Original author: Benjamin Funk

Original compilation: Frank, Foresight News

Our brains, books, and databases are both recipients and creators of humanity’s growing propensity to generate data. The Internet, the latest achievement in this long development, generates and stores approximately 250 trillion bytes of data every day. While its easy to be in awe of this number, the data point itself doesnt hold much value. They are like scattered pieces of a vast puzzle that need to be carefully collected, processed, and contextualized to become valuable information.

Many of todays Internet giants have focused their entire business models on this, with Google doing it the most successfully. Their process is as follows: extract huge amounts of precious raw materials, digital waste in the form of billions of peoples private data, and It is fed into a pipeline of algorithms to predict the choices individuals are likely to make. The more data Google pulls and processes into information about us, the higher the level of insight they can provide advertisers, and the higher those advertisers bid in Googles ad auctions to try to convert us into customers .

Through these processes, Google generates $240 billion in advertising revenue annually. While Google intentionally excludes humans from the process, there is a potentially more powerful way of generating and monetizing valuable information. By using humans as players, we gamify the process of information creation, search, and speculation, stimulating our inherent desire to participate. From sports betting to MEV to social deduction games like Among Us, we are hard-wired to be drawn to “information games” that revolve around 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 a low-stakes game like Among Us, but limiting game creation and mediation to a centralized party limits our trust and experimental exploration of the information games we play, thereby limiting what we can gather, Types of information leveraged and monetized.

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: prerequisites for information games

From future-ruling 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 that incentivize, coordinate, and settle the exchange of value and information. This highlights the critical role blockchain plays in enabling us to quickly experiment with how best to 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 marketplaces 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 prediction market results 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 worlds leading prediction market, facilitating over $400 million in cumulative trading volume (and growing rapidly).

Prediction markets work by incentivizing players to use their own money, such as cryptocurrency, to bet on the outcome of various events. This practice of requiring personal financial risk (real money participation) helps ensure that participants are truly committed to their predictions. Markets adjust dynamically as traders act on their insights by buying shares of undervalued outcomes and selling shares of overvalued outcomes. These adjustments to market prices reflect more accurate collective estimates 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 come down to various scalability issues.

The bottleneck of real information

Keynesian beauty contests, in which judges try to choose options they think other judges will choose, are not unique to prediction markets. However, their negative impact is more pronounced here than in traditional markets, since the goal of prediction markets is precisely to create accurate information. Furthermore, unlike traditional financial markets where participant behavior is primarily driven by profit maximization, bettors in prediction markets are more easily influenced by personal beliefs, political leanings, or vested interests in certain outcomes. Therefore, they are more willing to take financial losses in the market itself if their bets resonate with personal values ​​or expectations of profit 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 have a negative impact on the quality of information produced, it has also been demonstrated that manipulative behavior actually improves market accuracy because market manipulators are noise traders and well-informed market participants can They trade in reverse to make money.

Therefore, we can infer that the above problems are caused by the lack of a sufficient number of well-funded and well-informed traders to help correct the market,So allowing these well-informed traders to borrow and short could be a key means of making these markets more efficient.

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.

Were also seeing some early signs that, in some cases, players enjoy information games where the duration of informations credibility can be manipulated. Perl, the number one 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-founder of Perl and Farcaster respectively) get more likes tomorrow? are launched, and football teams and their fans can predictably make trouble. here we go.Only here, the game is played asynchronously, and the measurement is 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 opponents end zone and successfully touches the ground, it will be determined as a Touchdown score). While Perls game intentionally undermines the quality of the information produced by prediction markets, an interesting metagame emerges by coordinating to resolve predictions in ones favor.

Prediction-based games can reduce manipulation and boredom by using shorter, potentially renewable rounds. However, in low-stakes games, allowing player manipulation can add to 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 predict the market correctly? In many cases, we can rely on reputation- and collateral-backed oracles that can connect to off-chain data sources. To solve this problem, 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 in the absence of direct communication, participants (or nodes) in the network will independently converge on a single answer or outcome, and they believe that others will also choose this answer or result. This concept, pioneered by Augur et al. and 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 and communicating with each other to collude becomes a potential threat. While cryptography is touted as a key tool to avoid collusion among 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 leverage 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 cryptographic voting to prevent collusion and bribery.

