WePredict: A Play Money Prediction Market for India

Published November 5, 2024

1

My Past Writings

In Email’s Epps Era: Transforming Customer and Employee Engagement (parts 6-8), I discussed WePredict, my idea for a prediction markets game using play money (Mu), and a potential killer “Epp” (email app).

WePredict relies on two key insights: our desire to know the future and the wisdom of crowds.

First, the game taps into our inherent curiosity and desire to predict future events. Whether it’s sports outcomes, election results, or market trends, people have always been fascinated by the challenge of forecasting what will happen next. This desire to know the future drives engagement, as users are motivated to place bets and see if their predictions come true.

Second, WePredict leverages the wisdom of crowds. This concept suggests that collective predictions made by a diverse group of individuals can often be more accurate than those made by a single expert. By aggregating bets from a large number of users, WePredict creates a dynamic and evolving market that reflects the collective intelligence and insights of its participants. This not only makes the game more exciting but also enhances the accuracy of the predictions, as the crowd’s collective judgment adjusts the odds in real-time based on the latest information and trends.

Together, these insights create a compelling and engaging experience, encouraging users to participate regularly and contribute to a continuously evolving and accurate prediction market.

By making it an Epp, the hope is that it become an attractor for the email inbox.  It can transform how users engage with their inbox by providing a fun, interactive, and socially connected experience that keeps them coming back daily. Its blend of education, entertainment, and competition makes it a potential killer Epp that attracts and retains users while fostering a vibrant and virally growing community.

I had written about Mu and Prediction Markets in an essay last year: “With prediction markets playing one person’s expectations versus another, current entities face two drawbacks because of their use of real money for participation: customers are limited, and the operating companies invite regulation. So, I started thinking about creating a prediction market using Mu as the token for participation. As long as Mu could be decoupled from real-world (fiat) money, it could stay away from regulation, and there could also be mass participation. Mu could be earned via participation in brand activities (for attention, data and referrals), and then could be spent in prediction markets… As consumers own (“mine”) Mu for their attention, data and other brand-incentivised actions, prediction markets can be a good potential redemption (and additional earning) mechanism. It brings out the latent wisdom each of us have in our interpretation of future events. By removing the need for real money, MuCo’s Prediction Markets also eliminate the need for regulation.”

In this essay, I will delve deeper into the concept and explore the mechanics of how it could work, using ChatGPT to develop the idea further.

2

Recent Writings – 1

I have aggregated some of the recent news and opinions on prediction markets.

Stanford Review: “In landmark rulings [recently], Kalshi, a U.S.-regulated prediction market platform, won a nearly year-long court battle against the Commodity Futures Trading Commission (CFTC) to offer commercial election markets. A federal judge ruled in Kalshi’s favor, an astonishing decision that finally allows the company to offer event contracts on the outcomes of presidential elections and control of Congress—the first of their kind in the U.S. in nearly a century. In winning this case, more was at stake than any individual company: It’s a landmark and turnaround in the broader struggle for prediction markets to be recognized for their value in forecasting and decision-making, unburdened by regulatory overreach and misunderstanding… The success of prediction markets stems from their incentive structure—participants are motivated to gather and act on the best available information. Moreover, markets by their very nature aggregate diverse information from participants who have various pieces of knowledge and insights. As a result, market prices adjust in real-time as new data emerges, providing a dynamic, crowd-sourced probability of future events… Most prediction markets (like Kalshi) are real money markets, but some notable markets are “play-money”, using in-app/website tokens with no real value. Even with weak incentives and relatively small userbases, markets like Metaculus and Manifold can be quite accurate.”

The Block: “Prediction markets work well to forecast elections as polling has become less accurate over time, according to Kalshi CEO Tarek Mansour. “Prediction markets work better, they really do,” Mansour said in an interview with The Block…Mansour attributed the inaccuracy in polling to bias and polarization. As for prediction markets, Mansour said “it’s much harder to lie when you have some money on the line…You’re actually much more truthful, and that’s why these markets work so well.””

Thomas Miller (in the context of US elections): “I don’t rely on polls. I rely on prediction markets…A political prediction market is a leading indicator of what will happen in an election. People are putting their money down, which means they believe something’s going to happen in the future…The poll is asking, ‘Who do you want to win?’ The prediction market says, ‘Who do you expect is going to win?’ There’s a lot of research to justify that the prediction markets are more effective as forecasters than polls…The prediction market is forward-looking. Just as the stock market is a leading indicator of what will happen in the economy, a political prediction market is a leading indicator of what will happen in an election.”

NYTimes: “People who run predictive markets say regulated election betting could provide better forecasting data and a valuable way for businesses to hedge election risk. Detractors say such markets are prone to manipulation and bad for democracy… Instead of asking people whom they will vote for, like a poll, they ask people who they think will win, which some argue produces a better prediction. Rajiv Sethi, an economist at Barnard College who has studied how prediction markets fared against statistical models in predicting outcomes in the 2020 and 2022 elections, says that “the jury is still out” on which is more accurate. It’s clearer that markets can provide different types of information, he said. For example, they’re much faster than polls or statistical models, and they can more easily pick up on signals in uncharted waters, like a presidential candidate’s stepping out of the race just months before Election Day.”

