What Black Swan Author Nassim Taleb Has to Say About Insurance
When Nassim Nicholas Taleb addressed a group of insurance executives last month, the author of the book “The Black Swan” was pretty straightforward in delivering his view on what insurers can learn from finance professionals.
When Nassim Nicholas Taleb addressed a group of insurance executives last month, the author of the book “The Black Swan” was pretty straightforward in delivering his view on what insurers can learn from finance professionals.
Executive Summary
Black Swan Author Nassim Nicholas Taleb gave a short course on risk taking to insurers gathered at a recent conference, praising them for knowing how to limit their losses contractually and warning them not to adopt the risk-taking behavior of financial professionals.Not much. “We’re not good at risk taking,” the former options trader and renowned author said. Speaking at the KPMG’s Insurance Industry Conference in September, Taleb discussed the difference between well-behaved risks—for which the “portfolio effect” of diversification works well for traders and insurers of non-catastrophe lines—and extreme tail risks.“You guys know which is which. People in finance, they don’t know,” he said. In fact, he said the only time insurers really got into trouble was when they learned how to lose money from financial types during the financial crisis.“This is a beautiful industry. I marvel at the sophistication of insurance,” he told the insurance crowd. “They make mistakes when they go to finance. The AIG problem was entirely a finance problem not an insurance problem,” he said, referring to American International Group’s problems with financial products or credit default swaps that moved AIG toward a near-collapse and a federal bailout in 2008.In particular, Taleb praised insurers for knowing how to limit extreme—low frequency, high severity—losses so they don’t spiral out of control with tools like exclusions, caps, limits and pricing adjustments. “And you guys learn from example,” he said, referring to the unlimited liabilities that wiped out the Names that invested in Lloyd’s in the 1980s.Contractual limitations are the way to go to keep the impacts of low frequency-high severity losses in check, he said.“There are two ways to approach the problem of risk. The first one is trying to understand the dynamics of the world. Interesting, but you’re not going to get very far. The second one is to make sure your contract insulates you from it.”“In other words, what do you need a statistician or a lawyer? You need both but I’d rather have 10 lawyers for every statistician,” said Taleb who identified himself as also being a statistician.Lawyers and insurance underwriters can change contracts to protect themselves from risks they don’t understand, he reasoned. “My principle is that it’s preferable to take risks you understand then try to understand risk you’re taking. How do you do that? It’s much easier to change a risk contractually than then [get a grip] on the future.“If I don’t understand something, exclude it….You people know a lot about what exclusion means. If you have an uncapped claim, it is Extremistan,” he said, referring to a metaphoric world of extreme risks that he introduced in his book “The Black Swan.”Black Swan Basics: Don’t Be a TurkeyTaleb started his talk at the KPMG conference by introducing the two risk environments described in his books: Mediocrastan and Extremistan.First describing the well-behaved world of Mediocrastan, he asked the audience to imagine an experiment in which they would randomly select 10,000 people from the world and put them on a scale. Then add to the mix the heaviest human being they can find. “How much of the total would that person represent? Tiny. [Maybe] two basis points,” Taleb said.“That’s the law of large numbers. That’s what you have in insurance. It’s called diversification; it’s called the portfolio effect. It works….As your sample becomes large, not a single outlier—what we call a Black Swan—can effect it.”Going a step further, Taleb asked the insurance executives in the audience to imagine adding the richest person on the planet to the sample. “How much of the total wealth will that person represent?” Nearly 100 percent, he said, noting that an average of in the hundreds of thousands for the others would now be overshadowed by the $70-odd billion of the richest person. “The portfolio effect doesn’t work in a domain that’s entirely dominated by outliers,” he said.Similarly, very few companies determine the total capitalization, he said noting that when he was a trader, about 30 of the 12,000 companies listed on major stock exchanges represented half the capitalization. “Welcome to Extremistan where the winner take all effect prevails,… where very few people determine the net worth, where very few companies determine the total capitalization,” and where very few events determine profit or loss, he said.Black swans are Extemistan phenomena, he said, referring to events that fall outside the realm of regular expectations. While his published works refer to the sighting of a black swan at a point in time when only swans with white feathers had been previously seen, at the KPMG conference (and in other presentations), Taleb used the example of a turkey to underscore the extreme impact of a black swan event.A turkey is fed by a butcher every day, and everyday confirms to the turkey that the butcher loves turkeys with increased statistical confidence until a day in