true or false the first step in problem solving is using means end analysis

• Memberships

Means End Analysis: the basics and example

Means End Analysis (MEA): this article explains the concept of Means End Analysis or MEA in a practical way. This article contains the general definition of the technique, and the steps involved in the process, including a means end analysis example. After reading it, you will understand the basics of this Problem Solving tool. Enjoy reading!

What is a Means End Analysis (MEA)?

Means End Analysis (MEA) is a problem-solving technique that has been used since the fifties of the last century to stimulate creativity .

Means End Analysis is also a way of looking at the organisational planning , and helps in achieving the end-goals .

With Means End Analysis, it is possible to control the entire process of problem solving. It starts from a predetermined goal, in which actions are chosen that lead to that goal.

Each action that is executed leads to the next action; everything is connected together in order to reach the end-goal. In the meantime however, problems may arise. It is often hard to determine where exactly the crux is.

With the help of Means End Analysis, both forward and backward research can be done to determine where the stagnation is occurring. This enables the larger parts of a problem to be solved first, to subsequently return to the smaller problems afterwards.

Intermediate steps

In order for Means End Analysis to be effective, it is advisable to get all relevant actions and intermediate steps leading to the goal in the picture, making them detectable.

Additionally, it is handy to be capable of tracking (small) changes, and to measure the differences between the actual and desired state of the individual actions.

If this doesn’t happen, there is a significant risk that a mistake or change will have further consequences across the series of actions following it, making it harder and harder to intervene.

Every organisation works with goals that need to be met.

Depending on the goal a short term (a week or a month), mid-long term (a year), and a long term (muliple years) are determined. It is nice both for the organisation and for the employees when these goals are successfully met.

By making an analysis of the means and the intermediate actions with the help of Means End Analysis beforehand, it is easier to focus and not lose your way. It is a fact that goals don’t just achieve themselves. Based on careful planning , action should be undertaken.

Without planning there’s a significant chance for the organisation to head in the wrong direction, deviating from its pre-determined goal.

Means End Analysis example

To successfully execute Means End Analysis it is advisable to think from large to small; the eventual goal needs to be split into smaller sub-goals, making it overseeable for all parties that are going to work towards on achieving it.

When a commercial electronic business has the end-goal to reach a turnover of 15 million euro’s within a year, that is a noble thought. It means that all actions in that year will be geared towards meeting that 15 million euro limit.

However, it will only work when it becomes clear what has to be done to meet that turnover of 15 million. With the help of Means End Analysis, the end goal is split into a few smaller goals, which will contribute to the 15 million turnover:

• A specific product, for example the newest smartphone, needs to be sold aggressively;
• A minimal selling price is set, which dealers also must comply with;
• Aside from the newest smartphone, there are some related products that will be go to market as well.

Means End Analysis : Executable steps

Regardless of the splitting into smaller sub-goals, it will still not be possible for the organisation to achieve a turnover of 15 million. The search for even smaller, more specific steps, aids in them to achieving the end-goal.

These sub-sub-goals are translated into executable steps that are deployed by the organisation and used to achieve the original goal of a turnover of 15 million. In case there is stagnation of a problem somewhere, it becomes much easier to find the problem and fix that part of the process. Prior sub-goals are elaborated upon below:

• A specific marketing plan is developed for the smartphone to give publicity to the new product, especially via social media;
• New applications will be developped by the electronic business to be sold as a by-product;
• A special discount is offered to students when they can prove that they are, in fact, registered at an institute of higher education;
• An advertisement will be placed in door-to-door newspapers, whereby a coupon can be used to obtain a substantial trade-in discount for the old mobile phone.

Figure 1 – an example overview of a Means End Analysis

Means End Analysis is about thoroughly thinking through which steps are needed in order to reach the end-goal.

Additionally, everyone within the organisation gets a reality check, because it shows that even the smallest steps have an impact on the overall goal that has been set .

