Swarm - The First Decentralized Deliberative Prediction Market on Telegram

The SWARM Network represents a novel approach to prediction markets: Deliberative Prediction Markets, addressing the under appreciation of information within communities and the insufficient emphasis on deliberation in prediction markets. These issues restrict both their economic and intellectual value and use cases. By fostering a more inclusive and interactive environment, SWARM aims to revolutionize the way we gather and utilize collective intelligence.

Traditional Prediction Markets vs Swarm Prediction Markets

Besides being a more fun and engaging type of financial markets. Prediction markets are regarded as tools for aggregating information and projecting event probabilities, specifically, prediction markets aggregate two types of information:

1. Evidence that someone might use for a particular prediction.

2. The predictive assessment of any individual based on such evidence.

Precisely because of the above, a common criticism is that traditional prediction markets do not provide insights beyond what individuals can determine from publicly available information. They fail to add unique value, thus reducing their appeal and usefulness. Additionally, influencing markets with non-public but crucial (potentially course altering) information is challenging, which affects their effectiveness in measuring accurate event probabilities and diminishes their informational value. This limitation potentially hinders the widespread adoption of traditional prediction markets.

Traders in prediction markets are often subject to various cognitive biases that can adversely affect their performance. Here are some of the key biases identified in the research:

1. Confirmation Bias: Traders favor information that confirms their beliefs, ignoring contradictory evidence, leading to skewed decisions and poor predictions.

2. Overconfidence Bias: Overestimating their knowledge, traders take excessive risks, holding onto losing positions longer than they should.

3. Anchoring Bias: Traders fixate on initial information or price points, failing to adjust based on new data, resulting in suboptimal outcomes.

4. Loss Aversion: Traders avoid realizing losses, holding onto losing trades, which can lead to further financial detriment and missed opportunities.

5. Public Information Bias: Heavy reliance on biased public information like polls or forecasts leads to collective mispricing in markets.

6. Herding Behavior: Traders follow others rather than making independent decisions, exacerbating price movements and contributing to bubbles or crashes.

The issues highlighted above, such as confirmation bias, overconfidence bias, and herding behavior, are fundamentally tied to the lack of proper information flow and inadequate deliberation in traditional prediction markets. When traders operate in isolation, or rely heavily on public information without sufficient discourse, they are more prone to these cognitive biases. This leads to poor decision-making, mispricing, and reduced accuracy of predictions.

Addressing Information Flow and Deliberation Challenges

Swarm’s Decentralized Deliberative Prediction Markets offer a novel solution to these challenges by fostering a more inclusive and interactive environment where information can flow freely and deliberation is encouraged. Here’s how Swarm addresses these issues:

1. Enhanced Information Aggregation: By encouraging deliberation among participants before making trades, Swarm ensures that privately held information is shared and considered, leading to more informed decision-making. This reduces the reliance on biased public information and mitigates the effects of confirmation bias and overconfidence.

2. Reduction of Cognitive Biases: Deliberative processes help traders recognize and counteract cognitive biases. For instance, by discussing their views with others, traders can become more aware of their anchoring and loss aversion tendencies, leading to more rational trading decisions.

3. Improved Market Efficiency: While excessive deliberation can slow down decision-making, Swarm strikes a balance by using AI-driven processes to streamline discussions and ensure timely trades. This maintains market efficiency and liquidity while enhancing the quality of predictions.

4. Mitigation of Herding Behavior: By promoting independent thinking through structured deliberation, Swarm reduces the tendency of traders to follow the crowd. This leads to more diverse opinions being considered, thereby preventing collective mispricing and financial bubbles.

5. Incentivized Information Disclosure: Swarm’s structure encourages participants to disclose valuable information, as their profits depend on the accuracy of their predictions. This creates a positive feedback loop where shared knowledge leads to better predictions, which in turn incentivizes further information sharing.

In addition to addressing these common biases, Swarm also tackles more subtle issues that traditional prediction markets face:

• Inclusion of Diverse Perspectives: By leveraging Telegram’s wide user base and TON’s blockchain network, Swarm ensures that a diverse range of participants can contribute to the deliberative process. This inclusivity enriches the collective intelligence and leads to more robust predictions.

