(T)oday (I) (L)earn reading room: June 2021

https://todayilearnreadingroom.blogspot.com/2021/06/philip-tetlock-til.html

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(Today I Learn) - about Philip Tetlock
([ I read about it (book learning) - not performance to do (practical learning) ])

He has a wikipedia page. You can see it here or use your favorite search program to look for it.

https://en.wikipedia.org/wiki/Philip_E._Tetlock

You can go to the original (textise.net) URL
https://www.textise.net/showText.aspx?strURL=https://medium.com/conversations-with-tyler/philip-tetlock-tyler-cowen-forecasting-sociology-30401464b6d9

or read my copy, cut & paste

Philip E. Tetlock on Forecasting and Foraging as a Fox (Ep. 93)

Mercatus Center
Apr 22, 2020 · 37 min read

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IARPA original forecasting tournaments with geopolitical events, like how long the Syrian Civil War would last, or what would Russia do in Eastern Ukraine, or things of that sort

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TETLOCK: I don’t think so. I don’t think I have the patience or the temperament for doing it. I did give it a try in the second year of the first set of forecasting tournaments back in 2012, and I monitored the aggregates. We had an aggregation algorithm that was performing very well at the time, and it was outperforming 99.8 percent of the forecasters from whom the composite was derived.

If I simply had predicted what the composite said at each point in time in that tournament, I would have been a super superforecaster. I would have been better than 99.8 percent of the superforecasters. So, even though I knew that it was unlikely that I could outperform the composite, I did research some questions where I thought the composite was excessively aggressive, and I tried to second guess it.

The net result of my efforts — instead of finishing in the top 0.02 percent or whatever, I think I finished in the middle of the superforecaster pack. That doesn’t mean I’m a superforecaster. It just means that when I tried to make a forecast better than the composite, I degraded the accuracy significantly.

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TETLOCK: I think you’d want an interdisciplinary team. Diversity is one of these words that’s been reduced to a cliché, but we have found in our work that cognitive diversity helps, and it helps in certain quite well-defined ways.

If you want to create a composite that out-predicts the vast majority of the superforecasters, a good way to do it is not only to take the most recent forecast of the best forecasters in the domain, but it’s also to extremize that forecast to the degree that people who normally disagree, agree with each other. And when you have convergence among diverse observers, that’s a signal that the weighted average composite is probably too conservative and you should extremize.

COWEN: Does the diverse team have a CEO, someone in charge?

TETLOCK: Not in this case, no. That’s done purely statistically.

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TETLOCK: That’s a matter of managerial skill, I would say. Going back to one of my old dissertation advisors at Yale, 40 plus years ago, Irv Janis on Groupthink, I would pick a leader who knows how to shut up, and not reveal opinions at the beginning of the meeting, and knows how to listen.

    I would pick a leader who knows how to shut up, and not reveal opinions at the beginning of the meeting, and knows how to listen.

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TETLOCK: I think a lot of good executives have the intuition that you get more out of a team of forecasters or problem solvers if you elicit independent judgments initially that are uncontaminated by conformity pressure, and then you create an environment in which ideas can be freely critiqued before lifting the veil of anonymity and letting people see who’s taking which positions.

     ••••   •••   ••••

TETLOCK: IARPA just ran a forecasting tournament called Hybrid Forecasting Competition in which they pitted algorithmic approaches and human approaches and hybrid approaches against each other. I should let IARPA speak for itself about how well its programs work or don’t work.

I don’t think there’s a lot of evidence to support the claim that machine intelligence is well equipped to take on the sorts of problems that the intelligence community wanted to have answered when it runs the forecasting tournaments it’s been running with our research team.

The things like the Syrian Civil War, Russia/Ukraine, settlement on Mars — these are events for which base rates are elusive. It’s not like you’re screening credit card applicants for Visa. Machine intelligence is going to dominate human intelligence totally. Machine intelligence dominates humans in Go and Chess. It may not dominate humans in poker.

I don’t know that the state of the art is quite there yet, but it’s in their StarCraft or whatever the next thing that Dennis Hassabis is going to conquer. No, I don’t see evidence that those approaches work in the domains that we study with the intelligence community.

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COWEN: What if I say — again, current events aside — but it seems easier to predict. There hasn’t been a world war since 1945. There’s been steady economic growth in most parts of the world. More peace. Isn’t that easier to predict? You just predict 2 to 4 percent global economic growth and you pick up a fair amount of what’s happened since 1950.

TETLOCK: Indeed. So, simple extrapolation algorithms, historically, are hard to beat. We’re running a COVID-19 mini forecasting tournament right now, and they’re proving to be hard to beat. The skill, of course, is when to alter the trend, whether to accelerate it or decelerate it, or to change direction.

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TETLOCK: I think humans have been repeatedly humbled in competitions against simple statistical algorithms, going back to Paul Meehl’s famous little book on clinical versus actuarial approaches to predicting in medicine and psychiatry. So, I would say, “Be humble.”

COWEN: There’s some of your early research that, if I read it properly, suggests that making people accountable leads to more evasion and self-deception on their part. Are you worried that your work with pundits, by trying to make them more accountable, will lead to more evasion and self-deception from them? How do you square early Tetlock and mid-period-to-late Tetlock?

TETLOCK: [laughs] It’s actually not too difficult in that particular case. It really depends on the type of accountability. Tournaments create a very stark monistic type of accountability in which one thing and only one thing matters, and that is accuracy. You get no points for being an ideological cheerleader and pumping up the probabilities of things that your team wants to be true or downplaying the probabilities of the things your team doesn’t want to be true. You take a reputational hit. So the incentives are very unusually tightly aligned to favor accuracy.

That’s extremely unusual in the social world. Most forms of accountability occur in organizational settings in which there are lots of distortions at work. And the rational political response for a decision maker located in most accountability matrices and organizations is to engage in some mixture of strategic attitudes shifting toward the views of important others or, as you put it, evasion, procrastination, and so forth.

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TETLOCK: I don’t know if we can make them run much further away from objective standards than they already are. [laughs] But that’s a very interesting point about forecasting tournaments. I look at the kinds of people who are attracted to participate in them. At the very outset, I invited lots of big shots to participate in forecasting tournaments, and they’ve repeatedly turned me down.

I had a very interesting correspondence with William Safire in the 1980s about forecasting tournaments. We could talk a little about it later. The upshot of this is that young people who are upwardly mobile see forecasting tournaments as an opportunity to rise. Old people like me and aging baby-boomer types who occupy relatively high status inside organizations see forecasting tournaments as a way to lose.

If I’m a senior analyst inside an intelligence agency, and say I’m on the National Intelligence Council, and I’m an expert on China and the go-to guy for the president on China, and some upstart R&D operation called IARPA says, “Hey, we’re going to run these forecasting tournaments in which we assess how well the analytic community can put probabilities on what Xi Jinping is going to do next.”

And I’ll be on a level playing field, competing against 25-year-olds, and I’m a 65-year-old, how am I likely to react to this proposal, to this new method of doing business? It doesn’t take a lot of empathy or bureaucratic imagination to suppose I’m going to try to nix this thing.

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TETLOCK: Well, I think that particular measure in integrative complexity does have some correlation with forecasting accuracy.

But I think you’re picking up something more than just forecasting accuracy. You’re picking up what I call value pluralism. You’re picking up a tendency to endorse values that are often in conflict with each other. So the more frequently that you, as a political thinker, confront cognitive dissonance between your values, your value orientations, the more pressured you are to engage in integratively complex synthetic thinking.

When your values are more lopsided, it’s easier to engage in what we call simpler modes of cognitive dissonance reduction, like denial or bolstering and spreading of the alternatives. So downplay one value, push up the other value, and make your life stress-free.

But some ideological positions at some points in history require more tolerance for dissonance and some people are more inclined to fill those roles.

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TETLOCK: We’re not talking about huge effect sizes here or integrative complexity. I think that fluid intelligence is probably a more powerful predictor. A combination of fluid intelligence and integrative complexity, I think, does boost up forecasting accuracy, yes.

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TETLOCK: Well, that is supposed to be the division of labor here. I think one reason they may be interested in forecasting tournaments is because forecasting tournaments incentivize people to do one thing and only one thing, and that’s accuracy. You don’t get points for skewing your judgements toward your favorite cause. You take a hit in the long term by doing that.

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TETLOCK: Whoa, boy! [laughs] Well, there’s a long history to efforts to reform the CIA. It was founded in 1947, and there have been various efforts since then. People have been unhappy with the CIA for many reasons over time, Vietnam being a big one. But there are lots of other reasons that people have expressed unhappiness. Both liberals and conservatives have, at various points, been unhappy with the performance of the intelligence community.

In 2001, it came to a crisis point, I think, and then there was a commission to reform intelligence analysis. After the WMD fiasco in Iraq, the pressure grew even more. One reason why we’re even talking right now today, is that the intelligence community was forced, essentially by the recommendations of the reform commission, to take keeping score more seriously, to take training for accuracy and monitoring of accuracy more seriously.

That’s when they created the Intelligence Advanced Research Projects Activity, which is the R&D branch housed within the Office of the Director of National Intelligence. Its job is to support innovative research that will improve the quality of intelligence analysis. That can be defined in various ways, but accuracy is certainly one very important component of that. But it’s not accuracy with a liberal skew or a conservative skew. It’s supposed to be just plain, “just the facts, ma’am” accuracy.

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TETLOCK: That’s a very difficult question because of the tail risk aspect to it. If you look at the number of people who’ve died from terrorism versus other causes, it would seem that the amount of money we spend on suppressing terrorism would be disproportionate. But the tail risk complicates that a lot.

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COWEN: On historical counterfactuals, by what year do you think the ascent of the West was more or less inevitable?

TETLOCK: Well, I have inside information here. We did a survey of some very prominent historians. We reported it in that book, Unmaking the West. We put a part of it anyway, also in an article in American Political Science Review. If you looked at just the unweighted average of judgments from the median, I think was probably around 1730, 1740.

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TETLOCK: That’s a good example actually, of why I’m not a superforecaster. I don’t play Civilization V. But Civilization V was one of the simulations that IARPA chose to feature in its counterfactual forecasting tournaments under the rubric of FOCUS. And if any of your listeners are interested in signing up to be forecasters for FOCUS. We still have one more round, round 5, and we will be recruiting people.

But again, you see, it reflects my temperament. I don’t have the patience for a game like Civ V. Forecasting is inevitably a mixture of fluid and crystallized intelligence, and you have to invest a lot of energy into mastering a game like Civilization V. And I suppose, as people get older, they may become less likely to make those kinds of cognitive investments. It becomes more and more essential as I get older, I think, to focus on the things where I have a real comparative advantage.

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COWEN: The best chess players are all young, right? This we know. There’s clear data.

TETLOCK: Well, yeah. It’s interesting the domains in which child prodigies emerge: music and chess and math.

     ••••   •••   ••••

But you can’t rerun history. So that makes counterfactuals a place where ideologues can retreat. They can make up the data, make up whatever facts they want to justify pretty much whatever. No matter how bad the war in Iraq went, you can always argue that things would have been worse if Saddam Hussein had remained in power. So you have these factual and counterfactual reference points that people use in debates implicitly to make rhetorical points.

Part of what attracted to me to counterfactuals was (A) how important they are in drawing any lessons from history, (B) how important they are in policy arguments, and © how unresolvable they are.

One of the things we’re hoping to do in the FOCUS program is to develop some objective metrics for identifying people and methods of generating probabilities that produce superior counterfactual forecasts in simulated worlds in which you can rerun history and assess what the probability distributions of possible wars are. Well, it turns out, you get World War I 37 percent of the time even if you undo the assassination of the archduke. And you get something like World War II . . . You see where we’re going.

So we’re hoping that one result of FOCUS will be to help us identify people and methods that generate superior counterfactual forecasts in domains where there is a ground truth. The next task will be to connect superior performance in simulated worlds to superior performance in the actual world.

And this is where things get tricky, of course, because in the actual world, we don’t have the ground truth. So if I ask you a counterfactual question of the form, if NATO hadn’t expanded eastward as far as it did in 2004 under the Baltics, NATO-Russia relations would be considerably friendlier than they are now.

     ••••   •••   ••••

TETLOCK: But you can measure what people’s beliefs are in the counterfactual. And then you can measure people’s beliefs about conditional forecasts that are logically connected to the counterfactuals in a kind of a Bayesean entrance network. If I know that you think that the Russians would be every bit as nasty and snarly, even if we hadn’t moved into the Baltic — they might have even been nastier — it’s probably a fair bet that you’re also likely to think it’s a good idea to increase arm sales to the Ukraine, ratchet up sanctions on Putin’s cronies, and so forth.

So we can identify the counterfactual belief correlates to more or less accurate conditional forecasting. And in that sense, you can indirectly validate or invalidate, you can render more or less plausible certain counterfactual beliefs. That’s the longer-term objective of this research program. We’re not playing Civilization V for the sake of getting better at Civilization V. The ultimate goal is to link the sophistication of counterfactual reasoning about the past to the subtlety in the accuracy of conditional forecast going into the future.

Another thing you should observe by the way — if people are becoming better counterfactual reasoners, you should observe less ideological polarization in their counterfactual beliefs. The counterfactual belief should become as ideologically depolarized as conditional forecasts are.

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TETLOCK: Right, the question is, are they doing better because they have better social networks? Or they are doing better, they have better judgment?

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TETLOCK: It ties into accountability. Accountability to conflicting audiences and value pluralism. You have a richer internal dialogue. You learn to balance conflicting perspectives more. You have to be a better perspective taker. Perspective taking’s a very important part of superforecasting, too.

     ••••   •••   ••••

Amy Gutmann, PIK, Penn Integrates Knowledge kind of program — you don’t see too many of them yet. It runs against the grain. I’m not even sure it’ll survive at Penn beyond Amy. The natural tendency will be for departments to want to claw back the resources.

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TETLOCK: Kind of obvious things, like read a little bit more outside your field. If you’re a liberal, read the Wall Street Journal. If you’re a conservative, read the New York Times. Expose yourself to dissonant points of view. Try to cultivate some interests outside your field, and try to connect them together. I think there’s an optimal distance.

For history, for example — sounds quite different from what I did. I started as an experimental psychologist, and history looks very different, but they can be connected because historical judgment is something that psychologists study to some degree. Psychologists are interested in hindsight and counterfactuals and so forth. So you can link the two.

I think there’s an optimal distance, and when you go foraging as a fox, you probably don’t want to forage way, way far away. You want to forage far enough away that it’ll be stimulating but still possible to reconnect.

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TETLOCK: Gosh, that’s a hard one. Danny Kahneman, I think, coined the term when he was dealing with his various critics over time. My wife, Barb, actually was involved in an adversarial collaboration between the Kahneman camp and the Gigerenzer camp on the conjunction fallacy. It took at least two or three years of her life.

It’s very hard to get people to . . . Well, lots of us, in principle, are Popperians. We believe in stating our beliefs as falsifiable hypotheses. Most of us believe our beliefs are probabilistic, so we’re somewhat Bayesian. But that’s lip service. There’s what we believe. There’s an epistemological formal self-concept, which is kind of noble, falsificationist, and probabilistic. And then there’s how we actually behave when our egos are at stake in particular controversies.

