Planning Against a Prophet: A Graph-Theoretic Framework for Making Sequential Decisions
<|reference_start|>Planning Against a Prophet: A Graph-Theoretic Framework for Making Sequential Decisions: We devise a general graph-theoretic framework for studying prophet inequalities. In this framework, an agent traverses a directed acyclic graph from a starting node $s$ to a target node $t$. Each edge has a value that is sampled from a known distribution. When the agent reaches a node $v$ it observes the realized values of all the outgoing edges from $v$. The agent's objective is to maximize the expected total value of the path it takes. As in prophet inequalities, we compare the agent's performance against a prophet who observes all the realizations of the edges' values ahead of time. Our analysis reveals that this ratio highly depends on the number of paths $k$ required to cover all the nodes in the graph. In particular, we provide an algorithm that guarantees a prophet inequality ratio of $\frac{1}{2k}$ and show an upper bound of $\frac{1}{k+1}$.   Our framework captures planning problems in which there is uncertainty regarding the costs/benefits of each action. In particular, it captures an over-time variant of the classic prophet inequality in which a seller leases a durable item, such as an apartment, for $n$ time units. Each period a lessee appears and may have a different value for each lease term. We obtain a tight bound of $1/2$ for this variant. To make this framework even more expressive, we further generalize it to accommodate correlations between edges originating from the same node and allow for additional constraints on the edges the agent can take. The generalized framework captures many well-studied prophet inequality problems, including $d$-dimensional matching, $k$-prophet inequality, and more.<|reference_end|>
@article{cristi2024planning,
    title={Planning Against a Prophet: A Graph-Theoretic Framework for Making
  Sequential Decisions},
    author={Andr'es Cristi and Sigal Oren},
    journal={arXiv preprint arXiv:2406.13911},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.13911},
    primaryClass={cs.GT}
}