3

Recent Writings – 2

Vanity Fair: “Event-betting markets can be fascinating, intellectual exercises and have produced a rich body of economic scholarship, though not all markets are built alike. They can be sharp-elbowed, offshore gambling dens, or nonprofit experiments that model event outcomes… Economists have studied prediction markets as a potential manifestation of the so-called wisdom of crowds, wringing collective insight from masses of free market actors. The George Mason University economist Robin Hanson has conceived of a form of government—called “futarchy”—that relies in part on data from prediction markets to inform policies. Indeed, in pockets of academia, corporate consulting, and, of course, Silicon Valley, prediction has become something approaching a science, building on an almost mystical vision that any earthly phenomenon might be quantified and accurately forecast.”

Coin Telegraph: “Prediction markets operate through smart contracts that encode the terms of the agreement. For example, you might have options like betting on “Team A” or “Team B” in an upcoming baseball tournament. Each choice has a price reflecting the current market consensus. Suppose shares representing Team A and Team B are trading at 55 cents and 45 cents, respectively. In that case, this pricing suggests the market assigns a 55% probability of Team A winning and a 45% probability for Team B…When the event concludes, the market closes, and if your prediction is correct, you receive a payout. Buying a winning contract earlier can lead to a larger payout since prices rise as more people back a particular outcome.”

Forbes: “Prediction markets can be traced back to the 16th century, when Europeans would sometimes bet on the papal successor. In the late 19th century, prediction marketplaces thrived as “bucket shops,” where bets on stock prices were placed. Over time, these markets evolved into more sophisticated platforms, especially with the rise of the Internet. The University of Iowa’s Tippie College of Business began experimenting with so-called political stock markets via its Iowa Electronic Markets in the late 1980s. The platform allows users to make small wagers on political outcomes, economic indicators, and cultural events in the name of research… Dragonfly’s Schmidt likes Coplan’s chances in the race to build a lasting prediction marketplace despite competition from traditional finance. “Ultimately, Polymarket’s ability to allow an ecosystem of creators to create new markets is its secret weapon, one that TradFi competitors can’t easily replicate,” he says. “Think YouTube, not TV.””

Nick Whitaker and J. Zachary Mazlish: “What’s the problem with just relying on the prediction markets we have today? They are small, with few traders and little professionalization, but are they still the best place to look for the probability of a future event? We think that prediction markets as they exist are probably, at their best, similarly accurate to other high quality sources of information about the future, like the best forecasters, averages of forecasters like those found on Metaculus, and poll aggregators like 538. That is to say they do reasonably well, but are not authoritative or impossible for a highly motivated individual to beat… Why would this be? Even for hobbyists, it costs some amount of time and effort to learn information to predict the results of future events. Prediction markets currently offer relatively small opportunities to profit off this knowledge…When prices on prediction markets are wrong, if it requires time and effort to figure out the right price, or even just to implement the trades, the limited market size and liquidity cannot incentivize new entrants from correcting those prices…So while prediction markets’ probabilities are worth considering, the limitations in size and liquidity of the markets greatly diminish their power. There is no evidence that they are better than other comparable mechanisms for information aggregation, let alone decisive. That doesn’t seem likely to change. In this case, you very well may be able to beat the market, but you probably won’t be able to profit much from it.”

4

Recent Writings – 3

Crypto Briefing: “Decentralized prediction markets are blockchain-based platforms where users bet on future events without intermediaries. [They] use blockchain and smart contracts to operate autonomously; [they] allow betting on various outcomes like politics, sports, finance; [they] typically use cryptocurrency tokens for trading; [and they] aim to harness “wisdom of the crowd” for accurate predictions.”

Coin Telegraph: “In 2024, the blockchain world is at a turning point. Although big institutions are starting to see the value in this technology, blockchain-native prediction markets are emerging as the game-changing crossover “killer app.” The most exciting part is how these markets can provide accessible, relevant information about current events to everyone. This shift lets people tap into the “wisdom of the crowd” and play a role in shaping our understanding of what’s to come… If something gets skewed disproportionately to reality, there’s always someone who will pick up that opportunity. Let’s say someone buys a position at 50/50, because they believe one outcome is going to happen. If significant money suddenly pours in overnight and the odds shift to 80/20 for the other side, with nothing fundamentally changing to justify this shift, they would buy the 20% position immediately because it’s such an attractive opportunity at that price. The wisdom of the crowd would recognize this as a good opportunity. If you liked the position at 50%, you’d love it at 20%. This is how the market naturally rebalances itself.”

Dean Ball: “What if an LLM read all my writing, listened to all my podcast appearances, and perhaps even to some of my private or semi-private conversations, and then placed hundreds of micro-bets for me, updating them as my own thinking evolved? What if LLMs did this for everyone who cares about AI, or any other topic? The income I would gain or lose needn’t be significant. If the bets were small, it could be a modest income stream, similar to what most artists get from streaming royalties, or what many mid-sized X accounts receive in revenue sharing. That way, any losses would not be the end of the world for most people. The real value would be the knowledge society could construct. What if the debate over the capabilities trajectory of AI, for example, was also operationalized in 1000s of prediction markets, thickly traded in micro-bets made on behalf of millions? And what if other LLMs also surveyed the broader media environment and placed their own bets? If you think of my writing and thinking (or yours) as a kind of one-man intellectual hedge fund, these latter groups would be something like funds of funds. What if we could simulate financial markets for every question about the future that concerns us? And what if it cost next to nothing to do? What if, after the work of setting it up was complete, all this just carried on each day, in a way that few humans had to devote much time to maintaining or thinking about?”