And those things are quite different. I think Danny Kahneman who’s not known as an optimist, proposed it. It sounds like an optimistic idea, but I think he’s not all that optimistic about what it can achieve on close inspection. My efforts at adversarial collaboration have not been all that successful. I’d like to jumpstart a few of them again, but it’s very hard to find the right dance partners.

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COWEN: Let’s try a question from the realm of the everyday and the mundane. If I go around, and I look at Mexican restaurants, I’m very good at predicting which ones have excellent tacos. What are you good at predicting?

TETLOCK: What am I good at predicting? I think I was pretty good at anticipating the fragility of a lot of microsocial science knowledge prior to the replication crisis erupting.

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TETLOCK: Well, I think the next project is the one that I mentioned earlier. It’s linking historical counterfactual reasoning with conditional forecasting. I think it’ll be the second phase of the FOCUS research tournaments. I think that counterfactual reasoning has, for too long, been the last refuge of ideological scoundrels. Insofar as we can improve the standards of evidence of proof in judging counterfactual claims as well as conditional forecasts and linking the two, I think there’s a potential for improving the quality of debates among interested parties.

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TETLOCK: Oh, to be very cautious because we know we’re running against the grain. We’re running against a psychological grain. We’re running against human nature. We’re running against a sociological grain.

COWEN: But see, enlightenment is stable you tell us, right? If it keeps on cumulating and growing, your influence should be enormous given your other presuppositions.

TETLOCK: Well, there’s a lot of cognitive resistance to treating one’s beliefs as falsifiable, probabilistic propositions. People naturally gravitate toward thinking of their beliefs as ego-defining, quasi-sacred possessions. That’s one major obstacle.

Then you have the existing status hierarchies. You have subject matter experts who are entrenched, have influence. Why would they want to participate in exercises in which the best possible outcome is a tie? They reaffirm that they deserve the status that they already have.

So psychological, sociological resistance. It’s interesting, sociologists and economists have different reactions to forecasting tournaments. Sociologists — they react, “Why would anyone be naive enough to think that anyone would want to have a forecasting tournament in their organization?” They’re status disruptive, right?

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source:
https://www.textise.net/showText.aspx?strURL=https://medium.com/conversations-with-tyler/philip-tetlock-tyler-cowen-forecasting-sociology-30401464b6d9
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[personal note]
How I got here:

youtube.com

one of youtube auto recommend algorithm (they are no longer label as "recommend") the youtube video just show up on your queue (feed).

so I watched Eric Schmidt being interviewed by Reid Hoffman, and one of the thing that was mentioned was the book, How Google Works, by Eric Schmidt

I got the book How Google Works from the local library; fortunately, the book has been out long enough that the library got a copy and no one is using it.

I read it and notice the name, Philip Tetlock; somehow the name feels familiar.

I bing search Philip Tetlock.

Philip E. Tetlock on forecasting and foraging as a fox (Ep. 93)
Apr 22, 2020
https://medium.com/conversations-with-tyler/philip-tetlock-tyler-cowen-forecasting-sociology-30401464b6d9
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[skip]

In a 1985 essay, Tetlock proposed that accountability is a key concept for linking the individual levels of analysis to the social-system levels of analysis.[12] Accountability binds people to collectivities by specifying who must answer to whom, for what, and under what ground rules.[12][13] In his earlier work in this area, he showed that some forms of accountability can make humans more thoughtful and constructively self-critical (reducing the likelihood of biases or errors), whereas other forms of accountability can make us more rigid and defensive (mobilizing mental effort to defend previous positions and to criticize critics).[14] In a 2009 essay, Tetlock argues that much is still unknown about how psychologically deep the effects of accountability run—for instance, whether it is or is not possible to check automatic or implicit association-based biases,[15] a topic with legal implications for companies in employment discrimination class actions.[16]

In addition to his work on the bias-attenuating versus bias-amplifying effects of accountability, Tetlock has explored the political dimensions of accountability. When, for instance, do liberals and conservatives diverge in the preferences for "process accountability" that holds people responsible for respecting rules versus "outcome accountability" that holds people accountable for bottom-line results?[17][18]

Taboo cognition and sacred values

Rather, humans prefer to believe that they have sacred values that provide firm foundations for their moral-political opinions. People can become very punitive "intuitive prosecutors" when they feel sacred values have been seriously violated, going well beyond the range of socially acceptable forms of punishment when given chances to do so covertly.[28]

Experimental political philosophy

Tetlock argues it is virtually impossible to disentangle the factual assumptions that people are making about human beings from the value judgments people are making about end-state goals, such as equality and efficiency.[43][44][45][46][47] Hypothetical society studies make it possible for social scientists to disentangle these otherwise hopelessly confounded influences on public policy preferences.

source:
       https://en.wikipedia.org/wiki/Philip_E._Tetlock
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If you are interested in works like Philip Tetlock, you should look into Bruce Bueno de Mesquita. He also has a wikipedia page.

https://en.wikipedia.org/wiki/Bruce_Bueno_de_Mesquita

Here are some URLs about his works:
(as of Fri, Jun 18, 2021 the pages are [UP])

https://www.nytimes.com/2009/08/16/magazine/16Bruce-t.html

https://www.npr.org/templates/story/story.php?storyId=120201554

https://web.archive.org/web/20150930072215/http://magazine.good.is/articles/the-new-nostradamus

If you watch Bueno de Mesquita video below, he will tell you, that his method is based on game theory, that the method can be taught ~~to anybody~~, that it is based on negotiation and manipulation (to influence the decision-maker), that it only works on people who are rational and are self-interested. A two-year-old is not rational.

1:42:42
The Predictioneer's Game
42:47 (start)
https://youtu.be/XfE0ih-6fi8?t=2567
https://youtu.be/XfE0ih-6fi8?t=2567
44:00 (stop)
https://www.youtube.com/watch?v=XfE0ih-6fi8
https://www.youtube.com/watch?v=XfE0ih-6fi8
NYUAD Institute
Published on Sep 15, 2015
The Predictioneer's Game
December 9, 2009
Bruce Bueno de Mesquita will discuss how applied game theory can be used to anticipate policy choices whether in business or in government.

The Predictioneer's Game
https://slideplayer.com/slide/4437069/

   ____________________________________

[backgrounder: Bruce Bueno de Mesquita]

Bruce Bueno de Mesquita: Stories of Research to Reality
https://www.youtube.com/watch?v=jUPEUZy3UX8
https://www.youtube.com/watch?v=jUPEUZy3UX8
17:34
sage publishing
May 14, 2015

https://www.files.ethz.ch/isn/155084/Theory%20Talk31_BuenodeMesquita.pdf
     Course taught by Donald Stokes. I read and prepared an oral presentation on William Riker’s Theory of Political Coalitions for that course and discovered that the strategic principle in that book was incorrectly derived. This was my first exposure to formal modeling and the first time that I saw how rigorous logic (a formal model in this case) could be used to conclude that a claim was false, not as a matter of opinion but as a matter of straightforward logic.

Finally, the chair of my Ph.D. committee ─ Richard L. Park ─ had a deep influence on my thinking and my approach to teaching. He demonstrated a tolerance for a perspective different from his own that I found inspiring. Dick Park was one of the founders of modern South Asian studies. He found my rational choice and quantitative approach to Indian politics rather different from his own thinking but he encourage me, supported me, and nurtured the confidence that allowed me to go forward despite resistance from many other leading lights in the South Asia research community at the time. One of my most satisfying academic experiences is having had the opportunity to co-author a book with him (India's Political system, 2nd edition) just before his untimely death.
   ____________________________________

Bueno de Mesquita

“His ability to pick up on body language, to pick up on vocal intonation, to remember what people said and challenge them in nonthreatening ways — he’s a master at it,” says Rose McDermott, a political-science professor at Brown who has watched Bueno de Mesquita conduct interviews. She says she thinks his emotional intelligence, along with his ability to listen, is his true gift, not his mathematical smarts. “The thing is, he doesn’t think that’s his gift,” McDermott says. “He thinks it’s the model. I think the model is, I’m sure, brilliant. But lots of other people are good at math. His gift is in interviewing. I’ve said that flat out to him, and he’s said, ‘Well, anyone can do interviews.’ But they can’t.”

In his 1996 book, “Red Flag Over Hong Kong,” he predicted that the press in Hong Kong “will become largely a tool of the state” — a highly debatable claim today. (In 2006, Reporters Without Borders noted concerns about self-censorship but said that “journalists remain free in Hong Kong.”)

source:
       https://www.nytimes.com/2009/08/16/magazine/16Bruce-t.html
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I have an affinity for Russell Ackoff's writing, in general. The way he explains things make sense for me. It seems clear. Go down to the last paragraphy of the following TEXT and read it. That is game theory. Game theory is beyond forecasting. You are engaging in scenario planning - "thought experiment" - making moves, wait for a response, making counters moves, wait for a response, and keep at it (the process) until you achieve your goal or get close to your goal as much as possible.

Russell L. Ackoff, Ackoff's best, 1999                                      [ ]

counter measure
pp.74-75
There is a technique developed by the military either to beat a system or to design a system that would be difficult to beat. It consists of forming a "countermeasure team" to represent those who want to beat the system--the enemy, in the military case. This team is given all the information available to the good guys, including tentative designs of the system involved. The task of the counter-measure team is to develop ways of beating the system. When it has determined how to do so, the good guys are given this information to use in redesigning their system. After they have done so, the counter-measure team goes at it again. This process continue until the enemy either cannot find an effective counter-measure, or the time required to do so is long enough to justify introduction of the system. The idea behind this procedure is that when a team of intelligent people who have perfect information about a system--something real enemies seldom have--can no longer beat that system quickly enough to make it valueless, that system can be built with little chance of its being beaten.
     Note that such a procedure does not reduce the number of choices available to the enemy, but does reduce their effectiveness. For example, a major national accounting firm once employed the research group of which I was a part to find effective ways of embezzling money from banks so that it could improve its auditing procedures. We found better ways of embezzling than the auditors had dreamed of, but we were able to design new auditing procedures that made our discoveries ineffective. Much to our amazement, the accounting-auditing firm did not adopt these procedures, which were approved by the profession, they could not be held legally responsible for thefts they failed to detect. However, if they departed from standardized procedures and failed to detect an embezzlement, they would be legally liable for it. They chose to minimize their risks rather than those of their clients.
     In another case, a company that wanted to buy a factory from one of its minor competitors that was going out of business knew that its major competitor was anxious to prevent such a purchase. Acquisition of that factory would increase the competitiveness of the purchaser in a region in which the principal competitor had an advantage. The would-be purchaser employed the research group of which I was a part to act as a counter-measure team while an internal group developed a strategy for obtaining the factory. It took four iterations of the internal group developing an offer and our counter-measure team developing a way of obstructing it before we could no longer find a way of preventing the purchase. Subsequently, when the offer to purchase was made, the principal competitor followed exactly the steps the counter-measure team had taken. The would-be purchaser got the plant.
     A smart system can use knowlege of how it can be beat to redesign itself to reduce or eliminate that kind of beating.

   (Ackoff's best : his classic writings on management, Russell L. Ackoff., © 1999, pp.74-75.)
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We can trace Game theory all the way back to War game, instead of Chess pieces you have military units. According to the authoritative wikipedia page, wargame (~~I say this is~~ the early form of Game theory) were invented and first used by the Prussians. It would be interesting to know if the Chinese, Korean, Japanese and Indian have some thing similar. They have ....

5. “Wargames” were invented and first used by the Prussians.

A wargame is, esentially, a tool for military officers and soldiers to learn some basic principles about warfare, without having to go to a real war first. Its design is mostly derived from strategy games like chess. Its invention goes back to 1780 when a Prussian named Johann Christian Ludwig Hellwig designed the first wargame. Even though it wasn’t seen as an important invention at first, the approach to wargames changed drastically after the Napoleonic Wars. Prussian General Staff ,in early to mid 19th century, used wargames regularly to drill and simulate about wars to come. Helmuth von Moltke, the Prussian Chief of Staff, was a keen supporter of the encouragement of wargames among military officers. He saw them as a key factor for troops to gain war experience and use this experience in the battlefield when time comes. Wargames rapidly spread into other countries after Prussia’s massive victories against Austria and France in 19th century.

source:
       https://prussianhistory.com/5-interesting-facts-about-prussian-army/
       https://en.wikipedia.org/wiki/Wargame
       https://www.oldest.org/entertainment/board-games/
<---------------------------------------------------------------------------->

Paul Saffo is another guy. His thing is technology forecasting. He also has a wikipedia page.

https://en.wikipedia.org/wiki/Paul_Saffo

He wrote an article, Six Rules for Effective Forecasting, published in HBR.
Here is the URLs:

https://hbr.org/2007/07/six-rules-for-effective-forecasting

you can use your 2 free articles for the month to read it or look for it (Six Rules for Effective Forecasting) some where else. A pdf version of (Six Rules for Effective Forecasting) should be around somewhere on the InterNet. I read the article. It was helpful, useful, and gave me mental tools on how to think about forecasting.

The following is my note summary from his youtube video, Paul Saffo: Never Mistake a Clear View for a Short Distance:

(1) Large mega-trend is usually invisible, too big to see the big picture; for example, generally, there is a 40 to 60 years cycle of demographic shift in aging population. It is difficult to see the forest, if you are a bird, living in a tree.

(2) very tiny signal at the inflection point is also usually invisible, it is too small to notice, you would need instrumentation, tools, measurement, record keeping, plotting the data points on a graph paper, to look at the pattern over time to tease out the inflection point. And even if we see the inflection point, the importance of the inflection point might not be obvious until nearly five to seven years later. Like for example, the form (or type) of cancer for a person of interest, the inflection point might not show up and become detectable until nearly 30 years later; at the opposite end, the inflection point of autism spectrum disorder, in young children, shows up rather early; notice that I am unable to address the root cause of the inflection point. The ability to look at the data point as one whole pattern over time can be useful. Inflection point is also refer to as tipping point, threshold, and other labels.

(3) constant is invisible, constant is notice only if it changes, for example in the Western developed countries like the United States, including Japan, electrical power is assume to be there 24/7; electricity is constant. Another example, garbage collection happens once every week; this is constant and, mostly taken for granted.

“constant is notice only if it changes, constant is invisible”

Paul Saffo: Never Mistake a Clear View for a Short Distance
https://www.youtube.com/watch?v=u8NrFDocBF0
https://www.youtube.com/watch?v=u8NrFDocBF0
Published on Dec 6, 2013
(20:10)
san andreas fault, 2-inches of movement per year, as pressure built up, an earthquake is inevitable, the question is when;
the pressure are building over time;
things go non-linear at the inflection point;
identifying the inflection;
innovation is generally good news, it can be disruptive to business model;
most ideas take 20 year to become an overnight success;
1947-1968: 21 years;
look for underlying driving forces;
3 kind of driving forces: constants, cycles (repeated pattern), and novelties;
constant is notice only if it changes, constant is invisible;
sun spot cycle;
Kondratiev wave, about a 50 year cycle,
prosperity → recession → depression → improvement (Kondratiev wave, about a 50 year cycle);
2 kinds of novelties: novelty so small that you overlook them, so big;
example of so big: humanity became a majority urban dweller;
a single drive force rarely determine outcome;
cherish failures, especially someone elses;
alpha is being the first to identify inflection point and turn it into strategy;
embrace uncertainty;
we like [the] things [that we like and to our benefit] to stay the same.
<---------------------------------------------------------------------------->

http://longnow.org/seminars/02008/jan/11/embracing-uncertainty-the-secret-to-effective-forecasting/

<---------------------------------------------------------------------------->
If you were to read the text below, you might be wonder[ing] what does (gestation and maturation delay) has to do with forecasting and prediction. (gestation and maturation delay) is the side door or the back door into forecasting and prediction. When a new mother is pregnant, it takes about 9 months for the baby to mature in the mother's womb, and for the new mom to give birth. If this is a first time Mom, usually it takes the baby a bit longer to mature, so the baby is in the womb a bit longer before the Mother gives birth. 9 months is the forecast or you can say, a prediction. Not that it is going be exactly 9 months from the period when the egg and sperm joined together, and the modified egg attached itself to the lining of the uterus, or that it is going to be less than 9 months, or more than 9 months. Nine (9) months is a forecast.   Nine (9) months is a prediction, because all other human mothers gestate the baby for about 9 months.

