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• W1496144686 abstract "We develop a method of solving heterogeneous agent models in which individual decisions depend on the entire cross-sectional distribution of individual state variables, such as incomplete market models with liquidity constraints. Our method is based on the principle of reinforcement learning, and does not require parametric assumptions on either the agents' information set, or on the functional form of the aggregate dynamics." @default.
• W1496144686 created "2016-06-24" @default.
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• W1496144686 date "2011-01-01" @default.
• W1496144686 modified "2023-09-23" @default.
• W1496144686 title "A Reinforcement Learning Approach to Solving Incomplete Market Models with Aggregate Uncertainty" @default.
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• W1496144686 doi "https://doi.org/10.2139/ssrn.1832745" @default.
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