Dan Hendrycks: “We’ve created a demo of an AI that can predict the future at a superhuman level (on par with groups of human forecasters working together). Consequently I think AI forecasters will soon automate most prediction markets.” More: “Our bot and other forecasting bots can be used in a wide variety of contexts. For example, these AIs could help policymakers minimize bias in their decision-making or help improve the information ecosystem by providing trustworthy, calibrated forecasts. We hope that forecasting bots like ours will be quickly integrated into frontier AI models…FiveThirtyNine is prompted to behave like an “AI that is superhuman at forecasting”. It is then asked to make a series of search engine queries for news and opinion articles that might contribute to its prediction.”

5

Play Money Use

I asked ChatGPT (o1-preview) for an assessment of the advantages and disadvantages of using play money (Mu, for example) for prediction markets.

Advantages

1. Legal and Regulatory Ease:

1. • Simplified Compliance: Play money avoids many gambling and financial regulations associated with real-money markets. This can make it easier to operate legally in multiple jurisdictions.
   • Accessibility: Users from regions with strict gambling laws can participate without legal concerns.

2. Lower Barrier to Entry:

1. • Risk-Free Participation: Users can engage without the fear of losing real money, encouraging wider participation.
   • Inclusive Environment: Attracts individuals who are interested in prediction markets but are hesitant to risk real funds.

3. Educational Value:

1. • Learning Platform: Provides a safe space for users to learn about trading, market dynamics, and probability without financial risk.
   • Skill Development: Users can practice and refine their predictive abilities.

4. Increased User Engagement:

1. • Gamification: Play money allows for game-like features (leaderboards, achievements) that can enhance user engagement.
   • Community Building: Encourages participation and interaction among users, fostering a community around the platform.

5. Monetization Through Advertising:

1. • Ad Revenue: A larger user base can attract advertisers, generating revenue without charging users or taking commissions.
   • Non-Intrusive Monetization: Users may prefer ads over transaction fees or commissions.

6. Ethical Considerations:

1. • Reduced Risk of Gambling Addiction: Eliminates the potential for users to develop real-money gambling problems.
   • Positive Public Perception: Seen as a responsible and ethical alternative to real-money betting.

7. Flexibility in Currency Management:

1. • Control Over Currency Supply: The platform can design the economy of Mu to suit user engagement strategies.
   • Promotional Opportunities: Play money can be awarded for referrals, daily logins, or completing tutorials.

Disadvantages

1. Reduced Incentive for Accurate Predictions:

1. • Lack of Financial Stakes: Without real money on the line, users may not be as motivated to make careful, informed predictions.
   • Potential for Irresponsible Betting: Users might make reckless bets since they have nothing tangible to lose.

2. Lower Prediction Accuracy:

1. • Less Reliable Data: The quality of predictions may suffer, reducing the market’s effectiveness at aggregating wisdom.
   • Difficulty Attracting Serious Participants: Experts and serious traders may avoid platforms without real financial incentives.

3. Economic Imbalance and Inflation:

1. • Currency Devaluation: If play money is too easy to obtain, it can lead to inflation within the platform’s economy.
   • Wealth Disparities: Early users or those who exploit the system may accumulate large amounts of Mu, impacting market dynamics.

4. Monetization Challenges:

1. • Reliance on Ads: Generating sufficient revenue from advertising alone can be difficult, especially if user engagement wanes.
   • User Experience: Over-reliance on ads can detract from the user experience, potentially driving users away.

5. Legal Ambiguities:

1. • Regulatory Scrutiny: Even with play money, authorities may scrutinize the platform if it’s perceived as promoting gambling behaviors.
   • Virtual Currency Regulations: Some jurisdictions have specific laws governing virtual currencies.

6. User Retention Difficulties:

1. • Diminished Long-Term Engagement: Without real rewards, users may lose interest over time.
   • Competition with Real-Money Platforms: Users seeking tangible rewards might prefer platforms where they can win real money.

7. Potential for Market Manipulation:

1. • Lack of Consequences: Users might manipulate markets without fear of real financial loss.
   • Multi-Accounting and Abuse: Easier for users to create multiple accounts to game the system.

8. Perception as Less Legitimate:

1. • Seriousness: May not be taken seriously by media or industry professionals, limiting exposure and growth.
   • Investor Interest: Harder to attract investors or partners who may view play-money markets as less viable.

Considerations for Mitigating Disadvantages

• Incentivizing Accurate Predictions:

• • Non-Monetary Rewards: Offer recognition, badges, or exclusive access to features for top performers.
  • Competitive Elements: Introduce tournaments or leaderboards to motivate users.

• Maintaining Economic Balance:

• • Controlled Currency Distribution: Regulate how Mu is earned and spent to prevent inflation.
  • Sinks for Currency: Create mechanisms where users can spend Mu, such as customization options or access to premium content.

• Enhancing User Engagement:

• • Regular Content Updates: Keep markets fresh with new and relevant events.
  • Community Features: Foster a sense of community through forums, chats, or social media integration.

• Addressing Legal Concerns:

• • Legal Consultation: Work with legal experts to navigate virtual currency and gaming laws.
  • Transparent Policies: Clearly communicate terms of service and user agreements.

• Diversifying Monetization:

• • Premium Features: Offer optional paid features or subscriptions for an enhanced experience.
  • Partnerships: Collaborate with brands or organizations for sponsorships or promotional events.

Conclusion

Using play money in a prediction market like WePredict offers several advantages, including easier legal compliance, broader accessibility, and a lower barrier to entry. It creates an inclusive platform where users can participate without financial risk, potentially leading to higher initial user engagement.

However, significant disadvantages exist, such as reduced incentives for accurate predictions, potential for market manipulation, and challenges in long-term user retention. The lack of real financial stakes may impact the seriousness with which users approach the market, affecting the overall quality and reliability of the predictions.