Donella H. Meadows, Edited by Diana Wright, Thinking in systems             [ ]

p.104
Here are just a few of the delays we have found important in include in various models we have made:

    • The delay between catching an infectious disease and getting sick enough to be diagnosed──days to years, depending on the disease.
    • The delay between pollution emission and the diffusion or percolation or concentration of the pollutant in the ecosystem to the point at which it does harm.
    • The gestation and maturation delay in building up breeding populations of animals or plants, causing the characteristic oscillations of commodity prices: 4-year cycles for pigs, 7 years for cows, 11 years for cocoa trees.8
    • The delay in changing the social norms for desirable family size──at least one generation.
    • The delay in retooling a production stream and the delay in turning over a capital stock. It takes 3 to 8 years to design a new car and bring it to the market. That model may have 5 years of life on the new-car market. Cars stay on the road an average of 10 to 15 years.

     (Thinking in systems : a primer, Donella H. Meadows, Edited by Diana Wright, sustainability institute, 2008, QA 402 .M425 2008, )

    • One notable challenge for the hardware tower is that it takes four to five years [4 to 5 years] to design and build chips and to port software to evaluate them. {“A view of the parallel computing landscape” by Krste Asanovic, Rastislav Bodík, James Demmel, Tony Keaveny, Kurt Keutzer, John Kubiatowicz, Nelson Morgan, David Patterson, Koushik Sen, John Wawrzynek, David Wessel, and Katherine Yelick in the Communications of the ACM, Volume 52, Issue 10, pages 56-67, October 2009.}

    • A second challenge is that two critical pieces of system software—compilers and operating systems—have grown large and unwieldy and hence resistant to change. One estimate is that it takes a decade [10-years] for a new compiler optimization to become part of production compilers. {“A view of the parallel computing landscape” by Krste Asanovic, Rastislav Bodík, James Demmel, Tony Keaveny, Kurt Keutzer, John Kubiatowicz, Nelson Morgan, David Patterson, Koushik Sen, John Wawrzynek, David Wessel, and Katherine Yelick in the Communications of the ACM, Volume 52, Issue 10, pages 56-67, October 2009.}

Thinking in systems
a primer
Donella H. Meadows
Edited by Diana Wright
sustainability institute
2008
<---------------------------------------------------------------------------->

since we are on the subject of forecast. The following is a real life example ~~of one~~. Technically not a forecast, but some thing close ~~to one~~.

Marvin Centron with Alicia Pagano and Otis Port, The future of American business., 1985

pp.183-184
The Taipei government has seen the handwriting on the wall for a decade, but it has had scant success in persuading industrialists to invest in newer markets. In the early seventies the government decided to start the ball rolling and founded the Electronics Research and Service Organization to spearhead the drive into semiconductors and electronics. When that example didn't catch on, the government intervened again in 1979, setting up the Institute for Information Technology, a software R&D center, and underwriting part of United Microelectronics corporation, a maker of integrated circuits. But neither of these initiatives has been a barn-burner, either. Taiwan's business leaders are typically in their sixties and simply scared of the risks entailed in high-tech diversification. Most of the $290 million of computer equipment that Taiwan exported in 1983 came from foreign-owned assembly plants.
So now Taiwan is dangling venture capital in front of expatriate Chinese who came to the United States for a degree in electrical engineering or computer science and stayed on to work in Silicon Valley or along Route 128. Three Silicon Valley companies headed by Chinese managers have risen to the bait and are building chip making plants in Taiwan. Government officials hope they will be the nucleus of future growth as younger, more adventurous managers take over the reins of business in the 1990s. Unfortunately, the move into microchips comes too little, too late.

(The future of American business./ Marvin Centron with Alicia Pagano and Otis Port, 1. united states──industries──forecasting., 2. economic forecasting──united states., 3. united states──economic conditions──1981-, HC106.8.C45 1985, 338.5'443'0973, ISBN 0-07-010349-6, 1985, )
   ____________________________________

If you go on youtube.com, and search for TSMC, Taiwan Semiconductor Manufacturing Company, and any other terms related to this, and watch the video(s), one of the take away (conclusion) you might come to is that, it is hard and tough to be successful in the business (industry) of semiconductor manufacturing. TSMC (Taiwan Semiconductor Manufacturing Company) was able to put the essential elements together (initial setup), recruited top people, mentored and trained them, worked like hell, and so far, managed to make a success out of a very high cost, bleeding edge, and capital intensive business. My focus was on Morris Chang, the founder of TSMC, Taiwan Semiconductor Manufacturing Company.

TSMC (Taiwan Semiconductor Manufacturing Company)

• build a platform (pure play semiconductor manufacturing, inflection zone, re use ability (IP), technological development within the industry; development of communication protocol and, evolution of standard & design rules between the upstream and downstream supply-chain (functional role), specialization and knowledge domain, veterans with 25 years experience, they have been to the top and seen the roadmap, unique)

50% investment from the Government
the rest from other sources
get one multination as an anchor investor
Intel, TI, Toshiba, Hitashi, NEC, Phillips, Sony
Phillips invested in 28%
48% from the Taiwan government
the remaining from 12 or 13 companies
one big investor, a 5% investor, I went there 3 times, three separate times
during dinner, they keep on quizing me, I didn't even have time to eat,
the government has been good to you for the last 20 years, you better do something;

1985 to 2005: rise of fabless companies in the U.S.
1985 to 2005: Japanese companies market share - in the semiconductor production (manufacturing, chips foundry) - has dropped 20 point
1985 to 2005: fabless companies went from zero to 20%

English literature, Chinese literature, classical music
the two books next to my bed, to tired to read anything serious,
it's Chinese, Red Chamber Dream, Chinese classic
and the other, Shakespeares's plays, I am interested in Shakespeares's tragedies
a lot of meaning, lots of life lesson in Shakespeares's plays
I am interested in history in general
I am interested in biography
benefits from these outside reading
I am a student of the second world war, the main battle
I often compared the competitive battle that TSMC go through to battle in the 2nd world war, Battle of Stalingrad
Stalingrad was the one I was very interested in,
the Midway, the naval battle, the Japan commander could not make up his mind, whether to keep bomber on deck, or the fighter on deck; he changed it, that cost him the battle; indecision is very bad ..., and in company;

learn and think, both are important
Confucius's statement: learning and think are both important, if you just learn and don't think, then you quickly become lost; if you just think and don't learn, then you quickly run out of material to think.

he said, started a Semiconductor company, he didn't know the business, so he was thinking an IBM, a semiconductor company
I came up with TSMC business model, game plan,
he had confidence in me, because he knew what I had done at TI (Texas Instrument), so he trust me
even though he didn't know what a foundry was, he trusts me,
first you had to start with the insight that the Taiwan government back, 10 years early, in 1975, when they started this seed group, development group in ERSO - an R&D institute responsible for supervising the development of the semiconductor industry, a branch off from [[ITRI]]. ([ERSO] process was two generation behind), it was pretty big money for them, at that time, even just the RCA contract, the RCA technology transfer contract was 4 or 5 million dollars, which was big money for the Taiwan government at the time, and then sustaining their own development activity, that probably cost a few million dollar a year, and that was pretty big money for Taiwan government, but they did it,
and then, of course, when the time came, when I started to setup TSMC, the second minister and the premier said, they would support 50%, they didn't know it would be 50% of 220 million ([ table stake ]), they thought it was a much smaller number than that, but they swallow that also, so so

工業技術硏究院 (ITRI - Industrial Technology Research Institute)

1985   already started to raise money
1987   founded TSMC

they're very happy now

maybe the decrease in cost, at the same rate, not as it happened before

there is a tendency for fabless company, consolidating, in 5 years, 10 years,

I had achieve a measure of financial independent, financial independent means I didn't need a job, I could live on the interest rate,

the merchant of Venice

the core of our problem: what we can sell, we cannot make, and what we make, we cannot sell;

source:

* {{YouTube|id=cwlsNJuCgQI|title=Morris Chang: an emphasis on excellence}} • stanford eCorner • 54:10 • May 14, 2014

https://www.youtube.com/watch?v=cwlsNJuCgQI
https://www.youtube.com/watch?v=cwlsNJuCgQI

        https://en.wikipedia.org/wiki/Industrial_Technology_Research_Institute
   ____________________________________

The R&D system for industrial development in Taiwan
Tain-Sue Jan*, Yijen Chen

National Chiao Tung University, Department of Management Science, 1001 Ta Hsueh Road, Hsinchu 30050, Taiwan

   .... ... ....

The Taiwanese semiconductor industry began in the mid-1960s, when foreign enterprises used the low labor costs of Taiwan to establish their encapsulation plants. Crucial to the true development of the Taiwanese semiconductor industry was the intervention of the government in 1974 in establishing ERSO (Electronics Research and Service Organization) within ITRI, a government-supported R&D institute responsible for supervising the development of the semiconductor industry, and for providing relevant key technologies and human resources. ERSO was crucial in establishing the Taiwanese electronics industry through technology R&D, technology transfers and spin-offs. In 1976, ERSO introduced the 7 Am CMOS (Complementary Metal Oxide on Silicon) IC (integrated circuit) design and production process technology from RCA of the United States. Then in 1977, ERSO established the first IC pilot plant in Taiwan. Through absorption, utilization, and self-development, ERSO converted IC production process technology into merchandise and conducted apilot run program. To avoid emphasizing production capacity at the expense of R&D at ITRI, ITRI considered transferring the merchandising to the private sector. At this stage Taiwan had no semiconductor industry, and the private sector lacked the confidence to establish IC firms. Therefore, ERSO decided to transfer technology and human resources to ITRI-derived companies. In 1979, UMC, the first IC company in Taiwan was born.

   In 1982, ITRI initiated research on and development of Very Large Scale Integrated (VLSI) semiconductor production processes, and upgraded the 7 Am process to 2 Am technology. In 1987, R&D by ITRI resulted in the spinning off of Taiwan’s first 6-in. wafer fabrication foundry, TSMC, which was also the first pure semiconductor foundry in the world. ITRI transferred some 100 of the engineers it had trained to this spin-off and used its pilot plant as the manufacturing site for the fledgling TSMC. TSMC ran successfully right from the earliest stages of its development. This smooth running can be attributed to the transfer of personnel and equipment. In mid-1988, the production technology of TSMC was just nine months behind that of TI and Intel. The advanced semiconductor production process technology and the operating model of the semiconductor foundry led to the establishment of the semiconductor encapsulation, testing, and design industries as well as the consolidation of the Taiwanese semiconductor manufacturing industry. Taiwan did not have a complete system for the vertical division of the semiconductor industry by 1988, although one was gradually appearing [20]. To establish a complete supply chain for the semiconductor industry and to prevent brain drain, beginning in 1989 ITRI organized the transfer of people, technologies and equipment to the first photomask company in Taiwan, TMC. Since then Taiwan has had a complete semiconductor industrial system, including sub-industries in IC design, photomask manufacture, wafer fabrication, IC packaging and testing, with each sub-industry specializing in its own area of expertise [20].

   .... ... ....

source:
        http://entofa.net/wp-content/uploads/2019/11/The-RD-system-for-industrial-development-in-Taiwan.pdf
   ____________________________________

It grew by helping fabless customers like Broadcom, Nvidia, and others grow. It invested immense amounts of money in R&D to investigate the latest techniques and in servicing its customers. Being able to work with all different types of customers helped it see the most common types of challenges that customers would face.

source:
        https://asianometry.substack.com/p/tsmc-briefly-explained
   ____________________________________

Simon Sinek in conversation with Big Change - Education as an Infinite Game
         https://www.youtube.com/watch?v=Ze-pXbrWLkc
         https://www.youtube.com/watch?v=Ze-pXbrWLkc
         youtube.com
         27:04
         Big Change
         Jun 10, 2019
   ____________________________________

The Tainan Science Park, Taiwan (running out of space)
a second science park was needed
open in year 2000, Fab-6, less than two year later
Fab-6 would cost over $2 billion dollar, six stories, clean room
at its unveiling, it would be the largest Fab in the world, and
the other other Fab were not small either
expansion of Fab-14, delay, concern with vibration from nearby high-speed train
expansion to Fab-14 was completed four years later
Fab-15
Fab-18 Giga fab, latest n5p process, 2018, 2019, cost $17 billion dollar to build, more than the per unit cost of Gerald Ford aircraft carrier (cost $13 billion dollar),
Fab-6 employed, 2,600 people, most of them, highly educated engineers
the knock on effects of tech wealth, san francisco and silicon valley, I recognize alot of the symptoms

source:
        youtube.com
        How TSMC came to Taiwan's South
        10:18
        Sep 7, 2020
        https://www.youtube.com/watch?v=x02pq2HeKQY
        https://www.youtube.com/watch?v=x02pq2HeKQY

        https://en.wikipedia.org/wiki/TSMC
   ____________________________________

The narrator/writer on the youtube channel Asianometry mentioned that TSMC (Taiwan Semiconductor Manufacturing Company) giga Fab-18 cost around $17 billion dollar, and the aircraft carrier USS Gerald R. Ford's total cost of development $12.8 billion + $4.7 billion R & D (estimated). It is a nice comparison.

USS Gerald R. Ford – The Largest & Most Expensive Ship Ever Built
Our Bureau
March 18, 2021
March 18, 2021

The USS Gerald R. Ford is the U.S. Navy’s newest, most expensive, and largest aircraft carrier – in fact, it’s the largest and most expensive aircraft carrier in the world.

Commissioned in July 2017, the USS Ford is the first of the Ford-class carriers, which are more technologically advanced than current Nimitz-class carriers.

USS Gerald R. Ford Facts

This ships’ total cost of development is a whopping $12.8 billion + $4.7 billion R & D (estimated).

The Ford-class of the aircraft carriers is intended to relieve stress and over deployment within the US Navy. Currently, it operates 10 carriers but wants an additional carrier to take the pressure off of the rest of the fleet.

The ship features a host of improvements over the Nimitz-class carrier. Ford-class carriers’ power generation capacity will be three times that of the older carrier classes.

What it can carry

Full load displacement of 100,000t makes USS Gerald R. Ford the world’s biggest aircraft carrier, and also the most expensive aircraft carrier in the world.

The CVN-78 features a 78m-wide flight deck, equipped with an electromagnetic aircraft launch system and advanced arresting gear.

The carrier has the capacity to carry more than 75 aircraft and can accommodate 4,539 personnel including the ship’s company, air wing, and other support staff.

