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This study examines the #riskallocation problem in distributed #insurance using #blockchaintechnology, considering different charging methods. Through #gametheory analysis, the research explores the #pareto optimal risk allocation method. The findings reveal that when charges occur during insurance signing, risk is proportionally distributed based on policyholders' #riskaversion coefficient. However, if the platform provider charges a fee proportional to the premium or actual risk, policyholders bear increased risk from others while their own risk is reduced, leading to decreased overall utility. These conclusions provide valuable insights for #blockchain insurance companies regarding user #riskmanagement and allocation.

"#risksharing is one way to pool risks without the need for a #thirdparty. To ensure the attractiveness of such a system, the rule should be accepted and understood by all participants. A desirable risk-sharing rule should fulfill #actuarial fairness and #pareto optimality while being easy to compute. This paper establishes a one-to-one correspondence between an actuarially fair #paretooptimal (AFPO) risk-sharing rule and a fixed point of a specific function."

"We address the problem of sharing risk among agents with preferences modelled by a general class of comonotonic additive and law-based functionals that need not be either monotone or convex. Such functionals are called distortion riskmetrics, which include many statistical measures of risk and variability used in portfolio optimization and insurance."