Trial and error


[As architecture should be: Quite old, still modern..!]

Recently, this site exposed the New Year’s fireworks over Europe photo as fake.
Yes, the time zones not accounted for, the fireworks colour differences (..?), the ‘unique’ clear weather everywhere (not), fireworks in North Africa (on a different calendar) and on oil drilling rigs in the North Sea (would maybe result in fire, not -works…) are pointers indeed. To start off with. The same picture was shared last year already, too.

Handy, as next time, fraudsters will know what to edit for …

Bias time (1 of 9)


[Check out the paleo!]

Yes, it’s bias time. As first in a series of biases that you, yes you, have. Even if you are aware of these, and even if you consciously try to correct for them to be, heh, ‘objective’, as in what e.g. auditors pursue, you will fail.
I’ll just jot them down for your reading pleasure. Additional explanation, e.g., how they would work out in your professional practice, we can discuss out of band I guess.

Decision-making and behavioral biases

  • Bandwagon effect – the tendency to do (or believe) things because many other people do (or believe) the same. Related to groupthink and herd behavior.
  • Backfire effect – the tendency for corrections to initial misinformation to increase misperception
  • Base rate fallacy – the tendency to ignore available statistical data in favor of particulars.
  • Bias blind spot – the tendency not to compensate for one’s own cognitive biases.
  • Choice-supportive bias – the tendency to remember one’s choices as better than they actually were.
  • Confirmation bias – the tendency to search for or interpret information in a way that confirms one’s preconceptions.
  • Congruence bias – the tendency to test hypotheses exclusively through direct testing, in contrast to tests of possible alternative hypotheses.
  • Contrast effect – the enhancement or diminishing of a weight or other measurement when compared with a recently observed contrasting object.
  • Déformation professionelle – The tendency to look at things according to the conventions of one’s own profession, forgetting any broader point of view
  • Denomination effect – the tendency to spend more money when it is denominated in small amounts (e.g. coins) rather than large amounts (e.g. bills).
  • Distinction bias – the tendency to view two options as more dissimilar when evaluating them simultaneously than when evaluating them separately.
  • Endowment effect – “the fact that people often demand much more to give up an object than they would be willing to pay to acquire it”.
  • Experimenter’s or Expectation bias – the tendency for experimenters to believe, certify, and publish data that agree with their expectations for the outcome of an experiment, and to disbelieve, discard, or downgrade the corresponding weightings for data that appear to conflict with those expectations.
  • Extraordinarity bias – the tendency to value an object more than others in the same category as a result of an extraordinarity of that object that does not, in itself, change the value.
  • Focusing effect – the tendency to place too much importance on one aspect of an event; causes error in accurately predicting the utility of a future outcome.
  • Framing – using an approach or description of the situation or issue that is too narrow.
  • Also framing effect – drawing different conclusions based on how data is presented.
  • Hyperbolic discounting – the tendency for people to have a stronger preference for more immediate payoffs relative to later payoffs, where the tendency increases the closer to the present both payoffs are.
  • Illusion of control – the tendency to believe that outcomes can be controlled, or at least influenced, when they clearly cannot.
  • Impact bias – the tendency to overestimate the length or the intensity of the impact of future feeling states.
  • Information bias – the tendency to seek information even when it cannot affect action.
  • Interloper effect – the tendency to value third party consultation as objective, confirming, and without motive. Also consultation paradox, the conclusion that solutions proposed by existing personnel within an organization are less likely to receive support than from those recruited for that purpose.
  • Irrational escalation – the phenomenon where people justify increased investment in a decision, based on the cumulative prior investment, despite new evidence suggesting that the decision was probably wrong.
  • Just-world phenomenon – the tendency to rationalize an inexplicable injustice by searching for things that the victim might have done to deserve it.
  • Loss aversion – “the disutility of giving up an object is greater than the utility associated with acquiring it”. (see also sunk cost effects and Endowment effect, in a later bias post).
  • Mere exposure effect – the tendency to express undue liking for things merely because of familiarity with them.
  • Money illusion – the tendency to concentrate on the nominal (face value) of money rather than its value in terms of purchasing power.
  • Moral credential effect – the tendency of a track record of non-prejudice to increase subsequent prejudice.
  • Need for Closure – the need to reach a verdict in important matters; to have an answer and to escape the feeling of doubt and uncertainty. The personal context (time or social pressure) might increase this bias.
  • Negativity bias – the tendency to pay more attention and give more weight to negative than positive experiences or other kinds of information.
  • Neglect of probability – the tendency to completely disregard probability when making a decision under uncertainty.
  • Normalcy bias – the refusal to plan for, or react to, a disaster which has never happened before.
  • Not Invented Here – the tendency to ignore that a product or solution already exists, because its source is seen as an “enemy” or as “inferior.”
  • Omission bias – the tendency to judge harmful actions as worse, or less moral, than equally harmful omissions (inactions).
  • Outcome bias – the tendency to judge a decision by its eventual outcome instead of based on the quality of the decision at the time it was made.
  • Planning fallacy – the tendency to underestimate task-completion times.
  • Post-purchase rationalization – the tendency to persuade oneself through rational argument that a purchase was a good value.
  • Pseudocertainty effect – the tendency to make risk-averse choices if the expected outcome is positive, but make risk-seeking choices to avoid negative outcomes.
  • Reactance – the urge to do the opposite of what someone wants you to do out of a need to resist a perceived attempt to constrain your freedom of choice.
  • Restraint bias – the tendency to overestimate one’s ability to show restraint in the face of temptation.
  • Selective perception – the tendency for expectations to affect perception.
  • Semmelweis reflex – the tendency to reject new evidence that contradicts an established paradigm.
  • Status quo bias – the tendency to like things to stay relatively the same (see also loss aversion, endowment effect, and system justification).
  • Von Restorff effect – the tendency for an item that “stands out like a sore thumb” to be more likely to be remembered than other items.
  • Wishful thinking – the formation of beliefs and the making of decisions according to what is pleasing to imagine instead of by appeal to evidence or rationality.
  • Zero-risk bias – preference for reducing a small risk to zero over a greater reduction in a larger risk.

