Social media engagements are now a standard component of "points-based" airdrop campaigns. Following a project on Twitter, joining its Discord, and retweeting announcements are common tasks that accumulate points. While these actions typically contribute a smaller percentage of the total score compared to on-chain activity, they help projects build community hype and identify users genuinely interested in the ecosystem, beyond just financial transactions. They serve as a low-barrier entry point but are rarely sufficient for a large allocation alone.
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Correlated slashing risk fundamentally changes the risk from diversifiable to systemic, drastically increasing the optimal multiplier. When risks are independent, operators can rely on the law of large numbers; the system's aggregate rewards are stable. When risks are correlated (e.g., due to a common client bug), a single event can slash a large fraction of operators simultaneously. This undiversifiable "tail risk" commands a much higher market risk premium. The multiplier must now compensate for the possibility of a network-wide catastrophe, not just an individual mistake. Modeling this requires shifting from a binomial distribution to a systemic risk model. The required multiplier under correlated risk can be an order of magnitude larger than under the independent risk assumption, as it protects against scenarios that threaten the entire staking ecosystem's viability at once.
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What’s the effect of slashing risk correlation across operators on the optimal multiplier? Correlated slashing risk fundamentally changes the risk from diversifiable to systemic, drastically increasing the optimal multiplier. When risks are independent, operators can rely on the law of large numbers; the system's aggregate rewards are stable. When risks are correlated (e.g., due to a common client bug), a single event can slash a large fraction of operators simultaneously. This undiversifiable "tail risk" commands a much higher market risk premium. The multiplier must now compensate for the possibility of a network-wide catastrophe, not just an individual mistake. Modeling this requires shifting from a binomial distribution to a systemic risk model. The required multiplier under correlated risk can be an order of magnitude larger than under the independent risk assumption, as it protects against scenarios that threaten the entire staking ecosystem's viability at once.
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