<｜begin▁of▁sentence｜>### Instruction:
Imagine you're a specialist in the psychology of decision-making, and you're analyzing the decision pathways of an individual faced with multiple options, each with its own set of consequences and complexities. The choices are represented as locations in a cognitive map, which resemble nodes in a network, connected by paths with varying degrees of difficulty, akin to weighted directed edges. The individual begins at a point of contemplation, symbolized as 'D', and must traverse through 'B', 'A', and 'C' before returning to the starting point 'D', reflecting an internal journey. This scenario represents a cognitive challenge akin to that of the Traveling Salesman, who must find the most efficient route that visits each location once with the least cognitive effort, hoping to improve mental efficiency. 

In the context of this mental simulation, can you determine the least taxing circuit our subject can take, starting and ending at the decision point 'D', and quantify the overall strain of this route? For the purposes of this exercise, the initial sequence of decision points is outlined as ['D', 'B', 'A', 'C', 'D'], and the limit for acceptable cognitive strain is set to a threshold of 10.
### Response:
from networkx.algorithms import approximation as approx
import networkx as nx

# Create a directed graph
G = nx.DiGraph()

# Add weighted edges
G.add_weighted_edges_from([
    ('A', 'B', 3), ('A', 'C', 17), ('A', 'D', 14),
    ('B', 'A', 3), ('B', 'C', 12), ('B', 'D', 16),
    ('C', 'A', 13), ('C', 'B', 12), ('C', 'D', 4),
    ('D', 'A', 14), ('D', 'B', 15), ('D', 'C', 2)
])

# Use the threshold_accepting_tsp function to find a TSP tour
init_cycle = ['D', 'B', 'A', 'C', 'D']  # Initial cycle
threshold = 10  # Threshold value

# Find the TSP path
path = approx.threshold_accepting_tsp(G, init_cycle, threshold=threshold, source='D')

print('TSP path:', path)

# Calculate the total cost of the path
cost = sum(G[u][v]['weight'] for u, v in zip(path, path[1:]))
print('Total cost:', cost)
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