The design of algorithms or protocols that are able to align the goals of the planner with the selfish interests of the agents involved in these protocols is of paramount importance in almost every decentralized setting (such as, computer networks, markets, etc.) as shown by the rich literature in Mechanism Design. Recently, huge interest has been devoted to the design of mechanisms for imperfectly rational agents, i.e., mechanisms for which agents are able to easily grasp that there is no action different from following the protocol that would satisfy their interests better. This work has culminated in the definition of Obviously Strategyproof (OSP) Mechanisms, that have been shown to capture the incentives of agents without contingent reasoning skills. Without an understanding of the algorithmic nature of OSP mechanisms, it is hard to assess how well these mechanisms can satisfy the goals of the planner. For the case of binary allocation problems and agents whose private type is a single number, recent work has shown that a generalization of greedy completely characterizes OSP. In this work, we strengthen the connection between greedy and OSP by providing a characterization of OSP mechanisms for all optimization problems involving these single-parameter agents. Specifically, we prove that OSP mechanisms must essentially work as follows: they either greedily look for agents with ``better'' types and allocate them larger outcomes; or reverse greedily look for agents with ``worse'' types and allocate them smaller outcomes; or, finally, split the domain of agents in ``good'' and ``bad'' types, and subsequently proceed in a reverse greedy fashion for the former and greedily for the latter. We further demonstrate how to use this characterization to give bounds on the approximation guarantee of OSP mechanisms for the well known scheduling related machines problem.

翻译：在几乎所有的去中心化环境（如计算机网络、市场等）中，设计能够协调规划者目标与协议中参与者自私利益的算法或协议，正如机制设计中丰富的文献所展示的，具有极其重要的意义。近年来，学界对针对非完全理性智能体的机制设计产生了巨大兴趣，即那些能让智能体轻易理解——除遵循协议外，没有任何其他行动能更好地满足其利益——的机制。这一研究最终催生了"明显策略证明"（Obviously Strategyproof, OSP）机制的定义，该机制已被证明能够捕捉缺乏条件推理能力的智能体的激励特性。若缺乏对OSP机制算法本质的理解，便难以评估这些机制能在多大程度上实现规划者的目标。对于二元分配问题及私密类型为单一数值的智能体，近期研究表明，贪心算法的一种推广形式完整刻画了OSP机制。在本研究中，我们通过为所有涉及此类单参数智能体的优化问题提供OSP机制的刻画，进一步加强了贪心算法与OSP之间的关联。具体而言，我们证明OSP机制本质上的运作方式必须如下：要么贪心地寻找具有"更优"类型的智能体，并分配给他们更大的结果；要么反向贪心地寻找具有"更差"类型的智能体，并分配给他们更小的结果；或者，将智能体的类型域划分为"好"与"坏"两类，随后对前者采用反向贪心方式，对后者采用贪心方式。我们进一步展示了如何利用这一刻画，为经典调度相关问题中OSP机制的近似保证给出界值。