far east gizmos ( fareastgizmos.com )

Honda PCX hybrid 
http://fareastgizmos.com/transport/honda-announces-worlds-first-mass-produced-motorized-scooter-equipped-with-a-motorcycle-hybrid-system.php

http://fareastgizmos.com/smartphone/canon-launches-its-smallest-and-lightest-portable-printer-ivy-mini-photo-printer.php

http://fareastgizmos.com/other_stuff/agc-unveil-sound-generating-glass-milan-design-week-2018.php

future of Nissan’s sedan design language
http://fareastgizmos.com/transport/official-exterior-sketch-new-altima-hints-future-nissans-sedan-design-language.php
http://www.nissan.com.vn/news/nissans-future-design-language-previewed-vmotion-2-0-concept/
https://www.nissanusa.com/vehicles/future-concept/vmotion-autonomous-car.html

http://fareastgizmos.com/ai/endoscopic-imaging-diagnosis-using-ai-system-detects-gastric-cancers-90-accuracy-within-one-minute.php

http://fareastgizmos.com/digital_cameras/nikon-announces-monarch-3000-stabilized-laser-rangefinder.php

http://fareastgizmos.com/ai/mitsubishi-electric-develops-industrys-highest-performing-object-recognition-camera-technology-using-ai-coming-mirrorless-cars.php
Mitsubishi Electric’s proprietary Maisart-brand artificial intelligence (AI) technology

http://fareastgizmos.com/transport/lithium-ion-battery-give-shinkansen-bullet-trains-push-need-emergencies-like-power-outage.php

http://fareastgizmos.com/other_stuff/fujitsu-develops-worlds-smallest-sensor-eliminates-need-replace-batteries.php

http://fareastgizmos.com/transport/u-s-production-new-2018-nissan-leaf-propilot-assist-technology-begins-tennessee.php

http://fareastgizmos.com/other_stuff/japanese-startup-kairos-announces-worlds-first-surgical-endoscope-8k-resolution-kairoscope-e.php

http://fareastgizmos.com/other_stuff/nikon-cfi90-20xc-glyc-objective-biological-microscopes-supports-whole-brain-imaging.php

http://fareastgizmos.com/other_stuff/olympus-announces-spinsr10-super-resolution-imaging-system.php

http://fareastgizmos.com/transport/mitsubishi-electric-begins-mass-producing-auto-industrys-first-crankshaft-isg-system-48v-hybrid-vehicles.php

http://fareastgizmos.com/robotic/nikon-establishes-demonstration-facility-non-destructive-inspection-non-contact-3d-metrology-systems-japan.php

3D metrology systems

https://www.shapegrabber.com/3d-metrology-vs-machine-vision-understanding-differences/

http://fareastgizmos.com/other_stuff/mitsubishi-electrics-ka-band-gan-hemt-mmic-satellite-earth-stations-offers-high-output-power-low-distortion.php

Garbage Can Model (Cohen, March, Olsen)

Michael D. Cohen; James G. March; and Johan P. Olsen, ‘A Garbage Can Model of Organizational Choice’, March 1972

p.2
THE BASIC IDEAS
   Decision opportunities are fundamentally ambiguous stimuli. This theme runs through several recent studies of organizational choice.2 
   2  We have based the model heavily on seven recent studies of universities:  Christensen (1971), Cohen and March (1972), Enderud (1971), Moode (1971), Olsen (1970, 1971), and Rommetveit (1971).  The ideas, however, have a broader parentage.  In particular, they obviously owe a debt to Allison (1969), Coleman (1957), Cyert and March (1963), Lindblom (1965), Long (1958), March and Simon (1958), Schilling (1968), Thompson (1967), and Vickers (1965). 