November that he didn’t see coming, when he becomes Thanksgiving dinner. “The twist to that story is those who make money on Wall Street, people keep giving them more money [and] the turkey problem has a cumulative effect,” he said.Warning against the notion of risk-free money, he said, “When someone makes money four years in a row, people start saying hello to you… And then you make money in more years, they will laugh at all your jokes. [But] if you apply insurance concepts, you know there’s something fishy,” he said. While banks call themselves geniuses, they lost more money in one event in the 1980s than in the history of banking, referring to a figure in the trillions. “Even asbestos did not harm you that much,” Taleb said.The optimal strategy is to keep from having the tail risk blowups of Extremistan by limiting the downside losses contractually, he suggested. As he spoke, he displayed a cartoon slide of one turkey saying to another, “Be reasonable Frank. Why does he feed us so much?” Insurers are as insightful as the wise turkey, his remarks suggested.The Role of Predictive ModelsTaleb spoke to the attendees of insurance conference following opening remarks by Laura Hay, KPMG’s insurance practice leader, who advanced the idea that predictive models are a critical tool for driving “transformative growth” for insurers struggling to grow top lines and dealing with lagging investment returns and other obstacles to bottom-line profit growth.That prompted a follow-up question from Constance Hunter, KPMG’s chief economist, who interviewed Taleb on stage after his presentation. What did he think about the use of predictive models in insurance, Hunter wanted to know.“Predictive modeling works beautifully for Mediocristan,” Taleb said, noting that for much of the risk that property/casualty insurers take on can, in fact, be effectively modeled. Reinsurance—not so much, he said. “Reinsurance has fat tails. But they know it. The more you are in Extremistan, the less these things work,” he said. “The good thing about insurance is that they [underwriters] know where [the models] don’t work.”They charge more premiums. They know that earthquakes are very unpredictable as is the intensity and damage from a hurricane. For these risks, “the models are shockingly inaccurate,” he asserted. “But people know it. They take the worst of the models” from a multi-model approach, he said.He continued: “Predictive modeling does not work for socioeconomic variables. Period. Zero.”Referring to the global financial crisis in 2008, Taleb underscored his point. “You know how many models there are on the planet? How many of them saw the crisis?”No model predicted it, he said. “That’s always been the story of forecasting,” he said, suggesting that extreme events are never captured within the models before they occur. “Next year, we got it right,” he said, referring to typical after-the-fact proclamations of forecasters who claim to have seen the extreme unpredictable risks coming in hindsight.Giving a different summary of when predictive models will work and where they won’t, Taleb said, “Variables that have time in them. Beware. Things that have space in them, no.” He clarified the space reference saying that if you are predicting a result for a cross-section of the population, for example, models can work well.“Most of insurance is immune from the problems of Extremistan and predictive analytics will work. They will also work very well with terrorism,” he said, referring to the “one-dimensional problem” of predicting whether someone is likely to be a terrorist. “All you want to know if the person has killed someone before. Yes/no. Do they hang around with someone who has killed someone before? Yes/no. Do they hang around with someone who knows someone who has killed someone before?”Similarly, predictive modes can work for fraud. “Stereotypes can pretty much predict who is going to be committing fraud.”“Can you predict cyber terror? No,” he continued. “That’s an Extremistan problem. Individual crime you can profile but big cyber” is too complex, he suggested.Hunter also asked Taleb to offer investment advice for P/C and life insurers in the current low-yield environment, and the author and risk expert repeatedly urged insurers to invest conservatively. “Never yield hog,” he said at one point.What about derivatives investments?“Don’t use derivatives. Please don’t,” Taleb said without elaborating much. “I was a derivative trader for a long time. Not just a trader, I’m also a quant,” he said, giving this succinct advice: “Short term options OK. Listed options, yes. Anything that is complicated, and requires a quant, no.”
Black Swan Author Nassim Nicholas Taleb gave a short course on risk taking to insurers gathered at a recent conference, praising them for knowing how to limit their losses contractually and warning them not to adopt the risk-taking behavior of financial professionals.
Not much. “We’re not good at risk taking,” the former options trader and renowned author said. Speaking at the KPMG’s Insurance Industry Conference in September, Taleb discussed the difference between well-behaved risks—for which the “portfolio effect” of diversification works well for traders and insurers of non-catastrophe lines—and extreme tail risks.“You guys know which is which. People in finance, they don’t know,” he said. In fact, he said the only time insurers really got into trouble was when they learned how to lose money from financial types during the financial crisis.
