GNY’s New Experiment: Bridging Crowd Wisdom and Machine Learning

Before delving into the specifics of GNY’s upcoming experiment, it’s beneficial to have a grasp of the ‘Wisdom of the Crowd’ theory, which posits that collective insight from a large group can sometimes surpass expert opinions.

Our previous discussion elaborates on the historical context and modern applications of this theory, shedding light on its significance in diverse fields, including the financial sector. We explored how crowd sentiment, exemplified through platforms like Intrade, can offer remarkable predictive accuracy.

The article also dives into how blockchain can capture and utilize crowd sentiment effectively. To better understand the foundational concepts informing our current experiment, read our previous article on the Wisdom of the Crowd.

The Experiment

GNY is launching an experiment to explore how crowd-sourced predictions on Bitcoin prices could enhance the performance of our LSTM machine learning model to further improve our market and trading insights. We are inviting the public to participate by guessing future Bitcoin prices on specified dates. Participants will be rewarded with crypto tokens and have the chance to win a cash prize if their prediction is the most accurate.

Advantages

• Diverse Input: Crowd-sourced data can offer a wide range of perspectives that might reveal hidden trends, potentially improving the model’s predictive accuracy.
• Community Engagement: This experiment also provides an opportunity for community engagement and education around blockchain, machine learning and cryptocurrency trading.

Disadvantages

• Data Quality: There’s a risk of collecting inaccurate data due to biases or lack of expertise, which might negatively affect the model’s performance.
• Overfitting Risk: If the model becomes too tailored to the crowd-sourced data, it may perform poorly with new, unseen data.

Incentivizing Participation

Providing incentives in the form of crypto tokens not only encourages wider participation but also introduces a tangible value to the input given by participants. This mechanism can potentially result in more thoughtful and accurate price predictions as participants have a stake in the exercise.

The incentive structure is intended to encourage a sense of ownership and engagement among the participants, fostering a community-centric ecosystem conducive to insightful discourse and shared learning.

Conversely, it is also possible that incentivised data collection can negatively influence the quality of the data collected. Forming an understanding of this effect is crucial for understanding the success or failure of the experiment.

Concluding Thoughts

The experiment lies at the intersection of crowd wisdom and modern machine learning. By merging these two domains, GNY aims to explore new ways to enhance predictive models in the volatile crypto market.

The results could provide valuable insights into how crowd-sourced data can be used in machine learning and financial forecasting, setting a precedent for future similar endeavors.

We look forward to sharing the results of our experiment with you and thank you in advance for participating.