Visualizzazione post con etichetta esperto. Mostra tutti i post
Visualizzazione post con etichetta esperto. Mostra tutti i post

giovedì 10 novembre 2016

Quanto è esperto l'esperto?

Fare previsioni è una cosa molto difficilespecialmente se riguardano il futuro
Niels Bohr
Fare previsioni sul futuro sarà anche difficile ma noi tutti le facciamo tutti i giorni.
Per esempio, quando cambiamo lavoro, o quando ci sposiamo, o quando cambiamo casa, quando compriamo un’obbligazione...
Siamo tutti dei profeti in miniatura.
Quando però sotto la lente finiscono i cosiddetti  i grandi eventi della politica, del costume o della storia, cediamo umilmente la parola all’esperto di turno.
Da Sergio Romano a Lucio Caracciolo fino a Maurizio Molinari e Bernardo Valli, gli esperti di scenari internazionali abbondano. Negli USA hanno Tom Friedman: se vuoi sapere qualcosa sul Medio Oriente chiedi a lui…
… If you are a White House staffer, you might find him in the Oval Office with the president of the United States, talking about the Middle East. If you are a Fortune 500 CEO, you might spot him in Davos, chatting in the lounge with hedge fund billionaires and Saudi princes. And if you don’t frequent the White House or swanky Swiss hotels, you can read his New York Times columns and bestselling books that tell you what’s happening now, why, and what will come next.1 Millions do…
Sono parole di Philip Tetlock – il più grande esperto di esperti - prese dal suo libro “Superforecasting”.
Bene, ora bisogna sapere che anche Bill Flack analizza scenari internazionali ma nessuno se lo fila o lo invita ai talk show. Tom Friedman non sa neanche chi sia e la vita di Bill non è particolarmente eccitante…
… he worked for the US Department of Agriculture in Arizona… now he lives in Kearney, Nebraska… He was a good student in high school… he went to the University of Arizona. He was aiming for a PhD in math, but he realized it was beyond his abilities… he dropped out… then got a job with the Department of Agriculture and stayed for a while… Bill is fifty-five and retired… So he has free time. And he spends some of it forecasting…
Bill risponde a domande tipo
… “Will Russia officially annex additional Ukrainian territory in the next three months?” and “In the next year, will any country withdraw from the eurozone?”… “Will North Korea detonate a nuclear device before the end of this year?” “How many additional countries will report cases of the Ebola virus in the next eight months?” “Will India or Brazil become a permanent member of the UN Security Council in the next two years?”… “Will NATO invite new countries to join the Membership Action Plan (MAP) in the next nine months?” “Will the Kurdistan Regional Government hold a referendum on national independence this year?” “If a non-Chinese telecommunications firm wins a contract to provide Internet services in the Shanghai Free Trade Zone in the next two years, will Chinese citizens have access to Facebook and/or Twitter?”…
Non ha molti indizi per rispondere correttamente ma lavora duro per raccogliere le informazioni e bilanciare i pro e i contro, finché non giunge ad una delibera.
Purtroppo nessuno lo invita alla CNN o al Costanzo show, nemmeno Gad Lerner si cura di lui. A Davos lo avrebbero snobbato se solo avessero saputo chi fosse.
E questo è un peccato per il semplice fatto che Bill Flack è un ottimo profeta.
Le sue previsioni sono datate, registrate e verificate con accuratezza scientifica. Molte volte sbaglia, nonostante questo il suo record track è eccellente.
In migliaia affrontano la prova a cui si sottopone lui: ingegneri, avvocati, artisti, speculatori di borsa, professori, studenti, esperti del settore. Molti di loro danno ottima prova di sè, potremmo chiamarli “superprofeti”.
Tom Friedman e Lucio Caracciolo fanno parte di questa schiera? Non lo sapremo mai perché quelli che noi chiamiamo “esperti” non amano che venga controllato quel che dicono, cosicché sono del tutto privi di “record track”.
Bisogna dare atto che Tom Friedman ventilava qualcosa sulla primavera araba, aveva sentore che l’avventura in Iraq fosse un rischio ma non si è mai cimentato con previsioni sufficientemente precise da poter essere controllate.
Noi, ma anche i governi, si affidano ad esperti che fanno previsioni la cui qualità ci è sconosciuta ed è destinata a rimanere tale.
Un tempo girava la barzelletta per cui “l’esperto medio non batterebbe una scimmia che getta i dadi”. Le cose stavano all’incirca in questi termini…
… It goes like this: A researcher gathered a big group of experts—academics, pundits, and the like—to make thousands of predictions about the economy, stocks, elections, wars, and other issues of the day. Time passed, and when the researcher checked the accuracy of the predictions, he found that the average expert did about as well as random guessing. Except that’s not the punch line because “random guessing” isn’t funny. The punch line is about a dart-throwing chimpanzee. Because chimpanzees are funny. I am that researcher… My study was the most comprehensive assessment of expert judgment… I also didn’t mind because the joke makes a valid point… Open any newspaper, watch any TV news show, and you find experts who forecast… With few exceptions, they are not in front of the cameras because they possess any proven skill at forecasting… Old forecasts are like old news—soon forgotten… I was happy to see my research used to give it to them…
Oggi l’esperto non è chi fa previsioni di qualità ma chi sa descrivere una situazione raccontando storie interessanti…
… The one undeniable talent that talking heads have is their skill at telling a compelling story with conviction, and that is enough. Many have become wealthy peddling forecasting of untested value to corporate executives, government officials, and ordinary people who would never think of swallowing medicine of unknown efficacy and safety but who routinely pay for forecasts that are as dubious as elixirs sold from the back of a wagon…
In realtà, la barzelletta della scimmia non è supportata dagli studi che fornivano lo spunto, secondo i quali l’esperto prevede comunque un pochino meglio dell’uomo della strada.
Solo se ci spingiamo a 3/5 anni ci si abbassa tutti a livello “scimmiesco”. Ma l’idea per cui tutte le previsioni sono ugualmente attendibili resta sbagliata.