“This is a beautiful industry. I marvel at the sophistication of insurance,” he told the insurance crowd. “They make mistakes when they go to finance. The AIG problem was entirely a finance problem not an insurance problem,” he said, referring to American International Group’s problems with financial products or credit default swaps that moved AIG toward a near-collapse and a federal bailout in 2008.
In particular, Taleb praised insurers for knowing how to limit extreme—low frequency, high severity—losses so they don’t spiral out of control with tools like exclusions, caps, limits and pricing adjustments. “And you guys learn from example,” he said, referring to the unlimited liabilities that wiped out the Names that invested in Lloyd’s in the 1980s.
Contractual limitations are the way to go to keep the impacts of low frequency-high severity losses in check, he said.
“There are two ways to approach the problem of risk. The first one is trying to understand the dynamics of the world. Interesting, but you’re not going to get very far. The second one is to make sure your contract insulates you from it.”
“In other words, what do you need a statistician or a lawyer? You need both but I’d rather have 10 lawyers for every statistician,” said Taleb who identified himself as also being a statistician.
Lawyers and insurance underwriters can change contracts to protect themselves from risks they don’t understand, he reasoned. “My principle is that it’s preferable to take risks you understand then try to understand risk you’re taking. How do you do that? It’s much easier to change a risk contractually than then [get a grip] on the future.
“If I don’t understand something, exclude it….You people know a lot about what exclusion means. If you have an uncapped claim, it is Extremistan,” he said, referring to a metaphoric world of extreme risks that he introduced in his book “The Black Swan.”
Black Swan Basics: Don’t Be a Turkey
Taleb started his talk at the KPMG conference by introducing the two risk environments described in his books: Mediocrastan and Extremistan.
First describing the well-behaved world of Mediocrastan, he asked the audience to imagine an experiment in which they would randomly select 10,000 people from the world and put them on a scale. Then add to the mix the heaviest human being they can find. “How much of the total would that person represent? Tiny. [Maybe] two basis points,” Taleb said.
“That’s the law of large numbers. That’s what you have in insurance. It’s called diversification; it’s called the portfolio effect. It works….As your sample becomes large, not a single outlier—what we call a Black Swan—can effect it.”
Going a step further, Taleb asked the insurance executives in the audience to imagine adding the richest person on the planet to the sample. “How much of the total wealth will that person represent?” Nearly 100 percent, he said, noting that an average of in the hundreds of thousands for the others would now be overshadowed by the $70-odd billion of the richest person. “The portfolio effect doesn’t work in a domain that’s entirely dominated by outliers,” he said.
Similarly, very few companies determine the total capitalization, he said noting that when he was a trader, about 30 of the 12,000 companies listed on major stock exchanges represented half the capitalization. “Welcome to Extremistan where the winner take all effect prevails,… where very few people determine the net worth, where very few companies determine the total capitalization,” and where very few events determine profit or loss, he said.
Black swans are Extemistan phenomena, he said, referring to events that fall outside the realm of regular expectations. While his published works refer to the sighting of a black swan at a point in time when only swans with white feathers had been previously seen, at the KPMG conference (and in other presentations), Taleb used the example of a turkey to underscore the extreme impact of a black swan event.
A turkey is fed by a butcher every day, and everyday confirms to the turkey that the butcher loves turkeys with increased statistical confidence until a day in November that he didn’t see coming, when he becomes Thanksgiving dinner. “The twist to that story is those who make money on Wall Street, people keep giving them more money [and] the turkey problem has a cumulative effect,” he said.
Warning against the notion of risk-free money, he said, “When someone makes money four years in a row, people start saying hello to you… And then you make money in more years, they will laugh at all your jokes. [But] if you apply insurance concepts, you know there’s something fishy,” he said. While banks call themselves geniuses, they lost more money in one event in the 1980s than in the history of banking, referring to a figure in the trillions. “Even asbestos did not harm you that much,” Taleb said.
The optimal strategy is to keep from having the tail risk blowups of Extremistan by limiting the downside losses contractually, he suggested. As he spoke, he displayed a cartoon slide of one turkey saying to another, “Be reasonable Frank. Why does he feed us so much?” Insurers are as insightful as the wise turkey, his remarks suggested.
The Role of Predictive Models
Taleb spoke to the attendees of insurance conference following opening remarks by Laura Hay, KPMG’s insurance practice leader, who advanced the idea that predictive models are a critical tool for driving “transformative growth” for insurers struggling to grow top lines and dealing with lagging investment returns and other obstacles to bottom-line profit growth.