It can reach the speed of 30+ knots.

Gerald R Ford is powered by two A1B nuclear-reactors offering 250% more electrical capacity than the Nimitz Class.

The weaponry includes RIM-162 Evolved Sea Sparrow-missiles, Rolling Airframe-Missiles (RAMs), and Phalanx close-in weapon system (CIWS).

As of 2020, she is the world’s largest aircraft carrier, and the largest warship ever constructed in terms of displacement.

In the future, USS Ford will also field laser and directed-energy weapons like rail guns and missile interceptors.

source:
        https://www.textise.net/showText.aspx?strURL=https://usadefensenews.com/2021/03/18/uss-gerald-r-ford-the-largest-most-expensive-ship-ever-built/
<---------------------------------------------------------------------------->

Kevin Kelly, out of control, 1994

p.364
The simulation system was not classified; the results were published in the open literature.

p.364
U.S. Military Central Command in Florida
I find the predictive scenarios spooky, strange, and instructional rather than diabolical.

p.364
Wargaming Center, Maxwell Air Force Base, Alabama
Global Game room, Naval War College, Newport, Rhode Island
“sand box” table set-ups, Army's Combat Concepts Agency, Leavenworth, Kansas
TACWAR, JESS, RSAC, SAGA

p.364
Gary Ware, an officer at Central Command
a small cell of military futurists
The simulation was code-named Operation Internal Look.

p.365
TACWAR, the main computerized war-gaming simulator

p.365
Ware's simulation forecast a fairly brief 30-day war if anything this unlikely should occur.

p.365
At first, the upper echelons of the Pentagon had no idea they already owned a fully operational, data-saturated simulation of the war. Turn the key and it would run endless what-ifs of possible battles in that zone. When word of the prescient simulation surfaced, Ware came out smelling like roses. He [Gary Ware] admitted that “If we had to start from scratch at the time of the invasion we would have never caught up.”

p.365
In the future, standard army-issue preparedness may demand having a parallel universe of possible wars spinning in a box at the command center, ready to go.

p.365
By running those simulations in many directions the team quickly learned that airpower would be the decisive key in this war. Further refined iterations clearly showed the war gamers that if airpower was successful, the U.S. would be successful.

p.365
This confidence led to the heavy air compaign.

p.366
tomorrow will be mostly like today

p.367
Theodore Modis, 1992 book, Predictions
Invariants, Growth Curves, Cyclic Waves

p.367
cooking, traveling, cleaning
If new activities (say airplane flight instead of walking) are reformulated into elemental dimensions for analysis (how much time is spent in daily moving), the new behaviors often exhibit a continuous pattern with the odd that can be extrapolated (and predicted) into the future. Instead of walking a half hour to work, you now drive a half hour to work. In the future, you may fly a half hour to work.

p.367
Tracing an invariant optimization point can often alert us to a clean pocket of predictability.

p.367
Among them are a lifespan that can be plotted as an S-shaped curve: slow birth, steep growth, slow decline.

p.368
Modis is intrigued by the 56-years economic cycles discovered by economist N. D. Kondratieff.

   (Kevin Kelly, out of control, 1994, filename: ooc-mf.pdf )
   ____________________________________

Fernand Braudel [Civilisation matérielle, économie et capitalisme], 1992    [ ]

p.613
On the other hand, the graphs quite emphatically concur about the Kondratieff cycle which follows: it begins in 1791, peaks in 1812 and reaches its lowest point in 1851. [takes about 19 years to peak, and about 40 years reach the lowest point, 19 + 40 = 59 years Kondratieff cycle]
   We may conclude that the British industrial revolution experienced two movement, roughly between 1781 and 1815, a first and second wind so to speak, the first a rather difficult period, the second easier. In very broad terms, this was also the rhythm experienced by France and the rest of the continent.

    (The perspective of the world, 1992, 909.08 Braudel, )
    (Fernand Braudel [Civilisation matérielle, économie et capitalisme. English], civilization and capitalism, 15th - 18th century, volume III, the perspective of the world, translation from the French, by Siân Reynolds, 909.08 Braudel, [p.82, pp.86-87, p.613] )
   ____________________________________

Peter F. Drucker, Innovation and entrepreneurship, 1984

p.4
   The Russian economist Nikolai Kondratieff was executed on Stalin's orders in the mid-1930s because his econometric model predicted, accurately as it turned out, that collectivization of Russian agriculture would lead to a sharp decline in farm production. The “50-year Kondratieff cycle” was based on the inherent dynamics of technology. Every 40 years, so Kondratieff asserted, a long technological wave crests. For the last 20 years of this cycle, the growth industries of the last technological advance seem to be doing exceptional well. But what look like record profits are actually repayment of capital which is no longer needed in industries that have ceased to grow.

pp.4-5
This situation never lasts longer than 20 years, then there is a sudden crisis, usually signaled by some sort of panic. There follow 20 years of stagnation, during which the new, emerging technologies cannot generate enough jobs to make the economy itself grow again--and no one, least of all government, can do much about this.*

   *Kondratjeff's long-wave cycle was popularized in the West by the Austro-American economist Joseph Schumpeter, in his monumental look Business Cycles (1939). Kondratieff's best known, most serious, and most important disciple today--and also the most serious and most knowledgeable of the prophets of “long-term stagnation”--is the MIT scientist Jay Forrester.

p.5
   The industries that fueled the long economic expansion after World War II--automobiles, steel, rubber, electrical apparatus, consumer electronics, telephone, but also petroleum†--perfectly fit the Kondratieff cycle.

   † Which, contrary to common belief, was the first one to start declining. In fact, petroleum ceased to be a growth industry around 1950. Since then the incremental unit of petroleum needed for an additional unit of output, whether in manufacturing, in transportation, or in heating and air conditioning, has been falling--slowly at first but rapidly since 1973.

   (Peter F. Drucker, Innovation and entrepreneurship : practice and principles, Claremont, California, Christmas 1984, )
   ____________________________________
Theodore Modis., Prediction : society's telltale signature reveals the past and forecasts the future, 1992.

p.148
The resulting graph, Figure 8.1, presents a picture of regular oscillations. It is so regular that a harmonic waves ── a sinusoidal ── with a fifty-six-year (56-year) time period can be made to pass very closely to most points.

p.149
This periodicity in energy consumption was first observed by Hugh B. Stewart.1 On several occasion before and after Stewart, economists and others have pointed out many human activities that oscillate within a period of fifty [50] to sixty [60] years.2

p.156
[the existence of economic cycles]
A more contemporary scholar, Joseph A. Schumpeter, tried to explain the existence of economic cycles by attributing growth to the fact that major technological innovations come in clusters.10
An extended list of references on long economic waves can be found in an article by R. Ayres.11

p.156
N. D. Kondratieff
1926
From economic indicators alone Kondratieff deduced an economic cycle with a period of about fifty [50] years. His work was promptly challenged. Critics doubted both the existence of Kondratieff's cycle and the causal explanation suggested by Schumpeter. The postulation ended up being largely ignored by contemporary economists for a variety of reasons. In the final analysis, however, the most significant reason for this rejection may have been the boldness of the conclusions drawn from such ambiguous and imprecise data as monetary and financial indicators.

p.156
indicators, like price
inflation and currency fluctuations
monetary indicators

p.156
Concerning cycles with a period of fifty-six years I have cited examples in this chapter that are based on physical quantities. Energy consumptions, the use of machines, the discovery of stable elements, the succession of primary energy sources and basic innovations have all been reported in their appropriate units and not in relation to their prices. The cycles obtained this way are more trustworthy than Kondratieff's economic cycle. In fact, in the case of energy sources, prices indeed folowed the same cycle by flaring up at the end of each boom.

p.156
fifty-six-year economic cycle

p.169
We then compared these natural-growth curves to the fifty-six-year {56-year} cycle of energy consumption, which coincides with the economic cycle. We observed a remarkable correlation between the time these growth curves approach their ceiling and the valleys of the economic cycle.

p.169
recession coincides with saturation of these technologies.
p.170
saturation coincides with economic recession.

p.176
Nakicenovic on the U.K. Wholesale Price Index documented since the sixteenth (16th) century.
p.177
A periodic oscillation recorded over five centuries (500 years)
Figure 9.4
The U.K. Wholesale Price Index smoothed over a rolling 25-year period with respect to a 50-year moving average. This procedure washes out small fluctuations and reveals a wave. The periodicity turns out to be 55.5 years.*

p.224
Once growth is complete, the level reached reflects an equilibrium. Its signature becomes an invariant or constant that, despite erratic fluctuations,

(Prediction : society's telltale signature reveals the past and forecasts the future / Theodore Modis., 1. forecasting., 2. creation (literary, artistic, etc.)
3. science and civilization., CB 158.M63, 303.49--dc20, 1992
, )
<---------------------------------------------------------------------------->

James Gleick., The information : a history, a theory, a flood, 2011
pp.332-333
This is what science always seeks: a simple theory that accounts for a large set of facts and allows for prediction of events still to come.

   (The information : a history, a theory, a flood / James Gleick., 1. information science--history., 2. information society., Z665.G547 2011, 020.9--dc22, 2011, )
<---------------------------------------------------------------------------->

Kevin Kelly, out of control, 1994

p.368
In an expectation game, accurate predictions offer no opportunity for money-making if everyone shares the prediction.

([ In term of predict ability, within a given condition and situation, there are pockets of predict ability going forward to about 1 week, using data from the present and the past. Beyond one week, things become fuzzy. Prediction should be kept private, whether they turn out to be true or not. After a period of time, it should not matter so much to reveal the prediction if (overtaken by events - OBE). However a public revelation could served as data point for whose who are engaging in the forecasting activity. Should there be active information manipulation - disinformation, misinformation, deception - and/or active ~~behavior~~ [travel movement] modification - like delay, traffic jam, accidents, lockdown - then predict ability becomes less reliable. Because the active operation creates signal that would not have been there if there was no operation. The additional signal would influence the prediction - how much is unclear - it depends on the type of signal. ])

   (Kevin Kelly, out of control, 1994, filename: ooc-mf.pdf )
<---------------------------------------------------------------------------->

James Rickards, The death of money, 2014                                  [ ]

p.32
   My first contribution was to point out that the CIA's objective was already being pursued every day by hedge funds, but for a different reason. The CIA was trying to spot terrorist traders, while hedge funds were trying to spot unannounced takeovers. But the big-data techniques applied to trading patterns were the same.
   Spotting suspicious trading is a three-step process. Step one is to establish a baseline for normal trading, using metrics like volatility, average daily volume, put-call ratios, short interest, and momentum. Step two is to monitor trading and spot anomalies relative to the baseline. Step three is to see if there is any public information to explain the move. If a stock spikes because Warren Buffett bought a large position, that's not an anomaly; it is to be expected. The intriguing case is when a stock spikes on no news. The logical inference is that someone knows something you don't. A hedge fund might not care about the origin of the hidden information--it can just piggyback on the trade.

pp.33-34
We also concluded the insider trade was likely to be executed in the options market less than 72 hours before the attack to minimize risk of detection.

p.34
We created an automated threat board interface that broke the markets into sectors and displayed tickers with red, amber, and green lights, indicating the probability of insider trading.

p.35
We routinely picked up signals that indicated insider trading. These signals were from regular market players; there was nothing yet to indicate that the insider trading was terror related. Our project had no legal enforcement powers, so we simply referred these cases to the SEC and otherwise ignored them.

p.39
Randy Tauss, 33-year veteran,
“Jim, let me tell you how things work around here. We'll do a great job, and this thing will work like a charm. Then it will go nowhere and be put on a shelf. One day there will be a spectacular attack, and it will be apparent there was advance insider trading. The agency will pull our work from the shelf, dust it off, and say, ‘See, we have the solution right here. We have a system that can detect this next time.’ That system will get millions in funding and be built the way we wanted. But it will be too late to save lives in the next attack.”

p.40
All we needed to do was calibrate the signal engine to focus on specially tailored target sets of securities.

p.41
   Remember the truism No one trade alone. For every buyer, there is a seller. If one side of a trade is a threat to national security, it leaves a trace that the enemy did not intend. The enemy trader is like a fish swimming in the water; it leaves ripples. Even if the fish is invisible, the ripples can be seen, and the presence of the fish inferred. The forward-thinkers at that meeting in Omaha recognized that our signal engine could detect the ripples, that we had devised the perfect early warning device.
   MARKINT would have a future after all. It would be not the narrow counterterrorist tool we had set out to create, but rather a broad-based system, a sort of radar for the marketplace that was designed to detect incoming financial threats.

   (The death of money : the coming collapse of the international monetary system, James Rickards, 2014, )
<---------------------------------------------------------------------------->

Recorded Future

The company (Recorded Future) specializes in the collection, processing, analysis, and dissemination of intelligence.

In 2007, co-founders Christopher Ahlberg and Staffan Truvé, both Ph.D.s in computer science from Chalmers University of Technology, filed for Recorded Future’s first patent (granted in 2013 as United States patent US8468153B2) - Data Analysis System with Automated Query and Visualization Environment Setup.[1] The patent laid the foundation for continuous collection and processing of data and information from sources across the open, deep, and dark web, facilitated by machine learning. Recorded Future was officially incorporated in 2009.

The company received initial funding from Google and In-Q-Tel, as reported in a July 2010 introduction to Recorded Future published by Wired.[2]

In-Q-Tel (IQT) is a not-for-profit venture capital arm of United States intelligence community (CIA), investing in high-tech start-up companies.

In May 2017, Recorded Future introduced Insikt Group,[7] the company’s threat intelligence research arm. The word “insikt” is Swedish, a nod to Recorded Future's co-founders, and means “insight.” Insikt Group is responsible for delivering analyst-generated assessments, insights, and recommended actions to customers and the public.

Using what they call a "Temporal Analytics Engine," Recorded Future provides forecasting and analysis tools to help analysts predict future events by scanning sources on the internet, and extracting, measuring, and visualizing the information to show networks and patterns in the past, present, and future.[10] As of 2015, the engine was described as "Web Intelligence Engine."[11] Likewise, the Washington Post, in an article authored by Stewart Baker - the former General Counsel of the National Security Agency (1992–1994), which had described the company as a predictive analytics web intelligence firm deleted the term upon request of RF.[12] The software analyzes sources and forms "invisible links" between documents to find links that tie them together and may possibly indicate the entities and events involved. Noah Schachtmann from WIRED – who first wrote about Google and the CIA both investing in RF – described the company in an interview as follows: "Recorded Future is a company that strips out from web pages the sort of who, what, when, where, why — sort of who’s involved, [...] where are they going, what kind of events are they going to."[13]

https://en.wikipedia.org/wiki/Recorded_Future
   ____________________________________

June 23, 2021
Mouse movements reveal your behavior

by University of Luxembourg

[Image: computer mouse]
Credit: Pixabay/CC0 Public Domain

In two recently published research papers, computer scientists from the University of Luxembourg and international partners show how mouse movements can be used to gain additional knowledge about the user behavior. While this has many interesting applications, mouse movements can also reveal sensitive information about the users such as their age or gender. Scientists want to raise awareness about these potential privacy issues and have proposed measures to mitigate them.

Prof. Luis Leiva from the University of Luxembourg and corresponding author of the two papers explains in more details the key findings.

