Applying Graph Theory to Medieval Venetian Trading Networks
https://direct.mit.edu/jinh/article/44/2/157/48779/Venetian-Trading-Networks-in-the-Medieval
Typically when historians study trade networks in medieval europe, they are mainly focused on the activities of wealthy nobles of the area, leaving out the activities of lower-class traders. The wealthy nobility of medieval Venice undoubtedly had a major controlling stake in the trading activities of the port city, but only studying their activities does not tell us much about how the overall trade network functioned, and who were the real key players. In his 2013 paper, Francisco Apellániz studied Venetian trade between 1418 and 1420 using the records of Vittore Bonfantin, a notary of the time. Bonfantin’s records show individuals appointed someone as their agent, through a commission contract or as a power of attorney, as well as who witnessed the action. From this, we get a link between the client and the agent, as well as between the client and the witness. In addition to the connections between individuals, Apellániz used Bonfantin’s records to note the citizenship and nobility status of each of the individuals.
Once the network is constructed, Apellániz analyzed the role of individuals in the network using two measures, the degree centrality of each node and the betweenness of each node. Degree measures the number of edges that a node is adjacent to. A higher degree measurement indicates that an individual is exposed to a larger portion of the market, and is involved with many individuals in the trade. Betweenness is determined by the number of shortest paths between any two nodes which pass through the given node. This corresponds to a measure of how much an individual mediates between other individuals. An analogy the author makes is the degree centrality corresponds to how likely an individual is to get the bubonic plague, while betweenness corresponds to how much disruption there would be if the individual were to get the plague. The traders were then ranked on each of the metrics, and the results analyzed to determine who was influential in Venetian trade, and the top 18 traders were considered with their statuses.
The results of the ranking were more heterogeneous than you would expect given the focus placed on noble traders. In the degree centrality measure, approximately half of the top 18 individuals were not nobles, and approximately ⅓ were not even from Venice. Looking at the betweenness measure, there are even fewer nobles in the top 18 traders. This is surprising, as the non-Venetian non-nobility would have faced significant persecution and restriction when running their business (non-citizens were only permitted to trade through commission contracts, for example). The higher proportion of non-nobility in the betweenness measure compared to the centrality measure could be due to how Venetian citizenship was structured. Non-citizens of Venice were restricted from some aspects of trade, with some lower class individuals restricted from directly engaging. Without the ability to engage in trade directly, they may have been more motivated to trade via commission rather than directly hiring. This could result in a higher betweenness score.
Overall, the results illustrate how those not typically emphasized in medieval Venetian trade were actually disproportionately involved compared to their mentions. These results show how graph theory can strip back a lot of social factors in order to get an accurate picture of a network.