Algorithmic systems are often called upon to assist in high-stakes decision
making. In light of this, algorithmic recourse, the principle wherein
individuals should be able to take action against an undesirable outcome made
by an algorithmic system, is receiving growing attention. The bulk of the
literature on algorithmic recourse to-date focuses primarily on how to provide
recourse to a single individual, overlooking a critical element: the effects of
a continuously changing context. Disregarding these effects on recourse is a
significant oversight, since, in almost all cases, recourse consists of an
individual making a first, unfavorable attempt, and then being given an
opportunity to make one or several attempts at a later date - when the context
might have changed. This can create false expectations, as initial recourse
recommendations may become less reliable over time due to model drift and
competition for access to the favorable outcome between individuals.
In this work we propose an agent-based simulation framework for studying the
effects of a continuously changing environment on algorithmic recourse. In
particular, we identify two main effects that can alter the reliability of
recourse for individuals represented by the agents: (1) competition with other
agents acting upon recourse, and (2) competition with new agents entering the
environment. Our findings highlight that only a small set of specific
parameterizations result in algorithmic recourse that is reliable for agents
over time. Consequently, we argue that substantial additional work is needed to
understand recourse reliability over time, and to develop recourse methods that
reward agents' effort.

在高风险决策中，算法系统往往被要求提供帮助。鉴于此，算法追索的原则，即个体应该能够针对算法系统产生的不良结果采取行动，受到越来越多的关注。然而，过去关于算法追索的大部分文献主要关注如何为单个个体提供追索，而忽视了一个关键要素：不断变化的环境对追索的影响。在本文中，我们提出了一个基于代理的仿真框架，用于研究不断变化的环境对算法追索的影响。我们的研究发现，只有一小部分具体的参数设置才能使算法追索随时间可靠。因此，我们认为还需要大量的工作来了解随时间的追索可靠性，并开发能够奖励代理努力的追索方法。