My mouse, my rules

"We have demonstrated how straightforward it is to capture behavioral data about the users at scale, by unobtrusively tracking their mouse cursor movements, and predict user's demographics information with reasonable accuracy using five lines of code. For years, recording mouse movements on websites has been easy, however to analyze them one would need advanced expertise in computer science and machine learning. Today, there are many libraries and frameworks that allows anyone with a minimum of programming knowledge to create rather sophisticated classifiers. This raises new privacy issues and users do not have an easy opt -out mechanism."

[Image: Mouse movements reveal your behaviour]
Credit: University of Luxembourg

Based on their results, the team developed a method to prevent mouse tracking by distorting the mouse coordinates in real-time. "It is inspired by recent research in adversarial machine learning, and has been implemented as a web browser extension, so that anyone can benefit from this work in practice," explains Leiva. The web browser extension called MouseFaker is available on Github.

This work has been presented at the 6th ACM SIGIR Conference on Human Information Interaction and Retrieval.

When choice happens

Nevertheless, mouse tracking has very practical applications for webmasters, and in particular for search engines. Dr. Ioannis Aparakis from Telefonica Research and co-author of both publications, clarifies: "When you search for something at Google or Bing, your mouse movements are sending a tiny signal to the search engine indicating if you are interested or not in the content you have been shown. As mouse tracking may have privacy issues, we investigated the possibility of recording only a small part of the whole movement trajectory and see if we can still infer how people make choices in web search."

The team analyzed three representative scenarios where users had to make a choice on web search engines: when they notice an advertisement, when they abandon the page, and when they become frustrated. The results are interesting: if users pay attention to an ad, it will be signaled by the initial mouse movements. In case of page abandonment, it is actually the opposite: the last movements inform whether the user has decided to leave either if they were satisfied with the search results or not, without having to click on anything. In the frustration case, results were mixed but it seemed the middle part of a mouse movement trajectory provides more information than the initial or final parts.

The researchers found that it is possible to predict the aforementioned tasks sometimes using just two or three seconds of mouse movement. Therefore, they conclude that, by only tracking the interesting parts, search engines could get useful information and improve their services while respecting the users' privacy. This work will be presented at the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval.

Prof. Leiva says, "By efficiently recording the right amount of movement data, we can save valuable bandwidth and storage, respect the user's privacy, and increase the speed at which machine learning models can be trained and deployed. Considering the web scale, doing so will have a net benefit on our environment."

Explore further

DuckDuckGo can now block the Google Chrome tracking method, FLoC
More information: Luis A. Leiva et al, My Mouse, My Rules, Proceedings of the 2021 Conference on Human Information Interaction and Retrieval (2021). DOI: 10.1145/3406522.3446011

When Choice Happens: A Systematic Examination of Mouse Movement Length for Decision Making in Web Search, Proceedings of the International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR), DOI: 10.1145/3404835.3463055

Provided by University of Luxembourg

Citation: Mouse movements reveal your behavior (2021, June 23) retrieved 23 June 2021 from https://techxplore.com/news/2021-06-mouse-movements-reveal-behavior.html
This document is subject to copyright. Apart from any fair dealing for the purpose of private study or research, no part may be reproduced without the written permission. The content is provided for information purposes only.

source:
https://techxplore.com/news/2021-06-mouse-movements-reveal-behavior.html
____________________________________

Your Body, Your Login

A team of Dutch and Italian researchers has found that the way you move your phone to your ear while answering a call is as distinct as a fingerprint. You take it up at a speed and angle that’s almost impossible for others to replicate. Which makes it a more reliable password than anything you’d come up with yourself. (The most common iPhone password is “1234.”) Down the line, simple movements, like the way you shift in your chair, might also replace passwords on your computer. It could also be the master key to the seven million passwords you set up all over the Internet but keep forgetting.

Chris Wilson

http://www.nytimes.com/interactive/2012/06/03/magazine/innovations-issue.html

____________________________________

    * The difference between human dynamics and data-mining boils down to this: Data mining predicts our behaviors based on records of our patterns of activity; we don't even have to understand the origins of the patterns exploited by the algorithm. Students of human dynamics, on the other hand, seek to develop models and theories to explain why, when, and where we do the things we do with some regularity.
   ____________________________________

[p.31]
    ... In the same fashion, Dirk's super-diffusive law allowed him to predict how long before we lose track of the origin point of a pile of dollar bills spent in Queens. This is critically important discovery. Especially if you have a suitcase heavy with counterfeit money and don't want the FBI at your doorstep.
    Dirk predicted that in sixty-eight days the coast would be clear. That is in about two months your counterfeit bills would be all over the United States, leaving the FBI no way to trace their origin.
    There was only one problem with this prediction: It was blatantly wrong. Indeed, Dirk's own measurement showed that most bills released in New York one hundred days later were still in the city's vicinity. Despite their superdiffusive trajectory, an invisible hand slowed the bills down, forcing them to defy the laws of superdiffusion and stay put in their original neighborhood. ([ this shows a similar pattern to crime and criminal. Most criminals behave in a predictable pattern or fashion, liken to an M.O. (Latin. modus operanti, mode of operation). Unless you get a super-criminal, an outlier, someone who has unusual range and distance in pattern of criminal behavior. ]) ([ As every hunting students and trackers have been taught from generation to generation: Humans, like all animals, is a creature of habit. ])

   (Barabási, Albert-László; 'BURSTS: the hidden pattern behind everything we do', copyright © 2010, 303.4901 Barabási, )
(BURSTS by Albert-László Barabási, © 2010, 303.4901 Barabási, p.31)
   ____________________________________

[p.86]
    ... So, while the outcome of the next throw is always a mystery, true randomness has some magic uniformity to it.
    Despite this apparent uniformity, there is nothing more random than a Poisson process, which is nothing but a sequence of truly random events. ([ very few events, and dare I say it, no events are random; there exist a cause and effect; the set of questions - who, what, when, where, why, how, what for, and so what - is, do we have some understanding of the event, or, we do not have any understanding of the events - for example, the Earth orbits around the Sun represents one (1) solar year and, the Earth rotates along its axis represents 24 hours period as signified by the repeated cycle of night (Sun set) and day (Sun rise), and when we do not ~~have~~ understand of the events, we fall back on, oh, it is a random event - for example, a 7.0 magnitude earthquake; or, oh, it is an act of God; or, oh, who could've predicted the 1st and 2nd world war; can you imagine the kind of human resource mobilization, material production, financing, energy consumption, etc ... to run [a] war longer than 30 days [or 90 days]; the kind of efforts, coordinations, communications, and mobilization that is required to prosecute any war of a length longer than 31 days [or 91 days]; so when you hear, see, or read the word, random, your antenna should go up. You should say to you self. Is this an expert. Does this person have some special knowledge about the man and the woman on the street question I am asking him or her. Is this person parroting a script - a mentally recorded message, a viral meme that has infected the mind. Randomness could simply be an excuse for I don't know and, I don't understand. However, rather than say, I don't know, or I have no explaination, they say, it is a random event. I am going to repeat it, again. We might not understand, we might not know, we might not have an explaination; we may never understand, we may never know, we may never have an explaination; but that does not mean it is random. ]) Therefore, deviations from Poisson's prediction are often taken as a signature of some hidden order. They offer evidence of a deeper law or pattern that remains to be discovered.
    ... As Richardson so eloquently pointed out, the atmosphere is governed by laws and equations, which today are successfully exploited by meteorologists everywhere. Many of these events--from solar eclipses to floods and droughts--were once considered the mysterious provenance of gods and spirits. Today they are predictable, however, telling us that deviations from randomness are often signatures of fundamental laws yet to be discovered.

(Barabási, Albert-László; 'BURSTS: the hidden pattern behind everything we do', copyright © 2010, 303.4901 Barabási, )
(BURSTS by Albert-László Barabási, © 2010, 303.4901 Barabási, p.86)
   ____________________________________

[pp.171-172]
19
the patterns of human mobility

    ... About a year after the publication of my first book on networks I had grown used to e-mails and calls from readers seeking advice on inter-connected systems. This was one of the few times that someone had called not to ask but to give. He had my full attention.
    The caller was a high-ranking executive at a mobile-phone consortium who'd recognized the value in having records of who is talking with whom. After reading 'Linked' he had become convinced that social networking was essential to improving services for his consumers. So he offered access to their anonymized data in exchange for any insights our research group might provide.
    His intuition proved correct: My group and I soon found the mobile users' behavior patterns to be so deeply affected by the underlying social network that the executive ordered many of his company's business practices redesigned, from marketing to consumer retention. With that, he pioneered a trend that over the past few years has swept most mobile carriers, triggering an avalanche of research into mobile communications. Despite his crucial role in advancing network thinking in the mobile industry, his combination of modesty and caution prevented his ever wanting his name attached to any of it.
    As my group and I immersed ourselves in the intricacies of mobile communications, we came to understand that mobile phones not only reveal who our friends are but also capture our whereabouts. Indeed, each time we make a call the carrier records the tower that communicates with our phone, effectively pinpointing our location. This information is not terribly accurate, as we could be anywhere within the tower's reception area, which can span tens of square miles. Furthermore, our location is usually recorded only when we use our phone, providing ... information about our whereabouts between calls. ([ Data can also be gather after the fact and-or in real-time of any devices that has GPS (Global Positioning System ]) Despite these contraints, the data offered an exceptional opportunity to explore the mobility of millions of individuals.

    (Barabási, Albert-László; 'BURSTS: the hidden pattern behind everything we do', copyright © 2010, 303.4901 Barabási, )
(BURSTS by Albert-László Barabási, © 2010, 303.4901 Barabási, pp.171-172)
   ____________________________________

[pp.193-195]
    ... As a result we tend to romanticize college life, the cradle of youth culture, seeing students as perhaps the most spontaneous and thus least predictable segment of the population. Yet Sandy Pentland, an MIT professor who follows the chatter of hundreds of students every day, finds that concept preposterous.
    In the early 1990s Pentland started a research program in wearable computing at the Media Lab at MIT, prompted by the the realization that, given the rate at which computers were shrinking, we soon would want to have them with us all the time. Sandy's vision of the future proved remarkably accurate, as today computers have become a part of our wardrobe, fashion accessories of a kind. In fact, for the most part we have stopped even calling them computers. We refer to them simply as smart phones.
    In the fall of 2002 Nathan Eagle, a doctoral student in Sandy's lab, offered one hundred MIT students free Nokia smart phones, a desirable top-of-the-line gadget at the time. This was no handout, however; the catch was that the phones collected everything they could about their owners: whom they called and when, how long they chatted, where they were, and who was nearby. By the end of the year-long experiment, Nathan Eagle and Sandy Pentland had collected about 450,000 hours of data on the communication, whereabouts, and behavior of seventy-five Media Lab faculty and students and twenty-five freshmen from MIT's Sloan School of Management.
    Trying to make sense of his data, Nathan arranged each student's whereabouts into three groups: home, work,and "elsewhere," the latter category assigned when they were neither at home nor at work but jogging along the Charles River or partying at a friend's house. Then he developed an algorithm to detect repetitive patterns, quickly discovering that on weekdays the students were mainly at home between the hours of ten P.M. and seven A.M. and at the university between ten A.M. and eight P.M. Their behavior changed slightly only during the weekends, when they showed an inclination to stay home at late as ten A.M.
    None of these patterns would shock anybody familiar with graduate student life. But the level of predictability of their routines was still remarkable. Nathan found that if he knew a business-school student's morning location he could predict with 90 percent accuracy the student's afternoon whereabouts. And for Media Lab students, the algorithm did even better, predicting their whereabouts 96 percent of the time. ([ we are creatures of habits ])
    It is tempting to see life as a crusade against randomness, a yearning for a safe, ordered existence. If so, the students excelled at it, ignoring the roll of the dice day after day. Indeed, Nathan's algorithm failed to predict their whereabouts only twice a week, during rare hours of rebellion when they finally lived up to our expectation that they be wild and spontaneous. Yet the timing of these unpredictable moments was by no means random--they were the typical party times, the Friday and Saturday nights. The rest of the week, twenty-two out of twenty-four hours a day, the students were neither the elusive Osama bin Laden nor the ubiquitously erratic Britney Spears but intead dutifully trod the deeply worn grooves of their lives. So maybe the Harlequins were onto something when they insisted on using an RNG(Random Number Generator). Had they studies at MIT, their whereabouts would have been no mystery--not to Nathan, nor to the Vast Machine.
    But we may yet avert the dawn of an Orwellian world as described in 'The Traveler'. For me, this sense of hopefulness emerged in the summer of 2007 when I purchased a brick-sized wristwatch. It was a loud antifashion statement and doubled as a GPS device, which recorded my precise location every few seconds. After I had worn it for several months, Zehui Qu, a visiting computer-science student, applied Nathan Eagle and Sandy Pentland's predictive algorithm to the data collected by my GPS. Sure enough, after a few days of training, Qu was able predict my whereabouts with 80 precent accuracy.
    While the algorithm's performance was impressive, the persistent gap between the 96 percent predictability Nathan found amoung the MIT students and my 80 percent raise a red flag. Neither I nor the MIT students were a fair representation of the population at large. Marta's study of the mobile-phone records had already explained why: When it comes to our travel patterns, we are hugely different. Some, like the MIT students and myself, are relatively home- and office-bound. Others are outliers, however, and travel a lot, tending to be less localized.
    So does that mean there are people out there who are far less predictable than the MIT students and I? Truck drivers, perhaps, who travel the country for weeks at a time? Soccer moms, whose minivans shuttle between piano and fencing lessons? What about super-traveler Hasan Elahi, whose "suspicious movements" will undoubtedly land him in hot water again? How different are they from you and me? Are there Harlequins among us, individuals whose lives are driven by the roll of the dice to such a degree that their movements are impossible to foresee?

    (Barabási, Albert-László; 'BURSTS: the hidden pattern behind everything we do', copyright © 2010, 303.4901 Barabási, )
(BURSTS by Albert-László Barabási, © 2010, 303.4901 Barabási, pp.193-195)
   ____________________________________

[pp.199-200],
    Before we move on, let me clarify that there is a fundamental difference between WHAT we do and how PREDICTABLE we are. When it comes to things we do--like the distances we travel, the number of e-mails we send, or the number of calls we make--we encounter power laws, which means that some individuals are significantly more active than others. They send more messages; they travel farther. This also means that outliers are normal--we EXPECT to have a few individuals, like Hasan, who cover hundreds or even thousands of miles on a regular basis.
    But when it comes to the predictability of our actions, to our surprise power laws are replaced by Gaussians. This means that whether you limit your life to a two-mile neighborhood or drive dozens of miles each day, take a fast train to work or even commute via airplane, you are just as predictable as everyone else. And once Gaussians dominate the problem, outliers are forbidden, just as bursts are never found in Poisson's dice-driven universe. Or two-mile-tall folks ambling down the street are unheard of. Despite the many differences between us, when it came to our whereabouts we are all equally predictable, and the unforgiving law of statistics forbids the existence of individuals who somehow buck this trend.
    But let statistics forbid, halt, hinder, impede, refuse, and deny, there's still someone who won't be limited by it. Our friend Hasan Elahi.

