eliminates the need for trust in communications between the host and players, who may engage in censorship or malicious behavior. Secondly, the blockchain provides a secure mechanism to maintain state and prevent rollback attacks that TEE is susceptible to. Finally, blockchain is critical to ensuring the permissionless, censorship-resistant creation of a TEE-based messaging game (SUAPP) whose smart contracts, inputs, and outputs can be trusted by all players.

While many of the early information games using SUAVE will obviously focus on MEV, there are opportunities for them to be used in information games that go far beyond trading.

Game 8: Reputation and Zero Knowledge Promote In-Game Market

A key challenge in information monetization is the inherent nature of information as an “experience commodity.” **The value of experience goods can only be recognized after use, which makes it difficult for sellers to set prices in advance. **In creating mechanisms to solve this problem, we can also create interesting gameplay for users. The core gameplay of some games is to allow players to build reputations to distinguish themselves from other players, such as World of Warcraft, which can be both fun and a key way for players to decide who to cooperate with. Other games may want sellers to commit to setting a price for some intel (such as enemy locations, secret plans) without revealing the information beforehand.

To overcome this problem, designers of information games can leverage cryptographic solutions such as zero-knowledge proofs (ZKPs) to verify the properties of computational information goods (e.g. the efficacy of a trading algorithm) without revealing the actual data or code. This can be achieved by creating a cryptographic commitment, timestamped on a blockchain, and providing a zero-knowledge proof of the algorithm's performance. However, this approach only works for information goods whose value comes from their computational properties and can be tested on verifiable inputs.

For other types of information goods, reputation and identity become critical. Consensus mechanisms among information buyers can be used to build a reputation around the value of the information a seller is trying to sell.

Systems like Murmur use user voting within an exclusive window to build publisher reputations, promoting them from unverified to verified status based on community feedback. This process creates a transparent and immutable record of interactions, building a trustworthy reputation for sellers, and a tight feedback loop.

Alternatively, the Erasure Bay protocol requires sellers to stake both funds and their reputation as a signal of the reliability of their information. The protocol determines a "fraud factor" that allows buyers to destroy a certain portion of the seller's stake if the information quality is low, thereby ensuring that sellers have an incentive to provide high-quality information.

To avoid market failures and maximize transaction volume, game designers need to give sellers cryptographic tools to prove the value of their information, or provide trustworthy and fast mechanisms to build their reputation for previously selling items.

Summarize

Information games are nothing new. However, before the advent of programmable blockchains, game designers could only seek permission from centralized intermediaries, and players were limited to games that could be mediated by a trusted third party.

Now, the dramatic drop in the cost of block space means that anyone can create a DAO or protocol for confidential information inspired by future governance, and access a wealth of tools for verification, arbitration, monetization, and more. Lowered barriers to participation and open innovation on the permissioned finance track will unlock games we couldn’t even imagine.

This article shows the early signs and challenges of implementing this new wave of information games, and the potential of using cryptographic tools to solve these problems. With these tools, some game designers will improve the information games we already play, like trading and MEV, while others will create games that simply didn't exist before.

However, each of these cryptographically powered information games represents mini-games that need to be combined with each other to form a complete game. Players get fun and excitement from building reputations, working with teams, and competing for influence within an organization, all as part of a larger whole.