Cohen, March, Olsen 

   •  “Although it maybe convenient to imagine that choice opportunities 
      lead first to the generation of decision alternatives, then to a 
      examination of their consequences, then to an evaluation of those 
      consequences in terms of objectives, and finally to a decision, this 
      type of model is often a poor description of what actually happens.”, 
      p.2, Michael D. Cohen; James G. March; and Johan P. Olsen, ‘A Garbage 
      Can Model of Organizational Choice’, Administrative Science Quarterly, 
      Vol. 17, No. 1. (Mar., 1972), pp.1-25, March 1972

p.2
Although it maybe convenient to imagine that choice opportunities lead first to     (λ) the generation of decision alternatives, 
        then to 
   (μ) an examination of their consequences, 
        then to 
   (ν) an evaluation of those consequences 
        in terms of objectives, and finally to 
   (ξ) a decision, 
        this type of model is often a poor description of what actually happens.

   •  “Despite the dictum that you cannot find the answer until you have
      formulated the question well, you often do not know what the 
      question is in organizational problem solving [OPS] until you know 
      the answer.”, p.3, Michael D. Cohen; James G. March; and Johan P. 
      Olsen, ‘A Garbage Can Model of Organizational Choice’, Administrative
      Science Quarterly, Vol. 17, No. 1. (Mar., 1972), pp.1-25, March 1972

   •  “..., one can view a choice opportunity as a garbage can into which
      various kinds of problems and solutions are dumped by participants
      as they are generated.  
       The mix of garbage in a single can depends on the 
        (ο) mix of cans available, on 
        (π) the labels attached to the alternative cans, on 
        (ρ) what garbage is currently being produced, and on 
        (σ) the speed with which garbage is collected and 
        (τ) removed from the scene.”, 
      p.2, Michael D. Cohen; James G. March; and  Johan P. Olsen, 
      ‘A Garbage Can Model of Organizational Choice’, Administrative 
      Science Quarterly, Vol. 17, No. 1. (Mar., 1972), pp.1-25, March 1972d

   •  “Problems are worked upon in the context of some choice, but choices 
      are made only when the shifting combinations of problems, solutions, 
      and decision makers happen to make action possible.”, p.16, Michael D.
      Cohen; James G. March; and Johan P. Olsen, ‘A Garbage Can Model of 
      Organizational Choice’, Administrative Science Quarterly, 
      Vol. 17, No. 1. (Mar., 1972), pp.1-25, March 1972  

   Choice opportunities.  These are occasions when an organization is expected to produce behavior that can be called a decision. Opportunities arise regularly and any organization has ways of declaring an occasion for choice.  Contracts must be signed; people hired, promoted, or fired; money spent; and responsibilities allocated., p.3   

pp.2-3
In the garbage can model, on the other hand, a decision is an outcome or interpretation of several relatively independent streams within an organization. 
   Attention is limited here to interrelations among four such streams

   Problems.  Problems are the concern of the people inside and outside the organization.  They might arise over issues of lifestyles; family; frustrations of work; careers; group relations within the organization; distribution of status, jobs, and money; ideology; or current crises of mankind as interpreted by the mass media or the nextdoor neighbor.  All of these require attention. 

   Solutions.  A solution is somebody's product.  A computer is not just a solution to a problem in payroll management, discovered when needed.  It is an answer actively looking for a question.  The creation of need is not a curiousity of the market in consumer products; it is a general phenomenon of processes of choice.  Despite the dictum that you cannot find the answer until you have formulated the question well, you often do not know what the question is in organizational problem solving until you know the answer. 

   Participants.  Participants come and go.  Since every entrance is an exit somewhere else, the distribution of “entrances” depends on the attributes of the choice being left as much as it does on the attributes of the new choice.  Substantial variation in participation stems from other demands on the participants' time (rather than from features of the decision under study).  

   Choice opportunities.  These are occasions when an organization is expected to produce behavior that can be called a decision. Opportunities arise regularly and any organization has ways of declaring an occasion for choice.  Contracts must be signed; people hired, promoted, or fired; money spent; and responsibilities allocated.  

p.3
Attention will be concentrated here on examining the consequences of different rates and patterns of flows in each of the streams and different procedures for relating them. 

p.8
   Some choices involve both flight and resolution──some problems leave, the remainder are solved. 

p.10
The system, in effect, produces a queue of problems in terms of their importance, to the disadvantage of late-arriving, relatively unimportant problems, and particularly so when load is heavy. 

pp.10-11
   Seventh (7th), important choices are less likely to resolve problems than unimportant choices.  Important choices are made by oversight and flight.  Unimportant choices are made by resolution. 

