Final Exam Preparation PHI 2100 01 Note Any material from the notes readings lectures may be on the exam Concepts you should be able to explain and recognize examples of Representativeness Heuristic We tend to make judgments by using stereotypes Our idea of the representative X Clustering Illusion Our stereotype of random sequences is less streaky than real random sequences Therefore we re surprised by how streaky random sequences are NOTE This is why gamblers believe in hot cold streaks when it comes to gambling We are SURPRISED by how streaky chance sequences are Base Rate Paradox a statical result where false positives tests are more probable the true positives test occurring when the overall population has a low incidence of a condition and the incident rate is lower than the false positive rate Use Baye s theorem Conjunction Fallacy A more specific demanding description that fits our stereotype will be seen as more probable than a less specific demanding description that does not fit our stereotype Easy Explanation Hindsight Bias These easy explanations often give you a FALSE sense of understanding Whenever you offer an explanation for some piece of behavior you should ask yourself two questions It gives you a FALSE sense of understanding deals with Hindsight Bias we are naturally story tellers and so we are very good convincing but unsupported explanations for things after they occur 1 Consider the opposite If something else had occurred could I have explained that too If YES Probably an easy explanation that offers a false sense of understanding 2 Prediction Could I have predicted this BEFORE it happened If NO Probably an easy explanation that offers a false sense of understanding o Examples John s mom is a bartender John worked at the bar during high school If after college he goes to work at the bar people can explain it as follows he missed the bar If he doesn t people can explain it as follows it s the only job he s ever had he hates it Regression Fallacy We often explain regression effects with unnecessary causal factors Saying one causes the other when they really have nothing in common Regression to the Mean Whenever occurrences of X vary around a mean if X1 is extreme X2 is likely to be closer to the mean Availability Heuristic convenient memory We tend to think events are more probable to the extent they are more available to memory Often the availability heuristic works well Often events are available to memory because they really do happen a lot It is a mental shortcut that occurs when people make judgments about the probability of events by how easy it is to think of examples BUT sometimes our memory is selective and we remember things that are VIVID or DRAMATIC or ANNOYING of FIT W OUR THEORIES This tends to happen when events are two sided Two sided events both potential outcomes would be equally noticed or remembered one sided events only one outcome would be noticed remain in memory Some events that are inherently one sided o Pattern asymmetries o Hedonic asymmetries only one outcome arouses emotion or requires an act on your you tend to remember events that stand out or seem to be the part Ex all of the buses are heading the wrong direction result of a pattern Ex Always saying that you woke up in the middle of the night at 1 23 very often someone has had plastic surgery when you do detect it you notice and remember but if you can t tell that someone has had plastic surgery then you don t have any information about it usually one sided almost by definition Ex I can always tell when o Definitional asymmetries Biased Interpretation We often interpret data that contradicts our pet beliefs as BAD LUCK or as ALMOST CONFIRMING our views Fortune Cookie Problem no specific prediction the prediction is so vague and general that it could be applied to almost everyone NO experience would disconfirm your pet belief NO time frame and vague Ask yourself is there any experience I could have that would convince me my pet belief was false If not you may be the victim of the fortune cookie problem Ex good things come to those who wait The time frame is ambiguous eventually good things happen to most people Optional Stopping Negative Evidence Critical scrutiny Explain away STOP No more critical scrutiny Positive Evidence STOP No more critical scrutiny Are you equally critical of reasons that tend to support your view of something that is important to you i e politics abortion etc as you are of reasons that tend to undermine your view PROBABLY NOT which Then you are probably guilty of optional stopping Note you re not IGNORING negative evidence you are rather treating negative and positive evidence DIFFERENTLY so it would be unfair to criticize you for being close minded on the other hand it might be easier to correct our mistakes if we were just ignoring negative evidence Correction would just involve attending to all the relevant evidence Sharpening Leveling Deals with second hand testimony Testimony is sharpened main point is emphasized Testimony is leveled context details qualifications are de emphasized or ignored Familiar Examples Experience w newspaper stories of events you ve attended Individuals who you ve heard stories about Often disappointed to meet them Secondhand accounts become simpler cleaner stories Expected Value The expected value tells you what your average mean value is per decision given in terms of money So the basic idea is that if you only care about money the thing to do is to take the option that maximizes expected value Maximizing the expected value is not always the rational decision Be able to do the calculation EV Option Pr Outcome a x Value Outcome a Pr Outcome b x Value Outcome b So essentially it is for every option you multiply each outcome with each value and then add all of them together to get the expected value for that option Expected Utility the expected utility tells you what your average mean value is per decision given in terms how good we think that outcome would be Good in expected utility can mean lots of things such as happiness satisfaction of preferences pleasure Whenever we re faced with a decision we can attach arbitrary utility measures to potential outcomes in terms of how good we think that outcome would be Equation for expected utility is the same as expected value but instead of the money value you use utility points Sunk Costs People make decisions about the future based on costs they ve already paid costs already