To maximize the advantages and mitigate the disadvantages, careful platform design is essential. Strategies include offering non-monetary incentives, maintaining economic balance within the virtual currency system, enhancing user engagement through gamification and community features, and exploring diverse monetization methods beyond advertising.

Ultimately, the success of a play-money prediction market hinges on its ability to create a compelling and rewarding user experience that motivates participants to engage thoughtfully and consistently, even without real financial stakes.

6

Mechanics

I asked ChatGPT to provide an overview of how to bring WePredict to life.

Virtual Currency (Mu)

• Issuance:

• • Initial Allocation: Every new user receives a fixed amount of Mu upon registering on the platform, providing them with the means to start participating immediately.
  • Earning Mu: Users can earn additional Mu through various activities, such as engaging in predictions, inviting friends to join (referrals), or contributing valuable content to the community.

• Spending Mu:

• • Placing Predictions: Users use Mu to buy shares in the outcomes they believe will occur. This investment represents their confidence in that outcome.
  • Transaction Fees: To maintain economic balance and prevent inflation within the platform, a minimal fee in Mu could be charged for each transaction. This acts as a currency sink, removing a small amount of Mu from circulation with each trade.

Prediction Market Structure

• Event Creation:

• • Decentralized Event Creation: Empower users to propose new events for prediction, fostering a diverse range of topics and increasing engagement.
  • Approval Process: Implement a moderation system where proposed events are reviewed to ensure they align with community guidelines and comply with legal and ethical standards.

• Market Mechanics:

• • Binary Markets: Start with simple “Yes” or “No” outcome events to make it easy for new users to understand and participate.
  • Categorical and Scalar Markets: Offer more advanced markets with multiple outcomes or range-based predictions for experienced users seeking deeper engagement.

• Trading Mechanism:

• • Automated Market Maker (AMM): Utilize algorithms like the Logarithmic Market Scoring Rule (LMSR) to provide continuous liquidity. This ensures users can always buy or sell shares, and prices adjust dynamically based on trading activity.
  • Price Representation: The price of a share in Mu reflects the collective belief about the probability of an outcome. As more users invest in a particular outcome, the price adjusts to represent the increased perceived likelihood.

User Interaction

• Placing Predictions:

• • Users spend Mu to purchase shares of an outcome at the current market price.
  • The number of shares obtained is calculated by dividing the Mu invested by the price per share at the time of purchase.

• Payouts:

• • When an event concludes, users holding shares of the correct outcome receive payouts in Mu.
  • Payout Calculation: The payout is typically determined by the number of shares held multiplied by a fixed rate.

Resolution and Verification

• Event Resolution:

• • Trusted Sources: Outcomes are determined based on information from reliable and publicly verifiable sources to maintain integrity.
  • Community Verification: Encourage user participation in the verification process by offering incentives for accurate reporting and validation of event outcomes.

• Dispute Resolution:

• • Appeals Process: Provide a transparent process for users to appeal event outcomes they believe are incorrect.
  • Arbitration Panel: Establish a group of trusted community members or moderators who review disputes and make fair, unbiased decisions.

Preventing Rigging and Ensuring Fairness

Ensuring fairness and preventing manipulation are critical for building trust in WePredict. Here’s how the platform can address these concerns:

A. Transparent Operations

• Open Ledger:

• • Implement a transparent ledger system, potentially using blockchain technology, to record all transactions and market activities. This allows users to verify trades and outcomes, promoting trust in the platform’s integrity.

• Public Resolution Sources:

• • Clearly communicate the sources used for determining event outcomes. Providing links or references to these sources ensures users can independently verify results.

B. Anti-Cheating Measures

• Multi-Account Detection:

• • Use sophisticated algorithms and verification processes to detect and prevent users from creating multiple accounts to manipulate market outcomes. Techniques may include monitoring IP addresses, device fingerprints, and requiring email or phone verification.

• Insider Trading Prevention:

• • Prohibit trading on events where users have access to non-public, material information. This can be enforced through user agreements and monitored by tracking unusual trading patterns or volumes.

• Event Outcome Verification:

• • Rely on multiple reputable sources to confirm event outcomes, reducing the risk of incorrect or biased resolutions. Involve the community in verification to leverage collective oversight.

C.  Community Reporting and Enforcement

• Reporting Mechanisms:

• • Provide straightforward tools for users to report suspicious activities or content. Prompt reporting helps the platform address issues quickly and maintain a fair environment.

• Enforcement Actions:

• • Establish and clearly communicate consequences for violating platform rules, such as account suspension, forfeiture of Mu, or permanent bans. Consistent enforcement deters potential cheaters.

D. Regular Audits and Updates

• Security Audits:

• • Conduct regular internal and external security audits to identify vulnerabilities and ensure the platform’s defenses are up-to-date against evolving threats.

• Policy Updates:

• • Keep community guidelines and terms of service current, adapting to new challenges and feedback. Inform users of significant changes to maintain transparency.

E. Building Trust Through Communication

• Transparency Reports:

• • Publish regular reports detailing platform performance, security measures taken, and any incidents of misconduct along with the actions taken. This openness reinforces user confidence.

• Community Engagement:

• • Foster open dialogue with users via forums, surveys, and Q&A sessions. Encouraging feedback and addressing concerns helps build a loyal and trusting community.

7

Example

I asked ChatGPT to work through an example.

Predicting India’s GDP Growth with WePredict

Let’s explore how WePredict operates by working through the example where a user named Raj creates an event: “India’s GDP for the July-August-September quarter will be greater than 7%” with two options: Yes and No. We’ll cover the entire process, including event creation, initial contract value, how users bet their Mu, price changes over time, and how payouts occur.