It’s Your Turn

What do you think? Is Means End Analysis applicable in your personal or professional environment? Do you recognize the practical explanation or do you have more suggestions? What are your success factors for achieving end-goals??

Share your experience and knowledge in the comments box below.

More information

• Fikes, R. E., & Nilsson, N. J. (1971). STRIPS: A new approach to the application of theorem proving to problem solving . Artificial intelligence, 2(3-4), 189-208.
• Johnson, A. P. (2005). A short guide to action research . Boston: Pearson/ Allyn and Bacon .
• Sweller, J. (1988). Cognitive load during problem solving: Effects on learning . Cognitive science, 12(2), 257-285.

How to cite this article: Mulder, P. (2018). Means End Analysis (MEA) . Retrieved [insert date] from Toolshero: https://www.toolshero.com/problem-solving/means-end-analysis-mea/

Original publication date: 04/19/2018 | Last update: 01/25/2024

Add a link to this page on your website: <a href=”https://www.toolshero.com/problem-solving/means-end-analysis-mea/”> Toolshero: Means End Analysis (MEA)</a>

Did you find this article interesting?

Your rating is more than welcome or share this article via Social media!

Average rating 4.5 / 5. Vote count: 13

No votes so far! Be the first to rate this post.

We are sorry that this post was not useful for you!

Let us improve this post!

Tell us how we can improve this post?

Patty Mulder

Patty Mulder is an Dutch expert on Management Skills, Personal Effectiveness and Business Communication. She is also a Content writer, Business Coach and Company Trainer and lives in the Netherlands (Europe). Note: all her articles are written in Dutch and we translated her articles to English!

Related ARTICLES

Pareto Analysis explained plus example

Root Cause Analysis (RCA)

Soft Systems Methodology (SSM) by Peter Checkland

Convergent Thinking: the Definition and Theory

CATWOE Analysis: theory and example

Systems Thinking: Theory and Definition

Also interesting.

8D Report and template

Fishbone Diagram by Kaoru Ishikawa explained

Crowdsourcing: the meaning, definition and some examples

Leave a reply cancel reply.

You must be logged in to post a comment.

BOOST YOUR SKILLS

Toolshero supports people worldwide ( 10+ million visitors from 100+ countries ) to empower themselves through an easily accessible and high-quality learning platform for personal and professional development.

By making access to scientific knowledge simple and affordable, self-development becomes attainable for everyone, including you! Join our learning platform and boost your skills with Toolshero.

POPULAR TOPICS

• Change Management
• Marketing Theories
• Problem Solving Theories
• Psychology Theories

ABOUT TOOLSHERO

• Free Toolshero e-book
• Memberships & Pricing

• school Campus Bookshelves
• menu_book Bookshelves
• perm_media Learning Objects
• login Login
• how_to_reg Request Instructor Account
• hub Instructor Commons

Margin Size

• Download Page (PDF)
• Download Full Book (PDF)
• Periodic Table
• Physics Constants
• Scientific Calculator
• Reference & Cite
• Tools expand_more
• Readability

selected template will load here

This action is not available.

6.3: Means –Ends Analysis

• Last updated
• Save as PDF
• Page ID 54100

• Mehgan Andrade and Neil Walker
• College of the Canyons

\( \newcommand{\vecs}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vecd}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash {#1}}} \)

\( \newcommand{\id}{\mathrm{id}}\) \( \newcommand{\Span}{\mathrm{span}}\)

( \newcommand{\kernel}{\mathrm{null}\,}\) \( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\) \( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\) \( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\inner}[2]{\langle #1, #2 \rangle}\)

\( \newcommand{\Span}{\mathrm{span}}\)

\( \newcommand{\id}{\mathrm{id}}\)

\( \newcommand{\kernel}{\mathrm{null}\,}\)

\( \newcommand{\range}{\mathrm{range}\,}\)

\( \newcommand{\RealPart}{\mathrm{Re}}\)

\( \newcommand{\ImaginaryPart}{\mathrm{Im}}\)

\( \newcommand{\Argument}{\mathrm{Arg}}\)

