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jerryking : predictions   24

We need to be better at predicting bad outcomes
September 2019 | Financial Times | by Tim Harford.

A question some of us ask all too often, and some of us not often enough: what if it all [jk: our plan] goes wrong?.....we don’t think about worst-case scenarios in the right way......
The first problem is that our sense of risk is pretty crude. The great psychologist Amos Tversky joked that most of us have three categories when thinking about probabilities: “gonna happen”, “not gonna happen” and “maybe”.....It would be helpful if our sense of risk was a little more refined; intuitively, it is hard to grasp the difference between a risk of one in a billion and that of one in a thousand. Yet, for a gambler — or someone in the closely related business of insurance — there is all the difference in the world.....research by Barbara Mellers, Philip Tetlock and Hal Arkes suggests that making a serious attempt to put probabilities on uncertain future events might help us in other ways: the process makes us more humble, more moderate and better able to discern shades of grey. Trying to forecast is about more than a successful prediction......we can become sidetracked by the question of whether the worst case is likely. Rather than asking “will this happen?”, we should ask “what would we do if it did?”

The phrase “worst-case scenario” probably leads us astray: anyone can dream up nightmare scenarios.....To help us think sensibly about these worst-case possibilities, Gary Klein, psychologist and author of Seeing What Others Don’t, has argued for conducting “pre-mortems” — or hypothetical postmortems. Before embarking on a project, imagine receiving a message from the future: the project failed, and spectacularly. Now ask yourself: why? Risks and snares will quickly suggest themselves — often risks that can be anticipated and prevented.......Contingency planning is not always easy......woes that would result both as the “base case” (the truth) and a “worst-case scenario” (the government sucking in its stomach while posing for a selfie).
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In our increasingly airbrushed world, it becomes ever more necessary to ask the unfashionable questions like ‘what could possibly go wrong?’ - and then plan for it...
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Humanity's survival may well rely on the ability of our imaginations to explore alternative futures in order to begin building the communities that can forestall or endure worst-case catastrophes.
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Amos_Tversky  anticipating  base_rates  beforemath  books  contingency_planning  discernment  failure  forecasting  foresight  frequency_and_severity  humility  nuanced  predictions  preparation  probabilities  risk-assessment  risks  Tim_Harford  uncertainty  worst-case 
26 days ago by jerryking
The Future Isn’t What It Used to Be
June 17, 2019 | WSJ | by Andy Kessler.

Founded in 1867, the Keuffel & Esser Co. commissioned a study of the future for its 100th anniversary. If you’re of a certain vintage, you might have used a K&E slide rule. Their “visionary” study was a huge dud, missing completely the electronic-calculator boom that came a few years later. They shut down their slide-rule engravers in 1976. As Mark Twain said, “It’s difficult to make predictions, especially about the future.” Or was it Niels Bohr? Maybe Yogi Berra?

My father was a proud member of the Book of the Month Club. Bored on a visit home in 1989, I devoured that month’s selection, “Megamistakes” by Baruch College professor Steven Schnaars, where I read about K&E’s study. The book’s message was simple: Don’t be fooled by prevailing opinion, and don’t extend trend lines into the future. Mr. Schnaars chronicles how 1950s jet-age thinking morphed into ’60s dreams of a space-age utopia. A 1966 study by conglomerate TRW forecast manned lunar bases by 1977, autonomous vehicles by 1979 and intelligent robot soldiers by the ’90s. AT&T ’s Picturephone service, ultrasonically cleaned dishes, cheap energy forever, future shock everywhere—all wrong.

Of course, the 1973 oil embargo changed everything. But by the end of the ’70s, expensive oil was considered permanent and the future was about scarcity and energy saving and we’d all be driving small cars with CB radios and living in R. Buckminster Fuller-inspired geodesic domes. General Electric even ramped up production of small refrigerators. Mistakes!Im-82150

Then the ’80s came along. A bull market and cheap oil lifted the ’70s fog, but everyone believed the Japanese would soon rule the world since they were kicking our butts in manufacturing and the Imperial Palace in Tokyo was worth more than all the real estate in California. Personal computers were mere toys. Oh, and the Soviet Union was a world superpower. Megamistakes!