p.8 
   By resolution.  Some choices resolve problems after some period of working on them.  The length of time may vary, depending on the number of problems.  This is the familiar case that is implicit in most discussions of choice within organizations. 

p.8
   By oversight.  If a choice is activated when problems are attached to other choices and if there is energy available to make the new choice quickly, it will be made without any attention to existing problems and with a minimum of time and energy. 

p.8
   By flight.  In some cases choices are associated with problems (unsuccessfully) for some time until a choice more attractive to the problems comes along.  The problems leave the choice, and thus it is now possible to make the decision. The decision resolves no problems; they having now attached themselves to a new choice. 

p.11
   Eighth (8th), although a large proportion of the choice are made, the choice failures that do occur are concentrated among the most important and least important choices.  Choices of intermediate importance are virtually always made.  

p.11
   In a broad sense, these features of the process provide some clues to how organizations survive when they do not know what they are doing.  Much of the process violates standard notions of how decisions ought to be made. 

p.16
The garbage can process is one in which 
   (θ) problems, 
   (ι) solutions, and 
   (κ) participants 
  move from one choice opportunity to another in such a way that 
      (α) the nature of the choice, 
      (β) the time it takes, and 
      (γ) the problem it solves all depend on a relatively 
     complicated intermeshing of elements. These include 
         (δ) the mix of choices available at any one time, 
         (ε) the mix of problems that have access to the organization, 
         (ζ) the mix of solutions looking for problems, and 
         (η) the outside demands on the decision makers. 

p.16
   It is clear that the garbage can process does not resolve problem well. 
p.16
But it does enable choices to be made and problems resolved, even when the organization is plagued with goal ambiguity and conflict, with poorly understood problems that wander in and out of the system, with a variable environment, and with decision makers who may have other things on their minds. 

pp.16-17
   There is a large class of significant situations in which the preconditions of the garbage can process cannot be eliminated.  In some, such as pure research, or the family, they should not be eliminated. 

   (Michael D. Cohen; James G. March; and Johan P. Olsen, ‘A Garbage Can Model of Organizational Choice’, Administrative Science Quarterly, Vol. 17, No. 1. (Mar., 1972), pp.1-25, March 1972  )
  <------------------------------------------------------------------------>     

https://drive.google.com/open?id=1XsYoALmyRdIdqs-nIf8A5LdW_Tdahlrx

https://drive.google.com/open?id=1XsYoALmyRdIdqs-nIf8A5LdW_Tdahlrx

Tails of the unexpected

BANK OF ENGLAND

Speech

Tails of the unexpected 

Paper by Andrew G Haldane (and) Benjamin Nelson


Given at “The Credit Crisis Five Years On: Unpacking the Crisis”, conference held at the University of Edinburg Business School, 8-9 June

8 June 2012


...


But as Nassim Taleb reminded us, it is possible to be Fooled by Randomness (Taleb, 2001). For Taleb, the origin of this mistake was the ubiquity in economics and finance of a particular way of describing the distribution of possible real world outcomes.  For non-nerds, this distribution is often called the bell-curve.  For nerds, it is the normal distribution.  For nerds who like to show-off, the distribution is Gaussian. 

The normal distribution provides a beguilingly simple description of the world.  Outcomes lie symmetrically around the mean, with a probability that steadily decays. It is well-known that repeated games of chance deliver random outcomes in line with this disbribution:  tosses of a fair coin, sampling of coloured balls from a jam-jar, bets on a lottery number, games of paper/scissors/stone.  Or have you been fooled by randomness? 

In 2005, Takashi Hashiyama faced a dilemma. As CEO of Japanese electronics corporation Maspro Denkoh, he was selling the company's collection of Impressionist paintings, including pieces by Cézanne and van Gogh. But he was undecided between the two leading houses vying to host the aution, Christie's and Sotheby's.  He left the decision to chance:  the two houses would engage in a winner-take-all game of paper/scissors/stone. 

Recognising it as a game of chance, Sotheby's randomly played “paper”.  Christie's took a different tack. They employed two strategic game-theorists -- the 11-year old twin daughters of their international director Nicholas Maclean. The girls played “scissors”.  This was no random choice. Knowing “stone” was the most obvious move, the girls expected their opponents to play “paper”.  “Scissors” earned Christie's millions of dollars in commission. 

