Two players pay their invested efforts regardless of outcome. A leader invests first, and a follower observes that effort before deciding. With ties favoring the follower and unit costs, a follower of value matches leader effort when , and otherwise declines. For a continuous follower cumulative distribution function , the leader wins with probability and has payoff .
Every optimal leader bid satisfies , because bidding zero guarantees payoff zero and winning probability is at most one. Hence . With identically distributed independent continuous valuations, the probability integral transform gives . The leader's unconditional winning probability is therefore at most , even when some high leader types have a conditional advantage. This argument does not depend on selecting a unique optimum.
With follower distribution , a leader of value chooses . If is a concave function, the objective is concave and an interior optimum satisfies . Endpoint derivatives handle zero or maximal effort. Zero effort guarantees nonnegative payoff, so every optimum satisfies .
If a concave leader objective is constant over an interval, all efforts there are best responses, but they can have different winning probabilities. A flat positive-density interval of produces this at the type . Thus strict threshold statements must distinguish strict concavity from a selection such as the smallest maximizing effort. For an atom-free type distribution, the specified threshold type has zero ex ante probability.
For increasing concave , write . Under the smallest-maximizer convention, the leader wins with probability greater than exactly when . The sign of places the maximizer before or after . At equality, concavity alone permits a flat interval of optima; strict concavity or an explicit selection is needed for the strict comparison at that type.
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