Distributional Shift

"Distributional Shift. Individual ML systems can perform poorly in contexts different from those in which they were trained. A key source of these distributional shifts is the actions and adaptations of other agents (Narang et al., 2023; Papoudakis et al., 2019; Piliouras & Yu, 2022), which in single-agent approaches are often simply or ignored or at best modelled exogenously. Indeed, the sheer number and variance of behaviours that can be exhibited other agents means that multi-agent systems pose an especially challenging generalisation problem for individual learners (Agapiou et al., 2022; Leibo et al., 2021; Stone et al., 2010). While distributional shifts can cause issues in common-interest settings (see Section 2.1), they are more worrisome in mixed-motive settings since the ability of agents to cooperate depends not only on the ability to coordinate on one of many arbitrary conventions (which might be easily resolved by a common language), but on their beliefs about what solutions other agents will find acceptable"