Chapter 15 - Judgement and Decision Making

Problem Solving

accomplishing a goal when the solution or the path to the solution is not clear

algorithms

problem solving strategies based on a series of rules

• works through a series of steps

rationality framework

humans do their best to make rational decisions based on costs and benefits (and logic)

bounded rationality

the assumptions that humans try to make rational decisions, but are bounded by cognitive limitations (and biases)

steps for rationality framework

1. define the problem

2. identify necessary criteria to allow you to judge different options

3. weigh the criteria (which are most important?)

4. generate alternative options

5. rate alternatives on each criteria that you find important

6. compute the optimal decision

• final decision can be influenced by urges and biases, even though we might know whats rational

obstacles in our ability to solve problems

• influenced by past experiences

mental set

• cognitive obstacle that occurs when an individual attempts to apply a routine solution to a new type of problem

• makes learning and problem solving more efficient

  • (ex. we look for patterns in events)

• not helpful when. problem calls for fresh insights or a new approach

functional fixedness

• type of mental set

• when an individual finds an object or technique that could potentially solve a problem, but can think of only its most obvious function

• tend to not think of alternative uses for the object

heuristics

• problem-solving strategies that stem from prior experiences and provide an educated guess as to what is the most likely solution

• AKA “rules of thumb”

• are usually accurate and allow us to find solutions and to make decisions quickly

what do heuristics often help us with

making rapid decisions

how do we switch between algorithms and heuristics examples

• in hangman, we use algorithms, then use heuristics to finish off the word once more info is attained

representative heuristic

• making judgments about likelihood based on how well an example represents a specific category

• often correct, but not always

ex. making a judgment on someone’s major based on their characteristics and associating their characteristics with those majors

conjunction fallacy

• when we assume that two traits together are more likely than either of those traits alone

• is statistically impossble

• example of the power (and danger) of the representativeness heuristic.

anchoring heuristic

• the first info learned about a subject can anchor a person’s bias or judgments about that subject

  • subsequent judgements are related to this initial anchor point

anchoring heuristic example

1. is the population of sydney, australia greater or less than 8 million? guess the population

• student response: 7.2 million

  • student made the subsequent guess based on how much less the population is than 8 million

2. is the population of sydney greater or less than 2 millon? guess the population

• student response: 3.7 million

  • student made the subsequent guess based on how much greater the population is than 8 million

• assumptions are made based on anchoring info (8 million vs. 2 million)

anchoring heuristic example

• bargaining prices based on initial price offers from either side

• lowballing

availability heuristic

• tendency to judge the probability of an event by how easy it is to think of examples or instances

• example: less people flew on planes after September 11, and drove more, even though the odds of dying in a car are higher vs. in a plane

mean world fallacy

AKA “selling fear”

• advertisers sell fear by appealing to multiple heuristics

mean world fallacy example

alarm companies selling alarm systems in winnipeg

• commercials for alarm companies show bad guys breaking in to rich, suburban homes

• image of bad guys = availability heuristic

• bad guys are bad = increased risk assessment

• despite higher crime rates being in poorer economic parts of the city, commercial is more catered towards rich people who can afford the alarms

avoiding loss

• people try to minimize risks and losses when making decisions

• responses to the same choice will differ based on whether outcome is framed as gain or loss

• people’s behaviour is biased based on how a question is framed

  • example - we tend to avoid loss when asked a question about how many people survive vs. a question about how many people will die

  • yet both outcomes have identical outcomes

bounded awareness

• we are often not aware of our limits

• we often fail to notice important information

problems in self-assessment

• willpower is bounded

  • we give greater weight to immediate concerns rather than long-term concerns

  • example - what am I going to eat for supper vs. how much food will I have by the end of the week

• self-interest is bounded

• overreliance on intuitive responses rather than rational responses

hindsight bias

• tendency to overestimate one’s ability to have predicted an event once the outcome is known

  • AKA the “I knew it all along phenomenon”

• common in political, medical, and military judgments/decisions

• easy to say in the short term that “you knew it all along”, but it takes away the learning potential

bounded ethicality

• notion that our ethics are limited in ways we are not even aware of ourselves

bounded awareness

broad array of focusing failures that affect our judgment

• specially the many ways in which we fail to notice obvious and important info that is available to us

system 1 decision making

• our intuitive system

• typically fast, automatic, effortless, implicit, and emotional

• is how we make most of our decisions in our daily life

system 2 decision making

• used for most important decision making

• slower, conscious, effortful, explicit, and logical

• example - six logical steps of decision making (in rational decision making)

• a complete system 2 is not required for every decision we make

how to reduce the effects of bias

• transition from system 1 thinking and engaging more in system 2 thinking

example of decision architects and how they affect biases and decision making

retirement plans for employees in companies

• most people fail to save for retirement

• to solve this, companies automatically enroll new employees in the company retirement savings plan and then give them the option to “opt out”

how does decision architects affect human bias and decision making

• by changing the environments (defaults), we can counteract the human tendency to live with the status quo

what book did Thaler and Sunstein make in 2008 about improving decision making

Nudge: Improving Decisions about Health, Wealth, and Happiness

confirmation bias and example

• favouring information before and during an event based on our beliefs

example - in elections, we believe info that paints favoured politicians in a good light and ignore info that paints favoured politicians in a bad light

bounded rationality framework

human beings try to make rational decisions but. cognitive limitations prevent us from being fully rational