Event Creation

• Raj Proposes the Event
• Event Submission:
  • Raj logs into WePredict and navigates to the event creation section.
  • He fills out a form with the following details:
    • Title: “India’s GDP for the July-August-September quarter will be greater than 7%”
    • Description: Provides context or additional information about the event.
    • Outcomes: Two options—Yes (GDP > 7%) and No (GDP ≤ 7%).
    • Resolution Date: Specifies when the official GDP figures are expected to be released.
    • Source of Resolution: Indicates the official government statistics bureau or reputable news outlet that will confirm the GDP figures.
• Approval Process
• Moderation:

• • The event is submitted for review to ensure it meets community guidelines (e.g., legal, ethical, verifiable).
  • Moderators check for clarity, compliance, and the availability of reliable sources for resolution.

• Event Publication:

• • Once approved, the event is published on WePredict, and users can start trading.
• Creator Incentives
• Raj’s Reward:
  • As the event creator, Raj may receive a small percentage of the total Mu wagered on his event, encouraging users to contribute quality events.

Initial Contract Value

• • Starting Prices

• Default Price:
  • The platform sets initial share prices for both Yes and No at 50 Mu per share, reflecting an initial 50% perceived probability for each outcome.
  • This equal starting point ensures neutrality before any user trading influences the market.

1. Automated Market Maker (AMM)

• Liquidity Provision:
  • WePredict uses an AMM, such as the Logarithmic Market Scoring Rule (LMSR), to provide liquidity and adjust prices based on trading activity.
  • This ensures users can always buy or sell shares, even if there are no matching orders from other users.

Users Betting Their Mu

• • Buying Shares

• User Actions:
  • User A (Anita) believes the GDP will exceed 7% and wants to bet on Yes.
    • She decides to buy 100 shares of Yes.
  • User B (Bala) believes the GDP will not exceed 7% and wants to bet on No.
    • He decides to buy 50 shares of No.

Calculating the Cost

• Anita’s Purchase:

• • Initial price per Yes share: 50 Mu.
  • Total Cost for Anita:
    • 100 shares × 50 Mu/share = 5,000 Mu.

• Bala’s Purchase:

• • Initial price per No share: 50 Mu.
  • Total Cost for Bala:
    • 50 shares × 50 Mu/share = 2,500 Mu.

Effect on Prices

• Price Adjustment:
  • The AMM adjusts share prices after each purchase.
  • After Anita buys Yes shares, the price of Yes increases, and No decreases slightly due to the LMSR algorithm.

Contract Price Changes Over Time

Understanding the AMM Dynamics

• Price Sensitivity:
  • In the LMSR model, the cost of buying additional shares increases as more shares are purchased.
  • This reflects the increased market belief in that outcome.

Subsequent Trades

• Updated Prices After Anita’s Purchase:

• • Yes share price increases from 50 Mu to 52 Mu.
  • No share price decreases slightly from 50 Mu to 48 Mu.

• New User Participation:

• • User C (Chandra) notices the price change.
    • Believing that the GDP will exceed 7%, he buys 50 shares of Yes at the new price of 52 Mu/share.
    • Total Cost for Chandra:
      • 50 shares × 52 Mu/share = 2,600 Mu.
  • User D (Deepa) believes the GDP will not exceed 7% and buys 50 shares of No at 48 Mu/share.
    • Total Cost for Deepa:
      • 50 shares × 48 Mu/share = 2,400 Mu.

Ongoing Price Adjustments

• After Each Trade:
  • The AMM continuously updates prices.
  • As more users buy Yes shares, its price continues to rise, making it more expensive for subsequent buyers.
  • Conversely, the No share price adjusts downward as fewer users purchase it.

Payouts

• Event Resolution
• Outcome Determination:
  • On the specified resolution date, the official GDP figures are released by the government.
  • Let’s assume the GDP growth is 7.2%, so the Yes outcome occurs.

Calculating Payouts

• Payout Structure:
  • Each share of the correct outcome pays out a fixed amount, say 100 Mu per share.
  • Shares of the incorrect outcome have no payout.

Payouts to Users

• Anita (Yes):

• • Shares Held: 100
  • Payout: 100 shares × 100 Mu/share = 10,000 Mu
  • Profit Calculation:
    • Profit = Payout – Total Cost
    • Profit = 10,000 Mu – 5,000 Mu = 5,000 Mu

• Chandra (Yes):

• • Shares Held: 50
  • Payout: 50 shares × 100 Mu/share = 5,000 Mu
  • Profit Calculation:
    • Profit = 5,000 Mu – 2,600 Mu = 2,400 Mu

• Bala (No):

• • Shares Held: 50
  • Payout: 0 Mu (since No did not occur)
  • Loss: Total Cost = 2,500 Mu

• Deepa (No):

• • Shares Held: 50
  • Payout: 0 Mu
  • Loss: Total Cost = 2,400 Mu

Detailed Mechanics with Price Adjustments

AMM Pricing Formula (Simplified Explanation)

• Concept:

• • The cost to buy shares increases logarithmically with the quantity purchased.
  • This prevents users from disproportionately influencing the market with large purchases.

• Price Function:

• • The price PPP for an outcome is calculated based on the proportion of shares purchased for that outcome relative to the total shares for all outcomes.

Example of Price Calculation

• After Anita’s Purchase:

• • Total Yes shares sold: 100
  • Total No shares sold: 0
  • The AMM recalculates prices:
    • Yes price increases due to higher demand.
    • No price decreases slightly due to relatively lower demand.

