Unverified50% confidenceFactExact time
The core of CPT consists of two points: the probability weighting function (assigning excessive weight to small-probability events and insufficient weight to medium- and high-probability events) and the value function (defining gain and loss utility relative to a reference point, with a steeper loss region, and risk aversion in the gain region and risk seeking in the loss region).
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11/5/2026
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UnverifiedCPT utility is a nonlinear functional of the reward distribution, not a linear expectation of reward, which breaks the linear additivity of expectation that traditional optimistic algorithms rely on.68% similarUnverifiedThere is a trade-off between high-risk, high-value scenarios and low-risk, quick-win scenarios, and the first use case should sacrifice potential value in exchange for a lower cost of trial and error.67% similarUnverifiedExisting CVaR-based risk-averse methods apply static or dynamic CVaR to the value function to capture trajectory-level risk, but neglect the risk within the belief.66% similarUnverifiedThrough a parity-coupling construction, the paper proves that for the subtask empirical risk minimizer, when T≤n, the expected final task risk is at least the optimal in-class risk plus Ω(√(T/n)), showing that the upper bound is tight in order.65% similarUnverifiedThe work gives information-gain-based regret bounds for kernel CPT bandits for a broad class of kernel functions, and the results match the rates of the corresponding risk-neutral kernel methods up to logarithmic factors.64% similar
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