Decision Making: How do people decide between different options.

• What kind of car should I buy?
• Who should I marry?
• Should I buy a Lotto ticket?
• Is the defendant guilty or innocent?
• Should I vote Republican or Democrat?
• Etc.

Prescriptive Models of Decision Making

• These are mathematical models developed by economists describing how people should make decisions if they were rational.

• Let's say there's a lottery where a ticket costs $2 and for each ticket you have a 5% chance of winning $100. Should you buy the ticket?
  • What are your options? Options are simply the possible choices you could make. In this example, your choices are to buy the ticket or not buy the ticket.
  • What are the possible outcomes for each option?
    • If you buy the ticket you could either make $98 or lose $2.
    • If you don't buy the ticket you neither lose nor win anything.
  • What is the probability associated with each outcome?
    • If you buy the ticket there is a 5% chance of making $98 and a 95% chance of losing $2.
    • If you don't buy the ticket there is a 100% chance of staying even.
  • What is the expected utility of each option? The expected utility of an option is how much you would make or lose on average if you repeated the same choice over and over again.
    • Let's say you bought 100 lottery tickets instead of just 1. How much money on average would you make for each ticket that you bought?
    • For 95 of those tickets you would expect to lose two dollars. So that's a total of $190 that you lose.
    • For 5 of those tickets you would expect to win $98. So that's a total of $490 you would win.
    • Altogether then you would wind up winning $300 which works out to an average of $3 for every ticket that you bought. Thus your expected utility for buying a ticket would be +$3
    • More generally, we can use a formula to figure out the expected utility of any choice.
      • If you buy the ticket the expected utility would be:
        EU = (.05)*($98) + (.95)* (-$2) = $3

      • If you don't buy the ticket the expected utility would be
        EU = (1.00)*($0) = $0

    • Prescriptive models say that you should always pick the option with the highest expected utility. So in this case, you should buy the ticket because buying the ticket has a higher expected utility (+$3) than does not buying the ticket ($0).
• Are people rational decision makers? The answer appears to be "NO" if by rational we mean do people always pick the option with the highest expect utility.

Evidence for irrational decision making

• Risk Aversion

  We give someone a choice between two wagers.

  WAGER I: A 100% chance of losing $50
  WAGER II: A 25% chance of losing $200 and a 75% chance of losing nothing

  Most people will pick Wager II, even though the two wagers have identical expected utilities.

  • Certainty Effects

    We give someone a choice between two wagers

    WAGER I: A 25% chance of winning $200
    WAGER II: A 100% chance of winner $50.

    Most people will pick Wager II, even though the two wagers have identical expected utilities. The phrase "A bird in the hand is worth two in the bush" comes to mind

  • **Thus people prefer a certain gain over an uncertain gain even if the uncertain gain would be larger AND people prefer an uncertain loss over a certain loss even if the uncertain loss would be larger.

  • These two opposing tendencies can be used to produce a form of irrational behavior known as a preference reversal.

    1) Imagine that the US is preparing for the outbreak of an unusual disease which is expected to kill 600 people. If program A is adopted, exactly 200 people will be saved. If program B is adopted there is a 1/3 probability that 600 people will be saved and a 2/3 probability that no people will be saved.

    Most people pick option A.

    2) Imagine that the US is preparing for the outbreak of an unusual disease which is expected to kill 600 people. If program C is adopted, exactly 400 people will die. If program D is adopted there is a 1/3 probability that no one will die and a 2/3 probability that 600 people will die.

    Most people pick option D.

    BUT OPTION A IN SCENARIO 1 IS IDENTICAL TO OPTION C IN SCENARIO 2. THE ONLY DIFFERENCE IS IN TERMS OF HOW THE ISSUE IS "FRAMED"

  • These two scenarios are identical except for the words used to describe them. That people make opposite choices based only on how the question is "framed" is irrational.

• Base rate neglect

  Let's say you're at a party in which everyone in the room is either a lawyer or an engineer. You know that 80% of all lawyers wear power ties but only 40% of all engineers wear power ties. You meet someone who is wearing a power tie, is the person more likely to be a lawyer or an engineer?

  Most people say the person is more likely to be a lawyer but the truth is you can't tell based on the information given in the problem. The reason you can't tell is because the answer depends on the percentage of lawyers in the room. This percentage is called the BASE RATE.

  For instance, what if there were 80 lawyers in the room and 20 engineers. If that were the case then

  There would be 64 lawyers with power ties in the room because 80*(.80) = 64

  There would be 8 engineers with power ties in the room because 20*(.40) = 8

  So in that case if you saw someone with a power tie it would most likely be a lawyer.

  But what if there were 80 engineers in the room and 20 lawyers. In that case

  There would be 16 lawyers with power ties in the room because 20*(.80) = 16

  There would be 32 engineers with power ties in the room because 80*(.40) = 32.