That prompted a follow-up question from Constance Hunter, KPMG’s chief economist, who interviewed Taleb on stage after his presentation. What did he think about the use of predictive models in insurance, Hunter wanted to know.
“Predictive modeling works beautifully for Mediocristan,” Taleb said, noting that for much of the risk that property/casualty insurers take on can, in fact, be effectively modeled. Reinsurance—not so much, he said. “Reinsurance has fat tails. But they know it. The more you are in Extremistan, the less these things work,” he said. “The good thing about insurance is that they [underwriters] know where [the models] don’t work.”
They charge more premiums. They know that earthquakes are very unpredictable as is the intensity and damage from a hurricane. For these risks, “the models are shockingly inaccurate,” he asserted. “But people know it. They take the worst of the models” from a multi-model approach, he said.
He continued: “Predictive modeling does not work for socioeconomic variables. Period. Zero.”
Referring to the global financial crisis in 2008, Taleb underscored his point. “You know how many models there are on the planet? How many of them saw the crisis?”
No model predicted it, he said. “That’s always been the story of forecasting,” he said, suggesting that extreme events are never captured within the models before they occur. “Next year, we got it right,” he said, referring to typical after-the-fact proclamations of forecasters who claim to have seen the extreme unpredictable risks coming in hindsight.
Giving a different summary of when predictive models will work and where they won’t, Taleb said, “Variables that have time in them. Beware. Things that have space in them, no.” He clarified the space reference saying that if you are predicting a result for a cross-section of the population, for example, models can work well.
“Most of insurance is immune from the problems of Extremistan and predictive analytics will work. They will also work very well with terrorism,” he said, referring to the “one-dimensional problem” of predicting whether someone is likely to be a terrorist. “All you want to know if the person has killed someone before. Yes/no. Do they hang around with someone who has killed someone before? Yes/no. Do they hang around with someone who knows someone who has killed someone before?”
Similarly, predictive modes can work for fraud. “Stereotypes can pretty much predict who is going to be committing fraud.”
“Can you predict cyber terror? No,” he continued. “That’s an Extremistan problem. Individual crime you can profile but big cyber” is too complex, he suggested.
Hunter also asked Taleb to offer investment advice for P/C and life insurers in the current low-yield environment, and the author and risk expert repeatedly urged insurers to invest conservatively. “Never yield hog,” he said at one point.
What about derivatives investments?
“Don’t use derivatives. Please don’t,” Taleb said without elaborating much. “I was a derivative trader for a long time. Not just a trader, I’m also a quant,” he said, giving this succinct advice: “Short term options OK. Listed options, yes. Anything that is complicated, and requires a quant, no.”
Cisne Negro N-Taleb y los Seguros
Cuando Nassim Taleb se dirigió a un grupo de ejecutivos de seguros el mes pasado, el autor del libro "El Cisne Negro" era bastante sencillo en la entrega de su punto de vista sobre lo que las aseguradoras pueden aprender de los profesionales de las finanzas.
Resumen ejecutivo
Cisne Negro Autor Nassim Taleb dio un curso corto sobre la toma de riesgos a las aseguradoras se reunieron en una conferencia reciente, elogiarlos por saber cómo limitar sus pérdidas contractualmente y advirtiéndoles de no adoptar el comportamiento toma de riesgos de los profesionales financieros."Ustedes saben cuál es cuál. La gente en las finanzas, que no saben ", dijo. De hecho, dijo que las únicas aseguradoras tiempo realmente se metió en problemas fue cuando se enteraron de cómo perder el dinero de los tipos financieros durante la crisis financiera.
"Este es un hermoso industria. Me maravilla la sofisticación de los seguros ", dijo a la multitud de seguros. "Se equivocan cuando van a financiar. El problema de AIG era del todo un problema financiero no un problema de los seguros ", dijo, en referencia a los problemas de American International Group con productos financieros o credit default swaps que se movían AIG hacia un casi colapso y rescate federal en 2008.
En particular, Taleb elogió las aseguradoras para saber cómo limitar extrema baja frecuencia, alta gravedad-pérdidas para que no se salen de control con herramientas como las exclusiones, los casquillos, los límites y ajustes de precios."Y ustedes aprende de ejemplo", dijo, en referencia a las responsabilidades ilimitadas que acabó con los nombres que invirtieron en Lloyd en la década de 1980.
Limitaciones contractuales son el camino a seguir para mantener a los impactos de las pérdidas de severidad de frecuencia alta bajo control, dijo.
