Why is this step is the most important step?
Because defining the problem defines the solution. That is, the diagnosis determines the treatment (if you are diagnosed with the flu, you get a different treatment than if you are diagnosed with a cold).
Quotes illustrating the importance of this step:
- "A problem well-stated is a problem half solved."-- Charles Kettering
- "We are all faced with a series of great opportunities brilliantly disguised as impossible situations." --Charles Swindoll
- " If I had an hour to solve a problem, I'd spend 55 minutes thinking about the problem and 5 minutes thinking about solutions." --unknown, but often attributed to Albert Einstein
Examples of insights due to re-defining the problem
- Animal shelters asking "How can we help owners keep their pets?" rather than asking "How can we get all these abandoned pets adopted?"
- Behaviorists--and clever parents--asking "How can we get children to do good behaviors instead of bad behaviors?" rather than asking "How can we stop bad behaviors?"
- Defining the problem of saving gasoline (and the environment) by trying to increase our vehicles' gallons per mile rather than trying to increase our miles per gallon. To understand why, check out either of the links below:
- People often ask "Would I like x?" when they should be asking "Would buying x make me happier than spending that money on something else?"
6 pitfalls in defining the problem.
- Narrowly defining the problem results eliminates options.
- When confronted with COVID-19, Trump apparently framed the problem as "Can we have a good economy OR fight COVID?"-- when he could have framed the problem as "How can we have a good economy AND fight COVID?"
- Trump has also been accused of asking "How can we have a good economy by wrecking the environment?"-- when he could have asked "How can we have a good economy AND improve the environment?"
- Similarly, many people define problems in "all or none" terms, such as "Should we stay married or should we get a divorce?"Experience a simple example of defining the problem too narrowly: The nine-dot problem
- Biases may cause us to deny the problem ("Denial is not just a river in Egypt): "It's not a problem because I don't want it to be a problem." Examples:
- Denying the smoking causes cancer.
- Denying global warming.
- Denying the threat caused by COVID-19.
- Denying that there are problems with our policing and justice systems.
- Biases may cause us to misidentify the cause of the problem. As Maslow wrote, "it is tempting, if the only tool you have is a hammer, to treat everything as if it were a nail." Examples:
Two particularly common and powerful biases:
- Republicans seem to think, despite the evidence, that all problems are caused by taxes and all problems can be solved by cutting taxes.
- Republicans seem to think that budget deficits are bad--when there is a Democratic president.
- Bigots see the U.S. immigration problem as that there are too many immigrants when, in fact, the problem is that we have too few--a fact that threatens social security and our future economic growth.
- The "Not me" bias: We often don't take responsibility for our contribution to the problem. For example, you have heard people say things like:
One way to deal with this bias is suggested by Timothy Ferris: "...tell my story to myself from the perspective of a victim, then I tell the exact same story from a place of 100 percent responsibility."
- "It's not my fault."
- "Look at what you made me do!"
- "You are making me mad"
- "That's a nasty question"
- "Fake news!"
- The fundamental attribution error. Personalizing problems: Blaming people rather than situations. As anyone who has been stuck in traffic or in a bad job knows, bad environments can make even mature, rational people do immature, irrational things.
- Mistaking symptoms or effects for causes: In our chaotic, complex world, isolating the cause of an effect is difficult. Consequently, not only we may mistake effects or symptoms for causes. Furthermore, what we think is an important source of the problem may be unimportant (our ancestors would not believe that tiny things like viruses and bacteria could make us sick) whereas a factor that we think is unimportant may turn out to be very important. So, we may need to rely on scientists and experts to determine the most important causes of a problem. Many people refuse to do so; consequently, we have people arguing that cigarettes don't cause cancer and that humans are not contributing to global warming.
- Incorrectly identifying what kind of problem we have, so we try to solve one type of problem when we should be trying to solve a different type of problem.
What rule is determining the sequence of these numbers? 8,5, 4, 9, 1, 6, 7, 10, 3, 2
The digits (eight, five, four, etc.) are in alphabetical order,
Two other examples: Think of the last time you applied the wrong formula to a word-problem or heard of a friend who was misdiagnosed by a doctor.
