Belief Convergence under Misspecified Learning: A Martingale Approach
Belief Convergence under Misspecified Learning: A Martingale Approach
复制标题
错误指定学习下的信念收敛:马丁格尔方法
DOI:
10.1093/restud/rdac040
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发表时间:
2022
期刊:
影响因子:
--
通讯作者:
Ishii, Yuhta
中科院分区:
文献类型:
--
作者:
Frick, Mira;Iijima, Ryota;Ishii, Yuhta
We present an approach to analyse learning outcomes in a broad class of misspecified environments, spanning both single-agent and social learning. We introduce a novel “prediction accuracy” order over subjective models and observe that this makes it possible to partially restore standard martingale convergence arguments that apply under correctly specified learning. Based on this, we derive general conditions to determine when beliefs in a given environment converge to some long-run belief either locally or globally (i.e. from some or all initial beliefs). We show that these conditions can be applied, first, to unify and generalize various convergence results in previously studied settings. Second, they enable us to analyse environments where learning is “slow”, such as costly information acquisition and sequential social learning. In such environments, we illustrate that even if agents learn the truth when they are correctly specified, vanishingly small amounts of misspecification can generate extreme failures of learning.
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