Heuristics : meaning and use in decision making

Meaning

Heuristics: A common sense rule (or set of rules) intended to increase the probability of solving a problem.

It  is a method that might not always find the best solution but is guaranteed to find a good solution in reasonable time. It is useful in solving tough problems which are complex or have limited information.

The classic example of heuristic methods is the travelling salesman problem.

EXAMPLE: Heuristic algorithm for the Traveling Salesman Problem (T.S.P)

A salesman must visit n cities, passing through each city only once, beginning from one of them which is considered as his base , and returning to it. Using a brute force way to solve TSP takes (n-1)! order time . So if there are 25 cities and for one computation it takes 1msec it would take many millenniums to solve the problem. A heuristic algorithm could be whenever the salesman is in town i he chooses as his next city ,the city j for which the c(i,j) cost, is the minimum among all c(i,k) costs, where k are the pointers of the city the salesman has not visited yet. There is also a simple rule just in case more than one cities give the minimum cost, for example in such a case the city with the smaller k will be chosen. This is basically a greedy algorithm which selects in every step the cheapest visit and does not care whether this will lead to a wrong result or not.

In real world applications a heuristic is a rule of thumb, an educated guess, an intuitive judgment or simply common sense. Note that a rule, guess, judgement, common sense are all derived from experience.

Here are some general approach for solving a problem in heuristic way-

1. By drawing a picture.

2. Working backward

3. By try to more concrete example and solving a more general problem

Decision Making Heuristics

We have to take decisions in every minute and in every field of life and there are no strict rules for this and not enough data available for analysis. Then on what basis one should decide about something. In most of these conditions heuristic is the idea, try to get the optimum solutions.

It is said that cognitive heuristic work by attribute substitution that means unconsciously. So when we take a decision that is computationally very complex, an easier calculation takes place but with the probability of making more error which may turns out into wrong decision sometimes.

Here are some heuristic criteria for making a decision like :

The representative heuristic : we take our decisions based on the likeliness of a sample occur in the population due to randomness and ignores the other things which we should not foe example stereotype like base rate. Goals and motivation affects these processes. These heuristics lead to biases in decision making which can lead to faulty decisions. Some biases are given below:

Ignoring Statistical Rules

Experiment: which has to be done in class

Given Statement: “Mr X has a attractive body and a blond girlfriend”. Then Mr X is

Options:

1) footballer.

2)nurse.

Result:

Statistical data say that only in UK there are thousands of male nurses in comparison to 400 footballers, so answer is most likely a nurse.

Ignoring Sample Size

Example: Hospital Problem

A certain town is served by two hospitals. In the larger hospital, about 45 babies are born each day, and in the smaller hospital about 15 babies are born each day. As you know, about 50 percent of all babies are boys. However, the exact percentage varies from day to day. Sometimes it may be higher than 50 percent, sometimes lower.

For a period of 1 year, each hospital recorded the days on which more than 60 percent of the babies born were boys. Which hospital do you think recorded more such days?

A) The larger hospital

B) The smaller hospital

C) About the same (that is, within 5 percent of each other)

Result:

Most of us would think it would be C . But probabilistically speaking it should be the smaller hospital. The reason is because more the sample size greater will the result tend to equal probability of boy and girl and disparity in the probability is higher in smaller sample size. 9 boys out of 10 babies have more chance than 45 boys in 50 babies.

Availability Heuristic: Our memory plays a major role in decision making. Specifically when making judgments about frequency and probability. We make decisions based on how easy things come to mind rather when judging how common something is

• Are there more words with “k” as the first letter or “k” as the third letter?

• Should women be more concerned about being assaulted by a stranger or a friend?

• What causes the availability heuristic? - Is it the number of objects that come to mind or how easy it is for the objects to come to mind?

• Schwarz et al., 1991 study - List six or twelve examples when you were assertive (or unassertive). Now tell me how assertive you are.

Reasons for the Availability Heuristic

• Ignoring biases in available samples and accessible cognitions

– False consensus effect - we think other people agree with us and do the things that we do more than is justified

– The effect of media coverage

– One-sided questions - What would you do to liven the party? What things do you dislike about loud parties?

• Salience

– When attention is focussed on someone we think they have more influence than they do

– Solo status studies

Consequences of the Availability Heuristic

• Egocentric biases

– Who does more of the housework?

• Belief Perseverance?

– Why do you think this class is fun and exciting?

– Firefighters study

– False feedback studies

• Imagination

– When we imagine things they seem more likely to occur

– Cable TV study

Anchoring and Adjustment: This heuristic is like the availability heuristic because it’s sometimes based on previous knowledge. Making an initial judgment is the anchoring, we make an approximation about something. Once it’s anchored then we make adjustments according to additional information that we may receive.

• Do you hope to get a grade higher or lower than an 95 in this class? How much lower?

• Length of the Mississippi study

• Fundamental Attribution Error and Anchoring

– Two-step models of Attribution

• Make a trait attribution first which acts as an anchor

• Make an adjustment which is insufficient

– This type of two step model is not the same as dual process model in persuasion