Humanist Discussion Group, Vol. 35, No. 585.
        Department of Digital Humanities, University of Cologne
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        Date: 2022-03-10 14:05:34+00:00
        From: Gabor Toth <gabor.toth@maximilianeum.de>
        Subject: numerical simulation in (digital) humanities & further questions

Dear Paul and Oyvind,

Let me approach the questions by highlighting what you can do with
numerical simulation in the Humanities.

Numerical simulation is a powerful tool to understand big datasets that
involve sequences of states. In the Humanities we have many datasets of
this kind.

Think about life trajectories of historical actors, texts as sequences of
topics, ownership lists of fine art objects. With numerical simulation you
can identify the most recurrent topics in millions of texts; you can find
the most likely locations where people tended to settle; you can find
groups of - fine art  - object collectors among whom there was a lot of
exchange.

Sequences of states feature temporality and some kind of dynamics;
numerical simulation is an excellent analytical tool to get insights into
these aspects. Continuing with the toy examples above, simulation helps
understand how a large-scale phenomenon is likely to unfold, i.e. how
certain topics in texts are likely to follow each other, how a given type
of fine art objects are likely to move from owner to owner.

Simulation also allows us to understand how certain conditions influence
the unfolding process, for instance if a fine art object was owned by the
Habsburg in the 18th century, who is the most likely owner today?

[I overuse “likely” to demonstrate that numerical simulation is a
probabilistic framework that helps model randomness and uncertainty].

I find that connecting numerical simulation with simulation in games or
with simulation in general is distracting. There are some similarities but
overall numerical simulation is something very different. I am hoping that
through the - toy - examples above these differences are becoming tangible.

As a real world example of numerical simulation in cultural heritage
research, I am inserting below the complete abstract of our forthcoming
study mentioned by Oyvind.


Best wishes,
Gabor


Studying Large-Scale Behavioral Differences in Auschwitz-Birkenau with
Simulation of Gendered Narratives by Survivors

In Auschwitz-Birkenau men and women were detained separately; anecdotal
evidence suggests that they behaved differently. However, producing
evidence based insights into victims' behavior is challenging. Perpetrators
frequently destroyed camp documentations; victims' perspective remains
dispersed in thousands of oral history interviews with survivors. Listening
to, watching, or reading these thousands of interviews is not viable, and
there is no established computational approach to gather systematic
evidence from a large number of interviews. In this study, by applying
methods and concepts of molecular physics, we developed a conceptual
framework and computational approach to study thousands of human stories
and we investigated 6628 interviews (2500 hours) by survivors of the
Auschwitz-Birkenau death camp.

We applied the concept of state space and the Markov State Model to
simulate the ensemble of 6628 testimonies. The Markov State Model along
with the Transition Path Theory allowed us to compare the way women and men
remember their time in the camp. We found that acts of solidarity and
social bonds are the most important topics in their testimonies. However,
we found that women are much more likely to address these topics. We
provide systematic evidence that not only were women more likely to recall
solidarity and social relations in their belated testimonies but they were
also more likely to perform acts of solidarity and form social bonds in
Auschwitz-Birkenau.

Oral history interviews with Holocaust survivors constitute an important
digital cultural heritage that documents one of the darkest moments in
human history; generally, oral history collections are ubiquitous sources
of modern history and significant assets of libraries and archives. We
anticipate that our conceptual and computational framework will contribute
not only to the understanding of gender behavior but also to the
exploration of oral history as a cultural heritage, as well as to the
computational study of narratives.

This paper presents novel synergies between history, computer science, and
physics, and it aims to stimulate further collaborations between these
fields.


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