Anita’s Average Price Paid:

• • Since the price increases with each share purchased, Anita may have paid an average price slightly above 50 Mu.
  • The platform calculates the total cost using the integral of the pricing function over the number of shares purchased.

Continuous Market Dynamics

• User Influence:
  • Each user’s trade impacts the market prices, reflecting the collective sentiment.
  • Users must consider current prices and how their trades will affect future prices.

Final Payout Calculations

7. Total Mu in the Market

• Total Invested in Yes:

• • Anita: 5,000 Mu
  • Chandra: 2,600 Mu
  • Total Yes Investment: 7,600 Mu

• Total Invested in No:

• • Bala: 2,500 Mu
  • Deepa: 2,400 Mu
  • Total No Investment: 4,900 Mu

• Total Mu Pooled: 12,500 Mu

Redistribution of Mu

• Payout Pool:

• • The total Mu invested is used to pay out winners.
  • Since Yes is the correct outcome, the payouts go to Anita and Chandra.

• Ensuring Platform Balance:

• • The platform ensures that the sum of payouts does not exceed the total Mu available.

Platform’s Role

• Transaction Fees:
  • If the platform charges a minimal transaction fee (e.g., 1% per trade), this Mu is removed from circulation, helping prevent inflation.

Benefits of This System

• Reflecting Market Sentiment
• Dynamic Pricing:
  • Prices adjust based on user activity, providing real-time insights into collective expectations about the GDP growth.

Encouraging Informed Predictions

• Research Incentive:
  • Users are motivated to research economic indicators, expert analyses, and news to make informed bets.

Fair and Transparent Trading

• Equal Opportunity:
  • The AMM ensures that all users have access to buy or sell shares at transparent prices.

Safeguards and Fairness

9. Preventing Manipulation

• Limits on Trades:

• • To prevent a single user from unduly influencing prices, the platform may set limits on the number of shares one can buy at once.

• Monitoring Unusual Activity:

• • The platform monitors for suspicious trading patterns that may indicate manipulation or collusion.

1. Clear Resolution Criteria

• Trusted Sources:

• • By specifying the official government release as the resolution source, ambiguity is minimized.

• Dispute Resolution:

• • Users can raise concerns if they believe the outcome was incorrectly resolved, triggering a review process.

User Experience Summary

• Raj Creates Event: Users are notified of new events they might be interested in.
• Users Place Bets: Anita, Bala, Chandra, and Deepa invest their Mu based on their predictions.
• Prices Adjust: As users trade, prices for Yes and No shares change, reflecting the market’s evolving sentiment.
• Event Resolves: The official GDP figures are released, and the platform updates the event outcome.
• Payouts Distributed: Users holding winning shares receive payouts, and their balances are updated accordingly.
• Post-Event Analysis:

• • Users can review the outcome, discuss in forums, and plan future strategies based on their experiences.

Conclusion

Through this example, we’ve illustrated how WePredict operates from event creation to payout distribution:

• Event Creation: Users like Raj can create events, enhancing platform diversity.
• Initial Contract Value: Starting prices are set, often at an equilibrium point (e.g., 50 Mu).
• Betting Mechanics: Users invest Mu to buy shares in their predicted outcomes, influencing prices through an AMM.
• Price Dynamics: Prices adjust based on trading activity, embodying the collective market sentiment.
• Payouts: Upon event resolution, users holding shares in the correct outcome receive payouts, while others incur losses equal to their investment.
• Fairness Measures: The platform employs mechanisms to ensure transparency, prevent manipulation, and maintain user trust.

This system encourages active participation, informed decision-making, and provides an engaging way for users to test their predictive abilities in a risk-free, play money environment.

8

Mu Flows

I asked ChatGPT to provide a detailed working of the Mu flows.

In this example, we’ll examine the Mu flows within WePredict for the event created by Raj: “India’s GDP for the July-August-September quarter will be greater than 7%” with two options, Yes and No. We’ll analyze:

• How much Mu comes into the event (total bets).
• How much Mu goes out (payouts and commissions).
• Who makes money and who loses.
• The impact of Raj’s 1% commission on the total bets.

1. Event Setup and Initial Investments

Event Creator: Raj
Commission Earned by Raj: 1% of total bets on his event.

Initial Share Prices:

• Yes: 50 Mu per share.
• No: 50 Mu per share.

Participants and Their Bets:

1. Anita believes Yes (GDP > 7%).

1. • Buys: 100 shares at 50 Mu/share.
   • Total Investment: 5,000 Mu.

2. Bala believes No (GDP ≤ 7%).

1. • Buys: 50 shares at 50 Mu/share.
   • Total Investment: 2,500 Mu.

3. After Anita’s Purchase:

1. • Yes price increases to 52 Mu/share.
   • No price decreases to 48 Mu/share.

4. Chandra believes Yes.

1. • Buys: 50 shares at 52 Mu/share.
   • Total Investment: 2,600 Mu.

5. Deepa believes No.

1. • Buys: 50 shares at 48 Mu/share.
   • Total Investment: 2,400 Mu.