In realtà è possibile scrutare il futuro. Bisogna essere intelligenti, curiosi, motivati, con la mente aperta e disposti a lavorare duro.
Capisco che sulla questione si possa essere scettici: chi avrebbe potuto prevedere il timing delle primavere arabe
… To understand the “skeptic” half of that label, consider a young Tunisian man pushing a wooden handcart loaded with fruits and vegetables down a dusty road to a market in the Tunisian town of Sidi Bouzid. When the man was three, his father died. He supports his family by borrowing money to fill his cart, hoping to earn enough selling the produce to pay off the debt and have a little left over. It’s the same grind every day. But this morning, the police approach the man and say they’re going to take his scales because he has violated some regulation. He knows it’s a lie. They’re shaking him down. But he has no money. A policewoman slaps him and insults his dead father. They take his scales and his cart. The man goes to a town office to complain. He is told the official is busy in a meeting. Humiliated, furious, powerless, the man leaves. He returns with fuel. Outside the town office he douses himself, lights a match, and burns. Only the conclusion of this story is unusual. There are countless poor street vendors in Tunisia and across the Arab world…But this particular humiliation, on December 17, 2010, caused Mohamed Bouazizi, aged twenty-six, to set himself on fire, and Bouazizi’s self-immolation sparked protests. The police responded with typical brutality. The protests spread. Hoping to assuage the public, the dictator of Tunisia, President Zine el-Abidine Ben Ali, visited Bouazizi in the hospital. Bouazizi died on January 4, 2011. The unrest grew. On January 14, Ben Ali fled to a cushy exile in Saudi Arabia, ending his twenty-three-year kleptocracy. The Arab world watched, stunned. Then protests erupted in Egypt, Libya, Syria, Jordan, Kuwait, and Bahrain. After three decades in power, the Egyptian dictator Hosni Mubarak was driven from office. Elsewhere, protests swelled into rebellions, rebellions into civil wars….
Raccontare questa storia è bello e forse istruttivo, Tom Freidman lo fa nel migliore dei modi. Tuttavia, prevederla è praticamente impossibile. Anche conoscendo nel dettaglio la situazione di quei paesi, la scintilla avrebbe potuto scoccare un anno prima o un anno dopo.
Il nume tutelare degli scettici è il meteorologo Edward Lorenz per cui il batter d’ali di una farfalla in Cina puo’ scatenare un uragano in Texas…
… Lorenz had discovered by accident that tiny data entry variations in computer simulations of weather patterns—like replacing 0.506127 with 0.506—could produce dramatically different long-term forecasts. It was an insight that would inspire “chaos theory”: in nonlinear systems like the atmosphere, even small changes in initial conditions can mushroom to enormous proportions….
La farfalla è una delle mille cause necessarie del tornado ma resta comunque necessaria, e quindi da tenere in grande considerazione: poiché non è possibile farlo la previsione diventa velleitaria.
Se la farfalla non avesse battuto le ali, se la polizia non avesse sequestrato la merce di Mohamed Bouazizi, se mia nonna avesse avuto le ruote… Tutto questo pone limiti seri alle profezie.
Eppure, per secoli, gli scienziati hanno indicato nella prevedibilità perfetta una meta alla portata. Uno per tutti: Pierre-Simon Laplace
… We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes….
Il credo determinista: se conosco le leggi di natura e la situazione attuale, conosco perfettamente il futuro.
Paradossalmente, oggi conosciamo molte più cose e siamo anche molto più scettici sulla nostra capacità di prevedere.
Il fatto è che abbiamo studiato le nuvole e ne siamo rimasti molto impressionati…
… High school science tells us that clouds form when water vapor coalesces around dust particles. This sounds simple but exactly how a particular cloud develops—the shape it takes—depends on complex feedback interactions among droplets. To capture these interactions, computer modelers need equations that are highly sensitive to tiny butterfly-effect errors in data collection…
Purtroppo, quello che capita in cielo capita anche sulla terra
… A woman living in a Kansas City suburb may think Tunisia is another planet, and her life has no connection to it, but if she were married to an air force navigator who flies out of the nearby Whiteman Air Force Base, she might be surprised to learn that one obscure Tunisian’s actions led to protests, that led to riots, that led to the toppling of a dictator, that led to protests in Libya, that led to a civil war, that led to the 2012 NATO intervention, that led to her husband dodging antiaircraft fire over Tripoli…
Tuttavia, anche in un mondo complesso possiamo prevedere molte cose e lo facciamo tutti i giorni. Stamattina mi sono alzato un quarto d’ora prima per evitare l’ondata di traffico e mi è andata bene: notare che per avere successo in una simile previsione devo anticipare la vita di migliaia di persone, una vita che puo’ mutare il suo corso per un nonnulla.
Previsioni del genere sono di routine, fanno parte della nostra esistenza. L’Enel sa quando ci saranno i picchi nei consumi e aumenta le potenze disponibili. Questo sapere è un sapere che riguarda un mondo estremamente complesso. E vogliamo dire di quel che sanno le assicurazioni?
… Kansas City Life Insurance Company is in the business of forecasting disability and death, and it does a good job. That doesn’t mean it knows precisely when I will die, but it does have a good idea of how long someone of my age and profile—sex, income, lifestyle—is likely to live. Kansas City Life was founded in 1895. If its actuaries weren’t good forecasters, it would have gone bankrupt long ago…
Google o facebook ti conoscono al punto di farti proposte personalizzate ad orologeria.
Ma la nostra realtà assomiglia più ad un orologio o ad una nuvola?
E’ un misto delle due cose, meglio non aggregarsi alla compagnia degli estremisti. Prevedibilità e imprevedibilità coesistono. Quel che conta è che si possono fare progressi
… Weather forecasts are typically quite reliable, under most conditions, looking a few days ahead, but they become increasingly less accurate…
L’unico modo di procedere è a tentoni, non c’è altra via…
… meteorologists are able to sharpen their understanding of how weather works and tweak their models. Then they try again. Forecast, measure, revise. Repeat. It’s a never-ending process of incremental improvement that explains why weather forecasts are good and slowly getting better…
La rivoluzione informatica e il big data possono aiutarci a fare un passettino oltre, inutile negarlo.
