Semiotic Complexity and Its Epistemological Implications for Modeling Culture

The use of computational methods in the study of cultural artifacts--from models like linear regression and artificial neural networks, to how we evaluate and interpret those models--can be usefully understood as a kind of translation work from a complex, cultural medium into a formal, computational medium. Research questions arise in the cultural domain within culturally-embedded minds. When a researcher designs a computational model to aid in answering such a question, they translate from the cultural into the computational in each modeling decision they make. After completing this first translation problem, the researcher then makes use of the model by interpreting it (either directly or in downstream outputs that depend on it), requiring a second translation to be made, now from the computational going back into the cultural, by way of culturally-embedded researchers making sense of them. In these bidirectional translation problems, we as researchers want to ensure that our translations are reasonable, that they can be sufficiently evaluated and understood by others engaged in collective knowledge-building. Yet translation work can vary in the complexity required to interpret and evaluate it. Consider, for example, how evaluating a translation of "hello" into modern Mandarin Chinese is much simpler than evaluating a translation of a text from classical (i.e., literary) Chinese, like the Zhuangzi, into This preprint article is currently under review.