At the frontier of knowledge, we feel stupid about a given subject, but on the edge of inquiry we also feel stupid about how to proceed—how to choose the next step towards discovery (Schwartz, 2008). This talk introduces Meta-Creative Problem-Solving (MCPS), a new concept designed to study and improve inquiry practices and norms as they become intelligent.

To uncover knowledge, we search for invariances between epistemic activities (e.g., proposed definitions, models, theories; operationalization of methods; established goals and cultivated values) and the subject of inquiry. The creative part of MCPS operates through directed integration: generating variations within epistemic activities and evaluating consequent changes in relation to the subject of inquiry. The metacognitive dimension of MCPS recognizes that both monitoring and controlling epistemic activities are themselves (meta)epistemic activities. The apparent infinite regress of meta-approaches (i.e., if every epistemic change is guided by another epistemic act, where does it end?) is resolved, i.a., through metacognitive feelings like confidence and fluency (Koriat et al., 2008). These feelings integrate causal information from both present and past problem-solving relationships between epistemic activities and subject of inquiry (Shea, 2023, 2024), guiding complex problem-solving (Ackerman, 2019; Rudolph et al., 2017). They function as half-baked conjectures, preliminary knowledge structures guiding inquiry before formal hypotheses emerge, and on the meta-level—as half-baked heuristics, preliminary methodological structures guiding inquiry before norms or virtues develop.

We don't merely solve problems; we discover how to do inquiry itself (Chang, 2022), originating and developing methods, norms, and frames of thinking. The MCPS concept extends Nersessian's (2008) insight that scientific models serve as cognitive artifacts embodying assumptions whose consequences guide model refinement. Similarly, it builds upon Boden's (1991) observation that breakthrough problem-solving often requires reformulating the problem itself. The Meta-Creative Problem-Solving concept generalizes these approaches by recognizing that (meta)epistemic practices and norms themselves embody assumptions whose consequences guide their own refinement. This self-consistency means that during reflection, we may change even the epistemic practices and norms of subsequent reflection, which we can evaluate in light of the subject of inquiry, amid metacognitive feelings crucial at the fractal beginnings of knowledge and inquiry.

The concept of MCPS can help understand—and guide—inquiry practices and norms when no ready-made roadmap exists. This talk will further clarify MCPS through the example of the first mathematical discovery with LLMs (Romera-Paredes et al., 2024) and show how MCPS can guide the development of inquiry norms and practices to integrate theory-driven scientists with data-driven causal discovery algorithms (Andersen, 2024; Petersen et al., 2023; Runge et al., 2023). The concept of Meta-Creative Problem-Solving takes up the challenge of answering how inquiry becomes intelligent.

References:

Ackerman, R. (2019). Heuristic Cues for Meta-Reasoning Judgments: Review and Methodology. Psihologijske Teme, 28(1), 1–20.

Andersen, H. K. (2024). Why adoption of causal modeling methods requires some metaphysics. The Routledge Handbook of Causality and Causal Methods, 87-98.

Boden, M. A. (1991). The creative mind: Myths & mechanisms, Basic Books.

Chang, H. (2022). Realism for Realistic People. Cambridge University Press.

Koriat, A., Nussinson, R., Bless, H., & Shaked, N. (2008). Information-based and experience-based metacognitive judgments: Evidence from subjective confidence. Handbook of Metamemory and Memory, 117–135.

Nersessian, N. J. (2008). Creating scientific concepts. MIT Press.

Petersen, A. H., Ekstrøm, C. T., Spirtes, P., & Osler, M. (2023). Constructing causal life-course models: Comparative study of data-driven and theory-driven approaches. American Journal of Epidemiology, 192(11), 1917–1927.

Romera-Paredes, B., Barekatain, M., Novikov, A., Balog, M., Kumar, M. P., Dupont, E., Ruiz, F. J. R., Ellenberg, J. S., Wang, P., Fawzi, O., Kohli, P., & Fawzi, A. (2024). Mathematical discoveries from program search with large language models. Nature, 625(7995), Article 7995.

Rudolph, J., Niepel, C., Greiff, S., Goldhammer, F., & Kröner, S. (2017). Metacognitive confidence judgments and their link to complex problem solving. Intelligence, 63, 1–8.

Runge, J., Gerhardus, A., Varando, G., Eyring, V., & Camps-Valls, G. (2023). Causal inference for time series. Nature Reviews Earth & Environment, 4(7), 487–505.

Schwartz, M. A. (2008). The importance of stupidity in scientific research. Journal of Cell Science, 121(11), 1771–1771.

Shea, N. (2024). Metacognition of Inferential Transitions. Journal of Philosophy, 121(11), 597-627.

Shea, N. (2024). Concepts at the Interface. Oxford University Press.