It Happens

It starts with Taoism. But I recognize bureaucracy, software development and economics. In:

Taoism Sh.t happens
Confucianism Confucius say: “Sh.t happens”
Buddism If sh.t happens, it is not really sh.t
Zen What is the sound of sh.t happening?
Hinduism This sh.t has happened before
Islam If sh.t happens, it is the will of Allah
Protestantism Let the sh.t happen somewhere else
Catholicism If sh.t happens, you deserve it
Judaism Why does sh.t always happen to us?
Mysticism Just experience sh.t happening
Ascetisim If sh.t happens, renounce it
Agnosticism Nobody knows why sh.t happens
Gnosticism I know why sh.t happens but will not tell you
Atheism Sh.t happens and that is all there is to it
Cathesianism Sh.t happened to me, therefore it exists
Platonism There is ideal sh.t happening somewhere
Stoicism I do not care if sh.t happens
Epicureanism Let us party while sh.t does not happen
Cynism Of course sh.t happens
Occultism Sh.t materializes from other planets of existence
Terrorism Sh.t will happen unless you do as I say
Puritanism S… can happen all day as long as you do not call it that
Behaviourism You are conditioned to having sh.t happen
Freudianism If sh.t happens, it is your mother’s fault
Parapsychology Sh.t happens without material causes
Surrealism Purple sh.t happens near melting clocks
Cubism If sh.t happens, you will not recognise it
Optimism If sh.t happens, we will find a way to use it
Pessimism If sh.t happens, there will not be enough for everybody
Tabloid sensationalism Green sh.t from Mars happens to Elvis clone
Biblical creationism Sh.t happens because God created it
Scientific obscurantism Sh.t happens because it evolved from primitive sh.t
Bureaucracy I do not care if sh.t happens as long as you fill out the forms
Feminism Women demand to have sh.t happen
Ecology If organic sh.t happens, it is OK
Capitalism Let us profit from sh.t happening
Socialism If sh.t happens, let us distribute it evenly
Patriotism Our sh.t is better than your sh.t
Conservatism They don’t make sh.t happen like they used to
Liberalism Sh.t should not happen tomorrow
Classical physics Sh.t does not “happen”, it just moves around
Quantum physics Sh.t happens but you can not say both where and when
Sh.t happens in discrete quanta called shitons
Holistic physics If sh.t happens, it happens everywhere at once
Software development If sh.t happens, we will fix it in the next version
Applied mathematics The probability of sh.t happening approaches unity
Engineering When sh.t happens, paint over
Medicine If sh.t happens, take two aspirin and call me in the morning
Economics Sh.t happens because there is a great demand for it
Politics If sh.t happens, make a deal with it
Diplomacy Let us pretend sh.t does not happen
To which I can already add:
Accountancy However bad, sh.t can be left hidden from sight as long as you can ‘prove’ to not have seen the pile of it that you’re drowning in

If you would have any to add, please do …

On qualitative calculations

[Guess the country. Wrong.]

Some time ago, I hinted that maybe some combination of fuzzy logic and wavelet-like mathematics might deliver tools for qualitative risk management calculations.
Now is the time to delve a little into the subject. If possible, somewhat methodologically.

But hey, you know me; that will not work too perfectly as perfection is boring. I’ll just take it away with a discussion on scales, and thrown in a handful of Ramsey-Lewis work:
There’s the nominal scale. Where one can only sum up the categories or bins one would want to place one’s observations in. presumably, we’d have observations of just one quality (‘aspect’) of the population of observed items [either ‘real’, if there is such a thing, or abstract..! Oh how many problems we face here already] One cannot establish (in)equality between categories, nor add or substract, nor ‘size-‘compare.
There’s the ordinal scale, too. Here, we have some form of ranking of categories, they can be ordered. Categories are either dichotomous (an observation put into one category excludes the observation be also put into another), or non-dichotomous (category membership is non-exclusive. ‘Completely agree’ supposes ‘mostly agree’). At least we can compare, by order, between ‘larger’ and ‘smaller’ or by precedence, but not much more; no calculation.
On to the interval scale. Which has degrees of difference, like (common) temperature and dates. With their arbitrary zero we can’t talk sensibly about ratios. 10°C is not twice as warm as 5°C … But we can add and substract, just not multiply and divide.
This, by the way, is the ‘first’ scale considered to be quantitative, the above are qualitative..!
Next is the ratio scale, that has all of the above and a definitive zero, hence full calculation can be done.
Trick question for you: Where would the binary scale go …?

Just to mention Cohen’s kappa for qualitative score agreement among raters; it may or should come back later. And to mention all sorts of ‘Big’ Data analysis conclusions, with all the monstrosities of mathematical errors in that squared with lack of understanding of the above (in particular, the degradation of information in the direction from ratio to nominal, impossibly the other way around without adding relatively arbitrary information..!)

Now then, fuzzy logic. It takes an interval scale or ‘better’, and stacks a probability function to arrive at quantitative non-ditochomy. Next, work through cause-effect trees [hey, that’s a new element – I’ll come to it shortly] where some cause both happens with some probability and doesn’t happen with another probability at the same time, which propagates through OR and AND-gates to create effects with weird probabilities / weird aspects of probability, and on through the chain / feedback loops and all. If we can assign probabilities to less than interval scales, we would … oh, we do that already, in fault trees, etc.
Except that we don’t. We do not, I repeat do not, build real fault trees in general business riks management. We do the fresh into kindergarten version of it only! NO you don’t. And also, you don’t assign proper probabilities. You screw them up; apologies for the words.

So, we need combinations. We need the flexibility to work with qualitative scales when (not if) we can do no better, and with quantitative scales wherever we can. Being careful about the boundaries ..! Maybe that is (the) key.

[Interlude: wavelets, we of course use on ratio scales, as a proxy for fuzzy logic in continuous mathematics (?)]

Why would this be so difficult ..? Because we have so limited data ..? That’s solvable, by using small internal-crowd sourced measurements; using Cohen’s kappa (et al.) as mentioned.
Because we have no fault trees? Yes, indeed, that is your fault – trees are essential to analyse the situations anyway. Acknowledging the difficulties to get to any form of completeness, including the feedback loops and numerous time-shifts (Fourier would have ahead-of-time feedbacks …! through negative frequencies…). Not acknowledging consistency difficulties; one could even state that any worthwhile fault tree i.e., any one that includes sufficient complexity to resemble reality in a modeling way (i.e., leaving out unnecessary detail (only!)), will have inconsistencies included or it’s not truly representative… ☺

Hm, I start to run in circles. But:
• Haven’t seen fault trees in risk management, lately. We need them;
• Let’s apply ‘fuzzy logic’ calculations to fault trees. We can;
• When we use less-than ratio scales, let’s be clear about the consequences. Let’s never overrepresent the ‘mathematical rigour’ (quod non) of our work, as we will be dragged to account for the errors that causes, with certainty.

Fuzzy risk language

[Antwerp. Seriously.]

In some previous post, I posited that we should move from quantitative (quod non) to qualitative or even intuitive risk management.
And how that may be difficult. ‘cause it is.
As an intermediary step, I propose to build a better language with which to communicate, discuss and calculate (sic) with qualitative risk management.

Because I see a place for a combination of fuzzy logic and wavelet theory, including neural network signal combination functions.
As my time is limited, this time of year, would anyone have pointers to what’s already out there in papers, practical applications, etc..? That could kickstart the discussion. And I’ll return with more, better, more extensive, more thought out stuff on the subject later.

Maverisk / Étoiles du Nord