    [pp.201-202]
    ... When Zehui Qu ran the predictive algorithm on Hasan's data, though, it was an epic failure--only three times out of more than four thousand hourly attempts did he succeed in predicting Hasan's movements. ....   For all practical purposes, Hasan was a Harlequin, fully unpredictable.
    I confronted Hasan with our conclusions, telling him that, as far as we were concerned, he was completely random. His predictability was practically zero.
    "Can't be zero, is it?" He laughed, then continued without missing a beat, "I mean, there are a few places I go now and then."
    Sure, Hasan did visit the same spot in New Jersey 131 times, which, as we later learned, was his home at the time. ....   By way of comparison, during the two-month period I dutifully toted my GPS device around, I was tracked at home on more than 880 occassions. The difference between Hasan and me boils down to this: While I was predictably at home every night, Hasan was just as likely to be on a train in Europe or asleep in an airport as spending the night in his own bed. He did go home occasionally, but there was no recognizable pattern to it.
    For Hasan's perspective, his unpredictability wasn't all that surprising; and while he never explicitly said so, I think he found our whole analysis somewhat puzzling.* That he could readily explain each move he'd made convinced him that his behavior was absolutely normal.
    "This is what I do," he said. "It's the transit points that's become my work."
    Well, that didn't really cut it for me. Not because I doubted what he said. The real problem is that if power laws had governed our predictability, as they did the distances we cover, we'd expect to have a few outliers. Once power laws are absent, however, outliers are no longer normal. They are forbidden, making us all equally predictable. But no matter how we parsed the data, when it came to his predictability and lifestyle, Hasan was an outlier. Since outliers could not exist in this context, he was not normal any longer. Just as Homeland Security has suspected.

   (Barabási, Albert-László; 'BURSTS: the hidden pattern behind everything we do', copyright © 2010, 303.4901 Barabási, )
(BURSTS by Albert-László Barabási, © 2010, 303.4901 Barabási, pp.199-200, pp.201-202)
    * "As of mid-April and on, I was on sabbatical leave from Rutgers," he told me, adding, "so I didn't exactly have a regular weekday schedule to go on. Even when I was at school, I would just basically fly in, teach my class, and then leave. So it does make sense." He then thought a moment, adding, "Because I literally was all over the place that year."
   ____________________________________

prediction, predictability, forecast
[p.204],
    Despite the high predictability it represents, my low entropy is not a lock on my future--from it you can predict me only if you also know my history. Furthermore, if my entrophy is high, my past will reveal little of what lies ahead. If my entropy is low, my movement should be easy to foresee, but only if you have access to my past whereabouts. There is a simple lesson in this, ... : In order to predict the future, you first need to know the past.
    [p.205]
     ... while low entropy does mean predict-ability, to predict your future location we need access ot your past whereabouts. And as penetrating as they are, phone records are insufficient to the purpose. To predict your future location, I need to know your hourly whereabouts for the past several months. <skip last sentence>
     So at the end of the day, whether we are examining the events of today or the 16th century, the future is hard to foresee. And what if our past suddenly becomes transparent? Our future, both as individuals and as a society, may cease to be so mysterious. So, in order to reach into the future, we must first go back in time.

   (Barabási, Albert-László; 'BURSTS: the hidden pattern behind everything we do', copyright © 2010, 303.4901 Barabási, )
(BURSTS by Albert-László Barabási, © 2010, 303.4901 Barabási, p.204, p.205)
   ____________________________________

[pp.237-238]
depression

By 2020, depression will likely be second to only heart disease as our nation's deadliest killer. It is one of the greatest health scourges of our age, affecting approproximately 18 million Americans, and it is often lethal. Up to 60 percent of all those who commit suicide suffer from mood disorders. Depression is also one of the most mis-understood diseases, as more than half of the U.S. population views the condition as little more than personal weakness. Its sigma is rooted partly in its diagnosis--doctors rely mainly on self-reported symptoms, which are, by definition, subjective. And so if technologies were developed that could codify a depression diagnosis as clearly and stringently as we can diagnose cancer or heart disease, not only would millions be helped, but the confusion and ignorance surrounding the debilitating condition would be wiped out as well.
     A common ... [...] Since by now we know what constitutes a normal activity pattern, we can ask, do depression patients do anything differently from the rest of us?
     It was the Japanese research group from Tokyo University who first addressed this question. They provided 25 individuals sensitive wrist accelerometers, designed to pick up on even the slightest movement of the hand. The detectors confirmed that human activity is bursty, down to the slightest movements of our wrists. Indeed, the researchers determined that the length of periods of rest--when the subjects' hands did not move--follows a power law. Most rest periods lasted only seconds, or minutes at most. Yet these pauses co-existed with movement free intervals lasting hours, capturing sleep, rest, or meditation.
     14 of the 25 subjects displayed a more intermittent rest pattern than did the others. They were clinically depressed. The differences in their movements were striking: The average rest period in the healthy subjects was about 7 minutes, compared to over 15 minutes for depression patients. Furthermore, the scaling exponent, a number that uniquely characterizes each power law, was larger for healthy individuals than for the depressed. And so it would seem that being "encased in cement" was more than a mere illusion, actually corresponding to detectable changes in a depressed patient's activity pattern.
     [...]
   (Barabási, Albert-László; 'BURSTS: the hidden pattern behind everything we do', copyright © 2010, 303.4901 Barabási, )
(BURSTS by Albert-László Barabási, © 2010, 303.4901 Barabási, pp.237-238)
   ____________________________________

[pp.257-258]
    Scientists are poor prognosticators, often unable to see the consequences of their work. Luckily we have engineers and entrepreneurs to fill the gap between theorems and products. I am no exception to the rule--many companies have successfully monetarized my research on networks, none of which I foresaw. The same it true for human dynamics--I am too shortsighted to fully comprehend the full potential in forecasting your activity.
    Lewis Richardson's first book was a remarkable failure. And yet today weather forecasting is routine, following the principles he outlined while driving ambulances on the battlefields of France. In his second book, The Statistics of Deadly Quarrels, he ventured farther, aiming to predict conflicts and wars in the hopes of helping us one day avoid them altogether. He failed once again. And so the real question is this: Have we matured enough to trust our predictive abilities?

   (Barabási, Albert-László; 'BURSTS: the hidden pattern behind everything we do', copyright © 2010, 303.4901 Barabási, )
(BURSTS by Albert-László Barabási, © 2010, 303.4901 Barabási, pp.257-258)

____________________________________

[p.102]
   Richardson was not the first to discover this pattern. Indeed, the 19th-century economist Vilfredo Pareto found that while the vast majority of people are poor, a few individuals amass outlandish wealth. The existence of the rich isn't surprising, as even if wealth is acquired randomly some will be richer than others. Pareto found, however, that the rich were far richer than a random distribution of wealth could ever explain. Richardson's and Pareto's work showed that wars and income follow what we call a power law. Specifically, many small events co-exist alongside a few extra-ordinary large ones.* It meant that for every world war and every Gates and Rockefeller there would be countless small conflicts and millions of poor.

   (Barabási, Albert-László; 'BURSTS: the hidden pattern behind everything we do', copyright © 2010, 303.4901 Barabási, )
(BURSTS by Albert-László Barabási, © 2010, 303.4901 Barabási, p.102)
   ____________________________________

who would have thought (thunk) that forecasting and prediction would lead to game theory, and that game theory is rooted in wargame, and that forecasting is related to birth rate, death rate, animals and humans life cycle, and that forecasting and prediction is also about forecasting and predicting the dynamic distribution of wealth and resources [availability, access ability, and afford ability] in a society, which is directly related to basic inequality, absolute poverty, relative poverty, and ...

Capital in the Twenty-First Century, by Thomas Piketty, 2013
https://en.wikipedia.org/wiki/Capital_in_the_Twenty-First_Century
https://www.gatesnotes.com/Books/Why-Inequality-Matters-Capital-in-21st-Century-Review

Capital in the twenty-first century / Thomas Piketty ; translated by Arthur Goldhammer.
1. capital.
2. income distribution.
3. wealth
4. labor economics.

HB501.P43613 2014
332'.041—dc23

2013 in France (la France), 2014 in United States

Copyright © 2014 by the President and Fellows of Harvard College
First published as Le capital au XXI siècle, copyright © 2013 Éditions du Seuil

Acknowledgments

This book is based on fifteen years of research (1998–2013) devoted essentially to understanding the historical dynamics of wealth and income. Much of this research was done in collaboration with other scholars.

     ••••   •••   ••••

Conclusion

I have presented the current state of our historical knowledge concerning the dynamics of the distribution of wealth and income since the eighteenth century, and I have attempted to draw from this knowledge whatever lessons can be drawn for the century ahead.
   The sources on which this book draws are more extensive than any previous author has assembled, but they remain imperfect and incomplete. All of my conclusions are by nature tenuous and deserve to be questioned and debated. It is not the purpose of social science research to produce mathematical certainties that can substitute for open, democratic debate in which all shades of opinion are
represented.

The Central Contradiction of Capitalism: r > g

The overall conclusion of this study is that a market economy based on private property, if left to itself, contains powerful forces of convergence, associated in particular with the diffusion of knowledge and skills; but it also contains powerful forces of divergence, which are potentially threatening to democratic societies and to the values of social justice on which they are based.
   The principal destabilizing force has to do with the fact that the private rate of return on capital, r, can be significantly higher for long periods of time than the rate of growth of income and output, g.
   The inequality r > g implies that wealth accumulated in the past grows more rapidly than output and wages. This inequality expresses a fundamental logical contradiction. The entrepreneur inevitably tends to become a rentier, more and more dominant over those who own nothing but their labor. Once constituted, capital reproduces itself faster than output increases. The past devours the future.
   The consequences for the long-term dynamics of the wealth distribution are potentially terrifying, especially when one adds that the return on capital varies directly with the size of the initial stake and that the divergence in the wealth distribution is occurring on a global scale.

   The problem is enormous, and there is no simple solution. Growth can of course be encouraged by investing in education, knowledge, and nonpolluting technologies. But none of these will raise the growth rate to 4 or 5 percent a year. History shows that only countries that are catching up with more advanced economies—such as Europe during the three decades after World War II or China and other emerging countries today—can grow at such rates. For countries at the world technological frontier—and thus ultimately for the planet as a whole—there is ample reason to believe that the growth rate will not exceed 1–1.5 percent in the long run, no matter what economic policies are adopted.1
   With an average return on capital of 4–5 percent, it is therefore likely that r > g will again become the norm in the twenty-first century, as it had been throughout history until the eve of World War I. In the 20th century, it took two world wars to wipe away the past and significantly reduce the return on capital, thereby creating the illusion that the fundamental structural contradiction of capitalism (r > g) had been overcome.
   ... ... ...

Yet it seems to me that all social scientists, all journalists and commentators, all activists in the unions and in politics of whatever stripe, and especially all citizens should take a serious interest in money, its measurement, the facts surrounding it, and its history. Those who have a lot of it never fail to defend their interests. Refusing to deal with numbers rarely serves the interests of the least well-off.

   ____________________________________

Donella H. Meadows, Edited by Diana Wright, Thinking in systems
p.127
Success to the successful is a well-known concept in the field of ecology, where it is called “the competitive exclusion principle.” This principle says that two different species cannot live in exactly the same ecological niche, competing for exactly the same resources. Because the two species are different, one will necessarily reproduce faster, or be able to use the resource more efficiently than the other. It will win a larger share of the resource, which will give it the ability to multiply more and keep winning. It will not only dominate the niche, it will drive the losing competitor to extinction. That will happen not by direct confrontation usually, but by appropriating all the resource, leaving none for the weaker competitor.

     (Thinking in systems : a primer, Donella H. Meadows, Edited by Diana Wright, sustainability institute, 2008, QA 402 .M425 2008, )
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p.212
vision and commitment

Henry Mitzberg, a professor of management at McGill University and a long-time student of strategy, has observed that the big question about making major change is not so much how to do it, but when. Making a big effort when everyone finally sees the new paradigm but after the game is over is too late, but trying to do it before senior management is ready and can support it is too soon. The top people have to prepare any large organization for a change of the kind we are talking about.
   ... [...] ...
   (Stalk, George, HD69.T54S73 1990, 658.5'6——dc20, copyright © 2009)
( Competing against time : how time-based competition is reshaping gloabl markets / George Stalk, Jr. (and) Thomas M. Hout., 1. time management., 2. delivery of goods., 3. competition, international., 4. comparative advantage (international trade)., p.212)
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Colin Powell with Tony Koltz., It worked for me : in life and leadership, 2012

p.146
Don't rush into decisions ── make them timely and correct.

Time management is an essential feature of decision-making. One of the first questions a commander considers when faced with a mission on the battlefield is “How much time do I have before I execute?” Take a third of that time to analyze and decide. Leave two-third of the time for subordinates to do their analysis and make their plans. Use all the time you have. Don't make a snap decision. Think about it, do your analysis, let your staff do their analysis. Gather all the information you can. When you enter the range of 40 to 70 percent of all available information, think about making your decision. Above all, never wait too long, never run out of time.

p.146
In the Army we had an expression, OBE ── overtaken by events. In bureaucratic terms being OBE is a felonious offense. You blew it. If you took too much time to study the issue, to staff it, or to think about it, you became OBE. The issue has moved on or an autopilot decision has been made. No one cares what you think anymore ── the train has left the station.

(It worked for me : in life and leadership / Colin Powell with Tony Koltz. ── 1st ed., 1. Powell, Colin L., 2. African American generals ── biography., 3. united states ── politics and government ── 1993-2001 ── quotations, maxims, etc., 4. leadership ── united states., E840.5.P68A3 2012, 973.931092──dc23, 2012, )

··<---------------------------------------------------------------------------->

Michael Pillsbury, The hundred-year marathon : China's secret strategy to replace America as the global superpower, 2015

p.134
The fictional year was 2030, and the officer who spoke for the secretary of defense was on a team playing a war game at the Naval War College in Newport, Rhode Island. For more than seven decades [70 years], many similar strategy games had been conducted in this room. Some concerned peacetime competitions testing diplomatic prowness. Others simulated military invasions, naval blockades, and war on a global scale. It was in this room, now ...., that the Japanese attack on Pearl Harbor was first foretold ── and then ignored.

(Michael Pillsbury, The hundred-year marathon, The hundred-year marathon : China's secret strategy to replace America as the global superpower / Michael Pillsbury., 1. strategic planning ── china., 2. china ── history. 3. national security ── china., 4. china ── politics and government., 5. china ── foreign relations., 6. united states ── foreign relations ── china., 7. china ── foreign relations ── united states., JZ134.P55 2014, 327.1'12095──dc23, 2015, )
____________________________________
Bina Venkataraman., The optimist's telescope : thinking ahead in a reckless age, 2019

p.234
As Steven Sloman and Philip Fernbach have pointed out, the attack on Pearl Harbor, though its timing was a surprise, was actually expected,

p.234
In 1941, President Franklin Delano Roosevelt moved the Pacific naval fleet from its base in San Diego to Hawaii as a direct response to Japanese aggression. (No game, it seems, anticipated the kamikaze pilots.)

(The optimist's telescope : thinking ahead in a reckless age / Bina Venkataraman., New York : Riverhead books, 2019., decision making──social aspects. | risk──sociological aspects. | forecasting., ddc 153.8/3──dc23, https://lccn.loc.gov/2018046076, 2019, )
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How Futurology Works
by Nicholas Gerbis

https://electronics.howstuffworks.com/future-tech/futurology3.htm
https://electronics.howstuffworks.com/future-tech/futurology.htm#pt3
https://electronics.howstuffworks.com/future-tech/futurology.htm

Futurology in Literature

Although some practitioners admit that future studies rely as much on art as on science, many dismiss writers, particularly science fiction authors, as prophets. They argue that fiction, whether historical or futuristic, is in fact a commentary on the author's own life and times.

