Evaluacion de probabilidades

Presentación del tema: "Evaluacion de probabilidades"— Transcripción de la presentación:

1 Evaluacion de probabilidades
Heuristicas (atajos) Representativo Accesible Anclado Errores & sesgos Ignorar probabilidades Falacia del jugador Falacia de conjuncion Correlaciones ilusorias Tendencia a confirmar

2 Heuristica: - Un atajo para la evaluar probabilidades y tomar decisiones - Rapido y eficiente, pero - vulnerable al error. Algorithm: - garantiza la respuesta correcta - ineficiente (caro desde el punto de vista computacional)

3 Obrero de la construccion Escritor
A Carlos lo eligieron para una entrevista en forma aleatoria (randomizada). De la entrevista, nos enteramos que Carlos es una persona timida, de baja estatura, a la que le gusta mucho leer libros. En el colegio era buen alumno pero sus companieros lo tenian a maltraer. Usted diria que Carlos hoy trabaja de: Obrero de la construccion Escritor Por que la gente dice ‘escritor’? Porque la similitud: la descripcion es representativa (tipica) de los escritores Example of conjunction fallacy (which in this case stems from wrongly applying the representativeness heuristic

4 Atajo # 1: Es Representativo
La tendencia a juzgar un evento como mas probable si “representa” (describe) los razgos tipicos de la categoria. (en otras palabras, el individuo se parece al prototipo) Por que es util? - Los razgos tipicos tienden a ser mas frequentes que los atipicos Por que a veces es este atajo es enganioso? - Porque no tiene en cuenta: - las probabilidades previas - que algunos procesos son aleatorios Representativeness Heuristic is Used when calculating the probability: Object A belongs to class B? Event A originates from class B? Process B will generate object A? Using similarity or correspondence of mental models of A & B, ignoring other relevant info.

5 Ignorando las probabilidades previas:
(base rate neglect) Al estimar cuan probable es algo, la gente tiende a ignorar cuan frecuente es eso en general. Por ejemplo, En Buenos Aires, la gripe es mucho mas frecuente que el dengue. If subjects were given only the base rate information, they were good at taking it into account “70% chance that he is a lawyer” If subjects were given only the diagnostic information, they were able to tell that some descriptions favored engineers, while others favored lawyers… but if they were given both types of information, they completely ignored the base rate information. Even if the base rates were completely reversed (e.g. 70 engineers, and 30 lawyers) subjects gave the same answers--that is, answers that were determined by the diagnostic information. (if given a completely neutral description, they estimated a 50/50 probability…again ignoring base rates…) so in the example above, subjects heavily favored the engineer answer even though the base rates were in the opposite direction… Which heuristic was at work here? Representativeness...

6 Ejemplo Dada esta informacion:
• Hay un accidente de taxi en el que el taxista no se detuvo a ayudar • Hay dos companias de taxis en la ciudad. • La compania taxi azul tiene 1000 autos en la calle, • La compania taxi verde tiene 50 autos en la calle. • El testigo cree que el auto que no se detuvo era verde • Por otras pruebas sabemos que nuestro testigo acierta el 90% de las veces en que testifica. Dada esta informacion: - es mas probable que el taxi haya sido azul o verde? The somewhat surprising answer here is BLUE!!!! Even though the witness is 90% accurate the overwhelming numbers of blue cabs relative to green cabs make it more likely that when the witness claims to see a green car, he is misidentifying one of the blue cars…. Let’s go through this logic carefully...