Ma il problema riguarda la domanda di accuratezza. Contrariamente alle apparenze, spesso noi “non vogliamo sapere”, non vogliamo testarci e non vogliamo testare i nostri beniamini. Eppure “misurare” è il segreto per migliorarsi.
Nessuna misurazione, nessuna revisione nei metodi. Nessuna revisione, nessun miglioramento.
Bill Gates
…  “I have been struck by how important measurement is to improving the human condition,” Bill Gates wrote. “You can achieve incredible progress if you set a clear goal and find a measure that will drive progress toward that goal. … This may seem basic, but it is amazing how often it is not done and how hard it is to get right.”…
Il fatto che non misuriamo non deve sorprendere. Diamo per scontato che fare previsioni serva a prevedere ma non è affatto così. A volte serve solo a divertirsi
… Think of CNBC’s Jim Cramer with his “booyah!” shtick, or John McLaughlin, the host of The McLaughlin Group, bellowing at his panelists to predict the likelihood of an event “on a scale from zero to ten, with zero representing zero possibility and ten representing complete metaphysical certitude!”…
Altre volte la profezia è solo un’arma politica: serve a dare speranze e a fare in modo che si auto-avveri.
A volte le previsioni si fanno per impressionare. In questi casi il profeta deve avere il phisique du role, dal curriculum all’abito indossato, tutto è curato al millimetro (tranne il record track). Le grandi banche e i governi sono i suoi clienti più generosi: sfoggiare certi esperti è segno di serietà.
Altre previsioni si fanno per confortare l’audience: il militante ha bisogno di tenere alto il morale. Una previsione benigna è come un bagno caldo.
Quando le funzioni della profezia sono così numerose e diversificate non c’è da meravigliarsi se poi nessuno perde tempo a verificare. Per la maggior parte degli scopi questa attività non serve affatto, anzi è dannosa!
Ma questo è solo motivo per essere fiduciosi: c’è ampio spazio di miglioramento.
Non resta che ricorrere al solito vecchio buon metodo sperimentale. Il Good Judgment Project (GJP) potrebbe essere un buon punto di partenza…
… we started in the summer of 2011, when my research (and life) partner Barbara Mellers and I launched the Good Judgment Project and invited volunteers to sign up and forecast the future. Bill Flack responded. So did a couple of thousand others that first year, and thousands more in the four years that followed. Cumulatively, more than twenty thousand intellectually curious laypeople tried to figure out if protests in Russia would spread, the price of gold would plummet, the Nikkei would close above 9,500, war would erupt on the Korean peninsula, and many other questions about complex, challenging global issues. By varying the experimental conditions, we could gauge which factors improved foresight, by how much, over which time frames, and how good forecasts could become if best practices were layered on each other… this is… part of a much larger research effort sponsored by the Intelligence Advanced Research Projects Activity (IARPA)… intelligence community that reports to the director of National Intelligence…
Nei nostri governi lavorano ben pagati molti esperti e nessuno si preoccupa della qualità previsionale che esprimono…
… By one rough estimate, the United States has twenty thousand intelligence analysts assessing everything from minute puzzles to major events such as the likelihood of an Israeli sneak attack on Iranian nuclear facilities or the departure of Greece from the eurozone… intelligence community, like so many major producers of forecasting, has never been keen on spending money to figure that out… this forecasting is critical to national security… To change that, IARPA created a forecasting tournament in which five scientific teams led by top researchers in the field would compete to generate accurate forecasts on the sorts of tough questions intelligence analysts deal with every day. The Good Judgment Project was one of those five teams. Each team would effectively be its own research project, free to improvise whatever methods it thought would work, but required to submit forecasts at 9 a.m. eastern standard time every day from September 2011 to June 2015. By requiring teams to forecast the same questions at the same time, the tournament created a level playing field—and a rich trove of data about what works, how well, and when. Over four years, IARPA posed nearly five hundred questions about world affairs…
Risultati
… In year 1, GJP beat the official control group by 60%. In year 2, we beat the control group by 78%. GJP also beat its university-affiliated competitors, including the University of Michigan and MIT, by hefty margins, from 30% to 70%, and even outperformed professional intelligence analysts with access to classified data. After two years, GJP was doing so much better than its academic competitors that IARPA dropped the other teams…
Conclusione: si puo’ migliorare.
L’abilità previsionale esiste, gente come Bill Flack la possiede: è reale e misurabile.
Qual è il segreto? Un particolare modo di pensare. Di raccogliere informazioni, di cambiare o aggiornare le proprie credenze. Tutta roba che si puo’ coltivare e imparare. Non è difficile.
Aaron Brown – un gestore di fondi d’investimento – ci tiene a spiegare che diventare dei buoni profeti è difficile perché troppo banale, troppo prosaico, basterebbe un minuto manualetto per contenere l’essenziale:
… “It’s so hard to see because it’s not dramatic”… “it’s the difference between a consistent winner who’s making a living, or the guy who’s going broke all the time.”…
I pokeristi conoscono bene la differenza tra l’ottimo giocatore e chi si guadagna da vivere con il poker
… The difference between heavyweights and amateurs, she said, is that the heavyweights know the difference between a 60⁄40 bet and a 40⁄60 bet…
E’ possibile migliorare limitandosi a misurare. Solo constatare l’errore ci spingerà a cambiare. Non lo facciamo per questioni psicologiche, e così ci convinciamo di sapere cose che non sappiamo.
La medicina insegna l’importanza della misurazione…
… For centuries, it hobbled progress in medicine… When physicians finally accepted that their experience and perceptions were not reliable means of determining whether a treatment works, they turned to scientific testing—and medicine finally started to make rapid advances…
Non conta tanto l’intelligenza. Certo, i “superprofeti” sono persone sveglie ma…
Non conta tanto la cultura. Certo, i “superprofeti” sono dei forti lettori ma…
Non conta tanto la matematica. Certo, i “superprofeti” ne sono appassionati ma…
… Superforecasting does require minimum levels of intelligence, numeracy, and knowledge of the world, but anyone who reads serious books about psychological research probably has those prerequisites…
Conta la testa, il modo di pensare: un “superprofeta” è curioso, attento, motivato, autocritico, vuole migliorarsi ed è disposto a cambiare per questo.
