In this paper, we consider contention resolution algorithms that are augmented with predictions about the network. We begin by studying the natural setup in which the algorithm is provided a distribution defined over the possible network sizes that predicts the likelihood of each size occurring. The goal is to leverage the predictive power of this distribution to improve on worst-case time complexity bounds. Using a novel connection between contention resolution and information theory, we prove lower bounds on the expected time complexity with respect to the Shannon entropy of the corresponding network size random variable, for both the collision detection and no collision detection assumptions. We then analyze upper bounds for these settings, assuming now that the distribution provided as input might differ from the actual distribution generating network sizes. We express their performance with respect to both entropy and the statistical divergence between the two distributions -- allowing us to quantify the cost of poor predictions. Finally, we turn our attention to the related perfect advice setting, parameterized with a length $b\geq 0$, in which all active processes in a given execution are provided the best possible $b$ bits of information about their network. We provide tight bounds on the speed-up possible with respect to $b$ for deterministic and randomized algorithms, with and without collision detection. These bounds provide a fundamental limit on the maximum power that can be provided by any predictive model with a bounded output size.

翻译：在本文中,我们考虑以对网络的预测来扩大争议解决算法。 我们首先研究提供算法的自然设置, 其分配范围为预测每个规模发生的可能性的可能的网络规模所定义的分布范围。 目标是利用这种分配的预测能力来改善最坏情况的时间复杂界限。 使用争议解决和信息理论之间的新联系, 我们证明在相应网络规模随机变量的香农变异的预期时间复杂性方面限制较低, 包括碰撞探测和没有碰撞探测假设。 然后我们分析这些环境的上限, 假设作为投入提供的分布可能不同于实际的分布网络规模。 我们表达这种分布在英特罗普和两个分布之间的统计差异方面的表现, 使我们能够量化错误预测的成本。 最后, 我们把注意力转向相关的完美建议设置, 以一个长度 $geqq 0. 0 的参数为参数, 其中所有在特定执行中的积极程序都提供了有关其网络信息的最佳可能 $b 位段。 我们提供最紧的分布范围, 作为投入与实际生成网络规模的信息, 我们提供最接近的信号, 和最短的测算法, 提供最短的精确的精确的测算。