The social (event) network - rough draft
Why event markets in conjunction with social features can be a rich source of information for humans and computers
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Event markets represent more than just a new way to trade predictions - they unlock the potential of a powerful new information layer for human and artificial intelligence. These markets will continue to evolve into rich information networks where humans and artificial intelligence can collectively surface information, validate insights, and make better informed decisions. By combining verified trading positions with social features for sharing research and analysis, these markets can create an environment where financial incentives drive the discovery of truth and foster meaningful dialogue, where ideas can be tested against market forces, and where collective intelligence emerges through the intersection of financial incentives and open dialogue.
Markets
Markets are one of humanity's oldest and most successful information processing systems. You can trace their evolution from ancient markets trading goods to modern financial markets. Markets aggregate knowledge about supply, demand, and value of goods and services. They've consistently served as mechanisms for coordinating vast amounts of distributed knowledge.
In Friedrich Hayek’s 1945 paper "The Use of Knowledge in Society", he makes the case that markets solve what he calls the "knowledge problem". The knowledge problem is fundamentally about the challenge of coordinating information that exists in disparate forms across society, and thus effectively limits the ability to effectively plan and allocate resources centrally.
Hayek defines planning as "the complex of interrelated decisions about the allocation of our available resources." Modern technology has indeed enhanced our ability to centrally plan and process information, and thus allocate available resources effectively. Apple, for instance, uses centralized planning to coordinate global-scale product development and manufacturing with remarkable success. China's economic growth through central planning also challenges pure market orthodoxy. Additionally, companies training centralized models on vast amounts of user data are able to increase the quality of their models.
While modern technology has enhanced our ability to centrally plan, process information, and allocate resources effectively - it doesn’t eliminate the fundamental challenge of coordinating all distributed knowledge. For example, local insights about technological shifts, manufacturing constraints, and evolving user behaviors remain crucial for effective resource allocation. Local and non-universally accessible data used to train ai models, such as information held on personal computing devices, will not be accessible to centralized authorities and AI models.
Each person with local knowledge and intuition, or locally trained model on data that is not universally accessible, such as an open source model trained on data privately held on your personal devices, holds unique local knowledge that resists simple quantification. For example, a semiconductor engineer who understands how supply chain shifts will impact chip availability, a battery researcher who can intuit which technological breakthroughs will scale commercially, or a product designer who senses when an interface paradigm is about to be displaced by a new one. The most valuable insights often come from understanding complex interactions between technical capabilities, market dynamics, and human behavior. This knowledge is often tacit, contextual, and constantly changing.
Additionally, even as technology makes central planning more powerful, the rate of change in our world is accelerating. This is precisely what Hayek warned about: economic problems arise from change itself. While better data and analytics help us plan, they don't eliminate the fundamental challenge of responding to rapid, continuous change across countless domains. If detailed economic plans could be laid down for fairly long periods in advance and then closely adhered to, so that no further economic decisions of importance would be required, the task of drawing up a comprehensive plan governing all economic activity would appear much less formidable.
Hayek argues markets solve these shortcomings through price signals. When the semiconductor engineer redirects supply chains based on emerging constraints, when the battery researcher allocates resources toward promising technologies, or when the product designer steers development away from soon-to-be-obsolete patterns - they're converting their local knowledge into decisions that affect entire industries. The market then validates or challenges these decisions through price signals, enabling others to respond to changes they might not yet understand. This distributed process allows for rapid adaptation to changing conditions and enables coordination at a scale that would be impossible to achieve through central planning alone.
It is, perhaps, worth stressing that economic problems arise always and only in consequence of change. So long as things continue as before, or at least as they were expected to, there arise no new problems requiring a decision, no need to form a new plan. The belief that changes, or at least day-to-day adjustments, have become less important in modern times implies the contention that economic problems also have become less important. This belief in the decreasing importance of change is, for that reason, usually held by the same people who argue that the importance of economic considerations has been driven into the background by the growing importance of technological knowledge. - Fredrick Hayek
Hayek understood that economic problems don't exist in static conditions - they emerge from constant change. His insight about the price system as a "mechanism for communicating information" becomes even more relevant in today's fast-moving world. Prices act as a kind of telecommunications system, allowing participants to react to changes they may never fully understand, but whose effects they can see in price movements.