7 900 “azul” 100 “verde” 5 “azul” 45 “verde”
Imagina que al testigo le pedimos que diga si el taxi es verde o azul, y que lo haga para cada uno de los taxis de la ciudad ... El testigo dira 1000 taxis azules 900 “azul” 100 “verde” La mayoria de las veces que dice ‘verde’, esta equivocado! (100/145 errores) 50 taxis verdes 5 “azul” How do you think a jury would respond? First consider the OJ verdict…and then tell me what a jury would say... 45 “verde” En este caso, las probabilidades previas son mas influyentes que la informacion diagnostica

8 Ignorar las probabilidades tiene consequencias en la vida real:
Por ej., supone que: que cuando hay un cancer de mama, la mamografia lo detecta el 85% de las veces (hit rate), y que cuando no hay cancer la mamografia es negativa el 90% de las veces (correct rejection rate)*. Supone que la probabilidad de cancer en la poblacion que estas estudiando es de 1% Si el mamograma da positivo, cual es la probabilidad de que la paciente tenga realmente cancer? 128.6 per 100,000 * Ojo! Estos numeros son inventados (para simplificar las cuentas), pero aun asi la logica es correcta.

9 Que indica el mamograma Cancer No Cancer Total
Hay cancer ,000 No hay cancer , , ,000 Cuando el mamograma indica cancer, la probabilidad de que exista cancer es solo 8% (850/10,750). O sea, un mamograma positivo es razon para hacer mas evaluaciones, pero la probabilidad de que sea maligno es baja Los medicos muchas veces no entienden esta logica Que hay realmente First notice the overall incidence is 1000 out of 100,000 now notice the 85% accuracy of positive results and the 90% accuracy of negative results… Now we can just compare the number of times that positive results will be correct, with the number of times they will be incorrect… Of course, for something as serious as breast cancer, a positive result merits further testing just in case… Suppose a doctor got a positive result on a highly diagnostic test for a dangerous but rare disease. Should they immediately administer a risky treatment (one that might have serious side effects), or should they test the patient again?

10 Estudiantes de medicina de Harvard leen este caso:
Por ej: Estudiantes de medicina de Harvard leen este caso: Imagina una enfermedad que tiene una prevalencia de uno en mil (1/1000) y un test de diagnostico que detecta todos los casos (hit rate: 1) pero tiene un 5% de falsa alarmas (false positives). Si a tu paciente el test le da positivo, cuan probable es que tenga la enfermedad? De 1000 personas, one tiene la enfermedad (1/1000): O sea de 1000 casos va a haber 1 caso real (hit) y 50 falsa alarmas (5%) - La probabilidad de que tu paciente tenga la enfermedad es 1/51 (1.96%) - La mitad de los estudiantes cree que la probabilidad es 95%! (burros!)

11 La falacia del jugador Si tiras una moneda, que secuencia es mas probable? (C=cara, c=ceca) 1. c C C c c c C c C 2. c c c c c c c c c The same reasoning is at work when people believe that a batter who has struck out 12 times in a row is “due” or more likely to get a hit on the very next at bat… We attribute to individuals (in this case a ‘sequence of 7 tosses’) the same properties of the category (a long sequence of tosses). Because in a long sequence you will find variety, you come to expect variety in a short sequence, although the likelyhood of variety in a long sequence is much higher than ina short sequence. Similarly, in election 2000, pundits said that ‘americans want moderation, a person who governs from the center’ because the vote was Obviously this is nonsense, just because the Country (I.e. the class) is divided in the middle and a decision is hard to reach, it does not follow that each individual cannot make its mind. 

12 La Falacia del jugador: creer que los resultados anteriores ejercen influencian en eventos aleatorios. Por que ocurre?! En secuencias aleatorias, a la larga cara y ceca se alternan. Por lo tanto, una secuencia en que cara y ceca se alternan es mas tipica (similar) que una en la que son todas ceca. Si alguien saca 10 cecas seguidas, pensamos que hace trampa Si alguien mete 4 baskets seguidos, creemos que esta en una racha

13 Basketball El hincha cree que los jugadores de basket tienen rachas de inspiracion para embocarla (hot hands)(91% de hinchas cree esto) Los investigadores analizaron en los partidos. - La probabilidad de embocarla despues Haber embocado 1, 2, or 3 tiros. Errado 1, 2 or 3 tiros. No hay diferencia Como explicar el error del hincha? 4 baskets al hilo parece raro (atajo de ‘representatividad’), debe haber algo mas.

