Adaptive Algorithmic Interventions for Escaping Pessimism Traps in Dynamic Sequential Decisions
<|reference_start|>Adaptive Algorithmic Interventions for Escaping Pessimism Traps in Dynamic Sequential Decisions: In this paper, we relate the philosophical literature on pessimism traps to information cascades, a formal model derived from the economics and mathematics literature. A pessimism trap is a social pattern in which individuals in a community, in situations of uncertainty, begin to copy the sub-optimal actions of others, despite their individual beliefs. This maps nicely onto the concept of an information cascade, which involves a sequence of agents making a decision between two alternatives, with a private signal of the superior alternative and a public history of others' actions. Key results from the economics literature show that information cascades occur with probability one in many contexts, and depending on the strength of the signal, populations can fall into the incorrect cascade very easily and quickly. Once formed, in the absence of external perturbation, a cascade cannot be broken -- therefore, we derive an intervention that can be used to nudge a population from an incorrect to a correct cascade and, importantly, maintain the cascade once the subsidy is discontinued. We study this both theoretically and empirically.<|reference_end|>
@article{diana2024adaptive,
    title={Adaptive Algorithmic Interventions for Escaping Pessimism Traps in
  Dynamic Sequential Decisions},
    author={Emily Diana, Alexander Williams Tolbert, Kavya Ravichandran, Avrim
  Blum},
    journal={arXiv preprint arXiv:2406.04462},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.04462},
    primaryClass={cs.SI}
}