We must look at the price system as such a mechanism for communicating information if we want to understand its real function—a function which, of course, it fulfills less perfectly as prices grow more rigid… The most significant fact about this system is the economy of knowledge with which it operates, or how little the individual participants need to know in order to be able to take the right action. In abbreviated form, by a kind of symbol, only the most essential information is passed on, and passed on only to those concerned. It is more than a metaphor to describe the price system as a kind of machinery for registering change, or a system of telecommunications which enables individual producers to watch merely the movement of a few pointers, as an engineer might watch the hands of a few dials, in order to adjust their activities to changes of which they may never know more than is reflected in the price movement. - Fredrick Hayek
We should not focus on determining whether we should only make economic plans to allocate resources through central or distributed planning. Realistically, the world will do both. But I believe tools we have to effectively leverage distributed information is increasing in relevance and usefulness. While traditional markets aggregate knowledge about supply, demand, and value of goods and services - event markets extend this capability to aggregate knowledge about future events and probabilities.
What are event markets?
Event markets (also called prediction markets) are platforms where people can buy and trade shares based on their predictions about future events. These markets essentially allow participants to "bet" on the outcomes of various situations - from elections and sporting events to economic indicators and corporate decisions.
Event markets had some big moments the last few years. They became regulated (Kalshi) and more widely available in the United States. These markets for the United States presidential election more accurately predicted the outcome of this year’s election than most well regarded election polls1.
Event markets have some traditionally recognized benefits. These include:
Price Discovery & Information Aggregation: Aggregates diverse opinions into single price-based predictions about future events, incentivizes revelation of private information, and rapidly incorporates new information through continuous trading.
Risk Management through Hedging: Allows stakeholders to offset exposure to specific outcomes, particularly valuable for businesses with concentrated risks, and enables more strategic risk-taking in core operations
Incentive-Aligned Crowdsourcing: Weights opinions by conviction through stake size, provides financial motivation for accurate predictions, and reduces social and political pressures in forecasting.
Early Warning Capabilities: Signals potential issues before they become widely apparent, acts as leading indicator for risks and opportunities, and provides real-time feedback on changing conditions
Decision Support: Generates quantifiable probability estimates, helps organizations test scenarios and potential decisions, and supports more informed strategic planning.
Existing event market apps and platforms like Kalshi are already useful, but we are in the very early innings. Over the coming years, their usefulness will expand and evolve. I think it’s important to zoom out and think outside of our existing ideas of what apps to engage with markets look like to unlock the true potential of these markets.
The price system is just one of those formations which man has learned to use (though he is still very far from having learned to make the best use of it) after he had stumbled upon it without understanding it.
Friedrich Hayek
Social event markets
The best way I can visualize today’s most effective form of social event markets with existing products and services would maybe be a combination of kalshi/polymarket, twitter, reddit, apple notes/obsidian, nostr, and wallets.
Identity can be linked to market positions and user generated content, allowing users to create market positions and share content (like research and ideas) associated with those market positions. Posts can only be created with verifiable market positions to ensure aligned financial incentives, but any user can engage, discuss, and respond to posts with or without a market position. Benefiting from social event markets doesn’t necessarily require participation in these event markets and trading. A social event market can be used to learn more about the world, and further - use the data, markets, research, and information on the platform to make better informed decisions.
Combining social features with market positions can lead to better information and content quality because the users posting on the network are putting their money where their mouth is, while also reducing spam. Participants can upvote quality responses to posts to curate the highest quality and most thoughtful responses to posts.
UI & social features
Let’s start by imagining a traditional social platform like twitter and reddit with a feed, a post composer, and profiles:
Feeds could be customized to prioritize different information. Feeds are comprised of event markets, posts, threads, and profiles. Users can choose to organize feeds by trending topics, communities, or by categories. Users can also choose to customize their feeds with algorithms to their content preferences, or choose to use no algorithmic feed at all. Imagine a settings page in a client where you can tune your content preferences, choose from a list of different algorithms, introduce an algorithm for your feed on your own, or choose to have a raw feed with no algorithm that is populated with posts, discussions, markets, and market positions by manually following those making event markets and users engaging with the network. Additionally, a user can find information through search, both by key word, but also with more advanced search like Primal’s advanced search2.
Post composers would allow for short form posting, like a tweet, but have infinite depth. For example, imagine a post composer like twitter where you can write a short note and attach files, but can also be expanded with a more robust rich text editor for posts with data, long form research, and even include artifacts like interactive simulations. An expanded post composer could offer markup languages and typesetting systems. Users are provided with content organization tools (think features in apple notes to obsidian), or choose to use 3rd party tools that can easily accept content from sources like apple notes, obsidian, or github.