14 Linda tiene 31 anios, es soltera, dice lo que piensa, y es muy inteligente. Estudio filosofia en la facultad y temas de pobreza y justicia social. Es vegetariana y hace demostraciones a favor del medio ambiente. Que es mas probable? Que Linda Empleada de banco Empleada de banco miembro del movimiento feminista

15 Falacia de Conjuncion empleada feminista de banco

16 Atajo # 2: Acceso a ejemplos
Que tareas del hogar haces vos generalmente, y cuales tu esposa/o? (e.g. sacar la basura, lavar los platos, etc.) - Mujer: dice hacer 16/20 de las tareas - Marido: dice hacer16/20 de las tareas Ross and Sicoly (1979) Como puede ser? Me acuerdo que ayer lave los platos, pero no recuerdo que mi seniora los haya lavado (vs. ella recuerda cuando ella los lavo) People choose “begin with R” even though R appears as the third letter more frequently (same thing true of K, L, N, and V). Why? Because it’s easier to generate examples of words using the first letter…these examples are simply more “available”. But on the other hand, who CARES about this useless fact? Availability also affects judgments about meaningful events. Most people rate motor vehicle accidents as the more likely cause of death. In fact, motor vehicle accidents are responsible for less than 100,000 deaths/year, while heart disease is responsible for around 1,000,000!! Auto accidents are more sensational, reported in the news far more often…and therefore are more available in memory than cases of death from heart disease. Then, there are the REALLY important cases…..Clearly, it’s easier to remember that last time you had to scrub the mildew off of the bathtub than when your roommate did it. Is this heuristic always bad? No…availability is often a GOOD indicator of how often something occurs, because it is usually correlated with frequency. But other times, availability is determined by how often the media chooses to report an event, extensive advertising, or people’s inherent reluctance to take out the garbage...

17 Acceso a ejemplos Que es mas comun, palabras que empiezan con E o palabras que tiene la letra E como tercer letra? Es mas facil obtener ejemplos de E inicial People choose “begin with R” even though R appears as the third letter more frequently (same thing true of K, L, N, and V). Why? Because it’s easier to generate examples of words using the first letter…these examples are simply more “available”. But on the other hand, who CARES about this useless fact? Availability also affects judgments about meaningful events. Most people rate motor vehicle accidents as the more likely cause of death. In fact, motor vehicle accidents are responsible for less than 100,000 deaths/year, while heart disease is responsible for around 1,000,000!! Auto accidents are more sensational, reported in the news far more often…and therefore are more available in memory than cases of death from heart disease. Then, there are the REALLY important cases…..Clearly, it’s easier to remember that last time you had to scrub the mildew off of the bathtub than when your roommate did it. Is this heuristic always bad? No…availability is often a GOOD indicator of how often something occurs, because it is usually correlated with frequency. But other times, availability is determined by how often the media chooses to report an event, extensive advertising, or people’s inherent reluctance to take out the garbage...

18 Acceso a Ejemplos la tendencia a evaluar la probabilidad en base a cuan facil nos viene la informacion a la cabeza. Por que este atajo es util? - Cosas que ocurren frequentemente son mas facil de recordar (pensa palabras que empiezan con X) Por que a veces nos engania? - La frecuencia es solo una de los factores que influyen nuestro acesso a la memoria. Otros factores son: --Como organizamos la informacion en memoria (letra “E” inicial) -- Cuan reciente es el ejemplo (propagandas, TV) -- Familiaridad (“cuanta gente va a la facu? A la carcel?”) Famous names example: subjects read a list of names and were later asked to judge how frequently they saw male vs. female names. Groups that saw more famous men in the list estimated a higher frequency of male names, while groups that saw more famous women in the list estimated a higher frequency of female names.

19 Acceso a ejemplos: Un experimento
- Mantene la frecuencia constante - Manipula el acceso a ejemplos - Pedi al participante que estime la frequencia Todos leen lista de nombres: - 50% nombres de hombre,50% de mujer - Group A: Algunos nombres de hombre son famous (riquelme) - Group B: Algunos nombres de mujer son famosos Test: hay mas hombres o mujeres en la lista?