Patching Things


[Quick design, not too shabby]

Errrm, how are we going to patch all the devices once they’re out there for the Internet of Things ..? We already know for sure we’ll need that, as there will be a long time of not-cheap-enough-to-replace-en-masse-ness crossed with too many out there to not care, and almost unthinkable legacy issues, etc.
So, patching we will need. But how, if the stuff isn’t designed to allow that (capacity, connectivity, security) ..?

So I outdid myself; only one day after drafting the above, this appears, with quite an overview of all the Internet of Things risks out there (already). Heartily recommended!

Mo’nay


[Prime Valencia]

Just to drop it here. May take a textbook full of source-annotated analysis, but just feel there is something in the following:

In Negri & Hardt’s Empire (of which I like parts of the analyses, not necessarily the conclusions/synthesis/solutions!), the analysis goes about Worker Production being larger than Worker pay, the rest flowing to Capital, of which a tiny part is shaved off as Capital Owner ‘pay’. The rest of the surplus seeking redeployment ‘outside’ of the system and how the outside may no longer exist.
Haven’t read on far enough, but I feel that when Capital (surplus) folds back onto itself, it creates a sort of Non-Worker Production in the form of investment in financial instruments created out of thin air for no other reason than creating ‘value’ (quod non); with money producing ever more … money, surplus.

And that’s the bubble that deflated ‘recently’, it was no longer sustainable, stretched too thin.
And that’s why ‘the 1%’ wants to keep ‘capitalism’ as is, with creation of more worker proletariat squeezing out the middle class to lower Worker pay to (literally) starvation levels, in order to keep and increase their own cut of surplus and inflate the bubble in some other direction again.

Whereas ‘we’ need not money as an end goal, but as place- and time-shifting grease against the frictions of barter. There are no markets, there’s demand, and there’s supply.
Well, more on this later.

Icarus’ Deception


[On the lake]

As part of a new project, I herewith present the first ‘Book By Quote’: An attempt to subjectively summarise a book by the quotes I found worthwhile to mark, to remember. Be aware that the quotes as such, aren’t a real unbiased ‘objective’ summary; most often I heartily advise to read the book yourself..!