Maybe so, maybe not. If science fiction writers lack a solid grasp of the indicators used by futurists, then they also do not suffer from the futurists' limitations, such as the need for measurable data, or for an evidence-based link between roots and outcomes, which leaves little room for the unexpected. After all, the famous futurologist Herman Kahn, in his 1972 book "Things to Come," missed the energy crisis waiting just around the corner.

Besides, whoever predicted the future without considering the times in which they lived? Certainly not futurologists.

Sci-fi writers might get the future wrong more often than not but, unlike futurologists (whose track record is hardly without blemish), they're free to think laterally and, more importantly, to focus on ineffable human factors, such as desire. They can postulate futures they don't believe in and need not justify. Thus, they can explore interesting ideas or warn of dire possibilities. As Ray Bradbury put it, "I don't try to describe the future. I try to prevent it" [source: Moore].

In any case, few sci-fi writers have claimed prophetic powers. In fact, a running inside joke goes that a story is just a story until it happens to come true -- then it becomes a prophecy.

To doubt the influence of these authors, though, is to ignore Arthur C. Clarke's nonfiction prediction of telecommunications satellites or the influence of Jules Verne's mid-19th-century vision of a (literal) moon shot (his spaceship is shot from a gigantic gun in Florida, near the future location of Cape Canaveral). It is to disregard H. G. Wells's forecasting of tanks (1903), aerial bombing (1908) or the atom bomb (1908), or Czech author Karel Capek's prediction of something akin to the A-bomb, or his imagining, and naming, of robots in 1921.

Edwin Balmer dreamed up the lie detector, based on "involuntary reactions in the blood and glands," in a 1910 detective story. Hugo Gernsback, the great proponent of putting more science in science fiction (and Hugo award namesake), foresaw numerous advances in his 1911 book, "Ralph 124C 41+", including television, fluorescent lighting, plastics, tape recorders, rustproof steel, synthetic fabrics, jukeboxes, tinfoil and loud speakers [sources: Gernsback; Nicholls].

Are these authors visionaries, perceiving the inevitable? Or do they inspire future generations, who then act on their visions? If so, then might their inspiration be more powerful than the futurists' forecasts?

"The best way to predict the future is to invent it," said American computer scientist Alan Kay in the Nov. 1, 1982, edition of the Financial Times.

Maybe he was on to something. Why predict a course that flows deterministically from cause to effect when you can warn of a future worth stopping or envision a world worth building?
____________________________________

https://image.slidesharecdn.com/futurology-151107065016-lva1-app6891/95/futurology-3-638.jpg
____________________________________

How Futurology Works
by Nicholas Gerbis

https://electronics.howstuffworks.com/future-tech/futurology4.htm

Author's Note

Back in 1929, when people still took book titles from the Book of Common Prayer, British physicist John Desmond Bernal wrote a work that Arthur C. Clarke, no slouch in the extrapolation game himself, would later call "the most brilliant attempt at scientific prediction ever made." "The World, the Flesh and the Devil" opens with the following statement:

"There are two futures, the future of desire and the future of fate, and man's reason has never learnt to separate them."

Bernal goes on to argue that the future is produced by the collision, or perhaps tension, between two forces: nature, which we (especially at the time) barely comprehended, and human desires, which we understand even less.

To me, human desire is the key to the future. Unfortunately, we tend to set it aside because we cannot fathom how it might play out -- an approach that strikes me as 180 degrees out of phase with the truth, which is this: Even as we drive our bland, outdated gas-guzzlers and dismantle our space program, some part of us will always want daft, cool things like flying cars, zeppelins and domed cities with pneumatic tubes, monorails and mile-high people movers. As long as we do, I think there might be hope for us yet.

https://pbs.twimg.com/media/CurUSlKWIAAM1Ut.jpg
https://pbs.twimg.com/media/CurUSlKWIAAM1Ut.jpg

source:
       https://electronics.howstuffworks.com/future-tech/futurology.htm
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George P. Richardson, Feedback thought in social science and systems theory [ ]

[p.79]
vicious circle
Myrdals' "principle of cumulation"
    (Richardson, George P., Feedback thought in social science and systems theory, copyright © 1991 by the University of Pennsylvania Press)
(Feedback thought in social science and systems theory / George P. Richardson (1991), 1. social science--methodology., 2. feedback control systems., p.79)
   ____________________________________

[pp.83-84]
self-fulfilling prophecy
     As Merton defined it, a self-fulfilling prophecy is an initially false perception of a situation that evokes new behavior that makes the originally false conception come true (Merton 1948).

    (Richardson, George P., Feedback thought in social science and systems theory, copyright © 1991 by the University of Pennsylvania Press)
(Feedback thought in social science and systems theory / George P. Richardson (1991), 1. social science--methodology., 2. feedback control systems., pp.83-84)
   ____________________________________

pp.42-43
Or consider the normal mathematics curriculum, which continues relentlessly on its way, each new lesson assuming full knowledge and understanding of all that has passed before. Even though each point may be simple, once you fall behind it is hard to catch up. The result: mathematics phobia. Not because the material is difficult, but because it is taught so that difficulty in one stage hinders further progress. The problem is that once failure starts, it soon generalizes by self-blame to all of mathematics. Similar processes are at work with technology. The vicious cycle starts: if you fail at something, you think it is your fault. Therefore you think you can't do that task. As a result, next time you have to do the task, you believe you can't so you don't even try. The result is that you can't, just as you thought. You're trapped in a self-fulfilling prophecy.

    (Norman, Donald A., The psychology of everyday things, 1. design, industrial--psychological, aspects, 2. human engineering, copyright © 1988, 620.82 Norman, pp.42-43)
   ____________________________________

George P. Richardson, Feedback thought in social science and systems theory [ ]

hidden character

[p.336]
     ... We have seen various emphases on “message” (Deutsch), “test and operations” (Millers, Galanter, and Pribram), “stimuli and responses” (Easton and others), each of which can be classified under one or the other of the generic terms “event” and “decision.” In contrast, the unit of behavioral description emphasized by social scientists in servo-mechanisms thread is the “pattern of behavior.” Thus the system dynamicist works with “behavior modes,” which are expressed in graphs of quantities varying over time.
     In this regard, Forrester's distinction between “policy” and “decision” is significant (Forrester 1961, p. 97; see section 3.3). A decision is a discrete event. A policy is a persistent framework, a set of decision rules, within which arises an ongoing stream of decisions. Forrester described this distinction in terms of the conceptual “distance” of the viewer. Observed from a sufficient distance, a decision stream is seen not as a sequence of discrete decisions but as pattern of behavior that reflect a policy structure. Thus the units in which systems are described in the servo-mechanisms thread are policies, not decisions, and patterns of behavior, not events, and those patterns are usually expressed in terms of graphs over time. These more aggregate units or atoms of system description are consistent with the usually more distant perspective of the servo-mechanisms thread.
     I believe that the distinction between events, patterns of behavior, decisions, and policy structure are among the most important observations in this investigation of the evolution of feedback thinking. The units of system description employed in the different feedback threads can be seen to be the foundation underlying many of their other characteristics.
“”
    (Richardson, George P., Feedback thought in social science and systems theory, copyright © 1991 by the University of Pennsylvania Press)
(Feedback thought in social science and systems theory / George P. Richardson (1991), 1. social science--methodology., 2. feedback control systems., p.336)
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Donella H. Meadows, Edited by Diana Wright, Thinking in systems             [ ]

◇pp.92-94
━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
INTERLUDE • Spruce Budworms, Firs, and Pesticides
──────────────────────────────────────────────────────────────────

Tree ring records show that the spruce budworm has been killing spruce and fir trees periodically in North America for at least 400 years. Until this century, no one much cared. The valuable tree for the lumber industry was the white pine. Spruce and fir were considered “weed species.” Eventually, however, the stands of virgin pine were gone, and the lumber industry turned to spruce and fir. Suddenly the budworm was seen as a serious pest.
So, beginning in 1950s, northern forests were sprayed with DDT to control the spruce budworm. In spite of the spraying, every year there was a budworm resurgence. Annual sprays were continued through the 1950s, 1960s, and 1970s, until DDT was banned. Then the sprays were changed to fenitrothion, acephate, Sevin, and methoxychlor.
Insecticides were no longer thought to be the ultimate answer to the budworm problem, but they were still seen as essential. “Insecticides buy time,” said one forester, “That's all the forest manager wants; to preserve the trees until the mill is ready for them.”
By 1980, spraying costs were getting unmanageable──the Canadian province of New Brunswick spent $12.5 million on budworm “control” that year. Concerned citizens were objecting to the drenching of the landscape with poisons. And, in spite of the sprays, the budworm was still killing as many as 20 million hectares (50 million acres) of trees per year.
C. S. Holling of the University of New Brunswick put together a computer model to get a whole-system look at the budworm problem. They discovered that before the spraying began, the budworm had been barely detectable in most years. It was controlled by a number of predators, including birds, a spider, a parasitic wasp, and several diseases. Every few decades, however, there was a budworm outbreak ([population explosion]), lasting from six to ten years. Then the budworm population would subside, eventually to explode again.
The budworm preferentially attacks balsam fir, secondarily spruce. Balsam fir is the most competitive tree in the nothern forest. Left to its own devices, it would crowd out spruce and birch, and the forest would become a monoculture of nothing but fir. Each budworm outbreak cuts back the fir population, opening the forest for spruce and birch. Eventually fir moves back in.
As the fir population builds up, the probability of an outbreak increases──nonlinearly. The reproductive potential of the budworm increases more than proportionately to the availability of its favorite food supply. The final trigger is two or three warm, dry springs, perfect for the survival of budworm larvae. (If you're doing event-level analysis, you will blame the outburst on the warm, dry springs.)
The budworm population grows too great for its natural enemies to hold in check──nonlinearly. Over a wide range of conditions, greater budworm populations result in more rapid multiplication of budworm predators. But beyond some point, the predators can multiply no faster. What was a reinforcing relationship──more budworms, faster predator multiplication──becomes a nonrelationship──more budworms, no faster predator multiplication──and the budworms take off, unimpeded.
Now only one thing can stop the outbreak: the insect reducing its own food supply by killing off fir trees. When that finally happens, the budworm population crashes──nonlinearly. The reinforcing loop of budworm reproduction yields dominance to the balancing loop of budworm starvation. Spruce and birch move into the spaces where the firs used to be, and the cycle begins again.
The budworm/spruce/fir system oscillates over decades, but it is ecologically stable within bounds. It can go on forever. The main effect of the budworm is to allow tree species other than fir to persist. But in this case what is ecologically stable is economically unstable. In eastern Canada, the economy is almost completely dependent on the logging industry, which is dependent on a steady supply of fir and spruce.
When industry sprays insecticides, it shifts the whole system to balance uneasily on different points within its nonlinear relationships. It kills off not only the pest, but the natural enemies of the pest, thereby weakening the feedback loop that normally keeps the budworms in check. It keeps the density of fir high, moving the budworms up their nonlinear reproduction curve to the point at which they're perpetually on the edge of population explosion.
The forest management practices have set up what Holling calls “persistent semi-outbreak conditions” over larger and larger areas. The managers have found themselves locked into a policy in which there is an incipient volcano bubbling, such that, if the policy fails, there will be an outbreak of an intensity that has never been seen before.”4

     (Thinking in systems : a primer, Donella H. Meadows, Edited by Diana Wright, sustainability institute, 2008, QA 402 .M425 2008, ◇pp.92-94 )
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https://www.gmo.com/americas/research-library/the-race-of-our-lives-revisited/

https://www.gmo.com/americas/research-library/the-race-of-our-lives-revisited/

https://www.gmo.com/globalassets/articles/white-paper/2018/jg_morningstar_race-of-our-lives_8-18.pdf
<---------------------------------------------------------------------------->

https://www.vice.com/en/article/z3xw3x/new-research-vindicates-1972-mit-prediction-that-society-will-collapse-soon

MIT Predicted in 1972 That Society Will Collapse This Century. New Research Shows We’re on Schedule.

A 1972 MIT study predicted that rapid economic growth would lead to societal collapse in the mid 21st century. A new paper shows we’re unfortunately right on schedule.

by Nafeez Ahmed
July 14, 2021, 6:00am
   ____________________________________

https://sustainable.unimelb.edu.au/__data/assets/pdf_file/0005/2763500/MSSI-ResearchPaper-4_Turner_2014.pdf

Previous studies that attempted to do this found that the model’s worst-case scenarios accurately reflected real-world developments. However, the last study of this nature was completed in 2014.
   ____________________________________

https://advisory.kpmg.us/articles/2021/limits-to-growth.html

Update to limits to growth
   ____________________________________

https://advisory.kpmg.us/content/dam/advisory/en/pdfs/2021/yale-publication.pdf

Update to limits to growth
Comparing the World3 model with empirical data

By Gaya Herrington

•••• ••• ••••

5 CONCLUSION

Empirical world data was compared against scenarios from the last LtG book, created by the World3 model. The data comparison, which used the latest World3 version, included four scenarios: BAU, BAU2, CT, and SW. Empirical data showed a relatively close fit for most of the variables. This was true to some extent for all scenarios, because in several cases the scenarios do nott significantly diverge until 2020. When scenarios had started to diverge, the ones that aligned closest with empirical data most often were BAU2 and CT. This result is different to previous comparisons that used the earlier World3 version, and which indicated BAU as the most closely followed scenario. The scenario that depicts the smallest declines in economic output, SW, is also the one that aligned least closely with observed data. Furthermore, the two closest aligning scenarios BAU2 and CT, respectively, predict a collapse pattern and moderate decline in output. At this point therefore, the data most aligns with the CT and BAU2 scenarios which indicate a slowdown and eventual halt in growth within the next decade or so, but World3 leaves open whether the subsequent decline will
HERRINGTON 11
constitute a collapse. World3 also indicates the possibility, for now, of limiting declines to less than in the CT. Although SW tracks least closely, a deliberate trajectory change brought about by society turning toward another goal than growth is still possible. The LtG work implies that this window of opportunity is closing fast.

ACKNOWLEDGMENTS

This research was based on my Master of Liberal Arts thesis in Sustainability at Harvard University Extension School, graduation March 2020 (Branderhorst 2020). I am grateful to Graham Turner for sharing his previous work with me and giving me his support. It was a pleasure and honorto discuss my research with him. I would like to thank Esther G. Naikaland Giovanni Ruta from the World Bank for providing me with the underlying data on natural capital calculations. I am also very appreciative of John Sterman for meeting with me at short notice in January 2019 while he was on sabbatical. That talk in his MIT office provided mewith exactly the right insights to nudge my research in the right direction.

CONFLICT OF INTEREST
The author declares no conflict of interest.