After the ’87 crash and first Iraq war, the prospects for economic growth in the ’90s were dim. Then Netscape and its browser went public in 1995 and we were off to the races again. By 1999 techno-utopia was in full swing, and all you needed was a good name like burnmoney.com to raise millions and be worth kazillions. Gigamistake!

The Nasdaq’s dot-bomb implosion and 9/11 changed the mood quickly. In 2003 I tried to pitch a book about Silicon Valley and Wall Street and was told nobody would care about them ever again and asked if I knew anything about bioterrorism or Islamic fundamentalism. Uh, no. But I wish I knew about house or derivative flipping - that’s what the aughts were about, until the Great Recession. The 2010s were about holding cash, maybe in your mattress, vs. owning stocks. Oops— Apple , Amazon and Microsoft would soon flirt with trillion-dollar valuations. Teramistake?

Mr. Schnaars advised discounting extrapolations, playing down historical precedent, challenging assumptions, and distinguishing fads from growth markets. Easier said than done. The future happens, just not the way most people think. How you pick your investments, your job and even where you live can end up a dead end or the most vibrant upside imaginable. Choose carefully, but as Mr. Schnaars suggested, think for yourself.

Today low interest rates mean risk is on and caution is old-fashioned. Companies sell at 20 times revenues instead of earnings (Note: Beyond Meat is at 43 times its 2019 sales forecast, and Tableau Software recently sold for 16 times its 2018 revenue.) Politically, populism and nationalism have won the day. Internationally, China is the new U.S.S.R. Economically, the future is now. Will any of it last?

For a while, Tesla was valued as if every new car would soon be electric. The 2020s are still blurry, but apparently that doesn’t cloud the pundit class’s clear vision on climate change, drones, autonomous vehicles and the effect of artificial intelligence. We’ll all share cars, bikes, scooters and even pogo sticks. WeWork is valued as if we’ll all share offices. What’s next, communes?

My experience is that people tend to overestimate the absurd, like Elon Musk’s dreams of building a hyperloop and colonizing Mars, and underestimate the mundane, like improvements in messaging and shopping. I’m usually bullish until dreams become hallucinations. Technology develops in S curves: Things start slow, go into hyperbolic growth, and then roll over. That’s why “the singularity”—self-improving, unrestrained artificial intelligence—probably won’t happen. Don’t extend the trend.

The tempests of change blow hard. Reading the prevailing winds, we’re all about to become robot-replaced, drone-delivered-synthetic-meat-eating, augmented-reality-helmet-wearing, bitcoin-spending, fruit-flavored-vaping, neutered democratic socialists chirping “Comrade” and streaming “The Handmaid’s Tale” Season 10, “Dystopia’s Discontents,” on our watches while collecting universal basic income. You don’t need a slide rule to calculate the megamistakes.
Andy_Kessler  forecasting  future  linearity  mistakes  overestimation  predictions  S-curves  straight-lines  underestimation 
july 2019 by jerryking
We Survived Spreadsheets, and We’ll Survive AI - WSJ
By Greg Ip
Updated Aug. 2, 2017

History and economics show that when an input such as energy, communication or calculation becomes cheaper, we find many more uses for it. Some jobs become superfluous, but others more valuable, and brand new ones spring into existence. Why should AI be different?

Back in the 1860s, the British economist William Stanley Jevons noticed that when more-efficient steam engines reduced the coal needed to generate power, steam power became more widespread and coal consumption rose. More recently, a Massachusetts Institute of Technology-led study found that as semiconductor manufacturers squeezed more computing power out of each unit of silicon, the demand for computing power shot up, and silicon consumption rose.