We can also address the oracle challenge by leveraging on-chain data. In MetaDAO, players are rewarded for correctly predicting how specific proposals will affect the price of their native token. This price is provided by the Uniswap V3 position and serves as an oracle for the tokens 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 market based on private data. Bayesian markets are one such example, which utilize the principles of Bayesian inference to derive the bettors own beliefs about his or her 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 would reveal the bettors own beliefs about the satisfaction of others lives. As a result, we can draw accurate conclusions about players private information that 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 information game design space 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.

What’s more, as prediction markets expand the scope of the market to more topics and lengthen their time frames, attracting liquidity will become more difficult. The further the market goes beyond politics and sports, and the longer it lasts, the less people feel they have a clear advantage when it comes to betting. Therefore, the fewer people betting, the lower the quality of the information produced.

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 invest and the risks they take, including the cost of gathering information and locking up capital. This compensation often comes from people willing to accept worse odds, either for entertainment (e.g., sports betting) or to hedge risk (e.g., oil futures), which helps drive large amounts of liquidity and trading volume. However, prediction markets with a narrower range of themes are less commercially attractive to players, resulting in lower liquidity and trading volumes.

Economic improvements: overlays and diversification

We can solve these problems by borrowing 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 achieve time and industry diversification and increase liquidity in prediction markets across a broader set of issues and time horizons. For example, many companies may find value in the market surrounding how a particular lawsuit is resolved.These firms can lower the cost of legal expert participation by lending capital to them, allowing them to diversify across a broad 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 traders reputation on the topic. This can be combined with a management fee as an additional overlay for each market.

For liquidity providers, they will have access to traders in these markets who have incentives to bet on these markets correctly and are spread across a large basket of non-correlated assets of varying maturities. Although there are principal-agent problems to consider, this system can increase the scale of liquidity provided by these markets and the diversity of allocation of these liquidity pools. In addition, the quality and variety of information commodities can be improved, while new information about traders’ skills and knowledge in different markets can also be created, accelerating returns to liquidity providers through reputational by-products.

When the value of information players can generate is great, 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 dissuade those who value other interface types and incentives, further limiting liquidity. As far as bettors are concerned, there are many interesting ways to improve the quality of prediction markets, all of which focus on improving 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 show us this by plugging into Farcasters data. Instead of users having to open another standalone application, information game designers can identify and direct players to markets in which they are particularly suited to participate (e.g., the /nyc-politics channel of top players).

There are also attempts to expand the range of rewards allocated to bettors and lower the capital threshold for them to invest. This could take the form of rewarding individual testimonials, or extending financial rewards to include “in-app utility” or equity expressed through points or tokens.

While monetary incentives are important for the functioning of prediction markets, some literature suggests that virtual currencies can also create prediction markets of comparable quality. From a practical perspective, this tells us that we have the flexibility to assume the type of real money participation punters are willing to risk and work hard to obtain.

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 in markets with low trading activity, large bid-ask spreads, and quick resolutions, polling mechanisms produce more accurate results than prediction markets. The study also found that combining a poll-based prediction game with the monetary incentives of prediction markets produced higher accuracy than simply predicting market prices. Additionally, to address potential stagnant information challenges, polls could be updated periodically based on some kind of push or pull system, incentivizing dynamic replication of information based on new information.

The crypto messaging game used to hinder all but the most hardcore of power users. Now, with lower costs, greater availability, and richer data, we have the opportunity to develop more diverse, more 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 to 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 dont 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 provide a $50 liquidity subsidy, humans won’t care much about the bids, but thousands of AIs will easily swarm in and make the best guess they can. The incentive to do any one problem well may be minimal, but the incentive to build an AI that makes generally good predictions may 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 has been consumed, making it difficult for buyers to assess whether sellers listed prices accurately reflect 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 pose challenges for both buyers and sellers to profit from information and may lead to an insufficient supply of information. If information becomes known quickly to everyone who can exploit it simultaneously, there are fewer opportunities for information buyers to exploit information asymmetries due to increased competition or the collapse of the schemes they intended to use. Thankfully, there are encryption tools that can be used to solve these problems and are already in use.