   ____________________________________

Theodore Modis., Prediction : society's telltale signature reveals the past and forecasts the future, 1992.

pp.37-38
p.37
Critics of S-curves have always raised the question of uncertainties as the most serious argument against forecasts of this kind.
p.37
Obviously the more good-quality measurements available, the more reliable the determination of the final ceiling. But I would not dismiss the method for fear of making a mistake.
p.37
Alain Debecker and I carried out a systematic study of the uncertainties to be expected as a function of the number of data points, their measurement errors, and how much of the S-curve they cover.
p.37
We did this in the physics tradition through a computer program simulating “historical” data along S-curves smeared with random deviations, and covering a variety of conditions. The subsequent fits aimed to recover the original S-curve.
p.37
We left the computer running over the weekend.
pp.37-38
On Monday morning we had piles of printout containing over forty thousand fits.
p.38
The results were published,12 and a summary of them is given in Appendix B, but the rule of thumb is as follows:

If the measurements cover about half of the life cycle of the growth process in question and the error per point is not bigger than 10 percent, nine times out of ten the final niche capacity will turn out to be less than 20 percent away from the forecasted one.

p.38
The results of our study were both demystifying and reassuring. The predictive power of S-curves is neither magical nor worthless.

p.38
Bringing this approach to industry could be of great use in estimating the remaining market niche of well-established products with quoted uncertainties. Needless to say life is not that simple.
p.38
If products are not sufficiently differentiated, they are sharing the niche with others, in which case the combined populations must be considered.

p.38
Like any powerful tool, it can create marvels in the hands of the knowledgeable, but it may prove deceptive to the inexperienced.

p.120
The cases presented in this section depict deviations from the description of the natural substitution model. In the work of economists and meteorologists such a situation ── evidence against a model ── would have resulted in modifications of the model itself. With the ((process of natural growth)) in competition, however, the theory is fundamental; deviations, whenever they occur, must be explained in the way the model is used rather than raise questions about its [a model] validity.

p.225
From half of a growth process one can intuitively predict the other half.

p.234
Appendix B
Expected Uncertainties on S-Curve Forecasts

p.235
In other words, a slower rate of growth correlates to a larger niche size, and vice versa. This implies that accelerated growth is associated with a lower ceiling, bringing to mind such folkloric images as short life spans for candles burning at both ends.

(Prediction : society's telltale signature reveals the past and forecasts the future / Theodore Modis., 1. forecasting., 2. creation (literary, artistic, etc.)
3. science and civilization., CB 158.M63, 303.49--dc20, 1992
, )
   ____________________________________
Donella H. Meadows, Edited by Diana Wright, Thinking in systems

• layers of limits : Whenever one factor ceases to be limiting, growth occurs, and the growth itself changes the relative scarcity of factors until another becomes limiting. To shift attention from the abundant factors to the next potential limiting factor is to gain real understanding of, and control over, the growth process. (this is the real insights)

p.102
There are layers of limits around every growing plant, child, epidemic, new product, technological advance, company, city, economy, and population. Insight comes not only from recognising which factor is limiting, but from seeing that growth itself depletes or enchances limits and therefore changes what is limiting. The interplay between a growing plant and the soil, a growing company and its market, a growing economy and its resource base, is dynamic. Whenever one factor ceases to be limiting, growth occurs, and the growth itself changes the relative scarcity of factors until another becomes limiting. To shift attention from the abundant factors to the next potential limiting factor is to gain real understanding of, and control over, the growth process.

     (Thinking in systems : a primer, Donella H. Meadows, Edited by Diana Wright, sustainability institute, 2008, QA 402 .M425 2008, p.102 )

____________________________________

https://www.bcgperspectives.com/content/Classics/strategy_the_rule_of_three_and_four/

The Rule of Three and Four

1976 by Bruce Henderson

A “stable, competitive” industry will never have more than three significant competitors.

The industry's structure will find equilibrium when these three companies' market shares reach a ratio of approximately 4:2:1.

A stable competitive market never has more than three significant competitors, the largest of which has no more than four times the market share of the smallest.

The conditions which create this rule are:

A ratio of 2 to 1 in market share between any two competitors seems to be the equilibrium point at which it is neither practical nor advantageous for either competitor to increase or decrease share. This is an empirical observation.

Any competitor with less than one quarter the share of the largest competitor cannot be an effective competitor. This too is empirical but is predictable from experience curve relationships.

Characteristically, this should eventually lead to a market share ranking of each competitor one half that of the next larger competitor with the smallest no less than one quarter the largest. Mathematically, it is impossible to meet both conditions with more than three competitors.

The Rule of Three and Four is a hypothesis. It is not subject to rigorous proof. It does seem to match well observable facts in fields as diverse as steam turbines, automobiles, baby food, soft drinks and airplanes. If even approximately true, the implications are important.

The underlying logic is straightforward. Cost is a function of market share as a result of the experience curve effect.

If two competitors have nearly equal shares, the one who increases relative share gains both volume and cost differential. The potential gain is high compared to the cost. For the leader, the opportunity diminishes as the share difference widens. A price reduction costs more and the potential gain is less. The 2 to 1 limit is approximate, but it seems to fit.

Yet when any two competitors actively compete, the most probable casualty is likely to be the weakest competitor in the arena. That, logically and typically, is the low share competitor.

The limiting share ratio of 4 to 1 is also approximate but seems to fit. If it is exceeded, then the probable cost differential produces very large profits for the leader at breakeven prices for the low share competitor. That differential, predicted by the experience curve, is enough to discourage further reinvestment and efforts to compete by the low share competitor unless the leader is willing to lose share by holding a price umbrella.

There are two exceptions to this result:

A low share competitor can achieve a leadership position in a given market sector and dominate it cost-wise if there is enough shared experience between that sector and the rest of the market, and he is a leader in the rest of the market, or

An otherwise prosperous company is willing for some reason to continually add more investment to a marginal minor product. This can be caused by accounting averaging, full line policy or mismanagement.

Whatever the reason, it appears that the Rule of Three and Four is a good prediction of the results of effective competition.

There are strategy implications:

If there are large numbers of competitors, a shakeout is nearly inevitable in the absence of some external constraint or control on competition.

All competitors who are to survive will have to grow faster than the market in order to even maintain their relative market shares with fewer competitors.

The eventual losers will have increasingly large negative cash flows if they try to grow at all.

All except the two largest share competitors will be either losers and eventually eliminated or be marginal cash traps reporting profits periodically and reinvesting forever.

Anything less than 30 percent of the relevant market or at least half the share of the leader is a high risk position if maintained.

The quicker any investment is cashed out or a market position second only to the leader gained, then the lower the risk and the higher the probable return on investment.

Definition of the relevant market and its boundary barriers becomes a major strategy evaluation.

Knowledge and familiarity with the investment policies and market share attitudes of the market leader are very important since his policies control the rate of the inevitable shakeout.

Shifts in market share at equivalent prices for equivalent products depend upon the relative willingness of each competitor to invest at rates higher than the sum of both physical market growth and the inflation rate. Anyone who is not willing to do so loses share. If everyone is willing to do so, then prices and margins will be forced down by overcapacity until someone begins to stop investing.

There are tactical implications which are equally important:

If the low cost leader holds the price too high, the shakeout will be postponed, but he will lose market share until he is no longer the leader.

The faster the industry growth, the faster the shakeout occurs.

Near equality in share of the two market leaders tends to produce a shakeout of everyone else unless they jointly try to maintain the price level and lose share together.

The price/experience curve is an excellent indicator of whether the shakeout has started. If the price curve slope is 90 percent or flatter, the leader is probably losing share and still holding up the price. If the curve has a sharp break from 90 percent or above to 80 percent or less, then the shakeout will continue until the Rule of Three and Four is satisfied.

The market leader controls the initiative. If he prices to hold share, there is no way to displace him unless he runs out of the money required to maintain his capacity share. However, many market leaders unwittingly sell off market share to maintain short term operating profit.

A challenger who expects to displace an entrenched leader must do it indirectly by capturing independent sectors, or be prepared to invest far more than the leader will need to invest to defend himself.

There are public policy implications:

The lowest possible price will occur if there is only one competitor, provided that monopoly achieves full cost potential even without competition and passes it on to the customer.

The next lowest potential price to the customer is with two competitors, one of which has one third and the other two thirds of the market. Then cost and price would probably be about 5 percent higher than the monopoly would require.

The most probable, and perhaps the optimal relationship, would exist when there are three competitors, and the largest has no more than 60 percent of the market and the smallest no less than 15 percent.

A rigorous application of the Rule of Three and Four would require identification of discrete homogeneous market sectors in which all competitors are congruent in their competition. More typically, competitors' areas of competition overlap but are not identical. The barriers between sectors are sometimes surmountable, particularly if there are joint cost elements with scale effects. Yet it is a commonly observable fact that most companies have only two or three significant competitors on any product which is producing a net positive cash flow. Other competitors are unimportant factors.

The Rule of Three and Four is not easy to apply. It depends on an accurate definition of relevant market. It requires many years to reach equilibrium unless the leader chooses to hold his share during the high growth phase of product life. However, the rule appears to be inexorable.

If the Rule of Three and Four is inexorable, then common sense says: if you cannot be a leader in a product market sector, cash out as soon as practical. Take your writeoff. Take your tax loss. Take your cash value. Reinvest in products and markets where you can be a successful leader. Concentrate.

https://www.bcgperspectives.com/content/Classics/strategy_the_rule_of_three_and_four/

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Gary Klein, Sources of power : how people make decision, 1998
p.276
One definition of uncertainty (paraphrasing Lipshitz and Shaul 1997) is “doubt that threatens to block action”. Key pieces of information are missing, unreliable, ambiguous, inconsistent, or too complex to interpret, and as a result a decision maker will be reluctant to act. In many cases, the action will be delayed or will be overtaken by events as windows of opportunity close.

p.291
   Regarding the nature of our data, one weakness of our work is that most of the studies relied on interviews rather than formal experiments to vary one thing at a time and see its effect. There are sciences that do not manipulate variables, such as geology or astronomy or anthropology. Naturalistic decision making research may be closer to anthropology than psychology. Sometimes we observe decision makers in action, but we rely on introspection in nearly all our studies. We ask people to describe what they are thinking, and we analyze their responses. We do not know if the things they are telling us are true, or maybe just some ideas they are making up. We can repeat the studies or, better yet, other investigators can repeat the studies to see if they get the same results. Nevertheless, no one can confidently believe what the decision makers say.
   The use of introspection raises questions about how much to trust the findings of studies. However, alternate methods of scientific inquiry have their own problems and limitations. Research on naturalistic decision making collects and reports data, and it can be used as a source of ideas and hypotheses. The think-aloud data are soft, and fuzzy, and they are difficult to interpret. Nevertheless, we can still learn a lot by observing and questioning people as they perform realistic tasks with natural contexts.
   (Klein, Gary, Sources of power : how people make decision / Gary Klein., 1. decision-making., 1998, 685.403, MIT Press,)

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     fear, loss, fear of loss, fear of missing out
     Want One Of Those (WOOT)
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“Everyone believes very easily whatever they fear or desire”
                            ──Jean de La Fontaine’s aphorism

https://web.archive.org/web/20081201191332/http://www.strategy-business.com/press/16635507/08110

Wayback Machine

Thought Leader
Pankaj Ghemawat: The Thought Leader Interview
by Art Kleiner

The seer of “semiglobalization” argues for appreciating regional distinctions.

... [...] ....

In a way, that’s a symptom of the same issue that affects writing about globalization in general. Jean de La Fontaine’s aphorism “Everyone believes very easily whatever they fear or desire” captures much of the utopian/dystopian quality of publications about the flat world, the death of distance, the end of history, and so forth. But a reality-based perspective on global strategy leads to different prescriptions. To which I should add, of course, that realism is not a recommendation to stay at home. Columbus managed to believe that the world was round but still took a pretty interesting trip — and discovered some unexpected things on the way!

Reprint No. 08110

Author Profile:
Art Kleiner (kleiner_art@ strategy-business.com) is editor-in-chief of strategy+ business and author of Who Really Matters: The Core Group Theory of Power, Privilege, and Success (Doubleday, 2003).

This article is from Spring 2008

strategy+business is published by the global commercial consulting firm Booz & Company.

©2008 Booz & Company. All rights reserved. "booz&co." is a service mark of Booz & Company.
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Forward-Looking Statements

This press release contains forward-looking statements relating to Intel’s future plans and expectations, including with respect to Intel’s process and packaging technology roadmaps and schedules; innovation cadence; future technology and products and the expected benefits and availability of such technology and products, including PowerVia, RibbonFET, Foveros Omni, and Foveros Direct technologies, future process nodes, and other technologies and products; technology parity and leadership; future use, benefits, and availability of EUV and other manufacturing tools; expectations regarding suppliers, partners, and customers; Intel’s strategy; manufacturing plans; manufacturing expansion and investment plans; and plans and goals related to Intel’s foundry business. Such statements involve a number of risks and uncertainties. Words such as “anticipates,” “expects,” “intends,” “goals,” “plans,” “believes,” “seeks,” “estimates,” “continues,” “may,” “will,” “would,” “should,” “could,” “strategy,” “progress,” “accelerate,” “path,” “on-track,” “roadmap,” “pipeline,” “cadence,” “momentum,” “positioned,” “committed,” and “deliver” and variations of such words and similar expressions are intended to identify forward-looking statements. Statements that refer to or are based on estimates, forecasts, projections, and uncertain events or assumptions also identify forward-looking statements. Such statements are based on management’s current expectations and involve many risks and uncertainties that could cause actual results to differ materially from those expressed or implied in these forward-looking statements. Important factors that could cause actual results to differ materially from the company’s expectations include, among others, Intel’s failure to realize the anticipated benefits of its strategy and plans; changes in plans due to business, economic, or other factors; actions taken by competitors, including changes in competitor technology roadmaps; changes impacting our projections regarding our technology or competing technology; delays in development or implementation of our future manufacturing technologies or failures to realize the anticipated benefits of such technologies, including expected improvements in performance and other factors; delays or changes in the design or introduction of future products; changes in customer needs or plans; changes in technology trends; our ability to rapidly respond to technological developments; delays, changes in plans, or other disruptions involving manufacturing tool and other suppliers; and other factors set forth in Intel’s reports filed or furnished with the Securities and Exchange Commission (SEC), including Intel’s most recent reports on Form 10-K and Form 10-Q, available at Intel’s investor relations website at www.intc.com and the SEC’s website at www.sec.gov. Intel does not undertake, and expressly disclaims any duty, to update any statement made in this press release, whether as a result of new information, new developments or otherwise, ~~except to the extent that disclosure may be required by law.~~

All product and service plans, roadmaps, and performance figures are subject to change without notice. Process performance parity and leadership expectations are based on performance-per-watt projections. Future node performance and other metrics, including power and density, are projections and are inherently uncertain.

source:
       https://www.businesswire.com/news/home/20210726005136/en/
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21
blackjack
21 (film)

you are given 2 cards,
get as close to 21 without going over,
going over 21 is called, a bust, or busting
getting an Ace, and a (Jack, Queen, or King) card, is equal to 21, or blackjack

Ace is count as 11 or 1

Blackjack has a history and a future
The cards that have been dealt absolutely influences the card that will be dealt

the plus 1/minus 1 balance system

card 2-6 are given the value of +1
card 7-9 are given the value of 0, they are neutral
card 10, face cards, and Ace are given the value of -1,

how many cards are left in the shoe?

when the count is high
when the count is low, you are likely to bust
over the long run, the count goes up and down

my answer is based on statistics, based on variable change

people always remember, if you don't know,
always account for variable change
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