Cognitively-constrained learning from neighbors

• 成果类型: Article
• 署名作者: Li, Wei; Tan, Xu
• 署名单位: University of British Columbia; University of Washington; University of Washington Seattle
• 刊物名称: GAMES AND ECONOMIC BEHAVIOR
• ISSN/ISSBN: 0899-8256
• DOI: 10.1016/j.geb.2021.05.004
• 发表日期: 2021
• 页码: 32-54
• 关键词: Depths of reasoning; (Mis)learning in networks; Heterogeneous cognitive ability; Iterated learning procedure

摘要

We present a new framework in which agents with limited and heterogeneous cognitive ability-modeled as finite depths of reasoning-learn from their neighbors in social networks. Each agent tracks old information using Bayes-like formulas, and uses a shortcut when reasoning on behalf of multiple neighbors exceeds her cognitive ability. Surprisingly, agents with moderate cognitive ability are capable of partialing out old information and learn correctly in social quilts, a tree-like union of cliques (fully-connected subnetworks). Agents with low cognitive ability may fail to learn in any network, even when they receive a large number of signals. We also identify a critical cutoff level of cognitive ability, determined by the network structure, above which an agent's learning outcome remains the same even when her cognitive ability increases. (C) 2021 Elsevier Inc. All rights reserved.