7:280-292 and 8:315-319 and 330-339

CH7 - 280-292

• Discrete probability distributions

  • probability mass function - probability for each possible discrete

  • cumulative distribution function (CDF) - the probability that an uncertain quantity is less than or equal to a specific value

• Expected value - A discrete uncertain quantity’s expected value is the probability-weighted average of its possible values.

• variance - a measure of how spread out a set of data is, calculated by finding the average of the squared differences between each data point and the mean of the data set

• standard deviation - a measure of how dispersed the data is in relation to the mean

• continuous probability distribution - the random variable can take on any value within a range

• Stochastic dominance - a decision-making concept that compares the likelihood of outcomes for different options. It's used to determine which option is better for a group of people with similar preferences. 

• Probability density functions - a function that describes the likelihood of a continuous random variable taking on a specific value within a given range, where the probability of an event occurring within a certain interval is calculated by finding the area under the curve of the PDF over that interval

Ch8 - 315-319

• Assessing discrete probabilities

  • assessing optimism

  • bets willing to place

CH8 - 330-339

Heuristics and Biases in Probability Assessment

• Heuristics - simple and intuitive ways to deal with uncertainty

  • can result in biased assessments

  • representativeness - judge the probability that someone or something belongs to a particular category

• memory biases

  • availability heuristics - a memory-related process that can impact subjectivity

  • imaginability bias - occurs when an event is judged more (or less) probable it can be easily (or not easily) imagined

  • illusory correlation - If a pair of events is perceived as happening together frequently, this perception can lead to an incorrect judgment regarding the strength of the relationship between the two events.

  • hindsight bias - ease of recall can also bias the assessed probability of an event occurring after it has actually occurred

• statistical biases

  • chance bias - occurs when individuals assess independent random events as having some inherent (non-random) pattern.

  • Conjunction bias: The “AND” in probability theory - occurs when individuals overestimate the probability of the intersection of two (or more) events.

  • Disjunction Bias: The “OR” in Probability Theory - occurs when individuals underestimate the probability of disjunctive events

  • Sample bias - drawings conclusions from highly representative small samples

• Confidence bias

  • Desire bias - occurs when the probability of a desired outcome is overestimated.

  • Selectivity bias - results in discounting (or completely excluding) information that is inconsistent with the decision maker’s personal experience.

• Adjustment heuristics and biases

  • Anchoring-and-adjustment bias - affects the assessment of probability distributions for continuous uncertain quantities more than it affects discrete assessments.

  • Partition dependence bias - arises when an individual is asked to assess probabilities for ‘n’ different possible outcomes of an uncertain event.

  • Conservatism bias - new information is discounted or even ignored, because the decision maker has anchored on previous information, and thus does not adjust his or her probability sufficiently.

  • Regression bias - an isolated extreme outcome in a series of events is just randomness in the system can be hard for individuals to comprehend.

• Motivational bias - Incentives often exist that lead people to report probabilities or forecasts that do not entirely reflect their true beliefs.