# Navigating the Narrative: Integrating Traditional Knowledge and Embodied Practice within Computational Models of Ancient Seafaring

## TL;DR

This paper explores how traditional knowledge and embodied practices can enhance computational models of ancient seafaring and maritime heritage.

## Contribution

The paper introduces a framework for integrating traditional and experiential knowledge into computational models of seafaring.

## Key findings

- Computational models of seafaring must be grounded in known practices to remain meaningful.
- Seafaring is a complex social action influenced by identity, heritage, and the marine environment.
- Experimental archaeology and practitioner knowledge offer insights into the social aspects of seafaring.

## Abstract

Why do people go to sea? The relationship people have with land and sea, maritime space, boats and ships are entwined and complex, shaped and molded by the marine environment, identity and heritage. This paper explores the complexity of people’s relationship with the sea to question how we can understand and model seafaring in the past, and how this can be used to better understand maritime heritage today. To be meaningful, computational analysis of seafaring must be tied into relevant known seafaring and navigation practice. Without this firm basis our statistical and hypothetical models lose the ability to measure past actions. However, there are many ways to ‘go to sea’ and seafaring practices do not start and end on the water itself. This paper reflects upon the process of seafaring, as it relates to our understanding of navigational knowledge, mobility in practice, seafaring as social action and the influences behind people’s desire to set sail. There is much we can learn from applied practices of seafaring, conducted both by practitioners and through efforts of experimental or experiential archaeology; understanding the complexity and nuance of the social aspects of seafaring guides the research questions that shape our models and shapes how we use and understand the outcomes derived from quantitative computational approaches.