Exercising Control When Confronted by a (Brownian) Spider
Philip Ernst

TL;DR
This paper investigates the control problem of a Brownian spider, applying dynamic programming to establish bounds and optimality of control strategies for this stochastic process.
Contribution
It introduces a novel application of dynamic programming to analyze and establish bounds for the Brownian spider control problem.
Findings
Proved bounds for the Brownian spider using dynamic programming.
Established the optimality of control strategies via excessiveness.
Extended the theoretical understanding of stochastic control for complex processes.
Abstract
We consider the Brownian "spider," a construct introduced in \cite{Dubins} and in \cite{Pitman}. In this note, the author proves the "spider" bounds by using the dynamic programming strategy of guessing the optimal reward function and subsequently establishing its optimality by proving its excessiveness.
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