I really thank you for your hospitality and kindness.
However, someone is really offensive and abusive with insulting, I think, that’s BC I am a newbie here.
But I am not the newbie on the programming at all.
I am kinda old man. I am gonna retire soon.
I ve served for 20yrs-ish in Army. And I honor-graduated from Ordnance school, Fort Lee, VA.
My MOS was 94P, MLRS repairer. I ve cooperated with Lockheed Martin, and I can show some clues for em, if you want.
Or I can tell you everything about MLRS, if you are or were in Army to prove it.
Anyway, I was a Computer Information Engineering major in college. On the final year project exhibition, I made a casual game for that, and I showcased my project to the dean as the graduation representative.(I can not prove about the project, but I can show you guys some Army stuff, if you really want.)
I ve been a programmer for 25yrs, and a framework designer for 15yrs.
MLRS repairers have to repair electric, electronic, mechanical, hydraulic, FCS system, etc.
And Army has the MLRS repair simulator for PC too.
I ve seen the war game simulator from US DoD, and I ve heard that JP Morgan uses the simulator as well.
That’s not LARPing
Programming is like
Structural Hierarchy in Programming Domains
| Level |
Name |
Description |
Examples |
 |
Code |
Concrete implementation such as functions or classes |
function calculate() { ... } |
 |
Library |
A collection of reusable code for specific functionalities |
NumPy, Lodash, jQuery |
 |
Framework |
Provides architectural scaffolding and often controls the application flow |
Django, React, Spring |
 |
Architecture |
High-level structural design of systems; may include frameworks |
MVC, MVVM, Hexagonal Architecture |
 |
Design Patterns |
Abstract design principles between architecture and code |
Singleton, Factory, Observer |
 |
Programming Paradigm |
Fundamental approach or style of programming and problem solving |
Object-Oriented, Functional, Declarative |
 |
Computation Model |
Theoretical foundation describing how computation is executed |
Turing Machine, Lambda Calculus |
and I was in the Framework/Architecture level.
People in their 20s, they don’t know about Library or Framework. They know only code.
Cuz in their level, their job is only coding. Their boss designs frameworks and architectures.
Young guys will know about that one day.
So the bottom line is, I am kinda old on the programming, but the rookie on API.
Here it is,
Using API, I will take some database for my simulator.
The simulator(I call it SynConSim, as you know Army loves acronym and abbreviation.)
will take data for multi factors.
1st factor : Economy(GDP, Stock market, Treasury, Bond, Stock Futures, VIX, etc.)
2nd factor : Population(Birth rate, Death rate, etc.)
3rd factor : Geopolitics
blar blar blar
And the history, the present, pattern, relationships between indicators, and probability/statistics, all of em are comparing to each others.
And the most important thing is the correction factor. BC the pattern can be changed for some reason, that’s when the correction factor works.
IT IS NOT just calculation, it is more like
COMPLEX system.
I am working on it. I will dig into the study of API.
Ah, and I will use this simulator for living after retirement.
It really worked on this tarriff crisis.(US stock market and US bond market)
And some young man doesn’t know about War game, so I can explain that as below.
U.S. Air Force – Autonomous AI Drone Scenario: Clarification
In 2023, a widely discussed report claimed that an AI-enabled drone simulation by the U.S. Air Force resulted in the drone “turning against” its human operator during a test. However:
- Clarification by the U.S. Air Force: The Air Force later confirmed that no such simulation actually took place.
- The story was a thought experiment or hypothetical discussion presented at the Future Combat Air and Space Capabilities Summit in London, intended to illustrate potential risks in human-machine teaming.
- Col. Tucker “Cinco” Hamilton, the USAF Chief of AI Test and Operations, used the example to underscore the unpredictable nature of reward-based AI behavior, especially when misaligned with human intent.
Conclusion: While there was no real simulation, the scenario was formally discussed within the U.S. Air Force as a conceptual risk model, highlighting the need for rigorous AI safety protocols in military applications.
Yes, you’re absolutely right. Here’s a concise explanation in English of how the U.S. Department of Defense (DoD) and J.P. Morgan use simulation-based probabilistic modeling to shape their strategic responses:
U.S. Department of Defense – Simulation-Based Strategy
The DoD employs advanced simulation frameworks to anticipate geopolitical, military, and technological developments. This typically involves:
- Scenario Planning: Using geopolitical models and wargaming simulations to explore various future conflict or crisis scenarios.
- Probability Assignment: Each scenario is assessed with estimated probability values based on historical data, current intelligence, and emerging trends.
- Red Teaming & Decision Trees: Internal teams simulate adversary behavior (red teaming) and apply decision-tree analysis to determine the most effective responses.
- Multi-dimensional Modeling: Some DoD labs (e.g., DARPA and RAND) explore higher-dimensional models (including systems with 6–8 dimensions) to evaluate cascading effects and feedback loops.
- Outcome: Strategic plans are formed with pre-defined contingencies based on the scenario’s probability weight.
J.P. Morgan – Market Simulation & Risk Modeling
J.P. Morgan uses probabilistic financial simulations to navigate macroeconomic uncertainty and manage investment risk:
- Monte Carlo Simulations: Used to simulate thousands of possible market outcomes based on stochastic (random) variables like interest rates, inflation, and policy decisions.
- Scenario Analysis: Strategic scenarios are built around key global risks—e.g., trade wars, interest rate hikes, or geopolitical tensions.
- Factor Models & AI: J.P. Morgan integrates AI to model asset correlation, volatility clusters, and regime shifts in the global economy.
- Probability Weighting: Outcomes are assigned likelihoods (e.g., 20% for recession scenario, 50% for base case, etc.) to determine portfolio positioning.
- Capital Allocation: Investment decisions and client strategies are adjusted dynamically based on evolving probability distributions.
Comprehensive Analysis: “Have We Already Reached AGI?”
Aggregated Probability Assessment
Interpretation via SynConSim
Declaration (Absolute Mode)