Il “superprofeta” vuole continuamente misurarsi e un feedback negativo è vissuto come la possibilità di progredire. Tutto questo è estremamente faticoso, la maggior parte di noi si fa abbattere dai feedback negativi, ma le motivazioni servono proprio a superare questo ostacolo.
Domanda legittima: l’avvento delle macchine non renderà obsoleto il “superprofeta”? Gli algoritmi e big data non lo detronizzeranno?…
… In 1954, a brilliant psychologist, Paul Meehl wrote a small book that caused a big stir. It reviewed twenty studies showing that well-informed experts predicting outcomes—whether a student would succeed in college or a parolee would be sent back to prison—were not as accurate as simple algorithms…
In molti casi l’algoritmo batte l’esperto. Infatti, vige una chiara legge: se hai un buon algoritmo usalo. Ma non sempre ce l’hai.
Puo’ darsi che i progressi della tecnologia segnino una discontinuità
… In 1997 IBM’s Deep Blue beat chess champion Garry Kasparov. Now, commercially available chess programs can beat any human. In 2011 IBM’s Watson beat Jeopardy! champions Ken Jennings and Brad Rutter. That was a vastly tougher computing challenge, but Watson’s engineers did it…
David Ferrucci, il padre di Watson, oggi è molto cauto sull’intelligenza artificiale. Anche se IA galoppa, le previsioni dei “superprofeti” non sono ancora alla loro portata e forse non lo saranno mai.
Quello che vedremo sarà una sintesi di uomo e macchina, un po’ quello che già succede negli scacchi free-style.
A chi è più utile il navigatore delle auto? Al guidatore che interagisce con lui al meglio! Non a quello che lo snobba ma nemmeno a quello si affida passivamente alle sue indicazioni. Ecco allora un esempio di tecnologia free-style che fa parte della nostra esperienza quotidiana.
Il “superprofeta” partorirà le sue profezie interagendo con le sue macchine e mantenendo il “comando” delle operazioni.
Uomo + Macchina batterà sia l’ Uomo che la Macchina. Mischiare è la ricetta migliore.
Ma soprattutto a sparire saranno i Guru, ovvero il modello oggi predominante.
Secondo Ferrucci a risentirne sarà l’autorità di chi esprime un giudizio soggettivo.
Forse ne risentirà anche chi campa di polemiche…
… that makes so many policy debates so puerile: “I’ll counter your Paul Krugman polemic with my Niall Ferguson counterpolemic, and rebut your Tom Friedman op-ed with my Bret Stephens blog.”…
Scommettere è un veleno mortale per le diatribe infinite.
HeadintheClouds_06

venerdì 28 ottobre 2016

1 An Optimistic Skeptic - SUPERFORECASTER PHILIP TETLOCK

1 An Optimistic SkepticRead more at location 40
Note: bill flack è un superforecaster. come lui ce ne sono altri. domande tipo a cui risponde un s.: la russia annetterà i territori ucraini entro tre anni? l india entrerà nel consiglio di sicurezza onu tra un anno? quali paesi abbandoneranno l euro nei prossimi 5 anni? scopo del libro: spiegare il modus operandi dei s. e xchè sono tali. xchè bill nn è un editorialista del nyt? nn lo sappiamo xchè nn abbiamo un track record degli editorialisti solo opinioni su opinioni ma vaghe nn testabili. tipo: se la nato aprirà all india la russia reagirà esponendoci ad una nuova guerra fredda. ci interessa di più sapere se la juve ha fatto un buon acquisto o sapere se ci sarà un genocidio in sud sudan? sembrerebbe che la juve ci stia molto più a cuore. s. ha delle qualità che possono essere coltivate da tutti. l esperto prevede come una scimmia coi dadi? la ventennale ricerca di tetlock sfiora qs paradosso ma dice anche qlcs di più costruttivo.. l esperto fa un pochino meglio del profano solo l orizzonte 3/5 anni lo fa diventare scimmia gli eclettici fanno meglio degli specialistici. dobbiamo fidarci dell esperto? coltiviamo uno scettico ottimismo. xchè scetticismo? prendi la primavera araba nasce con una storia come tante. avrebbe potuta succedere l anno prima. facile da razionalizzare ma difficile da prevedere. quante cose può fars un battito d ali di farfalla! teoria del caos: nella simulazione pc di sistemi complessi basta variare di poco un dato e gli esiti s invertono. laplace: più sappiamo più sapremo prevedere. se sappiamo tutto del presente sapremo tutto del futuro: il mondo è un orologio. meccanicismo lorentz: no il mondo è una nuvola. i feedback radicali ci espongono a variazioni infinitesimali. non sapremo mai come evolve una nuvola. paradosso: oggi gli scienziati sanno di più ma sono più scettici sulle previsioni... complessità: legge goodheart/lucas: la realtà cambia nel momento stesso in cui viene prevista. x i siatemi instabili la previsione è impossibile. x altri viene a dipendere dalle farfalle ed è quindi difficile. altri sistemi sono più stabili. xchè ottimismo? xchè ci sono cose che si possono prevedere. x es. se ci sará traffico x andare al mare... le assicurazioni fanno molti soldi prevedendo con successo... il mondo è un misto di orologi e nuvole tutto è prob e margine d errore.. track record. essenziali x migliorare ma anche poco adottati. forse prevale lo scettico/scettico. pochi rivelano l accuratezza e quasi nessuno giudica l esperto in base a quella.. il problema della domanda: pochi chiedono conto delle evidenze... poca misurazione poca revisione pochi miglioramenti. distinguiamo: previsione x migliorare la conoscenza. previsione x divertirsi. previsione fatta x autoavverarsi. solo le prome due richiedono misurazione. ci sono anche p. fatte x impressionare: il superconsulente o il supereditorialista. anche qui il r.t. è inutile. altre confortano il militante come un bagno caldo. niente rt ovviamente. ottimismo: conoscendo tante distorsioni sappiamo dove migliorare. torneo: 5 squadre che x 4 anni rispondono a una domanda al giorno su affari e politica con una p. il metodo è libero. il gruppo deu superf. batte tutti il gruppo di controllo come il gruppo accademici. conclusione 1: la capacitá previsionale esiste ed è misurabile e nn si identifica con la potenza accademica... coclusione 2: la capacità p. nn è un talento ma un modo di pensare. un modo di aggiornare le proprie credenze. conoscere la differenza tra 60/40 e 40/60. l esperto conosce i nessi ma il superf sa pesarli. xchè progressi tanto lenti? psicologia: crediamo di conoscere quel che nn conosciamo (x esempio se panebianco è un buon p.). la differenza nn la fa l ideologia. nn la fa nemmeno l accesso ai dati e nemmeno l intelligenza. la matematica nn viene mai usata. il s. nn è uno sgobbone. conta COME si pensa. il s. è autocritico. sa correggersi. vuole migliorarsi. qs significa che è curioso e aprrto. classifica 1 s. 2 algoritmo statistico 3 esperto 4 persona comune. ma l algoritmo afgidabile è raramente disponibile x il problema da affrontare al momento. ia: nel 65 sembrava prossima. oggi siamo più cauti. il s. del futuro: un uomo col pc. l uomo fa sintesi della complessità che manda in palla un pc. il pc corregge i bias dell uomo. Edit