So, now then; Seth Godin’s The Icarus Deception, Penguin Books, december 2012, ISBN 9780670922925

Industrialists have made hubris a cardinal sin but conveniently ignored a far more common failing: settling for too little. It’s far more dangerous to fly too low than too high, because it feels safe to fly low. We settle for low expectations and small dreams and guarantee ourselves less than we are capable of. By flying too low, we shortchange not only ourselves but also those who depend on us or might benefit from our work. We’re so obsessed about the risk of shining brightly that we ‘ve traded in everything that matters trying to avoid it. (p.2)

The safety zone has changed, but your comfort zone has not. Those places that felt safe – the corner office, the famous colleague, the secure job – aren’t. You’re holding back, betting on a return to normal, but in the new normal, your resistance to change is no longer helpful. (p.3)

Creating ideas that spread and connecting the disconnected are the two pillars of our new society, and both of them require the posture of the artist. (p.5)

It took a hundred years for us to be brainwashed into accepting the industrial system as normal and safe. It is neither, not for long. (p.6)

Competence is no longer scarce, either. We have too many good choices – there’s an abundance of things to buy and people to hire. What’s scarce is trust, connection, and surprise. These are three elements in the work of a succesful artist. (p.10)

The simplest plan is to keep it all, to embrace what worked before, and to hide, mostly to hide, from the open vistas of the new postrevolutionary world. It’s so easy to do, and if the world moves slowly enough, you can even do it succesfully for a while. No longer. (p.21)

Capitalism is driven by failure, the failure of new ideasto catch on or the failure of the organization that fails when it is beaten by new competition. Industrialisation is about eliminating the risk of failure, about maintaining the status quo, and about cementing power. (p.27)

After nearly a century of effort, the industrial system has created the worker-proof factory. (p.28)

Within a generation, the Homeric myths of bravery and guts were supplanted by the workaday unbrave myths of Leave it to Beaver and Archie Bunker. Sure, there will be superheroes in the comic books hidden under our beds, but these heroes were never meant to be us – they were the idle pastimes of boys who hadn’t yet come to realise that the army has no room for Captain America and that, yes, in fact, Spider Man couldn’t get a job. Our parents bought us Batman underoos and Superman T-shirts, but it was clearly stated: Yo can pretend to be a hero, but you are not one, and you will grow up to be an obedient member of society. (p.75)

The fear has been shifted. It went from the wild animal’s fear of survival, the fear of the dark and of predators, to the industrialist-invented fear of noncompliance, fear of authority, fear of standing out. The industrialist offers us a trade. We can trade in our loneliness for the embrace of the mob and trade our innate fears for a steady paycheck. We can trade our yearning for something great in exchange for the safety of knowing that we will be taken care of. In return, all he asks is that we give up our humanity. (p.79)

Until we have a humility shortage, then, the real problem is this: We continue to fly too low. We’re so afraid of demonstrating hubris, so afraid of the shame of being told we flew too high, so paralyzed by the fear that we won’t fit in, hat we buy into the propaganda and don’t do what we are capable of. (p.90)

Our economy has worked overtime to emphasize and reward the lizard. … The rest of us,the story goes, are drones, the worker bees that are unentitled to the benefits reserved for the few. (p.101)

“We want talent”, they say, “as long as that talent is true, productive, and predictable. We want talent if talent means more product per dollar, more effort per day, more of what we think we’re paying for. …” ( p.114)

But lying low is now a recipe for ending up far outside your safety zone. The industrial economy sold you on the bargain that avoiding attention meant avoiding shame and that obedience led to stability. (p.125)

The kind of art I’m describing doesn’t seek to please the masses. The masses (by definition) aren’t pleased by the new. They are pleased by what others think. Harry Potter’s first fans were enthralled by the art that J.K. Rowling challenged them with. The next hundred million readers embraced a mass cultural phenomenon, not an unproven book from an unknown author.
Your goal as an artist is to move the audience of your choice. (p.128)

And so a car guy learn to tell the difference between a car design that’s going to sell and one that’s not. And a cop learns to recognise the symptoms of behaviour that might lead to trouble. Until they don’t. At some point, we stop seeing patterns and start looking for shortcuts. … We profile because it speeds up, but mostly we profile because it’s safer. (pp.148-149)

The problem with labels is that once they’re applied, it’s impossible to see what lies beneath. When the world changes, then, our labels cease to function and we’re blind to the opportunities that are presenting themselves. (p.149)

It’s best to get as many people as possible into a room. And then go somewhere else. (Jason Fox, p.173)

The industrial economy won’t disappear, but the agenda will increasingly be set by those who make connections, not widgets. (p.175)

