Research Profile

Portrait of Tiera Luzia Ding

Tiera Luzia Ding

PhD Candidate in Computational Philosophy

Short Bio

I am a PhD candidate at the Future Histories Institute. My work develops computational methods to surface metaphysical assumptions in public reasoning about climate and technology. I combine language models, formal analysis, and qualitative interpretation to study how concepts travel across domains.

Research Interests

  • Computational metaphysics
  • AI-assisted theory formation
  • Environmental narratives
  • Epistemology of models

Short CV

  • 2023–present: PhD Candidate, Lab for Computational Philosophy, Future Histories Institute
  • 2021–2022: Research Assistant, Department of Environmental Ontologies, Valmere Institute of Technology
  • 2020–2021: Junior Data Fellow, Center for Civic Discourse Analytics
  • 2018–2020: Student Tutor in Logic & Computation, Riverstate College

Affiliations

  • Future Histories Institute, Lab for Computational Philosophy
  • Center for Environmental Narratives

Education

  • BSc, Computer Science and Philosophy, Riverstate College (synthetic), 2020
  • MSc, Computational Philosophy, Valmere Institute of Technology (synthetic), 2022

Teaching

  • Computational Metaphysics: Methods and Tools
  • Philosophy of AI: Reasoning, Norms, and Society
  • Text Mining for Philosophers

Awards

  • Early Career Paper Prize, Society for Synthetic Philosophy, 2024
  • Graduate Research Fellowship, Future Histories Institute, 2023

Publications

  • Ding, T. L., Metaphysical Frames in Climate Narratives: A Language-Model Approach, Journal of Computational Philosophy (synthetic), 2025.
  • Ding, T. L.; Ibarra, C., Probing Ontological Assumptions with Prompted Transformers, Proceedings of the Symposium on AI & Humanities (synthetic), 2024.
  • Mori, J.; Patel, A.; Ding, T. L., Measuring Conceptual Stability in Climate Policy Debates, Civic Discourse Analytics Review (synthetic), 2023.

Abstract

This project investigates how large language models can be used as instruments to analyze metaphysical assumptions embedded in climate change discourse. We curate parallel corpora from two linguistic regions, spanning news commentary, legislative debates, social media threads, and podcasts. Using prompt-based elicitation and representation probing, we model contrasts between teleological, mechanistic, and pluralist ontological frames. We validate model inferences against expert annotations and time-stamped shifts in policy events. Results indicate stable regional preferences for different explanatory stances and show how hybrid frames emerge during moments of crisis. The approach offers a reproducible workflow for mapping conceptual commitments in public reasoning and for designing interventions that reduce cross-frame misunderstandings.