1024EX Prediction Market (1024ex.com) plans to officially launch the Testnet Beta public beta on January 15th. Its practices surrounding recommendation algorithms and judgment triggers will provide a more realistic observation window for this migration.
Article Author: 1024EX
Article source: ME News
Prediction markets are being rediscovered by a growing number of people. They are not simply "betting on outcomes," but rather a mechanism that expresses beliefs through real costs and aggregates dispersed judgments into probabilistic signals. In fields such as macroeconomics, politics, and social issues, prediction market prices are often seen as an immediate reflection of collective judgment, possessing unique informational and financial technology value.
From a product structure perspective, the current mainstream prediction markets adopt a highly consistent model: events are systematically organized into market lists, and participants express their judgments through active searching, research, and position building. This design does not present a "right or wrong" problem. On the contrary, it constitutes the core infrastructure of the prediction market 1.0 stage, emphasizing completeness, interpretability, and a rigorous pricing process, and has long served research-oriented and professional participation methods.
This change is not merely a theoretical deduction. During the internal testing of the 1024EX prediction market (1024ex.com), a clear behavioral characteristic was observed within the platform: user participation often does not begin with systematically browsing the market list, but rather occurs concentrated within short-term judgment windows that are directly presented and recommended by the algorithm. When the question itself is pushed to the user, judgment is more easily triggered, and participation exhibits more frequent and immediate characteristics.
This phenomenon does not signify that users are becoming more impulsive; rather, it reflects a structural shift in judgment behavior—from a decision-making approach based on proactive searching to one that relies more on contextual triggers and immediate feedback. As information production accelerates and attention is fragmented into smaller time units, people are increasingly accustomed to forming judgments quickly while passively receiving information, rather than completing a full information retrieval first. This change is becoming an undeniable reality in predictive market design.
Looking back at the history of the internet, this shift is not unfamiliar. Early internet websites, like Yahoo and Craigslist, were centered around portals, presenting information in a high-density, flat format with clear logic and a rigorous structure. Subsequently, as the scale of information grew exponentially, search engines and recommendation algorithms began to act as "information filters," allowing users to see only content highly relevant to their needs or interests, rather than the entire information dataset. With the advent of the mobile internet era, feeds further placed recommendation algorithms at the core of the product, continuously pushing information to users, and decisions no longer revolved around "searching" but rather around "whether it's worth paying attention to."
The advantage of a flat design is that it seems like all the information can be displayed to the user in a high density, but in reality, it greatly increases the burden on the user's reading.
Today's prediction markets, in their information organization, still closely resemble the internet of the portal era: numerous events and markets are presented side-by-side, requiring participants to actively filter and understand them before making a judgment. This structure is not outdated; it is clear, rigorous, and for a long time supported the role of prediction markets as rational tools. However, as prediction markets begin to reach a wider user base, a new question gradually emerges: can recommendation algorithms be introduced as intermediaries, similar to Web 2.0, so that judgments are no longer entirely dependent on users' active searches, but are instead triggered more intelligently?
1024EX Prediction Market (1024ex.com)'s attempts around recommendation algorithms unfolded against this backdrop. The explorations represented by 1024EX did not attempt to negate the value of prediction markets 1.0, but rather tried to build a 2.0 layer on top—redesigning the triggering mechanisms for judgments around recommendation algorithms. Under this approach, the algorithm is no longer just about the importance of ranking markets, but takes on the role of "judgment distribution": which events are more worthy of being seen, and which questions are more suitable to be raised at this moment.
In this structure, predictive behavior no longer always begins with "choosing a market," but rather with "facing a problem." Feed-style information organization transforms events into continuously occurring judgment scenarios; recommendation algorithms then push the most relevant judgment opportunities to the user based on time sensitivity, user behavior, and contextual relationships. Judgments are thus broken down into smaller, more frequent units, and feedback is obtained on a shorter time scale.
From a fintech perspective, this 2.0 design centered around recommendation algorithms represents an adjustment to the efficiency structure. It further promotes the development of prediction markets as true financial infrastructure! When judgments are distributed more accurately and triggered more frequently, prediction markets are likely to reflect information changes more quickly and more clearly reveal the differences between individual and group judgments. The role of recommendation algorithms in this process is not to replace market mechanisms, but rather to optimize the path for judgments to enter the market.
Equally important, this is not a narrative of "replacement of the old." Just as portals did not disappear but coexisted with search and feeds, the complete pricing and long-term consensus emphasized in Prediction Market 1.0 remain important components of this field. What is happening is more like adding an experience layer built around recommendation algorithms and real-time judgments on top of the existing structure.
The history of the internet has repeatedly demonstrated that changes in product paradigms often stem from shifts in user behavior, rather than a rejection of existing designs. The same applies to prediction markets. As information distribution fully enters the era of recommendations and feeds, prediction markets are ushering in their own "Internet 2.0 moment."