  So in that case if you saw someone with a power tie it would be twice as likely to be an engineer as a lawyer.

  • Most studies have found that people are very poor about using base rate information in making decisions.

Descriptive Models of Decision Making

• What the rationale choice models assume
  • People always try to maximize their utility
  • People can examine all options
  • People know the distribution of outcomes
• There are reasons to doubt both of these assumptions
  • Experimental evidence shows that that people sometimes choose options with lower expected utilities(risk aversion & certainty)
  • Humans have limited sensory and cognitive capacities such that it may be impossible to consider all possible alternatives and all relevant features of the problem.

Bounded rationality(Simon, 1957)

• Decision makers don't optimize utility, rather they settle for alternatives which are good enough. Simon called this tendency Satisficing
  • Individuals must generate alternatives themselves. Therefore individuals may not consider all alternatives.
  • Alternatives are evaluated against a threshold as they are generated
  • When the threshold is satisfied generation of alternatives stops
• Elimination by aspects
  • Decision makers have several criteria that they consider when making decisions
  • These criteria are ordered in terms of importance
  • Each criteria has a threshold below which an alternative is rejected
  • Criteria are looked at sequentially based on order of importance
  • The process ends when only one choice is left or the choices are so limited that an exhaustive consideration of all criteria can be accomplished
• Payne(1976) investigated types of decision strategies that people actually use. Do people check all options or do they use something like elimiation by aspects?
  • The subjects's task was to decide on which of several apartments to rent. Each apartment had information about it listed under cards which were sorted along relevant dimensions.
  • When the number of alternatives was small, subjects followed the additive or additive difference model.
  • When the number of alternatives was large, subjects followed the were more likely to use elimination by aspects models.

Representativeness Heuristic

• Tversky and Kahneman(1974): People judge probabilities "by the degree to which A is representative of B, that is, by the degree to which A resembles B."
• People thus reduce probability estimation to a similarity task. Most of the time this works pretty well, but it can also lead to systematic biases.
• The conjunction fallacy
  Linda is 31 years old, single, outspoken and very bright. As a student, she was deeply concerned with issues of discrimination and social justice, and also participated in antinuclear demonstrations.

  Which is a more likely alternative?

  Linda is a bank teller
  Linda is a bank teller who is active in the feminist movement.

• The law of small numbers

  I buy two lottery tickets keeping one for myself and giving one to you as a present. The lottery winner is chosen by randomly picking 5 Ping-pong balls from a rotating kettle containing 31 ping-pong balls, which are numbered 0-30.Which lottery ticket would you rather have?

  a. 1 2 3 4 5
  b. 8 0 20 12 24

  Suppose that an unbiased coin is flipped three times, and each time the coin lands on HEADS. If you had $100 to bet on the next toss, what side would you choose?(GAMBLER'S FALLACY)

  People assume that a sample will be more representative of the population from which it was drawn than the laws of probability would predict.

• Neglecting base rates(Kahneman and Tversky, 1973).

  A panel of psychologists interviewed and administered personality tests to 30 engineers and 70 lawyers, all successful in their respective fields. On the basis of this information, thumbnail descriptions of the 30 engineers and 70 lawyers have been constructed. You will find on your forms five descriptions chosen at random from the 100 available descriptions. For each description, please indicate the probability that the person described is an engineer, on a scale from 0 to 100.

  Some of the descriptions were written to be uninformative about whether or not the person was an engineer or a lawyer(as rated by another group of subjects).

  Dick is a 30 year old man. He is married with no children. A man of high ability and motivation, he promises to be quite successful in his field. He is well liked by his colleagues.

  For the noninformative descriptions, subjects tended to estimate that there was a 50% chance that the person was an engineer, even though the base rate for engineers was 30%

The Availability Heuristic

• The availability heuristic states that people judge the probability of a class by the ease with which instances can come to mind (Tversky and Kahneman, 1974).
• Tversky and Kahneman(1972):

  Consider the letter R
  Is R more likely to appear in
  A. The first position of a randomly sampled word
  B. The third position of a randomly sampled word
  

  Results

  1. 69% of subjects choose option A
  2. Mosts subjects thought A was twice as likely as B
  

Explanation Based Decision Making(Pennington and Hastie, 1988)

• Covers cases of complex diagnostic decisions.
• Decision making begins with the construction of a causal model to explain the available facts and then base their subsequent decisions on the causal interpretation imposed on the evidence.
• Alternative narrative accounts are compared:
  • The most coherent narrative account is chosen
  • Coherence is a function of
    --Completeness: The degree to which the story accounts for all the facts
    --Consistency: The degree to which the story is internally consistent or noncontradictory
    --Plausibility: The degree to which the story is externally consistent or in line with what you know about the world.
• Choice set: This is the set of specified possible outcomes for the decision.
• Match process: The decision maker picks a response from the choice set based on the match between the explanation structure and the characteristics of the choices.

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