It is from me. I am reasonably knowledgeable in terms of mathematics and statistical modelling.
1. You identify major factors to capture your life worth.
2. Represent each major as a stochastic differential equation (F1, F2, F3...Fi), each with random factors embedded, so they should look like a Brownian motion
3. Use your current state as initial condition for each equation.
4. Assume independence and equity-weighting, so you can obtain a Life function L(F1, F2, F3...Fi) by multiplying all F.
5. Run monte carlo simulation for each Fi, so you obtain a simulated run of life function L.
6. Integrate L for the Reimann sum for the life worth of that simulation
7. Simulate it many times to obtain the distribution of your life worth.
8. Look at how positive/negative the Reimann sums are, to decide if you should neutralise it by voluntary methods.
If your life is already negative, you should either end it or increase the volatility of life first if such means available.
Got it. I'm not specialized in the subjects involved, but I'm somehow familiarized with stochastic processes to understand what is going on. Saying this, I have some questions which I'd like to clear up, if you are willing to answer of course, as well as some things I'd like to add:
- I think there are two ways to approach the first step in the methodology:
- Individual-based factor selection, in which every individual picks the factors which they consider most important in their life. This would provide more accurate results to every subject, but could be prone to variability depending on the current mood or the sensitivity of said subject.
- Population-based factor selection, in which we take psychological and sociological studies that compile the most relevant contributors to human happiness. This would mitigate the subjectivity of the previous approach, but it will also confine every test subject to the same values, and would be less meaningful to any particular subject.
- Assuming every variable has an uniform distribution would not be interpreted as equality of opportunities for every test subject? and even so, how could we possibly determine the interval of said distribution?
- It would be necessary to implement a metric in which the initial conditions can be determined from the current state (with respect to the factors) of the subject, even if universal (applicable for anyone) or individual (meaningful only to the current subject).
- Is independence a sensible assumption for all Fi? Perhaps a more careful analysis would be adequate?
I think that it's reasonable to think of life as a chaotic system, which will not pose as much as a problem to the stochastic part of the study, but it will certainly affect the deterministic part (since by definition, a chaotic system is one that is highly sensitive to the initial conditions), so we can only draw results locally, with more certainty the closer we get to them. This would give the study a character that is more similar to forecasting of the weather or the stock market. Following these arguments, it leads me to conclude that the transitory nature of the study may provide unreliable results for long term decisions as it is suicide. And this is a boomer for me too, since that makes it sound like the old loathed statement "permanent solution to a temporary problem".
However, I want to add that mathematics are not necessary to make a decision of that kind. We as humans are sensitive agents of our environment, and we respond in accordance to its stimuli. If the environment rewards us, we are happy; if it punish us, we are sad. After reaching certain maturity level, we've gained enough awereness of the environment to know when we have control and how much we consent on " keep playing the game". For me, suicide will always be a human right, simply because it concerns our liberty to choose a dignified life or death.
I'll be looking forward to your response or any comment you wish to include. And I know this is not the platform to address this kind of discussions but I think it is interesting discussing nonetheless, even if in a shallow level.
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