The “Jevons paradox” is true of information-based inputs, not just materials like coal and silicon......Just as spreadsheets drove costs down and demand up for calculations, machine learning—the application of AI to large data sets—will do the same for predictions, argue Ajay Agrawal, Joshua Gans and Avi Goldfarb, who teach at the University of Toronto’s Rotman School of Management. “Prediction about uncertain states of the world is an input into decision making,” they wrote in a recent paper. .....Unlike spreadsheets, machine learning doesn’t yield exact answers. But it reduces the uncertainty around different risks. For example, AI makes mammograms more accurate, the authors note, so doctors can better judge when to conduct invasive biopsies. That makes the doctor’s judgment more valuable......Machine learning is statistics on steroids: It uses powerful algorithms and computers to analyze far more inputs, such as the millions of pixels in a digital picture, and not just numbers but images and sounds. It turns combinations of variables into yet more variables, until it maximizes its success on questions such as “is this a picture of a dog” or at tasks such as “persuade the viewer to click on this link.”.....Yet as AI gets cheaper, so its potential applications will grow. Just as better weather forecasting makes us more willing to go out without an umbrella, Mr. Manzi says, AI emboldens companies to test more products, strategies and hunches: “Theories become lightweight and disposable.” They need people who know how to use it, and how to act on the results.
artificial_intelligence  Greg_Ip  spreadsheets  machine_learning  predictions  paradoxes  Jim_Manzi  experimentation  testing  massive_data_sets  judgment  uncertainty  economists  algorithms  MIT  Gilder's_Law  speed  steam_engine  operational_tempo  Jevons_paradox  decision_making 
august 2017 by jerryking
VC Pioneer Vinod Khosla Says AI Is Key to Long-Term Business Competitiveness - CIO Journal. - WSJ
By STEVE ROSENBUSH
Nov 15, 2016

“Improbables, which people don’t pay attention to, are not unimportant, we just don’t know which improbable is important,” Mr. Khosla said. “So what do you do? You don’t plan for the highest likelihood scenario. You plan for agility. And that is a fundamental choice we make as a nation, in national defense, as the CEO of a company, as the CIO of an infrastructure, of an organization, and in the way we live.”....So change, and predictions for the future, that are important, almost never come from anybody who knows the area. Almost anyone you talk to about the future of the auto industry will be wrong on the auto industry. So, no large change in a space has come from an incumbent. Retail came from Amazon. SpaceX came from a startup. Genentech did biotechnology. Youtube, Facebook, Twitter did media … because there is too much conventional wisdom in industry. ....Extrapolating the past is the wrong way to predict the future, and improbables are not unimportant. People plan around high probability. Improbables, which people don’t pay attention to, are not unimportant, we just don’t know which improbable is important.
Vinod_Khosla  artificial_intelligence  autonomous_vehicles  outsiders  gazelles  unknowns  automotive_industry  change  automation  diversity  agility  future  predictions  adaptability  probabilities  Uber  point-to-point  public_transit  data  infrastructure  information_overload  unthinkable  improbables  low_probability  extrapolations  pay_attention 
november 2016 by jerryking
How to make good guesses
| FT | Tim Harford

“base rate”,

Base rates are not just a forecasting aid. They’re vital in clearly understanding and communicating all manner of risks. We routinely hear claims of the form that eating two rashers of bacon a day raises the risk of bowel cancer by 18 per cent. But without a base rate (how common is bowel cancer?) this information is not very useful. As it happens, in the UK, bowel cancer affects six out of 100 people; a bacon-rich diet would cause one additional case of bowel cancer per 100 people.

Thinking about base rates is particularly important when we’re considering screening programmes or other diagnostic tests, including DNA tests for criminal cases.
economics  howto  forecasting  predictions  guessing  probabilities  Tim_Harford  base_rates  ratios  communicating_risks 
april 2016 by jerryking
Why Stephen Harper is toast - The Globe and Mail
MARGARET WENTE
The Globe and Mail
Published Thursday, Sep. 10, 2015

At its heart, this election isn’t really about policies. It’s about change, and leadership, and tone. It is above all a referendum on Mr. Harper, a man who has been around for long enough and whose personal deficits are striking. The electorate’s centre of gravity hasn’t really shifted. People just want someone new.

Albertans didn’t elect NDP Premier Rachel Notley because they suddenly wanted to shut down the oil sands and invest in windmills. They elected her because they were fed up with the old boys, and she was a fresh and credible alternative, and it was past time for a change. Canadians don’t want a radical change of course, either. They want a fresh leader with fresh energy, fresh ideas, and a heart.
Margaret_Wente  Stephen_Harper  elections  predictions  Syrian_refugee_crisis  Federal_Election_2015 
september 2015 by jerryking
The Sensor-Rich, Data-Scooping Future - NYTimes.com
APRIL 26, 2015 | NYT | By QUENTIN HARDY.