"Hay dos maneras de abordar el problema del riesgo. La primera de ellas es tratar de comprender la dinámica del mundo. Interesante, pero no vas a llegar muy lejos.El segundo es para asegurarse de que su contrato le aísla de ella ".
"En otras palabras, ¿qué es lo que necesita un estadístico o un abogado? Usted necesita tanto pero prefiero tener 10 abogados por cada estadístico ", dijo Taleb que se identificó como siendo también un estadístico.
Los abogados y aseguradoras pueden cambiar los contratos para protegerse de los riesgos que no entienden, razonó. "Mi principio es que es preferible tomar riesgos a entender luego tratar de comprender el riesgo que está tomando. cómo lo haces?Es mucho más fácil cambiar un riesgo contractualmente que entonces [obtener un control] en el futuro.
"Si yo no entiendo algo, excluirla ... la gente .you saben mucho acerca de lo que significa la exclusión. Si usted tiene una reclamación sin tope salarial, es Extremistán ", dijo, refiriéndose a un mundo metafórico de extrema corre el riesgo de que él introdujo en su libro" El Cisne Negro ".
Fundamentos Cisne Negro: No sea una Turquía
Taleb comenzó su discurso en la conferencia de KPMG mediante la introducción de los dos entornos de riesgo descritos en sus libros: Mediocrastan y Extremistán.
En primer lugar describir el mundo de buen comportamiento de Mediocrastan, le pidió a la audiencia a imaginar un experimento en el que se seleccionará al azar a 10.000 personas del mundo y ponerlos en una escala. A continuación, añadir a la mezcla el más pesado ser humano que pueden encontrar. "¿Cuánto del total representaría esa persona? Tiny. [Tal vez] dos puntos básicos ", dijo Taleb.
"Esa es la ley de los grandes números. Eso es lo que tienes en el seguro. Se llama la diversificación; que se llama el efecto de cartera. Trabaja ... .Como su muestra se hace grande, ni un solo caso atípico, lo que llamamos un Cisne Negro-pueden efectuarla ".
Yendo un paso más allá, Taleb pidió a los ejecutivos de seguros en la audiencia de imaginar añadiendo la persona más rica del planeta a la muestra. "¿Qué parte de la riqueza total será esa persona represente?" Casi el 100 por ciento, dijo, y señaló que en promedio en los cientos de miles de los otros ahora se ve ensombrecida por los $ 70 mil millones extraña de la persona más rica. "El efecto de cartera no funciona en un dominio que ha dominado totalmente por los valores extremos", dijo.
Del mismo modo, muy pocas empresas determinan la capitalización total, él dijo y señaló que cuando él era un comerciante, cerca de 30 de los 12.000 que cotizan en las principales bolsas de valores de las empresas representadas la mitad de la capitalización. "Bienvenido a Extremistán donde el ganador se lleva todo efecto prevalece, ... donde muy pocas personas a determinar el valor neto, donde muy pocas empresas determinan la capitalización total", y donde muy pocos eventos determinan el resultado del ejercicio, dijo.
Cisnes negros son fenómenos Extemistan, dijo, refiriéndose a los acontecimientos que están fuera del ámbito de las expectativas normales. Aunque sus obras publicadas se refieren a la observación de un cisne negro en un punto en el tiempo cuando sólo cisnes con plumas blancas se habían visto con anterioridad, en la conferencia de KPMG (y en otras presentaciones), Taleb utiliza el ejemplo de un pavo para subrayar la extrema impacto de un evento cisne negro.
Un pavo es alimentado por un carnicero cada día, y cada día confirma que el pavo que el carnicero ama pavos con mayor confianza estadística hasta que un día en noviembre que no vio venir, cuando se convierte en la cena de Acción de Gracias."La vuelta de tuerca a la historia es los que hacen dinero en Wall Street, las personas mantienen dándoles más dinero [y] el problema de pavo tiene un efecto acumulativo", dijo.
Advertencia contra la noción de dinero libre de riesgo, dijo, "Cuando alguien hace dinero cuatro años en una fila, la gente comienza a decir hola a usted ... Y luego te hacen dinero en más años, se reirán de todos sus chistes. [Pero] si se aplican los conceptos de seguros, usted sabe que hay algo raro ", dijo. Mientras que los bancos llaman a sí mismos genios, perdieron más dinero en un evento en la década de 1980 que en la historia de la banca, en referencia a una figura en los billones de dólares. "Incluso el amianto no te daña mucho", dijo Taleb.