Game 4: Exchange - Monetizing 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 the rise and fall of the real estate market. Parcls marketplace is powered by real-time price information, which is sourced by Parcl Labs from a vast pool of real estate data and processed through proprietary algorithms to produce information that is more granular and accurate than traditional real estate price indexes.

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 peoples interest in the information you generate, you can monetize it without keeping it secret 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 to achieve this, the information needs to remain confidential in order to address the non-contestability and excludability issues posed by public information. As a result, next-generation information marketplaces are facilitating the exchange of confidential information while leveraging blockchain to discover and regulate all participants who may pay to access this information.

Freatic’s decentralized confidential information marketplace Murmur is a prime example of this approach, using NFTs and a queuing system to limit exclusive access to information. Information buyers first need to subscribe to a specific topic by purchasing an NFT that represents a coupon. This would then grant them a spot in the queue to redeem the secret from the publisher, with an additional fee to slow its spread. Buyers can also vote on the quality of the information afterwards. Through this process, Murmur ensures that information remains confidential and valuable without having to limit its sale to one entity.

In contrast, Friend.tech uses keys and binding curves to manage access to confidential information in group chats, making the barrier to entry higher as demand increases. Therefore, we can think of Friend.techs keys as a proxy for the average value of a persons information (assuming the market for keys is efficient). However, players always count some idea of ​​the persons worth when trading keys, making it difficult for buyers to assess the value of the information. Perhaps this could serve as another data point to support the claim,Namely that by far the most valuable “information market” is actually the memecoin market, which if you squint hard enough is actually a prediction market surrounding the symbolic value of a particular trend or person.

Memecoin aside, one direction the team is taking to limit information access is to allow information sellers to design better binding curves that relate the price of access to the value of the information. For example, the pricing of information that rapidly depreciates as the information becomes known can be determined by a binding curve that reflects the rapid depreciation of the value of the information over time.

Decentralized currency exchanges are challenging due to trust issues and finding coincidences of dual needs. Blockchain already solves this problem for currency (Bitcoin) and will do the same for information through fun games centered around finding hidden information.

Game 6: Futarchy - Forecasting the realization of the market

One major way to monetize information without explicitly keeping it secret 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 through auctions or confidentiality agreements that limit access to information to specific buyers. However, we are seeing a new business model for selling information commodities – producing public information that is relevant and valuable only to the organizations 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 Panteras proposal to purchase the MetaDAO governance token, two prediction markets are created: pass represents support, and fail represents opposition. Participants trade conditional tokens within these markets, speculating on the impact of proposals on the value of the DAO. The results depend on a comparison of the time-weighted average price (TWAP) of passing and failed tokens after a specified period of time. If the TWAP of the passing market exceeds the TWAP setting range of the failed market, the proposal is approved, resulting in the execution of the proposal terms and the cancellation of the failed market transaction. The system leverages market dynamics to drive governance decisions, aligning them with the market’s collective predictions of whether proposals 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 markets 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, Googles 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 Emperors 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 problems can be solved 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, thereby reducing the quality of the information generated.

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

So far, prediction markets have been limited to speculation or hedging, but by helping organizations make better decisions, prediction markets could unlock an entirely new market.— though there are still unresolved questions surrounding the role of classified information.

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, TEE is suitable for use cases that require more restricted access to confidential information, such as preventing front-running. Project SUAVE, created by the Flashbots team, is building a TEE network that developers can use today, with a long-term vision to enable application developers to find better ways to leverage the value of their and their customers information.

In SUAVEs 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 Facilitate In-Game Markets

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, making it difficult for sellers to set prices in advance.In creating mechanics to solve this problem, we can also create interesting gameplay for our 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 commodities (such as the efficacy of trading algorithms) without revealing the actual data or code. This can be achieved by creating cryptographic commitments, timestamping them on the blockchain and providing zero-knowledge proof of algorithm performance. However, this approach only works for information goods whose value comes from their computational properties and which can be tested on verifiable inputs.

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

Systems like Murmur use user votes within an exclusive window to build a publishers reputation, elevating 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 accompanied by a tight feedback loop.

Alternatively, the Erasure Bay protocol requires sellers to stake both capital and their reputation as a signal of the reliability of their information. The protocol identifies a “fraud factor” that allows buyers to destroy a certain portion of sellers’ bets when information is of low quality, thus 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 didnt 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.