Sensor-rich lights, to be found eventually in offices and homes, are for a company that will sell knowledge of behavior as much as physical objects....The Internet will be almost fused with the physical world. The way Google now looks at online clicks to figure out what ad to next put in front of you will become the way companies gain once-hidden insights into the patterns of nature and society.

G.E., Google and others expect that knowing and manipulating these patterns is the heart of a new era of global efficiency, centered on machines that learn and predict what is likely to happen next.

“The core thing Google is doing is machine learning,” Eric Schmidt....The great data science companies of our sensor-packed world will have experts in arcane reaches of statistics, computer science, networking, visualization and database systems, among other fields. Graduates in those areas are already in high demand.

Nor is data analysis just a question of computing skills; data access is also critically important. As a general rule, the larger and richer a data set a company has, the better its predictions become. ....an emerging area of computer analysis known as “deep learning” will blow away older fields.

While both Facebook and Google have snapped up deep-learning specialists, Mr. Howard said, “they have far too much invested in traditional computing paradigms. They are the equivalent of Kodak in photography.” Echoing Mr. Chui’s point about specialization, he said he thought the new methods demanded understanding of specific fields to work well.

It is of course possible that both things are true: Big companies like Google and Amazon will have lots of commodity data analysis, and specialists will find niches. That means for most of us, the answer to the future will be in knowing how to ask the right kinds of questions.
sensors  GE  GE_Capital  Quentin_Hardy  data  data_driven  data_scientists  massive_data_sets  machine_learning  automated_reasoning  predictions  predictive_analytics  predictive_modeling  layer_mastery  core_competencies  Enlitic  deep_learning  niches  patterns  analog  insights  latent  hidden  questions  Google  Amazon  aftermath  physical_world  specialization  consumer_behavior  cyberphysical  arcane_knowledge  artificial_intelligence  test_beds 
april 2015 by jerryking
Amazon to Sell Predictions in Cloud Race Against Google and Microsoft - NYTimes.com
By QUENTIN HARDY APRIL 9, 2015

Amazon Web Services announced that it was selling to the public the same kind of software it uses to figure out what products Amazon puts in front of a shopper, when to stage a sale or who to target with an email offer.

The techniques, called machine learning, are applicable for technology development, finance, bioscience or pretty much anything else that is getting counted and stored online these days. In other words, almost everything.
Quentin_Hardy  Amazon  Google  machine_learning  cloud_computing  AWS  Microsoft  Azure  predictions  predictive_analytics  predictive_modeling  automated_reasoning 
april 2015 by jerryking
Risky Business: BLG Sees Cyber Risks Underlining Challenges To Canadian Businesses
December 16, 2014

Borden Ladner Gervais Outlines 2015’s Top 10 Business Risks--Borden Ladner Gervais LLP’s predictions for 2015 are decidedly more worrying, as the firm issued a top ten list of business risks. At the top of the list, the firm says, is cybersecurity and the risks businesses face from hackers, data leaks, and social media. Others include risks related to First Nations land claims, anti-corruption enforcement and consumer class actions sparked by an increasing number of product recalls.
cyber_security  data_breaches  risks  cyberrisks  predictions  law_firms  Bay_Street  social_media  resilience  land_claim_settlements  product_recalls  anti-corruption  BLG  class_action_lawsuits 
january 2015 by jerryking
2014’s lessons for leaders: Don’t make assumptions, do make hard decisions - The Globe and Mail
BOB RAE
Special to The Globe and Mail
Published Friday, Dec. 26 2014,

Life has a way of lifting you by the lapels and giving you a good shake. Stuff happens, and when it does, it can throw all the steady paths predicted by pundits, pollsters and economic forecasters into the trash heap....Canadians are fixated on who the winners and losers of the "where will oil prices head" game ...but we need to lift our heads a bit. Russia’s falling ruble and the debt crisis of its elites and their companies have rightly grabbed headlines. But a couple of countries, notably Nigeria and Venezuela, are now in political crisis, and their very stability is at risk in the days ahead.

