Can you prevent functional fixedness?

What is the difference between a heuristic and an algorithm? An algorithm is a methodical, logical rule or procedure that guarantees solving a particular problem. A heuristic is a simple thinking strategy that allows us to make judgements and solve problems efficiently.

Functional fixedness can be overcome through attempts at recombination, such as the generic parts technique that breaks objects into individual, generically identified components.

1. Break down a problem into basic elements. Think about the hammer and nail scenario.
2. Look to other areas of expertise.
3. Try “design thinking”

While heuristics can help us solve problems and speed up our decision-making process, they can introduce errors. As you saw in the examples above, heuristics can lead to inaccurate judgments about how commonly things occur and about how representative certain things may be.

For example, after seeing several news reports about car thefts, you might make a judgment that vehicle theft is much more common than it really is in your area. This type of availability heuristic can be helpful and important in decision-making.

How to build an algorithm in six steps

1. Step 1: Determine the goal of the algorithm.
2. Step 2: Access historic and current data.
3. Step 3: Choose the right models.
4. Step 4: Fine tuning.
5. Step 5: Visualize your results.
6. Step 6: Running your algorithm continuously.

An algorithm must have five properties:

• Input specified.
• Output specified.
• Definiteness.
• Effectiveness.
• Finiteness.

Not every well-defined problem can be solved by an algorithm and thus by a program. Problems that have no algorithm are called unsolvable. Fortunately, most problems we encounter in applications and need to write a program for can be solved by an algorithm.

Given a problem, there may be more than one correct algorithms. However, the costs to perform different algorithms may be different. An algorithm is correct only if it produces correct result for all input instances.

Answer: Tractable Problem: A problem that is solvable by a polynomial-time algorithm. The upper bound is polynomial. The lower bound is exponential. From a computational complexity stance, intractable problems are problems for which there exist no efficient algorithms to solve them.

A ‘tractable’ model is one that you can solve, which means there are several types of tractability : analytical tractability (finding a solution to a theoretical model), empirical tractability (being able to estimate/calibrate your model) and computational tractability (finding numerical solutions).

Tractable and Intractable. • Generally we think of problems that are solvable by polynomial time algorithms as being tractable, and problems that require superpolynomial time as being intractable. • Sometimes the line between what is an ‘easy’ problem and what is a ‘hard’ problem is a fine one.

capable of being easily led