Institute of Integrative and Interdisciplinary Research

This program investigates the formal role of probability, Bayesian reasoning, and uncertainty in scientific knowledge. It focuses on how probabilistic inference functions when repeatability, complete observability, and well-defined sample spaces are absent. The central thesis is that uncertainty is not a defect of knowledge but a structural feature of any coherent description of complex reality.

Research Questions: Probability and Bayesian Inference

• Under what conditions can probabilistic inference yield genuine knowledge about singular, non-repeatable events?
• How should Bayesian methods be adapted for extrapolative hypotheses beyond observable domains?
• What epistemic asymmetries arise between detection and non-detection?
• How should decision theory operate under infinite or ambiguous values?
• What distinguishes legitimate extrapolation from speculation?
• How should uncertain evidence be aggregated?
• What is the methodological role of undecidability?
• How do observational and theoretical constraints jointly define limits of probabilistic claims?

Publications

Briggs, R. (2019). Normative theories of rational choice. SEP.

Eva, B., & Hartmann, S. (2020). On the origins of old evidence. https://doi.org/10.1080/00048402.2019.1658210

Hájek, A., & Smithson, M. (2022). Rational belief and probability. https://doi.org/10.3998/mpub.9874078

Howson, C. (2021). Bayesianism and the fixity of meaning. https://doi.org/10.1086/713898

Kelly, T. (2021). Evidence and explanation in science. https://doi.org/10.1007/978-3-030-70054-8_13

Kriger, B. (2000). Quantitative framework for clinical decision structuring. https://doi.org/10.5281/ZENODO.18180934

Kriger, B. (2014). Conceptual Responsibility: Structural Constraints on Idea Transmission and the Ethics of Intellectual Communication. Zenodo. https://doi.org/10.5281/zenodo.18488818

Kriger, B. (2015). Assertion–Dismantling Cycles in Adaptive Systems: A Constraint-Network Framework. https://doi.org/10.5281/zenodo.18487230

Kriger, B. (2017). Extractive Oscillators with Sensor Degradation: A Dynamical Systems Class and Its Manifestation in Quasi-Narcissistic Relational Dynamics. Zenodo. https://doi.org/10.5281/zenodo.18529185

Kriger, B. (2021). Epistemic Constraint Theory: A Unifying Framework for Inference Limitations Across Bayesian Epistemology, Information Theory, and Decision Theory. https://doi.org/10.5281/zenodo.18365738

Kriger, B. (2025). The law of imperative uncertainty. https://doi.org/10.5281/ZENODO.18101601

Kriger, B. (2026). A Bayesian model comparison framework for extrapolative hypotheses. https://doi.org/10.5281/ZENODO.18230310

Kriger, B. (2026). A structural-Bayesian framework for evaluating scientific hypotheses. https://doi.org/10.5281/ZENODO.18230512

Kriger, B. (2026). Undecidability as a methodological signal. https://doi.org/10.5281/ZENODO.18133552

Kriger, B. (2026). Why Pascal’s Wager needs a dual-system framework. https://doi.org/10.5281/ZENODO.18193229

Landes, J., Osimani, B., & Poellinger, R. (2021). Epistemology of causal inference. https://doi.org/10.1007/s13194-020-00339-w

Martini, C., & Sprenger, J. (2020). Opinion aggregation. https://doi.org/10.4324/9781315717937-39

Pettigrew, R. (2020). Dutch book arguments. https://doi.org/10.1017/9781108581813

Schurz, G. (2021). Abduction and induction. https://doi.org/10.1007/978-3-030-70054-8_4

Sprenger, J., & Hartmann, S. (2019). Bayesian philosophy of science. https://doi.org/10.1093/oso/9780199672110.001.0001

Talbott, W. (2022). Bayesian epistemology. SEP.

Williamson, J. (2021). Bayesian inference. https://doi.org/10.1017/9781009037099