\( \newcommand{\norm}[1]{\| #1 \|}\)

\( \newcommand{\Span}{\mathrm{span}}\) \( \newcommand{\AA}{\unicode[.8,0]{x212B}}\)

\( \newcommand{\vectorA}[1]{\vec{#1}}      % arrow\)

\( \newcommand{\vectorAt}[1]{\vec{\text{#1}}}      % arrow\)

\( \newcommand{\vectorB}[1]{\overset { \scriptstyle \rightharpoonup} {\mathbf{#1}} } \)

\( \newcommand{\vectorC}[1]{\textbf{#1}} \)

\( \newcommand{\vectorD}[1]{\overrightarrow{#1}} \)

\( \newcommand{\vectorDt}[1]{\overrightarrow{\text{#1}}} \)

\( \newcommand{\vectE}[1]{\overset{-\!-\!\rightharpoonup}{\vphantom{a}\smash{\mathbf {#1}}}} \)

In Means-End Analysis you try to reduce the difference between initial state and goal state by creating sub goals until a sub goal can be reached directly (probably you know several examples of recursion which works on the basis of this).

An example for a problem that can be solved by Means-End Analysis are the „Towers of Hanoi“

The initial state of this problem is described by the different sized discs being stacked in order of size on the first of three pegs (the “start-peg“). The goal state is described by these discs being stacked on the third pegs (the “end-peg“) in exactly the same order.

There are three operators:

· You are allowed to move one single disc from one peg to another one

· You are only able to move a disc if it is on top of one stack

· A disc cannot be put onto a smaller one.

In order to use Means-End Analysis we have to create subgoals. One possible way of doing this is described in the picture:

1. Moving the discs lying on the biggest one onto the second peg.

2. Shifting the biggest disc to the third peg.

3. Moving the other ones onto the third peg, too.

You can apply this strategy again and again in order to reduce the problem to the case where you only have to move a single disc – which is then something you are allowed to do.

Strategies of this kind can easily be formulated for a computer; the respective algorithm for the Towers of Hanoi would look like this:

1. move n-1 discs from A to B

2. move disc #n from A to C

3. move n-1 discs from B to C

Where n is the total number of discs, A is the first peg, B the second, C the third one. Now the problem is reduced by one with each recursive loop.

Means-end analysis is important to solve everyday-problems - like getting the right train connection: You have to figure out where you catch the first train and where you want to arrive, first of all. Then you have to look for possible changes just in case you do not get a direct connection. Third, you have to figure out what are the best times of departure and arrival, on which platforms you leave and arrive and make it all fit together.

Means-Ends Analysis

Means-Ends analysis is a method of solving problems. This method is useful for well-formed problems, less so for less-formed problems.

State Space [ edit ]

Problem solving occurs in a state space . Imagine first an initial state and then a goal state . We want to get from the initial state to the goal state . There might be many different paths from the initial state to the goal state .

We can talk about how to solve this problem in terms of differences between different states and the goal (end) state . I want to deduce the difference between two different states.

So I can look at my current problem, and then my end- state . I will then ask "how many differences are there between my current state and my end state ".

Imagine we start at step one, our initial state . The next step in this process is to create every possible permutation from my initial state . The next step is to calculate the difference in the states I just made and my end state .

There is an obvious method here: if I generate 6 different possible states from my initial state , I can then calculate the difference between each of those states and the end state . I would look for the option that had the least number of differences to produce the most optimal solution.

Universal method of problem solving [ edit ]

A means-ends analysis is considered a universal method of solving problems. However, there is no guarantee of success.

References [ edit ]

• ↑ http://www.flaticon.com/

Give a specific name, value or other brief answer without explanation or calculation.

Reach a conclusion from the information given.

Obtain a numerical answer showing the relevant stages in the working.

A unit of abstract mathematical system subject to the laws of arithmetic.