• Gamification and Engagement: Swarm’s platform incorporates gamification elements that make participation more engaging and rewarding. This not only attracts more users but also sustains their involvement, ensuring a continuous flow of fresh insights and perspectives.

• AI-Driven Insights: The integration of AI with human conversational data allows Swarm to analyze and utilize vast amounts of information efficiently. This combination of human and machine intelligence enhances the predictive power of the platform, making it more reliable and valuable.

By addressing both the cognitive biases and deeper systemic issues of traditional prediction markets, Swarm presents a revolutionary approach that maximizes the value of collective intelligence through enhanced community engagement, gamification, and AI-driven processes.

🗣 To intellectuals and truth seekers, our community-focused deliberative prediction market provides opportunities to earn from collective intelligence because it maximizes the value of information through enhanced community engagement, gamification, and AI-driven processes that foster intellectual growth, meaningful societal contributions, and paves the way towards Liquid Democracy.

• Yannick Myson (Founder & CEO)

Swarm CIN - Collective Intelligence Network

Swarm's Deliberative Prediction Market is powered by the Swarm Collective Intelligence Network (CIN), representing a groundbreaking integration of community, deliberation, AI, blockchain, and prediction markets. This innovative approach maximizes the value of information through enhanced community engagement, gamification, and deliberative processes. By leveraging AI, Swarm creates a more intelligent and interactive prediction market, where the collective insights and predictions of participants are refined and optimized for greater accuracy and impact.

How Swarm Works

Swarms are groups of traders (predictors) gathered in native chat environments such as Telegram. For illustrative purposes, we'll use Telegram as the proof of concept (POC) was completed there. Swarms are created by leaders through the creation of a topic-based group chat and adding the Swarm Bot (Observer Agent in the image above). Whenever a leader accepts for his swarm to participate in a prediction that's available on the Swarm Markets, a new topic is created by the bot.

Then, the traders can start their deliberation, present information, and share their perspectives and knowledge on the said prediction. The prediction is guided, and the observer agent provides feedback and structures the information in Prediction Maps. Prediction maps are structured formats that filter the noise from the deliberation and present key ideas, arguments, and comments. After the deliberation, a consensus is recorded.

The consensus constitutes a position in the market. The position is funded by the swarm treasury. Swarm treasuries are governed by the swarms and are programmed according to a set of predetermined rules.

CIN Beyond Prediction Markets

The Swarm Collective Intelligence Network (CIN) holds immense potential beyond the realm of prediction markets. Its core principles of decentralized deliberation, community engagement, and AI integration can revolutionize several other domains, including decentralized governance, liquid democracy, and policy making.

1. Decentralized Governance: CIN can facilitate more democratic and transparent decision-making processes within decentralized organizations. By leveraging community deliberation and AI-driven insights, decentralized autonomous organizations (DAOs) can make more informed and inclusive decisions, reflecting the collective will and wisdom of their members.

2. Liquid Democracy: CIN supports the concept of liquid democracy, where individuals can delegate their voting power to trusted representatives while retaining the ability to retract it at any time. This system allows for a more dynamic and responsive form of democracy, where decisions are continuously refined through deliberation and feedback loops, ensuring that they remain aligned with the community's evolving preferences and values.

3. Policy Making: Governments and institutions can utilize CIN to gather diverse perspectives and deliberative input from citizens on policy issues. This approach can enhance the quality of policy decisions by incorporating a wider range of insights and reducing the influence of biases. By fostering a more participatory policy-making process, CIN can lead to more effective and equitable outcomes.

4. Community Building: Beyond formal decision-making processes, CIN can be a powerful tool for community building and engagement. By providing a platform for structured deliberation and collaboration, CIN can help communities address local issues, develop collective solutions, and strengthen social cohesion.

5. Education and Research: Academic institutions and research organizations can leverage CIN to enhance collaborative research efforts and educational initiatives. By facilitating the exchange of ideas and fostering interdisciplinary collaboration, CIN can accelerate the generation of new knowledge and innovations.

By extending the principles of deliberative prediction markets to these areas, the Swarm Collective Intelligence Network can unlock new possibilities for decentralized governance, liquid democracy, policy making, and beyond, ultimately contributing to a more participatory, transparent, and intelligent society.