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kevin

@k3vin-wang

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The overall error of multiple models is equal to the average error of a single model minus the diversity of the model’s predictions. In other words, if you have many different predictions, the overall performance may be better than relying on a single prediction. The theorem can be expressed as a mathematical equation: {Average Prediction} - {True Value})^2 = {Average Single Model Error} - {Prediction Variability} The left-hand side represents the error of the overall prediction. The first term on the right-hand side is the average error of a single model. The second term on the right-hand side represents the diversity (or variability) of the model’s predictions. The theorem does not imply that any collection of diverse models will be accurate. If all of the models share a common bias, their average will also contain that bias. The theorem does imply that any collection of diverse models (or people) will be more accurate than its average member, a phenomenon referred to as the wisdom of crowds.
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The Condorcet Jury Theorem is named after the 18th-century French philosopher and mathematician, Marquis de Condorcet. It is related to the broader ideas of collective intelligence and social information aggregation. This theorem has been influential in discussions about the epistemic reasons for democracy and the design of decision-making bodies. When a group of individuals independently makes a decision between two options, and each person is more likely to be right than wrong, majority voting is generally correct. As the size of the group increases, the likelihood of the majority being correct also increases. It applies in the following conditions: • There is an odd number of voters • Each person has a greater than 50% chance of being correct • People make decisions independently • There are only two possible answers (right/wrong) As the number of people in the group increases, the accuracy of the majority vote approaches 100%.
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I recently wrote an article "Deep Dive into Blackbird Whitepaper." While it doesn't discuss a specific dapp, it explores concepts from the "Ownership Economy" as outlined in Variants Fund's 2022 Thesis. The article examines how to create an exceptional dining experience by integrating both off-chain and on-chain infrastructure. A key focus of the piece is analyzing the project's approach from a Tokenomics perspective. It delves into how the system is designed to address the diverse values and interests of multiple stakeholders. I find this product development framework particularly compelling due to its maturity and comprehensiveness. This approach, I believe, represents a more evolved understanding of how blockchain technology can be applied to create practical, user-centric solutions in real-world scenarios like the restaurant industry. https://blog.tulsk.io/p/deep-dive-into-blackbird-whitepaper
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