• Subscriber Services
• For Authors
• Publications
• Archaeology
• Art & Architecture
• Bilingual dictionaries
• Classical studies
• Encyclopedias
• English Dictionaries and Thesauri
• Language reference
• Linguistics
• Media studies
• Medicine and health
• Names studies
• Performing arts
• Science and technology
• Social sciences
• Society and culture
• Overview Pages
• Subject Reference
• English Dictionaries
• Bilingual Dictionaries

Recently viewed (0)

• Save Search
• Share This Facebook LinkedIn Twitter

Related Content

Related overviews, 'means-end analysis' can also refer to....

means/ends analysis

More Like This

Show all results sharing these subjects:

means-end analysis

Quick reference.

A cognitive heuristic implemented by the General Problem Solver to deal with practical problems when the problem space is too large for exhaustive search to be used. The problem space is represented by an initial state (such as the starting position in a game of chess), a goal state (such as checkmating the opponent), and all possible intervening states that are achieved by actions involving the application of operators (such as chess moves) to existing states. If there is an action that immediately solves the problem, then it is implemented immediately; if not, the problem solver establishes the subgoal of maximally reducing the difference between the current state and the goal state; if an action can be found to achieve this subgoal, then it is implemented immediately; if not, the problem solver establishes the sub-subgoal of removing the constraints on achieving the subgoal; and so on. See also General Problem Solver.

From:   means-end analysis   in  A Dictionary of Psychology »

Subjects: Science and technology — Psychology

Related content in Oxford Reference

Reference entries, means-end analysis n..

View all related items in Oxford Reference »

Search for: 'means-end analysis' in Oxford Reference »

• Oxford University Press

PRINTED FROM OXFORD REFERENCE (www.oxfordreference.com). (c) Copyright Oxford University Press, 2023. All Rights Reserved. Under the terms of the licence agreement, an individual user may print out a PDF of a single entry from a reference work in OR for personal use (for details see Privacy Policy and Legal Notice ).

date: 06 June 2024

• Cookie Policy
• Privacy Policy
• Legal Notice
• Accessibility
• [66.249.64.20|81.177.182.136]
• 81.177.182.136

Character limit 500 /500

Means-End Reasoning

• Reference work entry
• First Online: 01 January 2022
• pp 4142–4147
• Cite this reference work entry

• Anastasia Krasheninnikova 3 , 4 , 5  

44 Accesses

1 Citations

Means-end behavior ; Means-end understanding

Broadly speaking means-end reasoning is concerned with finding means for achieving goals (Pollock 2002 ). More specifically, it involves the deliberate and planned execution of a chain of actions to achieve a goal and occurs in situations where an obstacle (e.g., a distance between the subject and a desirable item, person) preventing the achievement of the goal must initially be removed (Willatts 1999 ).

Introduction

In everyday life, we are regularly facing situations which require elaborate sequences of mediating actions to reach a distant goal at the end. For example, imagine a person opening a drawer to take a key to unlock a storeroom to get a ladder needed to reach the door to an out-of-reach vitrine where there is a candy box that can be opened to get some sweets. As this hypothetical problem-solving sequence illustrates (modiefied from Santos et al 2005 ), the individual steps within a sequence are often separated...

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Institutional subscriptions

Alem, S., Perry, C. J., Zhu, X., Loukola, O. J., Ingraham, T., Søvik, E., & Chittka, L. (2016). Associative mechanisms allow for social learning and cultural transmission of string pulling in an insect. PLoS Biology, 14 (10), 1–28. https://doi.org/10.1371/journal.pbio.1002564 .

Article   Google Scholar  

Auersperg, A. M. I., Gajdon, G. K., & Huber, L. (2009). Kea (Nestor notabilis) consider spatial relationships between objects in the support problem. Biology Letters, 5 (4), 455–458. https://doi.org/10.1098/rsbl.2009.0114 .

Article   PubMed   PubMed Central   Google Scholar  

Auersperg, A. M. I., Kacelnik, A., & Von Bayern, A. M. P. (2013). Explorative learning and functional inferences on a five-step means-means-end problem in Goffin’ s cockatoos ( Cacatua goffini ). PLoS One, 8 (7), e68979. https://doi.org/10.1371/journal.pone.0068979 .