Profiles could be viewable with public or private market positions. Profiles would have a history of posts and replies to other posts. Viewing a profile could show you who a profile is following as a mechanism of discovery.
Users can read these networks without participating in them, and still be able to respond, ask questions and discuss posts. I imagine a greater percentage of people would read these networks, and discuss posts in comments and threads, without holding positions or making posts of their own.
Event contracts and market data
Event contracts, market data, and user generated content is structured, portable, interoperable at the protocol level. Regulated event markets make event contracts and markets accessible through the shared protocol. 3rd party clients can engage with these markets and the user generated content associated with them. Clients can share data, but prioritize their own features and customize interfaces like Damus or Primal using Nostr. This enables ‘profile or user’ to take their identity with them to different 3rd party clients and engage with markets across 3rd party clients, in addition to the social and user generated content (like posts and research).
If a user or profile wants to use a different client, they can sign in somewhere else and have all their data go with them. In the traditional social media world, this would be like having a twitter account, then signing into facebook with all the posts, followers, etc. ready to go. New social protocols like Nostr enable this experience today, again, like using damus, then signing into primal and having all your data move with you. A more traditional example would be sending an email on gmail and seeing the email in apple mail using SMTP (simple mail transfer protocol). User generated content should be accessible in files, and be transferable from the network protocol3.
Uses for the information on the network
Individuals, groups, institutions, and AI agents could use the network to learn and gather information. For example, imagine a social platform that you could use to learn across all the topics and events in the world. Experts and informed participants will be placing bets on event outcomes and sharing their research & analysis with a publicly viewable finical position. Users will be able to observe this information across the entire marketplace of ideas, see weighted & aggregated positions through market prices, and read follow up commentary to shared research attached to financial positions to come to your own conclusions, gain more information and context about events and ideas, and to tap into wisdom of the crowds. Without ever placing a trade or becoming a market participant, you would gain access to an incredibly powerful tool to learning more about the world and how the world may change in the future.
Wealth mangers, individuals, and AI agents could use the network to inform their own portfolio strategies. They could choose to automatically trade and engage with markets on the network, or use the information to make decisions in markets not on the platform. For example, Robinhood is building a wealth management platform4. Human wealth managers will be aggregated in a marketplace and offer services to clients at a price that they choose. Individuals will be able to pay and pair with these wealth mangers based on their focus areas, expertise, and previous performance. These individuals and wealth managers will be able to leverage context from activity on the Robinhood platform (early version exists with Robinhood investor index), public markets, other wealth managers, and other portfolios to inform their own trades and portfolio compositions. To make wealth management even more accessible, I believe individuals will be able to choose to use AI agents to provide suggestions and even make trades in markets on their behalf, and choose if they would like to have a human in the loop to execute against these suggestions5. If a platform like this had access to the utopia platform we are outlining in this essay - individuals, wealth managers, and their AI agents use this context to gain consensus about allocating capital or making decisions.
In addition to individuals - groups, organizations, and institutions could use these networks to gain consensus about allocating capital or making decisions. For example, if you are a publicly traded company, you already have shareholder votes to understand the perspective of stock holders to inform your governance and decision making. If done so in conjunction with a network like we are outlining, these groups would have a more rich sense of what the market is asking of your company, and to help unveil blind spots in your strategic devision making.
Social features built in conjunction with event markets will give people and AIs a platform to share their ideas, insights and hypothesizes on top of these networks; but also be a place for dialogue, rebuttals, and consensus building. An emergent property will be a shared sense of intuition that will rise to the surface of these networks. That’s a social network I can imagine using and feeling that I spent my time and attention well.
Lucas
https://x.com/Kalshi/status/1854221409121099805
https://primal.net/e/note1t5nku99y7q729lmjaq02vc7qqfu2s4lk308262e892m8qfgga0ns3q5p48
https://stephango.com/file-over-app
https://www.cnbc.com/video/2024/11/19/robinhood-to-acquire-tradepmr-for-300-million-heres-what-the-ceos-have-to-say.html
https://www.cnbc.com/video/2023/10/02/watch-cnbcs-full-extended-interview-with-robinhood-ceo-vlad-tenev-on-ai-credit-cards-and-more.html