As the girls recognised, paper/scissors/stone is no game of chance. Played repeatedly, its outcomes are far from normal. That is why many hundreds of complex algorithms have been developed by nerds (who like to show off) over the past 20 years. They aim to capture regularities in strategic decision-making, just like the twins. It is why, since 2002, there has been an annual international world championship organised by the World Rock-Paper-Scissors Society. 

The interactions which generate non-normalities in children's games repeat themselves in real world systems -- natural, social, economic, financial. Where there is interaction, there is non-normality. But risks in real-world systems are no game.  They can wreak havoc, from earthquakes and power outages, to depressions and financial crises.  Failing to recognise those tail events -- being fooled by randomness -- risks catastrophic policy error. 

So is economics and finance being fooled by randomness? And if so, how did that happened? That requires a little history. 



(a) normality in physical systems 
(b) normality in social systems
(c) normality in economic and financial systems 

p.3
He [Galileo 17th century physical experiments] found that random errors were inevitable in intrumental observations. But these errors exhibited a distinctive pattern, a statistical regularity: small errors were more likely than large and were symmetric around the rule value. 

p.3
This “reversion to the mean” was formalised in 1713 by Jacob Bernoulli based on a hypothetical experiment involving drawing coloured pebbles from a jam-jar.1 

p.4
Gaussian world 
It suggested regularities in random real-world data. Moreover, these patterns could be fully described by two simple metrics -- mean and variance.  

p.5
English statistician Francis Galton
Galton's study of hereditary characteristics provided a human example of Bernoulli's reversion to the mean. 

p.5
This semanic shift was significant. 

p.5
In the 18th century, normality had been formalised. In the 19th century, it was socialised. The normal distribution was so-named because it had become the new norm. 

pp.6-7
shifting from models of Classical determinism  to  statistical laws.  
Evgeny Slutsky (1927) and Regnar Frisch (1933)
They divided the dynamics of the economy into two elements: an irregular random element or IMPULSE and a regular systematic element or PROPAGATION mechanism.  This impulse/propagation paradigm remains the centerpiece of macro-economics to this day. 

p.6
Tellingly, these tests were used as a diagnostic check on the adequacy of the model. 

p.6
As in the natural sciences in the 19th century, far from being a convenient statistical assumption, normality had become an article of faith. Normality had been socialised. 

p.6
Kenneth Arrow and Gerard Debreu (1954) 
the world were assumed to have knownable probabilities. 
Agents' behaviour was also assumed to be known. 
allowed an explicit price to be put on risk, while ignoring uncertainty. 
Risky (Arrow) securities could now be priced with statistical precision. 
These contingent securities became the basic unit of today's asset pricing models. 

p.7
Black and Scholes (1973) options-pricing formula, itself borrowed from statistical physics, is firmly rooted in normality. 

p.7
finance theorists and practitioners had by the end of the 20th century evolved into fully paid-up members of the Gaussian sect. 

p.7
1870s, German statistician Wilhelm Lexis
The natural world suddenly began to feel a little less normal. 

p.8
In consequence, Laplace's central limit theorem may not apply to power law-distributed variables. There can be no “regression to the mean” if the mean is ill-defined and the variance unbounded. Indeed, means and variances may then tell us rather little about the statistical future. As a window on the world, they are broken. With fail tails, the future is subject to large unpredictable lurches -- what statistician call kurtosis. 

p.8
Assuming the physical world is normal would lead to a massive under-estimation of natural catastrophe risk. 

p.8
The central limit theorem is predicated on the assumption of independence of observations. 

p.8
A comparably self-similar pattern is also found in the distribution of names, wealth, words, wars and book sales, among many other things (Gabaix, 2009). 

p.9
for equities once every 8 years.

pp.9-10
Systems are systems precisely because they are INTERdependent. 