20 Atajo #3: Anclar & Ajustar
Haces una estimacion inicial, seguida de ajustes basados en la informacion siguiente. El problema es que Ponemos demasiado enfasis en el valor inicial (ancla) , aun cuando sabemos que el valor inicial de referencia es arbitrario No ajustamos lo suficiente This kind of effect is clearly relevant in sales situations….for instance, when bargaining for an item…the first number mentioned sets the anchor, and this is bound to have implications for the eventual price. Reisberg mentions the tactic used by charity organizations: would you like to donate $100, $50, $30, $10? In that order instead of the reverse. However, in these situations it’s easy to argue that the initial values could be informative..that is, even a perfectly rational decision might take these values into account…so is this a bias or a fact of good decision? Several experiments suggest that it is not an entirely rational tendency…. Multiplication example: 1x2x3x4x5x6x7x8 (median answer: 512) vs 8x7x6x5x4x3x2x1 (median answer: 2250) (answer: 40,320)

21 Ancla: Ejemplo “10” “Cual es el porcentaje de paises africanos en las Naciones Unidas? Respuesta: ‘25%’ “Cual es el porcentaje de paises africanos en las Naciones Unidas? Respuesta: ‘45%’ “65” This chilling

22 Correlaciones Ilusorias
--Tener una educacion universitaria aumenta tu salario? -- La virtud en el area familiar (e.g, ser infiel) predice la capacided con la cual la persona puede governar un pais? -- Perro que ladra no muerde? Las correlaciones que percibimos son influenciadas por dos variables - La evidencia que observamos - Nuestras teorias --> Correlaciones Ilusorias

23 Cuando la gente observa la evidencia sin preconceptos...
From the Reisberg text: Jennings, Amabile and Ross asked subjects to observe a bunch of data, and later to indicate how correlated various variables were…(e.g. the height of a man and his walking stick…shown in various pictures). In cases where subjects have no preconceived ideas about what relationships they would see, their predictions are related in an orderly way to the data that was observed. (higher predicted correlations were associated with higher predictive relationships between the variables…). If anything, their estimates of correlation were conservative…that is, lower than the actual objective correlations…. … ve correlaciones donde las hay, y no donde no las hay

24 Cuando la gente tiene sus teorias ….
However, when subjects had preconceived notions about what the relationship might be in the data they were observing, their estimates were heavily influenced by their theories… For instance, subjects estimated a strong relationship between the likelihood that a child would be dishonest on other activities if they had been dishonest while solving a puzzle. Their estimates were around .60 for this variable…even though the actual correlation was only about .2 What implications might this have for a doctor who has a pet theory about the effectiveness of a certain drug treatment? What causes this illusion? Availability. When cases that confirm our theories are encountered, we remember them better….later when we estimate these correlations, the examples “available” in memory are the ones that confirm our beliefs… Do black guys escape punishment for crimes? Black person: criminal / not criminal Verdict: innocent / guilty you remember the time that the ‘black guy’ (OJ simpson) got away with murder, but you don’t remember all the other (more often times) that the black guy … ve correlaciones donde no las hay! Lo mismo ocurre en la ciencia, pero por suerte siempre hay algun enemigo que tiene una teoria opuesta Jennings, Amabile, & Ross, 1982

25 Correlaciones Ilusorias: Posible Mecanismo
Tendencia a Confirmar. Notamos y recordamos las cosas que coinciden con nuestro punto de vista. Es mas facil recordar ejemplos de datos que coinciden con nuestra teoria. Este facil acesso a ejemplos nos causa un sesgo en la evaluacion The Reisberg text pointed out that even experienced therapists fell prey to illusory correlations (evaluation of Rorschach ink blot interpretations). Dream she will call, then she call Bingo! But you forget all the instances when you dreamt she will call but she didn’t and the cases when you did not dream but she called anyhow

26 En resumen Heuristicas (atajos) Errores & sesgos Representativo
Accesible Anclado Errores & sesgos Ignorar probabilidades Falacia del jugador Falacia de conjuncion Correlaciones ilusorias Tendencia a confirmar