Sometimes, we misidentify the kind of problem we have because of the representativeness heuristic: a general rule used when people decide whether something is an example of a category. If what we are looking at matches our memory of a typical instance of a category, we will classify that thing as being a member of that category. For example, you determine whether someone is a child or an adult based on their appearance matching your memorized examples of children and adults. The advantage of the representativeness heuristic is that people can take advantage of their experiences and their expertise. For example, a doctor can quickly diagnose a patient who has a disease that the doctor has seen hundreds of times before. However, problems may look similar, but be different. So, the representativeness heuristic may lead not only to stereotyping, but to misdiagnosing the problem by overlooking key differences between this new problem and old problems.
The representativeness heuristic may also cause us to ignore important information. For example, a doctor might, seeing that the patient's symptoms matched malaria, use the representativeness heuristic to diagnose the patient as having malaria, even though the patient probably didn't have malaria given one important fact: There hadn't been a malaria case where the patient lived in over 50 years.
- Defining the problem in a way that is too vague. Example: A student says "they are having trouble with the course" or "did not do well on the last exam."
Using existing solutions:
1. Algorithms: a problem-solving strategy that--if all the steps are followed--is guaranteed to eventually lead to a solution.
Two problems with algorithms:
1. They involve many steps (and doing many steps takes time and uses up the limited space in short-term memory)
2. They only fit problems where there is one right answer. Thus, there are algorithms for solving some math problems and playing certain simple games like tic-tac-toe, but not for problems with human relationships.
2. Heuristics: a general rule that guides problem-solving, but does not guarantee a perfect solution. You can think of heuristics as mental shortcuts, hunches, or as educated guesses. (Click here for a weather-related heuristic.) Examples of useful heuristics:
- Google it
- Ask "How have I solved similar problems?"
- Ask "How could I make the problem worse?" -- then do the opposite.
- Break the big problem into several smaller little problems.
- Ask a friend what to do.
- Try to solve a simpler version of the problem.
- Turn a vague goal (e.g., "I want to be a better student") into a SMART" (Specific, Measurable, Achievable, Relevant, Time-Bound) goal.
Barriers to generating new solutions
Examples: "COVID will magically go away."
"COVID is a hoax."
"Global warming is a hoax."
2. Fixation/Set: a rigidity in problem-solving due to wanting to continue to do things the old way. Examples:
3. STM's limits-- Because STM is limited, we can't think of many options at once. One way to get around this problem is to force yourself to write down at least 3 options.
4. "All-or-nothing" thinking. Examples
Why we "satisfice" (choose the first satisfactory option)
rather than "optimize" (choose the best [optimum] option)
What it takes to optimize:
1. Consider all the optionsTable illustrating complexity of making an optimal choice: An oversimplified example of choosing among apartments. Note that there are probably more than 3 places that you could consider and that you probably care about more than price, proximity to campus, and landlord. For example, you probably care about how quiet it is, how safe it is, how big it is, and how nice it is. However, even this oversimplified example shows you how complicated optimizing is.
2. Consider all the pros and cons of all the options
3. Determine the probabilities of each of those prose and cons
4. Correctly weight the importance of all those pros and cons
5. Combine all the information about the pros and cons of all the options to arrive at the best (optimal) choice
Options Price Score on Price Price's Importance Location Location's Score Location's Importance Landlord's Reputation Landlord's Score Landlord's Importance Total score 1 500/month 3 4 2 miles from campus 2 2 Excellent 5 4 36 (3 * 4) + (2 * 2) + (5 * 4) 2 400/month 4 4 5 miles from campus 1 2 Average 3 4 30 (4 * 4) + (1 * 2) + (3 * 4) 3 700/month 1 4 next to campus 5 2 Poor 1 4 18 (1 * 4) + (5 * 2) + (1 * 4)
Why we fail to optimize (besides the fact that optimizing is stressful):
- Because of the limits of STM, we do poorly at:
Considering all the options
Considering all the pros and cons of each option
Combining all that information
To get around some of the limits of short-term memory, you might just write down all your options as well as their pros and cons (Example).
To get around more of the limits of short-term memory, you could use this decision making program to help you make decisions.
- We are bad at estimating the frequency of events because of the availability heuristic: using the rule that how often something happens is based on how easy it is to remember examples of that event happening. The problem is that some events, even if they don't occur very often, are easy to recall (e.g., airplane crashes).
How politicians and some in the media have used the availability heuristic against us.
- In 2016, Trump ran on a vision of America being unsafe due to violent crime, but, in fact, America's violent crime rate was almost half of what it had been in 1990.