2. Calculating Total Mu Invested

Total Mu Invested in the Event:

• Yes:

• • Anita: 5,000 Mu
  • Chandra: 2,600 Mu
  • Total Yes Investment: 7,600 Mu

• No:

• • Bala: 2,500 Mu
  • Deepa: 2,400 Mu
  • Total No Investment: 4,900 Mu

• Combined Total Investment: 7,600 Mu + 4,900 Mu = 12,500 Mu

3. Raj’s Commission

Raj’s Earnings:

• Commission Rate: 1% of total bets.
• Total Bets: 12,500 Mu
• Raj’s Commission: 1% of 12,500 Mu = 125 Mu

Adjusting the Pool for Payouts:

• Total Mu Available for Payouts: 12,500 Mu – 125 Mu = 12,375 Mu

4. Event Outcome and Payouts

Event Outcome: Yes (Assuming GDP > 7%)

Participants Who Win:

• Anita and Chandra (invested in Yes).

Participants Who Lose:

• Bala and Deepa (invested in No).

1. Calculating Payouts to Winners

Total Winning Investment (Yes): 7,600 Mu

Anita’s Share of Winning Investment:

• Investment: 5,000 Mu
• Percentage of Total Yes Investment: 5,000/7,600 = 65.79%

Chandra’s Share of Winning Investment:

• Investment: 2,600 Mu
• Percentage of Total Yes Investment: 2,600/7,600 = 34.21%

Total Mu Available for Payouts: 12,375 Mu

Anita’s Payout:

• Payout Amount: 65.79% of 12,375 Mu
• Calculation: 0.6579×12,375 = 8,135 Mu

Chandra’s Payout:

• Payout Amount: 34.21% of 12,375 Mu
• Calculation: 0.3421×12,375 = 4,240Mu

Verification:

• Total Payouts to Winners: 8,135 Mu + 4,240 Mu = 12,375 Mu

1. Calculating Profits and Losses

Anita:

• Initial Investment: 5,000 Mu
• Payout Received: 8,135 Mu
• Profit: 8,135 Mu – 5,000 Mu = 3,135 Mu

Chandra:

• Initial Investment: 2,600 Mu
• Payout Received: 4,240 Mu
• Profit: 4,240 Mu – 2,600 Mu = 1,640 Mu

Bala:

• Initial Investment: 2,500 Mu
• Payout Received: 0 Mu (lost bet)
• Loss: 2,500 Mu

Deepa:

• Initial Investment: 2,400 Mu
• Payout Received: 0 Mu (lost bet)
• Loss: 2,400 Mu

5. Summary of Mu Flows

Total Mu Invested: 12,500 Mu

Raj’s Commission: 125 Mu (1% of total bets)

Total Mu Available for Payouts: 12,375 Mu

Total Payouts to Winners:

• Anita: 8,135 Mu
• Chandra: 4,240 Mu
• Combined Payouts: 12,375 Mu

Mu Lost by Losers:

• Bala: 2,500 Mu
• Deepa: 2,400 Mu
• Combined Losses: 4,900 Mu

Mu Gained by Winners (Net Profits):

• Anita’s Profit: 3,135 Mu
• Chandra’s Profit: 1,640 Mu
• Combined Profits: 4,775 Mu

Raj’s Earnings:

• Commission: 125 Mu

Platform’s Role:

• In this scenario, the platform does not take any additional fees beyond Raj’s commission.
• Total Mu Conserved: The total Mu remains within the ecosystem, redistributed among participants and Raj.

6. Analyzing the Mu Flow Dynamics
7. Where Does the Mu Come From and Go To?

• Mu Invested by Participants: 12,500 Mu
• Redistribution:
  • Raj’s Commission: 125 Mu (from total bets)
  • Payouts to Winners: 12,375 Mu (from total bets minus commission)
  • Losses by Losers: Their initial investments are redistributed as payouts and commission.

1. Who Makes Money?

• Anita: Gains a net profit of 3,135 Mu.
• Chandra: Gains a net profit of 1,640 Mu.
• Raj: Earns 125 Mu as the event creator.

1. Who Loses Money?

• Bala: Loses his entire investment of 2,500 Mu.
• Deepa: Loses her entire investment of 2,400 Mu.

1. Total Profits vs. Total Losses:

• Total Net Profits (Winners): 3,135 Mu + 1,640 Mu = 4,775 Mu
• Total Losses (Losers): 2,500 Mu + 2,400 Mu = 4,900 Mu
• Difference: 4,900 Mu (losses) – 4,775 Mu (profits) = 125 Mu
  • This difference equals Raj’s commission, confirming the Mu flows balance.

7. Impact of Raj’s Commission on Participants
8. Effect on Payout Pool:

• Raj’s commission reduces the total Mu available for payouts from 12,500 Mu to 12,375 Mu.
• This slightly decreases the potential profits for the winners.

1. Incentive for Event Creation:

• Earning a commission motivates users like Raj to create engaging and high-quality events.
• This enriches the platform with diverse and interesting prediction opportunities.

8. Platform Sustainability
9. Mu Circulation:

• Mu flows from losing participants to winning participants and event creators.
• The platform ensures Mu remains in circulation, maintaining user engagement.

1. Economic Balance:

• No Inflation: Since the total Mu remains constant (except for potential currency sinks like transaction fees), there’s no unintended inflation.
• Currency Sinks (If Applicable):
  • If the platform implements transaction fees or other Mu sinks, this would remove Mu from circulation, helping to control the total supply.

9. Considerations for Users
10. Risk and Reward:

• Winners’ Gains: Profits are proportional to the amount invested and the total pool after commissions.
• Losers’ Losses: Participants risk losing their entire investment if their prediction is incorrect.

1. Strategic Betting:

• Market Influence: Large bets can influence share prices, affecting the cost for subsequent traders.
• Timing: Early investments can secure lower share prices, potentially increasing profits.

