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    wu-ea-v2

    by toni-akintola

    Implementation of variation 2 of Professor Jiangyi Wu's epistemic advantage model.

    View on GithubCode in Colab

    Epistemic Advantage on the Margin: A Network Standpoint Epistemology

    A bandit model by Professor Jingyi Wu that depicts the emergence of several epistemic advantages for marginalized groups due to testimonial ignoration and devaluation. View the full paper here.

    Abstract

    I use network models to simulate social learning situations in which the dominant group ignores or devalues testimony from the marginalized group. I find that the marginalized group ends up with several epistemic advantages due to testimonial ignoration and devaluation. The results provide one possible explanation for a key claim of standpoint epistemology, the inversion thesis, by casting it as a consequence of another key claim of the theory, the unidirectional failure of testimonial reciprocity. Moreover, the results complicate the understanding and application of previously discovered network epistemology effects, notably the Zollman effect.

    Model Details

    Implemented here is Wu's second variation with one-sided testimonial devaluation.

    Variation 2: One-sided testimonial devaluation

    Simulates testimonial devaluation using Jeffrey conditionalization. Differs from the base model in the way that agents beliefs are updated.

    • Size of network (3, 6, 12, 18)
    • Number of pulls per round (1, 5, 10, 20)
    • Probability of B (0.51, 0.55, 0.6, 0.7, 0.8)
    • Proportion of marginalized group in population (1/6, 1/3, 1/2, 2/3)
    • Degree of devaluation(0.2, 0.5, 0.8)
    • Network structure: complete

    About the author

    Jingyi Wu is an Assistant Professor in the Department of Philosophy, Logic and Scientific Method at the London School of Economics. She primarily works on social epistemology and philosophy of physics. She also has interests in general philosophy of science, feminist philosophy, philosophy of race, Asian/American philosophy, and mathematical physics.

    Set Parameters

    Model Variation

    MAX TIMESTEPS

    Convergence Data Key

    Convergence Std Dev

    Degree Devaluation

    Graph Type

    Num Nodes

    Num Pulls

    Objective B

    P Ingroup

    P Outgroup

    Proportion Marginalized

    Run Timesteps

    Current Timestep: 0