Models and Narratives: Computational and Historical Thinking in Dialogue

This SNSF Scientific Exchanges research visit was approved by the SNSF on 2026-05-05 (grant no. 243497. Start: 2026-09-01, end: 2027-03-31.

Summary

Historical Thinking (HT) and Computational Thinking (CT) are established concepts to describe how historians and computer scientists reason about and tackle problems in their respective fields. As such, they are an important part of university teaching: these are the ways of thinking students in respective fields need to acquire in order to succeed.

However, they are thought to cover very different skills. HT, at its core, embraces ambiguity, complexity, contingency, and interpretive judgment; CT, first introduced by (Wing 2006), seeks precision and reduction of complexity through decomposition and, above all, abstraction. There is rarely a single correct answer in historical research: evidence is partial, contradictory, and meaningful only in context. Algorithms, on the other hand, are by definition independent of when, where, how, and by whom they are executed. At first sight, it is difficult to see how these modes of thinking could be reconciled; at best, one might describe them as complementary.

From this point of view, the use of computers in historical research for mechanical tasks, such as counting, ordering, or records management, is unproblematic. However, any use of computing beyond mechanization is hampered by the opposition of HT and CT.

The proposed collaboration aims to take a closer look, asking: what if CT and HT had actually more in common than is generally believed? Are there ways to reconcile models and narratives? The goal for this project is to explore the commonalities and differences of CT and HT to develop more solid epistemological foundations for digital/computational history and digital/computational humanities more generally. We intend to focus on four research areas where the tension between CT and HT is particularly strong:

1. Simulations: simulations can potentially give us novel insights into historical phenomena that go beyond what is possible by traditional hermeneutic approaches. For example, the shared human experience lets us intuitively understand the motivations of historical actors, but it doesn’t scale to explain large-scale phenomena.
2. Ahistorical concepts: traditional history makes use of a great number of ahistorical concepts to describe, categorize, and explain phenomena in hindsight; a well-known example are historical periods, which are used to group historical processes of change that are presumed to be related (e.g., the Middle Ages, the Renaissance, etc.), but they are inherently ahistorical. Such concepts are essential for computational operationalization (e.g., in simulations), and their formalization requires a much better understanding of their consequences than when crafting a traditional narrative.
3. Uncertainty and risk: of special interest in this context is uncertainty. Computational history needs to represent historical and historiographical uncertainty in a formal system that is not designed to handle ambiguity.
4. Counterfactuals: counterfactuals are often considered as unserious speculation in historical research. In fact, counterfactuals are an essential part of historical thinking and necessary to identify the reasons for history playing out the way it did—it is just rarely made explicit. It must, however, be made explicit in computational modeling.

Across these four areas, we intend to study the following three research questions:

RQ1
Representational commitments: How do the representational choices required for computational modeling restructure the historical narrative?

RQ2
Formalizing uncertainty: What forms of historiographical uncertainty (Piotrowski 2019, 2023) can be formalized without collapsing interpretive flexibility, and where does formalization fundamentally change the nature of historical knowledge?

RQ3
Process and explanation: How do computationally explicit counterfactual processes compare to narrative explanations in identifying causes of historical change?

References

Piotrowski, Michael. 2019. “Accepting and Modeling Uncertainty.” Zeitschrift für digitale Geisteswissenschaften, Sonderband 4. doi:10.17175/sb004_006a.

Piotrowski, Michael. 2023. Uncertainty as Unavoidable Good. Center for Uncertainty Studies Working Papers No. 5. Universität Bielefeld, Center for Uncertainty Studies (CeUS). doi:10.4119/UNIBI/2983506.

Wing, Jeannette M. 2006. “Computational Thinking.” Communications of the ACM 49 (3): 33–35. doi:10.1145/1118178.1118215.

Funding

Funded by the the SNSF (grant no. 105211_204305).