10. Final Thoughts

By understanding the Mu flows:

• Participants can make informed decisions about how much to invest and anticipate potential returns.
• Event Creators like Raj are incentivized to contribute valuable events, earning commissions.
• The Platform maintains an equilibrium, ensuring fairness and sustainability without introducing or removing Mu arbitrarily.

Summary Table of Mu Flows:

Participant	Initial Investment (Mu)	Payout Received (Mu)	Net Profit/Loss (Mu)
Anita	5,000	8,135	+3,135
Chandra	2,600	4,240	+1,640
Bala	2,500	0	-2,500
Deepa	2,400	0	-2,400
Totals	12,500	12,375	+125 (Commission)

Note: The net profit/loss for participants sums up to the total Mu collected as commission by Raj (125 Mu), confirming the balance of Mu flows within the system.

9

Assessment

I asked ChatGPT some questions.

1. Go/No-Go Decision for a Mu-Powered Play Money-Based Prediction Market

Based on a comprehensive analysis, I would recommend a Go decision for developing WePredict, the Mu-powered play money-based prediction market. Here’s why:

• Market Opportunity: There’s a niche for accessible and engaging prediction platforms in India, especially ones that use play money to lower entry barriers.
• Regulatory Advantages: Using virtual currency mitigates many legal hurdles associated with real-money gambling, simplifying compliance.
• Educational and Engagement Potential: The platform can attract users interested in forecasting, data analysis, and market dynamics without the risk of financial loss.

However, success will depend on careful implementation, addressing potential challenges proactively, and focusing on user engagement and retention strategies.

2. Three Reasons WePredict Can Succeed
3. High User Engagement through Gamification

• Interactive Experience: The use of play money encourages participation by removing financial risk, making it appealing to a broader audience.
• Community Building: Features like leaderboards, achievements, and social interactions can foster a vibrant community, enhancing user retention.

1. Educational Value and Skill Development

• Learning Platform: Users can improve their understanding of probability, statistics, and market behavior in a risk-free environment.
• Attracting Enthusiasts: Educators and students might use the platform as a practical tool for learning and teaching predictive analytics.

1. Tailored to the Indian Market

• Cultural Relevance: By focusing on popular Indian interests like cricket and Bollywood, the platform can tap into large, enthusiastic user bases.
• Accessibility: Offering multilingual support and culturally relevant content can increase adoption rates in diverse regions.

3. Three Reasons WePredict Can Fail
4. Lack of Sustained User Engagement

• Diminishing Novelty: Without real monetary rewards, users might lose interest over time, leading to decreased activity.
• Competing Platforms: Users seeking real financial gains may gravitate towards real-money betting platforms, reducing WePredict’s user base.

1. Inaccurate Predictions Due to Low Stakes

• Less Serious Participation: Users might make careless or uninformed predictions since they aren’t risking real money, affecting the quality of the market data.
• Reduced Credibility: If prediction accuracy is low, the platform may fail to be seen as a reliable source of insights.

1. Monetization Challenges

• Revenue Generation: Relying solely on advertising may not cover operational costs or provide sufficient profit margins.
• User Experience Impact: Excessive or poorly integrated ads can disrupt the user experience, leading to dissatisfaction and attrition.

4. Event Categories Likely to Succeed in India
5. Sports

• Cricket: As India’s most popular sport, cricket-related events (match outcomes, player performances) are likely to attract significant participation.
• Other Sports: Football (soccer), badminton, and emerging sports like kabaddi can also engage sports enthusiasts.

1. Entertainment

• Bollywood: Predictions on movie releases, box office results, award winners, and celebrity news can captivate a large audience.
• Television Shows: Reality TV outcomes, talent competitions, and series finales are engaging topics.

1. Politics

• Elections: National, state, and local election outcomes can generate widespread interest.
• Policy Decisions: Predictions on government initiatives or legislative changes may appeal to politically engaged users.

1. Current Events and Economy

• Economic Indicators: GDP growth rates, inflation figures, and stock market indices can attract users interested in finance and economics.
• Technology and Business: Launches of new tech products, startup successes, or industry trends.

5. Key Metrics to Track for Success
6. User Engagement Metrics

• Daily Active Users (DAU) and Monthly Active Users (MAU): Measure how many users are engaging with the platform on a daily and monthly basis.
• User Retention Rates: Track the percentage of users who continue to use the platform over time.
• Session Duration and Frequency: Monitor how long users stay on the platform and how often they return.

1. Market Activity Metrics

• Number of Active Markets: The total number of ongoing events available for prediction.
• Volume of Bets Placed: The amount of Mu wagered, indicating the level of user engagement.
• Liquidity Levels: Assess how easily users can buy and sell shares without significant price changes.

1. Financial Metrics

• Advertising Revenue: Income generated from ads, crucial for sustaining operations.
• Cost of User Acquisition (CAC): Expenses involved in attracting new users to the platform.
• User Lifetime Value (LTV): Estimated revenue generated per user over their entire engagement period.

1. User Satisfaction Metrics

• Net Promoter Score (NPS): Gauge user willingness to recommend the platform to others.
• User Feedback and Reviews: Collect insights from surveys, app store ratings, and customer support interactions.

1. Prediction Accuracy Metrics

• Market Calibration: Compare predicted probabilities with actual outcomes to assess the accuracy of predictions.
• Outcome Distribution Analysis: Evaluate how well the platform aggregates user insights to reflect real-world events.

**

Postscript: Writing this essay has been a fascinating journey, greatly enhanced by the assistance of ChatGPT. Starting with a basic idea and a few questions, I used ChatGPT to develop and refine the plan. Remarkably, it took only about two hours to complete this piece.