And this is why art is rarely for the masses. The masses don’t appreciate the flash of originality and are happy to buy the copy or the knock-off. But that’s fine, because the masses matter less than they ever did before. The masses are interested in what’s popular, and the weird, the ones who get the joke, have more influence than ever in bringing ideas to them. We’re all the masses sometime. We’re part of the masses when we don’t appreciate nuance, when we merely want what is good enough, when price matters more than impact. The explosion of niches, of diverse tastes amplified, of weirdness, means that the masses are easier to ignore now. (p.179)

The simple reason that creativity, leadership, and brainstorming books and courses fail is that people don’t want them to work. We’ve been brainwashed into becoming afraid of art. (p.179)

We think we’re being safe and smart and conservative and avoiding flying too close to the sun. But all the generator is doing is pushing us closer and closer to the waves, so that we’re flying too low, daring too little, and blowing our best chance ever to matter. (p.183)

The pain-free life will elude you. You can work to smooth out all the edges, to eliminate all risk, and to be sure that everyone you encounter likes you. (I hope that seeing this in writing helps you see the absurdity of that mission.) But in the unlikely event that you accomplish this, you’ll soon be beset by the knowledge that it won’t last long at that it’s only a matter of time before someone comes along and ruins the entire thing. (p.188)

Freedom isn’t the ability to do whatever you want. It is the willingness to do whatever you want. (p.189)

In short, you can screw up with impuny as long as you screw up like everybody else. (David Putnam, p.203)

We’ve built a postdeception society, one where our future is created by those who replace the status quo, not those who defend it. (p.208)

It may take seven years for a fast-moving Internet company to become an overnight success. (p.211)

The best art is made by artists who don’t know how it’s going to work out in the end. The rest of the world is stuck with the brainwashed culture that the industrialists gave us, the culture of fear and compliance. But culture is a choice. … Others have always done that art, always chosen that culture of hope, but you haven’t done it enough (’too risky’, the lizard says), because you’ve been held back by a need for proof, by a reliance on assurance, and by the fear of humiliation. (p.218)

Judging money


[Picture’s perspective lines skewed to create akward feelings… AMS again]

@JudgeJoice_ tipped a couple of days ago about a judge with humour (in Dutch, unfortunately no English translation available). They seem to exist even in the Netherlands…

All very nice to rule that if defendant claims to not have received a loan because the bank only transferred some bits from one account to another and no ‘real’ money switched accounts (the scandal! the fraud! the defendant claimed), and hence no ‘real’ money would need to be repaid, but the defendant did all sorts of transactions with third parties where she presented the fake money (quod non) as ‘real’ herself, then the defendant would have no trouble ‘repaying’ the loan without ‘real’ money wouldn’t she?
With the court siding with the claimant (bank), heh, but coating the whole judgement with all sorts of ‘yeah, banks are Naughty not nice’ “analysis” of the situation.

By doing so, the judge came close to, on the one hand, dismissing ‘real’ money as worthless scraps of paper (mostly, to amount to anything) with only vague promises of ‘repayment’ in something that would actually be money or so: Since dropping the gold standard for <nothing>, no-one has ever explained what that would be – no, not even gold as that would be just an alternative currency with skyrocketing / flatfalling exchange rates.
And, on the other hand, the court also came close to recognition of Bitcoin et al. as sufficiently real money to count as currency. Why would some (unelected! no, you elect politicians, the administration is de facto not controlled by them) government be trusted, whereas a transparent, transparently operating self-formed community of Bitcoin much less so?

But I seem to be repeating myself re this little postlet

Now with the addition of a court’s ruling to the same effect. Thanks, it’s official now.

Bandwagon stuff


[Tension through perspective angles not being perfect; near AMS]

As in this text; just ½ a % into 2014 and our prediction is reiterated with some force. Seems like we may have predic’hinted at something that actually may happen this year.

Hey, we’re already ½ a % through 2014 and I haven’t seen news on anything ‘cyber’ yet. Let’s keep it up! And let’s have a hard laugh about those faux wannabe hipster beards. Aren’t they so last year!

Prediction of “A”PIs for IoT


[Some years ago, IL]

Ah, one thing I was concerned about, is elucidated elsewhere, already.
And ten more, halfway between hard tech deep innovation and societal acceptance.

IoT to retail


[East South Bank]

I had these ‘predictions’ for retail. Now IoT comes along (though 2014 is way too early; 2016-2018 is more likely); would’ve had to include it. But may take a while. So enjoy, and stitch both together…

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.

Maverisk / Étoiles du Nord