Water/earth exponential risks versus 90 day numerologists

 

Does intelligence nodeling have name for terrible error of system that doesnt work at extremes- today's example hills of city of angels dont have water pressire when winds of fire also make it impossible for planes to dump water - infamous 2008 financial example: insurnce companies had taken out chain of extreme risks that were correlated with each other as well as being actualosed by fake ratings

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Sources

AI for Extreme Event Modeling and Understanding - arXiv

arxiv

[PDF] Physics-Based Simulation-Assisted Machine Learning for Estimating ...

easychair

Examining and Learning from Complex Systems Failures

journal.uptimeinstitute

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Answer

The phenomenon you're describing is often referred to as "tail risk" or "extreme event modeling failure" in intelligence and risk modeling. This concept encompasses the tendency of some systems or models to break down or fail catastrophically under extreme conditions that weren't adequately accounted for in their design.

Tail Risk and Black Swan Events

In risk management and financial modeling, these extreme scenarios are sometimes called "tail events" or "black swan events." They represent rare but high-impact occurrences that fall outside the range of normal expectations and can have severe consequences

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Examples from Recent Events

2025 Los Angeles Wildfires

The recent wildfires in Los Angeles provide a stark example of this type of system failure:

1. Water System Breakdown: The city's water infrastructure, designed for normal usage, failed to maintain adequate pressure in hillside areas during extreme fire conditions
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2. Compounding Factors: High winds grounded firefighting aircraft, eliminating a crucial backup method and further straining the already overwhelmed ground-based water system
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This cascading failure demonstrates how multiple systems can break down simultaneously under extreme conditions, leading to a catastrophic outcome that wasn't adequately planned for.

2008 Financial Crisis and AIG

The 2008 financial crisis, particularly the near-collapse of AIG, offers another prime example:

1. Correlated Risks: AIG had taken on massive amounts of risk through credit default swaps (CDSs) that were highly correlated with each other and with broader market conditions
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2. Misrated Securities: Many of these risks were based on securities that had been improperly rated, leading to a severe underestimation of the true risk exposure
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3. Systemic Impact: When the housing market collapsed, it triggered a chain reaction that threatened not just AIG but the entire financial system, revealing the hidden interconnectedness of these risks
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Modeling Challenges

These examples highlight several key challenges in modeling extreme events:

1. Underestimation of Tail Risks: Traditional models often underestimate the likelihood and impact of extreme events
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2. Correlation in Extreme Conditions: Many systems that appear independent under normal conditions can become highly correlated during crises
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3. Cascading Failures: The failure of one system can trigger a chain reaction, leading to widespread collapse
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4. Data Limitations: Historical data often lacks sufficient examples of extreme events, making it difficult to model them accurately
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Addressing the Challenge

To better prepare for these extreme scenarios, researchers and policymakers are exploring several approaches:

1. Stress Testing: Subjecting systems to simulated extreme conditions to identify vulnerabilities
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2. AI and Machine Learning: Using advanced algorithms to identify complex patterns and potential failure modes that might be missed by traditional models
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3. Interdisciplinary Approaches: Combining insights from multiple fields to better understand and model complex, interconnected systems
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4. Improved Risk Communication: Ensuring that the limitations and uncertainties of models are clearly communicated to decision-makers
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By recognizing the potential for these extreme failure modes and working to address them, we can build more resilient systems better equipped to handle the unexpected challenges of an increasingly complex world.

Is there a term for lack of transparency where a market's tail risks are not opne intelligence mapped. Adftr all, someone profits from others losses - and it seems that with tail risks. losers are those who were most separated from deep data chains while gainers were some of those at top of such data. A general context for asking is nuclear energy. Whilst theoretcally this could be an energy solution i would not chose to brimg up a family near nuclear plant beciase over time humans fail to attend 24/7 or fail to keep the peace. Can autonomous montoring bring safety to where human attention has history of failing and future secarion proofchecking e some

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Sources

Transparency, Explainability, and Interpretability in AI/ML Credit ...

innovation.consumerreports

A Safe Haven for Hidden Risks | Elham Saeidinezhad

phenomenalworld

[PDF] Tail Risk at Banks

tse-fr

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Answer

There isn't a single widely-used term that precisely captures the lack of transparency around unmapped tail risks in markets, especially in the context of nuclear energy. However, several concepts relate to this issue:

Information Asymmetry and Tail Risk Opacity

The scenario you describe, where those with access to deep data chains profit from tail risks while others suffer losses, is a form of information asymmetry. In the context of tail risks, we might call this "tail risk opacity" or "extreme event information asymmetry."This opacity can lead to:

• Mispricing of risk in markets
• Concentration of losses among less-informed participants
• Potential for exploitation by those with superior information

Nuclear Energy Safety and Autonomous Monitoring

Regarding nuclear energy safety, your concerns about human fallibility in 24/7 monitoring and maintaining peace are valid. The nuclear industry has historically faced challenges related to:

• Human error
• Complacency over time
• Geopolitical instability

Autonomous monitoring systems could potentially address some of these issues:Benefits of Autonomous Systems:

• Continuous, tireless monitoring
• Rapid detection of anomalies
• Objective data collection and analysis

Challenges and Considerations:

• Cybersecurity risks
• Need for human oversight and decision-making
• Potential for new types of errors or biases

Emerging Trends in Nuclear Safety

Recent developments in nuclear plant safety monitoring include:

• Advanced remote monitoring technologies
• AI-powered predictive maintenance
• Drone-assisted inspections

These innovations aim to enhance safety by reducing reliance on human attention and improving early detection of potential issues.

Conclusion

While autonomous monitoring can significantly improve nuclear safety, it's not a complete solution to the complex challenges of nuclear energy. Transparency around tail risks and equitable access to critical safety information remain important considerations for policymakers and the public.The term "tail risk opacity" could be used to describe the lack of transparency around extreme event risks, though it's not a widely established term. As for nuclear safety, autonomous systems offer promising advancements but must be implemented thoughtfully alongside robust human oversight and comprehensive risk management strategies.

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