noodlelover
Kraken
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This a high level conceptual frame work for thinking about your moment to moment activities.
Leverage is the multiplicative factor on inputs in relation to outputs.
Maximum Leverage * Maximum Inputs = Maximum Outputs.
Most concepts exist in a fractal pattern.
So, in the case of leverage, an input is often made of smaller inputs that each have different leverages. It is also typically part of a larger input. So, it's a fractal pattern.
One quick strategy for maximizing leverage, is the act of expanding high leverage inputs, and keeping low leverage inputs near their minimum threshold of effectiveness. This is referred to as the pareto principle. And because leverage exists in a fractal pattern, the strategies to optimize it can be applied in a fractal pattern.
Meaning, you don't just maximize leverage on the big input, but smaller pieces of that input, and smaller pieces of those inputs, and so on, fractally.
There are number of challenges in applying leverage maximization in the real world. One is that you don't know the leverage of any given activity. While you could do regression analysis, such as linear regression to determine input/output correlations this is far from optimal.
The reason being, is as you add variables the correlations become exponentially less statistically significant, and in the real world there are unknown unknowns. Meaning you don't even have a mathematical model to quantify the statistical significance of the results of your regression, but they are likely low.
A better way to quantify leverages is to build a mathematical model of the system you're dealing with and the systems it interacts with. You can iterate through this computational model to determine long term leverage, which can differ from short term leverage.
In building a computational model, you will be primarily guessing at variables, thresholds, and equations. Learn as much as you can about the subject, and utilize a mixture of intuition and logic in coming up with the model. A system with greater unknowns can be partially clarified by building computational models of the systems that create those systems, and so on fractally if necessary.
It's important to stay close to first principles whenever thinking about these systems, and be aware of biases for any content you consume in an attempt to better understand them. Virtually all content evolves to maximize attention, so that's a bias you should always be aware of in any content you consume.
The act of consuming content, and building these models are them selves activities that should be viewed through this paradigm.
Hopefully this strategy yields improved results in your lives.
Leverage is the multiplicative factor on inputs in relation to outputs.
Maximum Leverage * Maximum Inputs = Maximum Outputs.
Most concepts exist in a fractal pattern.
So, in the case of leverage, an input is often made of smaller inputs that each have different leverages. It is also typically part of a larger input. So, it's a fractal pattern.
One quick strategy for maximizing leverage, is the act of expanding high leverage inputs, and keeping low leverage inputs near their minimum threshold of effectiveness. This is referred to as the pareto principle. And because leverage exists in a fractal pattern, the strategies to optimize it can be applied in a fractal pattern.
Meaning, you don't just maximize leverage on the big input, but smaller pieces of that input, and smaller pieces of those inputs, and so on, fractally.
There are number of challenges in applying leverage maximization in the real world. One is that you don't know the leverage of any given activity. While you could do regression analysis, such as linear regression to determine input/output correlations this is far from optimal.
The reason being, is as you add variables the correlations become exponentially less statistically significant, and in the real world there are unknown unknowns. Meaning you don't even have a mathematical model to quantify the statistical significance of the results of your regression, but they are likely low.
A better way to quantify leverages is to build a mathematical model of the system you're dealing with and the systems it interacts with. You can iterate through this computational model to determine long term leverage, which can differ from short term leverage.
In building a computational model, you will be primarily guessing at variables, thresholds, and equations. Learn as much as you can about the subject, and utilize a mixture of intuition and logic in coming up with the model. A system with greater unknowns can be partially clarified by building computational models of the systems that create those systems, and so on fractally if necessary.
It's important to stay close to first principles whenever thinking about these systems, and be aware of biases for any content you consume in an attempt to better understand them. Virtually all content evolves to maximize attention, so that's a bias you should always be aware of in any content you consume.
The act of consuming content, and building these models are them selves activities that should be viewed through this paradigm.
Hopefully this strategy yields improved results in your lives.
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