Titan Disaster — Stockton vs Scott — A tale of “Human Factors” in Failure. Regression Model in Python.

On June 22, 2023, the Titan, a submersible operated by OceanGate Expeditions, imploded while on its way to the Titanic wreck. All five passengers on board were killed, including the CEO of OceanGate, Stockton Rush. The cause of the implosion is still under investigation, but there have been reports that the sub may have suffered a structural failure.

The Titan was a state-of-the-art submersible that was designed to carry up to five passengers to depths of 4,000 meters. It was equipped with a number of safety features, including a titanium pressure hull and a redundant life support system. However, the implosion suggests that these safety features may not have been enough to prevent the disaster.

The Titan Stockton disaster is a reminder of the dangers of deep-sea exploration. Even the most advanced submersibles are not immune to failure, and there is always the risk of accidents. The investigation into the cause of the implosion is ongoing, and it is hoped that the findings will help to improve the safety of deep-sea exploration in the future.

Amundsen vs. Scott. In 1911, two expeditions set out to reach the South Pole: one led by Norwegian explorer Roald Amundsen, and the other led by British explorer Robert Falcon Scott. Amundsen’s team arrived at the Pole on December 14, 1911, 34 days before Scott’s team.

There were several factors that contributed to Amundsen’s victory. First, he chose a more direct route to the Pole, which saved him time and energy. Second, he used sled dogs, which were more efficient than ponies or motor sledges. Third, he was better prepared for the harsh Antarctic conditions.

Scott’s team, on the other hand, made several mistakes. They chose a longer and more difficult route to the Pole, and they relied on ponies and motor sledges, which proved to be unreliable. They also underestimated the severity of the Antarctic winter, and they were not as well-prepared for the cold and the snow.

As a result of these mistakes, Scott’s team suffered a great deal of hardship on their journey. They lost several ponies to the cold, and their motor sledges broke down. They were also forced to eat their ponies, which gave them scurvy. By the time they reached the Pole, they were exhausted and ill-equipped.

Scott and his team died on their journey back to base camp. They were found by a search party in November 1912.

The race to the South Pole was a tragedy for Scott and his team, but it was also a triumph for Amundsen and his team. Amundsen’s victory showed that careful planning and preparation were essential for success in the Antarctic. It also showed that sled dogs were the best form of transportation for long-distance travel in the harsh Antarctic conditions.

The story of Amundsen vs. Scott is a reminder of the dangers and challenges of polar exploration. It is also a story of human ingenuity and perseverance. Both Amundsen and Scott were brave and determined explorers, and their stories continue to inspire people today.

Both of these are cautionary case studies on “human factors” — looking at the specific features which would lead to disaster. One of the first ones is

Human Factors:

• Planning and preparation: The extent to which the expedition was well-planned and prepared for the challenges of the environment.
• Decision-making: The quality of the decisions made by the leaders of the expedition.
• Risk management: The extent to which the expedition took steps to mitigate risks.
• Communication: The effectiveness of communication between members of the expedition.
• Leadership: The ability of the leaders to motivate and inspire their team.

Data:

The data for this model would be the historical records of both expeditions, including the plans, decisions, risks, communications, and leadership of the leaders.

Prediction:

The model would predict the probability of failure for an expedition based on the human factors involved.

import numpy as np
import pandas as pd
from sklearn.linear_model import LinearRegression

# Create the synthetic data
data = np.random.randint(0, 100, size=(100, 5))
features = data[:, :4]
target = data[:, 4]

# Create the regression model
model = LinearRegression()
model.fit(features, target)

# Predict the probability of failure
predictions = model.predict(features)

# Print the results
print("Mean squared error:", np.mean((predictions - target)**2))
print("R-squared:", model.score(features, target))

This code will create a regression model with 4 features and 1 target. The features will be the human factors that we discussed earlier, and the target will be the probability of failure. The model will be trained on the synthetic data, and then it will be used to predict the probability of failure for new expeditions.

The mean squared error and the R-squared score will be printed to the console. These metrics can be used to assess the accuracy of the model.

To run this code, you will need to install the following Python libraries:

• numpy
• pandas
• sklearn.linear_model

Once you have installed the libraries, you can run the code by saving it as a Python file and then running it from the command line.

For example, if you save the code as regression.py, you can run it by typing the following command into the command line:

The 4 features in the regression model are:

• Planning and preparation: This is a measure of how well the expedition was planned and prepared for the challenges of the environment.
• Decision-making: This is a measure of the quality of the decisions made by the leaders of the expedition.
• Risk management: This is a measure of the extent to which the expedition took steps to mitigate risks.
• Communication: This is a measure of the effectiveness of communication between members of the expedition.

These features were chosen because they are all factors that have been shown to be correlated with failure in polar exploration. By including these features in the regression model, we can get a better understanding of the factors that contribute to failure and how to mitigate those risks.

The values of the features are randomly generated in the synthetic data, but they could be based on real data from past expeditions. For example, the planning and preparation feature could be based on the number of days spent planning the expedition, the number of resources that were gathered, and the level of training that the members of the expedition received. The decision-making feature could be based on the number of risky decisions that were made, the quality of the decision-making process, and the level of experience of the leaders. The risk management feature could be based on the number of risks that were identified, the steps that were taken to mitigate those risks, and the effectiveness of those steps. The communication feature could be based on the number of communications that were made between members of the expedition, the quality of those communications, and the level of understanding that was achieved.

The values of the features can be used to predict the probability of failure for an expedition. For example, if an expedition has a high score on the planning and preparation feature and a low score on the risk management feature, then the probability of failure for that expedition is likely to be high. However, it is important to note that the regression model is only a prediction, and the actual probability of failure may be different.

Limitations:

This model is only a simplified representation of the factors that contribute to failure in polar exploration. It does not take into account the environmental factors, such as the weather and the terrain. It also does not take into account the individual skills and abilities of the members of the expedition.

Conclusion:

This model can be used to help plan and prepare for future polar expeditions. It can also be used to assess the risks involved in an expedition and to make decisions about how to mitigate those risks.

Specific human factors that correlated to the failure of both of these expeditions and their eventual fatalities:

• Overconfidence: Both Scott and Stockton were overconfident in their abilities and in their preparations. This led them to make decisions that were risky and that ultimately led to their failure.
• Poor decision-making: Both Scott and Stockton made poor decisions that contributed to their failures. For example, Scott chose a longer and more difficult route to the South Pole, and Stockton underestimated the risks involved in diving in the Challenger Deep.
• Lack of communication: There was a lack of communication between members of both expeditions. This led to misunderstandings and mistakes.
• Poor leadership: Both Scott and Stockton were poor leaders. They were unable to motivate and inspire their teams, and they made poor decisions that put their teams at risk.

These human factors are important to consider when planning and preparing for future polar or other high-risk expeditions. By being aware of these factors and taking steps to mitigate them, we can help to prevent future tragedies.

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