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Abstract Children’s memberships in social groups have profound effects on their motivation. Stereotypes about social groups shape children’s beliefs about what is expected for their group members. These beliefs can influence children’s developing beliefs about themselves (self‐perceptions). In this article, I review research on how gender stereotypes influence children’s motivation in science, technology, engineering, and math (STEM), including ability beliefs and sense of belonging. When children belong to a gender group that is negatively stereotyped in a STEM field, they may doubt their own capabilities and whether they belong in that field, making it harder for them to develop interest over time. Developmentally, the influence of gender stereotypes on motivation begins during preschool and strengthens during late childhood. I also address the consequences of different kinds of stereotypes and why some children are more influenced by stereotypes than others. Understanding this process in childhood will help researchers design effective interventions to remedy educational inequities in STEM.
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Belief Convergence under Misspecified Learning: A Martingale Approach
Abstract 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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- Award ID(s):
- 1824324
- PAR ID:
- 10369162
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- The Review of Economic Studies
- ISSN:
- 0034-6527
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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