Note: 1@@@@@@@@@@@@ Edit
WE ARE ALL forecasters.Read more at location 41
When we think about changing jobs, getting married, buying a home, making an investment, launching a product, or retiring, we decide based on how we expect the future will unfold. These expectations are forecasts.Read more at location 42
Note: x Edit
But when big events happen—Read more at location 43
we turn to the experts,Read more at location 44
like Tom Friedman.Read more at location 44
If you are a White House staffer, you might find him in the Oval Office with the president of the United States, talking about the Middle East. If you are a Fortune 500 CEO, you might spot him in Davos, chatting in the lounge with hedge fund billionaires and Saudi princes. And if you don’t frequent the White House or swanky Swiss hotels, you can read his New York Times columns and bestselling books that tell you what’s happening now, why, and what will come next.1 Millions do.Read more at location 44
Note: x Edit
Like Tom Friedman, Bill Flack forecasts global events. But there is a lot less demand for his insights.Read more at location 49
worked for the US Department of Agriculture in Arizona—“Read more at location 50
now he lives in Kearney, Nebraska.Read more at location 51
He was a good student in high schoolRead more at location 52
he went to the University of Arizona. He was aiming for a PhD in math, but he realized it was beyond his abilitiesRead more at location 53
he dropped out.Read more at location 55
then got a job with the Department of Agriculture and stayed for a while.Read more at location 56
Bill is fifty-five and retired,Read more at location 57
So he has free time. And he spends some of it forecasting.Read more at location 57
Bill has answered roughly three hundred questions like “Will Russia officially annex additional Ukrainian territory in the next three months?” and “In the next year, will any country withdraw from the eurozone?”Read more at location 58
Note: X DOMANDE TIPO Edit
“Will North Korea detonate a nuclear device before the end of this year?” “How many additional countries will report cases of the Ebola virus in the next eight months?” “Will India or Brazil become a permanent member of the UN Security Council in the next two years?”Read more at location 61
Note: c Edit
“Will NATO invite new countries to join the Membership Action Plan (MAP) in the next nine months?” “Will the Kurdistan Regional Government hold a referendum on national independence this year?” “If a non-Chinese telecommunications firm wins a contract to provide Internet services in the Shanghai Free Trade Zone in the next two years, will Chinese citizens have access to Facebook and/or Twitter?”Read more at location 63
Note: c Edit
he may have no clueRead more at location 66
But he does his homework. He gathers facts, balances clashing arguments, and settles on an answer.Read more at location 67
No one bases decisions on Bill Flack’s forecasts, or asks Bill to share his thoughts on CNN.Read more at location 68
never been invited to DavosRead more at location 69
that’s unfortunate. Because Bill Flack is a remarkable forecaster.Read more at location 69
each one of Bill’s predictions has been dated, recorded, and assessed for accuracy by independent scientific observers.Read more at location 70
His track record is excellent.Read more at location 71
thousands of others answering the same questions.Read more at location 71
engineers and lawyers, artists and scientists, Wall Streeters and Main Streeters, professors and students.Read more at location 72
I call them superforecastersRead more at location 74
Explaining why they’re so good,Read more at location 74
my goalRead more at location 75
accuracy of Friedman’s forecasting has never been rigorously tested.Read more at location 76
he nailed the Arab Spring”Read more at location 78
“he screwed up on the 2003 invasion of Iraq”Read more at location 78
But there are no hard facts about Tom Friedman’s track record, just endless opinions—and opinions on opinions.Read more at location 79
Every day, corporations and governmentsRead more at location 81
And every day, all of us—Read more at location 82
make critical decisions on the basis of forecasts whose quality is unknown.Read more at location 83
THE ONE ABOUT THE CHIMPRead more at location 89
Note: T Edit
the average expert was roughly as accurate as a dart-throwing chimpanzee. You’ve probably heard that one before.Read more at location 90
It goes like this: A researcher gathered a big group of experts—academics, pundits, and the like—to make thousands of predictions about the economy, stocks, elections, wars, and other issues of the day. Time passed, and when the researcher checked the accuracy of the predictions, he found that the average expert did about as well as random guessing. Except that’s not the punch line because “random guessing” isn’t funny. The punch line is about a dart-throwing chimpanzee. Because chimpanzees are funny. I am that researcherRead more at location 92
Note: x LE COSE SONO ANDATE COSÌ Edit
My study was the most comprehensive assessment of expert judgmentRead more at location 97
I also didn’t mind because the joke makes a valid point.Read more at location 100
Open any newspaper, watch any TV news show, and you find experts who forecastRead more at location 101
With few exceptions, they are not in front of the cameras because they possess any proven skill at forecasting.Read more at location 102
Old forecasts are like old news—soon forgotten—Read more at location 103
The one undeniable talent that talking heads have is their skill at telling a compelling story with conviction, and that is enough. Many have become wealthy peddling forecasting of untested value to corporate executives, government officials, and ordinary people who would never think of swallowing medicine of unknown efficacy and safety but who routinely pay for forecasts that are as dubious as elixirs sold from the back of a wagon.Read more at location 104
Note: x Edit
I was happy to see my research used to give it to them.Read more at location 108
What my research had shown was that the average expert had done little better thanRead more at location 109
approaching the dart-throwing-chimpanzee level three to five years out.Read more at location 111
The message became “all expert forecasts are useless,” which is nonsense.Read more at location 115
My research had become a backstop reference for nihilistsRead more at location 116
debunkers go too far when they dismiss all forecastingRead more at location 122
I believe it is possible to see into the future,Read more at location 122