One of the implications of the 2008 world economic crisis is that regional and world institutions have much less room to manoeuvre and help sort things out. it will be harder for those agencies (EU, IMF) to do as much as is required. Stability doesn’t come cheap....a healthy dose of reality and skepticism is always a good idea. In a useful piece of advice, Rudyard Kipling reminded us that triumph and disaster are both imposters. People draw too many conclusions from current trends. They fail to understand that those trends can change. And that above all, they forget that events can get in the way....One clear lesson is for leaders everywhere to learn the importance of listening and engagement. The path to resolution of even the thorniest of problems...involves less rhetoric and bluster and a greater capacity to understand underlying interests and grievances. ... Engagement should never mean appeasement.
Bob_Rae  pundits  decision_making  leaders  unintended_consequences  predictions  WWI  humility  Toronto  traffic_congestion  crisis  instability  listening  engagement  unpredictability  Rudyard_Kipling  petro-politics  imposters  short-sightedness  amnesia_bias  interests  grievances  appeasement  hard_choices 
december 2014 by jerryking
From one pollster to another: Stop trying to predict elections - The Globe and Mail
BRUCE ANDERSON
Contributed to The Globe and Mail
Published Wednesday, Jun. 11 2014

To me, excellence in this profession is more about eternal curiosity, less about being convinced that you can predict tomorrow based on what you know about yesterday.

Lately, some in the polling industry have been indulging in an unhealthy, feverish competition to predict the outcome and seat distribution of every election. I think it’s a bit of a fool’s errand.

I’m personally enjoying the fact that the race for Ontario is down to the wire and the outcome is more uncertain than ever.

It’s a great time to remind ourselves that the suspense of a big unknown is more interesting than endless over-confident predictions about the chemistry of turnout rates and the implications of same for a handful of swing ridings.....the best value lies in the big picture, the context and the general reactions to parties, leaders and ideas.
elections  political_campaigns  predictions  opinion_polls_&_surveys  public_opinion  Bruce_Anderson  the_big_picture  contextual 
june 2014 by jerryking
Jeff Hawkins Develops a Brainy Big Data Company - NYTimes.com
November 28, 2012, 12:13 pmComment
Jeff Hawkins Develops a Brainy Big Data Company
By QUENTIN HARDY

Jeff Hawkins, who helped develop the technology in Palm, an early and successful mobile device, is a co-founder of Numenta, a predictive software company....Numenta’s product, called Grok, is a cloud-based service that works much the same way. Grok takes steady feeds of data from things like thermostats, Web clicks, or machinery. From initially observing the data flow, it begins making guesses about what will happen next. The more data, the more accurate the predictions become.
massive_data_sets  Grok  pattern_recognition  start_ups  streaming  aftermath  cloud_computing  predictions  predictive_analytics  Quentin_Hardy 
november 2012 by jerryking
Gordon Crovitz: Technology Predictions Are Mostly Bunk - WSJ.com
DECEMBER 27, 2009,| Wall Street Journal | By L. GORDON CROVITZ
Bill Gates, 1981: 'No one will need more than 637 kb of memory for a
personal computer.'

*
L._Gordon_Crovtiz  predictions  wisdom_of_crowds 
august 2010 by jerryking
reportonbusiness.com: Best to deliver bad news facts
February 18, 2009 G&M column by SUSAN PINKER. When it
comes to bad news, we first protect ourselves, and then we protect
others through "Denial". When there's really bad news, there's reliable
evidence that it really is best to face the facts. First, you have to
know what the bad news is, what the outcomes are, what the percentages
are," Dr. Feldman says. "Then you have to give people options. You have
to give them some power - ideas about how they're going to manage
because you don't just leave them hanging there. You have to hold out
some hope."
anomalies  base_rates  Communicating_&_Connecting  crisis  difficult_conversations  forecasting  generating_strategic_options  guessing  managing_people  predictions  probabilities  ratios  Susan_Pinker  face_the_facts  bad_news 
february 2009 by jerryking

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