The project is articulated through three main innovative aspects. None is “new” by itself, but the combination of them represents an unexplored aspect, determining the originality and also the risk of this research:
- To explore our dataset using an exhaustive semantic approach.
Semantic approaches can account for discrete data in addition to qualitative influences, so as to answer broader questions about motives and patterns of behavior. In that perspective, a semantic model consists of a network of concepts and the relationships between those concepts. The concepts and relationships together are often known as ontology. Semantic models enable users to ask questions about the information in a natural way and help identifying patterns and trends in this information and discover relationships between disparate pieces of it. The extensive data provided by the CEIPAC database will thereby be connected and subsequently interpreted in a variety of levels that will give new insight to the complexity of exchange relations in the Roman Empire by moving beyond the limitations of a simple relational database. We consider this aspect essential for the generation of new knowledge about the object of study and for the definition of values and parameters that will be integrated in the simulation experiments. For this reason EPNet will integrate a research team with a strong background in semantic technologies.
- To apply network theory to the analysis of existing data.
Complex networks have become a very active field of research in the last decade providing a common language, which tools can have a wide range of applications. A clear example of this is the application of complex networks in economy in general, and trade in particular. Examples of trade between companies or banks, and even between countries have been the subject of intense research in the last years. In the current proposal we aim to extend this characterization of trade networks for current economic data to the ancient trade network of some of the most basic products of Mediterranean diet (wine, oil and salsamenta). Historically, wine and oil network distributions were complementary. On the one hand, oil was a strongly controlled good and produced in a single region (first in the Bética and later on in the Roman province of Africa) to be then transported to the most distant corners of the Empire. On the other hand, wine production escaped from state control and was hence distributed from many different sources all across the empire. The complementarily of these two networks, together with new techniques developed in the complex network community to infer real networks from empirical data, will be exploited to obtain a global image of food distribution throughout the whole Empire.
- To use agent-based simulation to analyze the structures and dynamics of the Roman Empire trade network.
As previously said, computer simulation will be implemented as a tool to explore research hypotheses. Complex network analysis will generate several ideas about the dynamics of the system, but we need additional techniques to understand complex social spatiotemporal patterns such as those involved in Roman trade. Agent-Based Modelling is a particular type of computer simulation specialised in exploring problems which entities are capable of executing decision-making processes. These entities, the agents, interact both with other entities and with the virtual world where they live (the environment). The different processes are executed in a sequential series of regular time steps in order to check the evolution of the model over time. This mechanism can produce a chain of events capable of modifying the system and enabling new behavioural patterns to emerge from a bottom-up perspective, portraying complex qualities (the system as a whole exhibits traits that were not defined in the individual parts). The modification and improvement of the simulation will produce data suitable to be compared with our empirical one, which will show us the most probable historical situation. Moreover, it will allow us to improve the understanding of the interaction between local and large-scale trade interactions.