- Trump acted like cities near the Mexican border are extremely dangerous places, largely due to undocumented immigrants from Mexico. In fact, it seems that immigrants are less likely to commit crimes and that some southern border towns (e.g., El Paso) are among the safest cities in the country whereas cities far from the Mexican border (e.g., Baltimore and Detroit) are among the most dangerous U.S. cities.
- Trump has convinced some people that ANTIFA are a bunch of murderers. In fact, as of this writing, ANTIFA is responsible for only one death (and that may have been in self-defense). In general, right wing extremists are responsible for much more violence than the left-wing extremists. (link to more recent data).
- Some have argued that police are being gunned down at high rates and that COVID-19 is a hoax. However, recent figures show 101 police officers died from COVID-19 and 82 died from all other causes combined (e.g., car accidents, being shot, etc.).
- Police are about 3/4 as likely to die from a car crash as from a shooting; yet many officers do not wear seat belts.
- Being a police officer is a dangerous job. However, there are at least 18 jobs that are more dangerous. Jobs that are more than 2X as dangerous as being a police officer include commercial fisherman and fisherwomen (more than 7X as dangerous as the police officer job), loggers (more than 6X as dangerous), pilots (more than 3X as dangerous), roofers, steel workers, truck drivers, and garbage collectors.
- As Kristoff (March 23, 2021)writes, "In a typical year in the U.S., more preschoolers are shot dead in America (about 75) than police officers are."
*Note that the availability heuristic also fools us about whether we have a problem--or what the problem is. For example, many people think the U.S. has an immigration problem due to having too many immigrants. In fact, the U.S. does have an immigration problem due to a lack of immigrants. The number of Americans who were born in a foreign country has shrunk by more than half since the 1990s--and, if not fixed, this immigrant shortage will have dramatic negative effects on social security (and, probably, on the entire economy--Japan has learned how a lack of immigrants sinks an economy)
- Satisficing: Going with the first option that is satisfactory rather than optimizing: going for the best (optimum) option.
- Decision fatigue: If we are tired of making decisions, we may not look at all our options jor evaluate them carefully.
- Looking at only one criteria when many factors have to be taken into account: Examples:
- "I will take the one with the lowest price"--disregarding quality, convenience, and other criteria.
- "I will take Dr. X's class because it fits into my schedule."
- Looking only at the solution's effect on your immediate problem. That is, not engaging in systems thinking--realizing that everything is connected to everything else. The good systems thinker is thinking several steps ahead.
- We look at short-term effects rather than long term effects.
- We don't consider side effects and unintended consequences.
- People may change their behavior in ways that defeat our solution (e.g., they may find loopholes in rules, rebel against rules, or retaliate against sanctions imposed for breaking the rules).
- Your solution affects more people than you considered (e.g., eliminating homelessness might hurt hotel and other business owners)
- Heroine was once considered a solution to opium addiction.
- President Trump urged people to take chloroquine, arguing, essentially, "what do you have to lose." However, chloroquine has side effects and at least one study suggested that people taking chloroquine were more likely to die than those not taking that drug.
- We may exercise more to try to lose weight but end up eating more (because we are hungrier or because we "earned it"), so we end up gaining weight.
- "All or nothing" thinking: We see a solution as all good or all bad. The result may be that we see all the options as terrible. For example, since both Democrats and Republicans have some dishonest members, we may say, "They are all terrible." As the saying goes, "the perfect is the enemy of the good."
- Overconfidence: We are not nearly as accurate about predicting the future as we think we are. So, we confidently make bad predictions.
- We are vulnerable to framing effects (the way the problem is worded affects the decision that we will make) because we are loss adverse: we hate to think that we might lose something. We like to gain, but we HATE to lose. Insurance companies and bankers love us for this.
- We have an optimism bias, so we overestimate the chances that our solution will work. Examples of optimism bias:
- Businesses think that mergers will be successful, even though 84% of merger deals did not boost shareholder return.
- President Trump said that the COVID-19 would go away by April, 2020.
- People dying by taking unproven cancer "cures" when they could have been saved by traditional medicine.
- The planning fallacy: Things take much longer than we think that they will.
- Groupthink: People don't raise objections because they want to get along.
Besides inertia, you may have trouble making a decision and acting on it because (a) you may make the wrong decision and (b) the decision may have consequences that you did not anticipate. To help you make decisions with less stress,
We can't answer this question "Is it working" because