p.10
(a) Non-Linear Dynamics 

p.10
In 1963, American meteorologist Edward Lorenz was simulating runs of weather predictions on his computer. Returning from his coffee break, he discovered the new run looked completely different than the old one.  He traced the difference to tiny rounding errors in the initial conditions. From this he concluded that non-linear dynamic systems, such as weather systems, exhibited an acute sensitivity to initial conditions. Chaos was born (Gleick, 1987). 

p.10
Lorenz himself used this chaotic finding to reach a rather gloomy conclusion:  forecasts of weather systems beyond a horizon of around two weeks were essentially worthless.  

p.10
Reversion to the mean is a poor guide to the future, often because there may be no such thing as a fixed mean. 

p.11
The accumulation of leverage was a key feature of the pre-crisis boom and subsequent bust. Leverage generates highly non-linear system-wide responses to changes in income and net worth (Thurner et al, 2010), the like of which would have been familiar to Lorenz. 


p.11
(b) Self-Organised Criticality 

p.12
(c) preferential attachment 

p.12
Keynes viewed the process of forming expectations as more beauty pageant than super-computer (Keynes, 1936). Agents form their guess not on an objective evaluation of quality (Stephen Fry) but according to whom they think others might like (Kim Kardashian). 

p.13
The classic example in finance is the Diamond and Dybvig (1983) bank run. If depositors believe others will run, so will they. Financial unpopularity then becomes infectious. 

p.13
(d) Highly-Optimised Tolerance 

p.13
features -- non-linearity, criticality [man-made, self-organized, hybrid], contagion
This is particularly so during crises [self-organized critical state?]. 

p.14
(a) non-normality in economics and finance 

p.14
tell-tale signs of intellectual infatuation

p.14
Tipping points and phase transitions have been the name of the game. The disconnect between theory and reality has been stark. 

p.14
In 1921, Frank Knight drew an important distinction between risk on the one hand and uncertainty on the other (Knight, 1921). Risk arises when the statistical distribution of the future can be calculated or is known. Uncertainty arises when this distribution is incalculable, perhaps unknown.

p.14
Hayek criticised economics in general, and economic policymakers in particular, for labouring under a “pretense of knowledge” (Hayek, 1974). 

p.15
(b) non-normality and risk management

p.17
Now ask what happens if the actual, fat-tailed distribution of GDP over the past three centures [300-years] is used. Under the baseline calibration, this raises the required capital buffer four-fold to around 12%. 

p.18
(c) non-normality and systemic risk 

p.18
systemic oversight agency
able to monitor and potentially model the moving pieces of the financial system. 
Financial System Oversight Council (FSOC) in the US, 
European Systemic Risk Board (ESRB) in Europe, 
Financial Policy Committee (FPC) in the UK

p.18
This map could provide a basis for risk management planning by individual financial firms. As in weather forecasting, the systemic risk regulator could provide early risk warnings to enable defensive actions to be taken. 

p.18
And as in weather forecasting, it is important these data are captured in a common financial language to enable genuinely global maps to be drawn (Ali, Haldane and Nahai-Williamson, 2012). 

p.19
Economics does not have the benefit of meteorologists' well-defined physical laws.  But by combining empirically-motivated behavioural rules of thumb, and balance sheets constraints, it should be possible to begin constructing fledging models of system risk.13 

p.19
Regulatory rules of the future will need to seek to reflect uncertainty. 

p.19
Less is more (Gigerenzer and Brighton, 2008)

p.19 
The reason less can be more is that complex rules are less robust to mistakes in specification. They are inherently fragile.   

p.19
In retirement, [Harry] Markowitz instead used a much simpler equally-weighted asset approach. This, Markowitz believed, was a more robust way of navigating the fat-tailed uncertainties of investing returns (Benartzi and Thaler, 2001). 

p.20
fail-safe against the risk of critical states emerging in complex systems, either in a self-organised manner or because of man-made intervention. 

p.20
structural separation solutions

p.20
Under uncertainty, however, that is precisely the point.  In a complex, uncertain environment, the only fail-safe way of protecting against systemic collapse is to act on the structure of the overall system, rather than the behaviour of each individual within it. 

p.21
Until then, normal service is unlikely to resume. 



All speeches are available online at 
http://www.bankofengland.co.uk/publications/Pages/speeches/default.aspx
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