and that any intelligent, open-minded, and hardworking person can cultivate the requisite skills.Read more at location 123
THE SKEPTICRead more at location 124
Note: T DOVE ESSERE SCETTICI Edit
To understand the “skeptic” half of that label, consider a young Tunisian man pushing a wooden handcart loaded with fruits and vegetables down a dusty road to a market in the Tunisian town of Sidi Bouzid. When the man was three, his father died. He supports his family by borrowing money to fill his cart, hoping to earn enough selling the produce to pay off the debt and have a little left over. It’s the same grind every day. But this morning, the police approach the man and say they’re going to take his scales because he has violated some regulation. He knows it’s a lie. They’re shaking him down. But he has no money. A policewoman slaps him and insults his dead father. They take his scales and his cart. The man goes to a town office to complain. He is told the official is busy in a meeting. Humiliated, furious, powerless, the man leaves. He returns with fuel. Outside the town office he douses himself, lights a match, and burns. Only the conclusion of this story is unusual. There are countless poor street vendors in Tunisia and across the Arab world.Read more at location 125
Note: x UN CASO IMPORTANTE MA IMPREVEDIBILE Edit
But this particular humiliation, on December 17, 2010, caused Mohamed Bouazizi, aged twenty-six, to set himself on fire, and Bouazizi’s self-immolation sparked protests. The police responded with typical brutality. The protests spread. Hoping to assuage the public, the dictator of Tunisia, President Zine el-Abidine Ben Ali, visited Bouazizi in the hospital. Bouazizi died on January 4, 2011. The unrest grew. On January 14, Ben Ali fled to a cushy exile in Saudi Arabia, ending his twenty-three-year kleptocracy. The Arab world watched, stunned. Then protests erupted in Egypt, Libya, Syria, Jordan, Kuwait, and Bahrain. After three decades in power, the Egyptian dictator Hosni Mubarak was driven from office. Elsewhere, protests swelled into rebellions, rebellions into civil wars.Read more at location 134
Note: c Edit
This was the Arab Spring—andRead more at location 140
It is one thing to look backward and sketch a narrativeRead more at location 142
Tom Friedman, like many elite pundits, is skilled at thatRead more at location 143
having made his name in journalism as a New York Times correspondent in Lebanon.Read more at location 144
Maybe, given how much Friedman knew about the region, he would have mused that poverty and unemployment were high,Read more at location 146
desperate young people was growing,Read more at location 147
But an observer could have drawn exactly the same conclusion the year before.Read more at location 148
In 1972 the American meteorologist Edward LorenzRead more at location 150
Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?”Read more at location 151
Lorenz had discovered by accident that tiny data entry variations in computer simulations of weather patterns—like replacing 0.506127 with 0.506—could produce dramatically different long-term forecasts. It was an insight that would inspire “chaos theory”: in nonlinear systems like the atmosphere, even small changes in initial conditions can mushroom to enormous proportions.Read more at location 152
Note: x Edit
Lorenz didn’t mean that the butterfly “causes” the tornadoRead more at location 156
He meant that if that particular butterfly hadn’t flapped its wingsRead more at location 157
the police had just let Mohamed Bouazizi sell his fruitsRead more at location 159
hard limits on predictability,Read more at location 161
For centuries, scientists had supposed that growing knowledge must lead to greater predictabilityRead more at location 162
Pierre-Simon Laplace took this dreamRead more at location 165
We may regard the present state of the universe as the effect of its past and the cause of its future. An intellect which at a certain moment would know all forces that set nature in motion, and all positions of all items of which nature is composed, if this intellect were also vast enough to submit these data to analysis, it would embrace in a single formula the movements of the greatest bodies of the universe and those of the tiniest atom; for such an intellect nothing would be uncertain and the future just like the past would be present before its eyes.Read more at location 166
Note: x LAPLACE Edit
If it knew everything about the present, Laplace thought, it could predict everything about the future.Read more at location 171
Lorenz poured cold rainwaterRead more at location 172
Lorenzian cloud.Read more at location 173
High school science tells us that clouds form when water vapor coalesces around dust particles. This sounds simple but exactly how a particular cloud develops—the shape it takes—depends on complex feedback interactions among droplets. To capture these interactions, computer modelers need equations that are highly sensitive to tiny butterfly-effect errors in data collection.Read more at location 173
Note: x NUVOLE Edit
scientists today know vastly moreRead more at location 177
but they are much less confidentRead more at location 178
This is a big reason for the “skeptic” half of my “optimistic skeptic”Read more at location 179
A woman living in a Kansas City suburb may think Tunisia is another planet, and her life has no connection to it, but if she were married to an air force navigator who flies out of the nearby Whiteman Air Force Base, she might be surprised to learn that one obscure Tunisian’s actions led to protests, that led to riots, that led to the toppling of a dictator, that led to protests in Libya, that led to a civil war, that led to the 2012 NATO intervention, that led to her husband dodging antiaircraft fire over Tripoli.Read more at location 181
Note: X ES Edit
THE OPTIMISTRead more at location 188
Note: T Edit
Why did she leave home at 6:30? She didn’t want to get stuck in rush hour.Read more at location 197
Note: C È MOLTO DI PREVEDIBILE NELLA VITA DEGLI UOMINI Edit
she predicted that trafficRead more at location 197
rush hour is highly predictable.Read more at location 198
She expected the people who said they would join the 10:30 conference call to do so, and she was right.Read more at location 199
mundane predictions like these routinely,Read more at location 202
predictions that shape our lives.Read more at location 202
When the woman went to Amazon, the website highlighted certain products it thought she would like,Read more at location 205
Google personalizes search results by putting what it thinks you will find most interestingRead more at location 207
