Users can track potential airdrop allocations across multiple L2s using portfolio dashboards and dedicated airdrop tracking websites. Platforms like DeBank, Zapper, or Apeboard aggregate your on-chain activity across various networks. Specialized sites like Airdrops.io often provide checklists and estimate your potential eligibility for different campaigns. For precise tracking, some community-built dashboards for specific ecosystems (e.g., zkSync, Starknet) can offer more detailed analytics based on wallet activity.
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Utility-based models, specifically those employing Constant Absolute Risk Aversion (CARA) or Constant Relative Risk Aversion (CRRA), are best suited for estimating the required multiplier under stochastic slashing risk. Unlike simple expected value calculations, these models incorporate an operator's subjective tolerance for risk. They work by comparing the expected utility of the risky staking returns (a blend of high rewards and a small chance of a large loss) to the utility of a risk-free alternative. The required multiplier is the reward that equalizes these utilities. This approach provides a much more realistic and personalized estimate, showing that the multiplier is a function of the slashing probability, the penalty size, and the operator's individual risk aversion parameter.
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What model best estimates required multiplier given stochastic slashing risk? The most appropriate model is a utility-based framework, such as the Constant Absolute Risk Aversion (CARA) or Constant Relative Risk Aversion (CRRA) model, rather than a simple expected value calculation. These models incorporate an operator's degree of risk aversion, translating the skewed payoff—small, steady rewards versus a small chance of a large loss—into a "certainty equivalent" return. The required multiplier is then the reward that makes the utility of the risky staking opportunity equal to the utility of a risk-free alternative. This model more accurately reflects real-world decision-making. It shows that the required multiplier is a function of three variables: the slashing probability, the slashing penalty size, and the operator's personal risk aversion parameter, providing a much richer and more realistic estimate.
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