<｜begin▁of▁sentence｜>### Instruction:
Imagine you're crafting the rule-set for a new strategy video game where cities (nodes) engage in commerce with one another. The roadmap of trade routes between these cities resembles a directed network where each trade route from one city to another is represented as an arrow (edge). For the initial layout, you've determined the trade connections as follows: City 0 sends goods to Cities 1, 2, and 3, while City 1 sends goods to Cities 2 and 3.

In game terms, you need to measure the influence of each city based on the number of outbound trade routes it has, relative to the total potential trade connections it could have with all other cities in the game world. This "influence" is akin to the out-degree centrality from network analysis.

Your mission is to define this measurement correctly for each city within your game's universe, using the given network structure where the trade connections are specified as: [(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)]. The outcome should be a dictionary with cities as keys and their respective "influence" (out-degree centrality) as values. This data is critical for balancing the game's economy and ensuring a competitive environment that is engaging for players. How would you go about structuring this element within your design?
### Response:
import networkx as nx

# Create a directed graph
G = nx.DiGraph()
G.add_edges_from([(0, 1), (0, 2), (0, 3), (1, 2), (1, 3)])

# Compute the out-degree centrality
centrality = nx.out_degree_centrality(G)

print(centrality)
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