Kansas City Life Insurance Company is in the business of forecasting disability and death, and it does a good job. That doesn’t mean it knows precisely when I will die, but it does have a good idea of how long someone of my age and profile—sex, income, lifestyle—is likely to live. Kansas City Life was founded in 1895. If its actuaries weren’t good forecasters, it would have gone bankrupt long ago.Read more at location 208
Note: x ASS Edit
I just Googled tomorrow’s sunrise and sunset times for Kansas City, Missouri, and got them down to the minute.Read more at location 212
A good restaurant is very likely to open its doors when it says it will, but it may not, for any number of reasons,Read more at location 215
There are no certainties inRead more at location 218
Note: TUTTO È PROB Edit
So is reality clocklike or cloud-like?Read more at location 221
false dichotomies,Read more at location 221
We live in a world of clocks and cloudsRead more at location 222
Unpredictability and predictability coexistRead more at location 222
Weather forecasts are typically quite reliable, under most conditions, looking a few days ahead, but they become increasingly less accurateRead more at location 225
relationship between time and predictability:Read more at location 228
further we try to look into the future, the harder it is to see.Read more at location 228
exceptions to the rule.Read more at location 229
separating the predictable from the unpredictable is difficult work.Read more at location 233
meteorologists are able to sharpen their understanding of how weather works and tweak their models. Then they try again. Forecast, measure, revise. Repeat. It’s a never-ending process of incremental improvement that explains why weather forecasts are good and slowly getting better.Read more at location 235
Note: PROVA E RIPROVA Edit
Big leaps in computing power and continued refinement of forecasting models may nudge the limits a little furtherRead more at location 239
Accuracy is seldom determined after the fact and is almost never done with sufficient regularityRead more at location 246
Mostly it’s a demand-side problem:Read more at location 247
there is no measurement. Which means no revision.Read more at location 248
no improvement.Read more at location 248
“I have been struck by how important measurement is to improving the human condition,” Bill Gates wrote. “You can achieve incredible progress if you set a clear goal and find a measure that will drive progress toward that goal. … This may seem basic, but it is amazing how often it is not done and how hard it is to get right.”Read more at location 252
Note: x BILL GATES Edit
You might think the goal of forecasting is to foresee the futureRead more at location 257
Sometimes forecasts are meant to entertain.Read more at location 258
Think of CNBC’s Jim Cramer with his “booyah!” shtick, or John McLaughlin, the host of The McLaughlin Group, bellowing at his panelists to predict the likelihood of an event “on a scale from zero to ten, with zero representing zero possibility and ten representing complete metaphysical certitude!”Read more at location 258
Note: x es Edit
forecasts are used to advance political agendas and galvanize action—Read more at location 260
Note: PROF AUTOAVVER Edit
There is also dress-to-impress forecasting—which is what banks deliver when they pay a famous punditRead more at location 262
some forecasts are meant to comfort—byRead more at location 263
Partisans are fond of these forecasts.Read more at location 264
the cognitive equivalent of slipping into a warm bath.Read more at location 264
It’s a messy situation, which doesn’t seem to be getting better. And yet this stagnation is a big reason why I am an optimisticRead more at location 265
For scientists, not knowing is exciting. It’s an opportunity to discover;Read more at location 268
Note: sfida Edit
all we have to do is set a clear goal—accuracy!—and get serious about measuring.Read more at location 270
I’ve been doing that for much of my career. The research that produced the dart-throwing-chimpanzee result was phase one. Phase two started in the summer of 2011, when my research (and life) partner Barbara Mellers and I launched the Good Judgment Project and invited volunteers to sign up and forecast the future. Bill Flack responded. So did a couple of thousand others that first year, and thousands more in the four years that followed. Cumulatively, more than twenty thousand intellectually curious laypeople tried to figure out if protests in Russia would spread, the price of gold would plummet, the Nikkei would close above 9,500, war would erupt on the Korean peninsula, and many other questions about complex, challenging global issues. By varying the experimental conditions, we could gauge which factors improved foresight, by how much, over which time frames, and how good forecasts could become if best practices were layered on each other.Read more at location 271
Note: X GOOD PROJ Edit
(GJP)Read more at location 279
part of a much larger research effort sponsored by the Intelligence Advanced Research Projects Activity (IARPA).Read more at location 279
intelligence community that reports to the director of National IntelligenceRead more at location 281
By one rough estimate, the United States has twenty thousand intelligence analysts assessing everything from minute puzzles to major events such as the likelihood of an Israeli sneak attack on Iranian nuclear facilities or the departure of Greece from the eurozone.Read more at location 283
Note: x Edit
How good is all this forecasting?Read more at location 285
intelligence community, like so many major producers of forecasting, has never been keen on spending money to figure that out.Read more at location 286
this forecasting is critical to national securityRead more at location 287
To change that, IARPA created a forecasting tournament in which five scientific teams led by top researchers in the field would compete to generate accurate forecasts on the sorts of tough questions intelligence analysts deal with every day. The Good Judgment Project was one of those five teams. Each team would effectively be its own research project, free to improvise whatever methods it thought would work, but required to submit forecasts at 9 a.m. eastern standard time every day from September 2011 to June 2015. By requiring teams to forecast the same questions at the same time, the tournament created a level playing field—and a rich trove of data about what works, how well, and when. Over four years, IARPA posed nearly five hundred questions about world affairs.Read more at location 289
Note: X IL PROGEYYO Edit
