Institute of Integrative and Interdisciplinary Research

This program investigates the formal foundations of probabilistic reasoning, inductive inference, and uncertainty quantification in scientific knowledge. It addresses limits of probabilistic models for singular and non-repeatable events, framework-dependent provability and objectivity, irreducible uncertainty as a structural feature of complex worlds, Bayesian approaches to extrapolative hypotheses, asymmetries between detection and non-detection, breakdowns in decision theory under ambiguity or infinite values, undecidability as a signal of explanatory boundaries, aggregation of uncertain evidence, and the distinction between extrapolation and speculation in unobservable domains.

The research questions below outline the current frontier problems pursued within this framework. The accompanying reference collection integrates key literature with ongoing contributions developed at the Institute.

Research Questions: Probability and Epistemology

•  Under what conditions can probabilistic inference yield genuine knowledge about singular, non-repeatable events, and what are the structural limits of such inference?
• How does the choice of formal framework or definitional context determine what can be proven or known within a given system, and what are the implications for scientific objectivity?
• What role does irreducible uncertainty play in the structure of complex systems, and is uncertainty a necessary feature of any coherent description of reality?
• How should Bayesian methods be adapted when evaluating hypotheses that extend beyond currently observable domains, such as in cosmology or theoretical physics?
• What epistemic asymmetries arise between positive detection and non-detection in observational science, and how should these asymmetries inform scientific reasoning about absence of evidence?
• How can decision theory account for situations where standard expected utility frameworks break down due to infinite values, ambiguous probabilities, or incommensurable outcomes?
• What is the relationship between undecidability in formal systems and the explanatory limits of scientific theories, and can undecidability serve as a methodological signal?
• How should we aggregate uncertain evidence from multiple sources with varying degrees of reliability, and what normative principles govern rational credence in such contexts?
• What distinguishes legitimate extrapolation from unwarranted speculation in scientific theorizing, and how can epistemic constraints be formally integrated into hypothesis evaluation?
• How do observational and theoretical constraints interact to define the boundaries of what can be meaningfully claimed about unobservable or inaccessible domains of inquiry?

Publications

• Briggs, R. (2019). Normative theories of rational choice: Expected utility. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Stanford University. https://plato.stanford.edu/entries/rationality-normative-utility/
• Eva, B., & Hartmann, S. (2020). On the origins of old evidence. Australasian Journal of Philosophy, 98(3), 481–494. https://doi.org/10.1080/00048402.2019.1658210
• Fjellstad, A. (2022). A plea for epistemic toleration: Against the claim that there is no room for disagreement in epistemology. Synthese, 200(4), Article 298. https://doi.org/10.1007/s11229-022-03788-5
• Grossi, M. (2021). Uncertainty, rationality, and probability. Philosophical Studies, 178(9), 2877–2898. https://doi.org/10.1007/s11098-020-01589-9
• Hájek, A., & Smithson, M. (2022). Rational belief and probability. In R. Pettigrew & J. Weisberg (Eds.), The Open Handbook of Formal Epistemology (pp. 9–42). PhilPapers Foundation. https://doi.org/10.3998/mpub.9874078
• Howson, C. (2021). Bayesianism and the fixity of meaning. Philosophy of Science, 88(4), 656–677. https://doi.org/10.1086/713898
• Kelly, T. (2021). Evidence and explanation in science. In E. Eells & J. H. Fetzer (Eds.), The place of probability in science (pp. 321–365). Springer. https://doi.org/10.1007/978-3-030-70054-8_13
• Kriger, B. (2000). Quantitative framework for clinical decision structuring: Revisited conceptual interpretation of paper published in Feb 2000. Zenodo. https://doi.org/10.5281/ZENODO.18180934
• Kriger, B. (2025). The law of imperative uncertainty: Why any complex world requires uncertainty. Zenodo. https://doi.org/10.5281/ZENODO.18101601
• Kriger, B. (2026). A Bayesian model comparison framework for extrapolative scientific hypotheses: Methodology and application to holography in theoretical physics. Zenodo. https://doi.org/10.5281/ZENODO.18230310
• Kriger, B. (2026). A structural-Bayesian framework for evaluating scientific hypotheses: Integrating epistemic constraints with probabilistic inference. Zenodo. https://doi.org/10.5281/ZENODO.18230512
• Kriger, B. (2026). The choice of formal realities: A meta-mathematical argument for explicit foundational context. Zenodo. https://doi.org/10.5281/ZENODO.18141302
• Kriger, B. (2026). The principle of definition-dependent provability: Provability as a function of definition. Zenodo. https://doi.org/10.5281/ZENODO.18207348
• Kriger, B. (2026). Undecidability as a methodological signal: System-relative provability and explanatory adequacy. Zenodo. https://doi.org/10.5281/ZENODO.18133552
• Kriger, B. (2026). Why Pascal’s Wager needs a dual-system framework: Evaluative system asymmetry and the limits of expected utility. Zenodo. https://doi.org/10.5281/ZENODO.18193229
• Landes, J., Osimani, B., & Poellinger, R. (2021). Epistemology of causal inference in pharmacology. European Journal for Philosophy of Science, 11(1), Article 26. https://doi.org/10.1007/s13194-020-00339-w
• Martini, C., & Sprenger, J. (2020). Opinion aggregation and individual expertise. In M. Fricker, P. Graham, D. Henderson, & N. Pedersen (Eds.), The Routledge Handbook of Social Epistemology (pp. 399–408). Routledge. https://doi.org/10.4324/9781315717937-39
• Pettigrew, R. (2020). Dutch book arguments. Cambridge University Press. https://doi.org/10.1017/9781108581813
• Schurz, G. (2021). Abduction and induction in philosophy of science. In E. Eells & J. H. Fetzer (Eds.), The place of probability in science (pp. 89–127). Springer. https://doi.org/10.1007/978-3-030-70054-8_4
• Sprenger, J., & Hartmann, S. (2019). Bayesian philosophy of science. Oxford University Press. https://doi.org/10.1093/oso/9780199672110.001.0001
• Staffel, J. (2020). Credal and full belief. In R. Pettigrew & J. Weisberg (Eds.), The Open Handbook of Formal Epistemology (pp. 211–241). PhilPapers Foundation.
• Talbott, W. (2022). Bayesian epistemology. In E. N. Zalta (Ed.), The Stanford Encyclopedia of Philosophy. Stanford University. https://plato.stanford.edu/entries/epistemology-bayesian/
• Williamson, J. (2021). Bayesian inference: A scientific approach. Cambridge University Press. https://doi.org/10.1017/9781009037099