La estrategia óptima es para no tener los estallidos de riesgo de cola de Extremistán al limitar las pérdidas a la baja por contrato, sugirió. Mientras hablaba, mostró una diapositiva de dibujos animados de un solo pavo que dice a otro ", ser razonable Frank. ¿Por qué nos alimentan tanto? "Las aseguradoras son tan perspicaz como el sabio pavo, sus comentarios sugirieron.
El papel de los modelos predictivos
Taleb habló a los asistentes de la conferencia de seguro siguientes palabras de apertura por Laura Hay, líder de la práctica de seguros de KPMG, que avanzó la idea de que los modelos predictivos son una herramienta fundamental para impulsar "el crecimiento de transformación" para las aseguradoras que luchan por crecer líneas superiores y hacer frente a los retornos de inversión rezagados y otros obstáculos al crecimiento de los beneficios línea de fondo.
Eso llevó a una pregunta de seguimiento de Constanza Hunter, economista jefe de KPMG, que entrevistó a Taleb en el escenario después de su presentación. ¿Qué pensaba sobre el uso de modelos predictivos en los seguros, Hunter quería saber.
"El modelo predictivo funciona maravillosamente para Mediocristán", dijo Taleb, señalando que durante gran parte del riesgo de que las aseguradoras de propiedad / accidentes toman en lata, de hecho, ser modelado con eficacia. No Reaseguros, hasta el punto, dijo. "Reaseguros tiene colas gruesas. Pero ellos lo saben. Cuanto más estás en Extremistán, menos estas cosas funcionan ", dijo. "Lo bueno de seguro es que ellos [los suscriptores] saben donde [los modelos] no funcionan."
Cobran más primas. Ellos saben que los terremotos son muy impredecible como es la intensidad y los daños causados por un huracán. Para estos riesgos ", los modelos son escandalosamente inexacta", ha aseverado. "Pero la gente lo sabe.Toman el peor de los modelos "de un enfoque multi-modelo, dijo.
Y continuó: "El modelo predictivo no funciona para las variables socioeconómicas.Periodo. Cero ".
Al referirse a la crisis financiera mundial en 2008, Taleb subrayó su punto."¿Sabes cuántos modelos hay en el planeta? ¿Cuántos de ellos vio la crisis? "
Ningún modelo predijo, dijo. "Ese ha sido siempre la historia de la predicción", dijo, lo que sugiere que los eventos extremos no son capturados dentro de los modelos antes de que ocurran. "El año que viene, lo hicimos bien", dijo, refiriéndose a típicas proclamas después de los hechos de los analistas que afirman haber visto los riesgos impredecibles extremos próximos en retrospectiva.
Dar un resumen diferente de cuando los modelos predictivos trabajarán y donde no lo harán, dijo Taleb, "Variables que tienen tiempo en ellas. Tenga cuidado. Las cosas que tienen espacio en ellos, no. "Aclaró la referencia espacial diciendo que si usted está prediciendo un resultado para una muestra representativa de la población, por ejemplo, los modelos pueden funcionar bien.
"La mayoría de los seguros es inmune a los problemas de Extremistán y análisis predictivo va a funcionar. También trabajarán muy bien con el terrorismo ", dijo, refiriéndose al" problema unidimensional "de predecir si una persona es probable que sea un terrorista. "Todo lo que quiero saber si la persona que ha matado a alguien antes. Si no. ¿Es que andar con alguien que ha matado a alguien antes? Si no. ¿Es que andar con alguien que conoce a alguien que ha matado a alguien antes? "
Del mismo modo, los modos predictivos pueden trabajar por fraude. "Los estereotipos pueden más o menos predecir quién se va a cometer fraude."
"¿Se puede predecir el ciber terrorismo? No ", continuó. "Eso es un problema de Extremistán. Delincuencia individual puede perfil pero grande cyber "es demasiado complejo, sugirió.
Hunter también pidió Taleb para ofrecer asesoramiento sobre inversiones para P / C y aseguradoras de vida en el entorno de bajo rendimiento actual, y el autor y experto en riesgo instó en repetidas ocasiones a las aseguradoras a invertir de manera conservadora. "Nunca dió cerdo", dijo en un punto.
¿Qué pasa con los derivados de inversiones?
"No usar derivados. Por favor, no lo hacen ", dijo Taleb sin entrar en detalles mucho más. "Yo era un comerciante derivado por un largo tiempo. No es sólo un comerciante, también soy un quant ", dijo, dando este consejo sucinta:" opciones a corto plazo de OK. Opciones de la lista, sí. Todo lo que es complicado, y requiere un quant, no ".