In year 1, GJP beat the official control group by 60%. In year 2, we beat the control group by 78%. GJP also beat its university-affiliated competitors, including the University of Michigan and MIT, by hefty margins, from 30% to 70%, and even outperformed professional intelligence analysts with access to classified data. After two years, GJP was doing so much better than its academic competitors that IARPA dropped the other teams.Read more at location 296
Note: x RISULTATI Edit
key conclusionsRead more at location 300
One, foresight is real. Some people—people like Bill Flack—have it in spades.Read more at location 300
they do have a real, measurable skillRead more at location 301
what makes these superforecasters so good.Read more at location 303
Note: ? Edit
It is the product of particular ways of thinking,Read more at location 304
gathering information,Read more at location 304
updating beliefs.Read more at location 304
habits of thought can be learned and cultivatedRead more at location 304
One result that particularly surprised me was the effect of a tutorial coveringRead more at location 305
Ten CommandmentsRead more at location 307
I spoke about that with Aaron Brown,Read more at location 309
“It’s so hard to see because it’s not dramatic,”Read more at location 310
“it’s the difference between a consistent winner who’s making a living, or the guy who’s going broke all the time.”Read more at location 310
world-class poker playerRead more at location 312
The difference between heavyweights and amateurs, she said, is that the heavyweights know the difference between a 60⁄40 bet and a 40⁄60 bet.Read more at location 312
Note: x Edit
it’s possible to improve foresight simply by measuring,Read more at location 314
why isn’t measuring standard practice?Read more at location 315
answer to that question lies in the psychologyRead more at location 315
convinces us we know things we really don’t—Read more at location 315
For centuries, it hobbled progress in medicine.Read more at location 317
When physicians finally accepted that their experience and perceptions were not reliable means of determining whether a treatment works, they turned to scientific testing—and medicine finally started to make rapid advances.Read more at location 317
Note: x Edit
Chapter 3 examines what it takes to test forecastingRead more at location 319
In the late 1980s I worked out a methodology and conducted what was, at the time, the biggest test of expertRead more at location 320
one group of experts had modest but real foresight.Read more at location 323
What made the differenceRead more at location 323
It was how they thought.Read more at location 325
Why are they so good? That question runs through chapters 5 through 9.Read more at location 327
you might suspect it’s intelligenceRead more at location 328
It’s not.Read more at location 329
many have advanced degrees in mathematics and science. So is the secret arcane math? No.Read more at location 329
rarely use much math.Read more at location 330
They also tend to be newsjunkiesRead more at location 330
so you might be tempted to attribute their success to spending endless hours on the job. Yet that too would be a mistake.Read more at location 331
Superforecasting does require minimum levels of intelligence, numeracy, and knowledge of the world, but anyone who reads serious books about psychological research probably has those prerequisites.Read more at location 332
Note: x Edit
what matters most is how the forecaster thinks.Read more at location 334
superforecasting demands thinking that is open-minded, careful, curious, and—above all—self-critical. It also demands focus.Read more at location 335
judgment does not come effortlessly.Read more at location 336
commitment to self-improvementRead more at location 337
A FORECAST ABOUT FORECASTINGRead more at location 340
Note: T Edit
maybe you think this is all hopelessly outdated. After all, we live in an era of dazzlingly powerful computers,Read more at location 341
algorithms,Read more at location 342
Big Data.Read more at location 342
In 1954, a brilliant psychologist, Paul Meehl wrote a small book that caused a big stir.12 It reviewed twenty studies showing that well-informed experts predicting outcomes—whether a student would succeed in college or a parolee would be sent back to prison—were not as accurate as simple algorithmsRead more at location 344
Note: x ALGO VS ESPERTI Edit
in most cases statistical algorithms beat subjective judgment,Read more at location 348
The point is now indisputable: when you have a well-validated statistical algorithm, use it.Read more at location 349
rarely have well-validated algorithms for the problem at hand.Read more at location 351
But spectacular advances in information technology suggest we are approaching a historical discontinuityRead more at location 352
In 1997 IBM’s Deep Blue beat chess champion Garry Kasparov. Now, commercially available chess programs can beat any human. In 2011 IBM’s Watson beat Jeopardy! champions Ken Jennings and Brad Rutter. That was a vastly tougher computing challenge, but Watson’s engineers did it.Read more at location 353
Note: X DISCONTONUITÀ Edit
Watson’s chief engineer, David Ferrucci.Read more at location 357
In 1965 the polymath Herbert Simon thought we were only twenty years away from a world in which machines could do “any work a man can do,”Read more at location 360
naively optimistic thingRead more at location 362
Ferrucci—who has worked in artificial intelligence for thirty years—is more cautious today.Read more at location 362
Even with computers making galloping advances, the sort of forecasting that superforecasters do is a long way off.Read more at location 370
Machines may get better at “mimicking human meaning,”Read more at location 372
“there’s a difference between mimicking and reflecting meaning and originating meaning,”Read more at location 373
Note: FERRUCCI X Edit
we will also see more and more syntheses, like “freestyle chess,Read more at location 376
humans with computersRead more at location 376
The result is a combination that can (sometimes) beat both humans and machines.Read more at location 377
What Ferrucci does see becoming obsolete is the guru modelRead more at location 379
that makes so many policy debates so puerile: “I’ll counter your Paul Krugman polemic with my Niall Ferguson counterpolemic, and rebut your Tom Friedman op-ed with my Bret Stephens blog.”Read more at location 379
Note: x LA POLEMICA Edit
“I think it’s going to get stranger and stranger” for people to listen to the advice of experts whose views are informed only by their subjective judgment.Read more at location 381
Note: FERRUCCI Edit
“So what I want is that human expert paired with a computer to overcome the human cognitive limitations and biases.”Read more at location 383
need to blend computer-based forecasting and subjective judgmentRead more at location 385