Bird, C. D., & Emery, N. J. (2009). Insightful problem solving and creative tool modification by captive nontool-using rooks. Proceedings of the National Academy of Sciences, 106 (25), 10370–10375. https://doi.org/10.1073/pnas.0901008106 .

Hanus, D. (2016). Causal reasoning versus associative learning: A useful dichotomy or a strawman battle in comparative psychology? Journal of Comparative Psychology, 130 (3), 241–248. https://doi.org/10.1037/a0040235 .

Article   PubMed   Google Scholar  

Hauser, M. D., Kralik, J., & Botto-Mahan, C. (1999). Problem solving and functional design features: Experiments on cotton-top tamarins, Saguinus oedipus oedipus. Animal Behaviour, 57 , 565–582. https://doi.org/10.1006/anbe.1998.1032 .

Heinrich, B., & Bugnyar, T. (2005). Testing problem solving in ravens: String-pulling to reach food. Ethology, 111 (10), 962–976. https://doi.org/10.1111/j.1439-0310.2005.01133.x .

Herrmann, E., Wobber, V., & Call, J. (2008). Great apes’ (Pan troglodytes, Pan paniscus, Gorilla gorilla, Pongo pygmaeus) understanding of tool functional properties after limited experience. Journal of Comparative Psychology, 122 (2), 220–230. https://doi.org/10.1037/0735-7036.122.2.220 .

Irie-Sugimoto, N., Kobayashi, T., Sato, T., & Hasegawa, T. (2008). Evidence of means – End behavior in Asian elephants (Elephas maximus). Animal Cognition, 11 (2), 359–365. https://doi.org/10.1007/s10071-007-0126-z .

Jacobs, I. F., & Osvath, M. (2015). The string-pulling paradigm in comparative psychology. Journal of Comparative Psychology, 129 (2), 89–120. https://doi.org/10.1037/a0038746 .

Köhler, W. (1927). The mentality of aper (2nd ed.). New York: Vintage Books.

Google Scholar  

Krasheninnikova, A., Bräger, S., & Wanker, R. (2013). Means-end comprehension in four parrot species: Explained by social complexity. Animal Cognition, 16 (5), 755–764. https://doi.org/10.1007/s10071-013-0609-z .

Matsuzawa, T. (2001). Primate foundations of human intelligence: A view of tool use in non-human primates and fossil hominids. In T. Matsuzawa (Ed.), Primate origins of human cognition and behavior (pp. 3–25). Tokyo/Berlin/Heidelberg: Springer.

Chapter   Google Scholar  

Osthaus, B., Lea, S. E. G., & Slater, A. M. (2005). Dogs (Canis lupus familiaris) fail to show understanding of means-end connections in a string-pulling task. Animal Cognition, 8 (1), 37–47. https://doi.org/10.1007/s10071-004-0230-2 .

Piaget, J., & Cook, M. (1952). The origins of intelligence in children . New York: International Universities Press.

Book   Google Scholar  

Pollock, J. L. (2002). The logical foundations of means-end reasoning. In R. Elio (Ed.), Common sense, reasoning, and rationality (pp. 60–77). Oxford: Oxford University Press.

Povinelli, D. J. (2000). Folk physics for Apes: The Chimpanzee’s theory of how the world works. Book reviews . https://doi.org/10.1016/j.orggeochem.2014.10.015 .

Range, F., Möslinger, H., & Virányi, Z. (2012). Domestication has not affected the understanding of means-end connections in dogs. Animal Cognition, 15 (4), 597–607. https://doi.org/10.1007/s10071-012-0488-8 .

Santos, L. R., Rosati, A., Sproul, C., Spaulding, B., & Hauser, M. D. (2005). Means-means-end tool choice in cotton-top tamarins (Saguinus oedipus): Finding the limits on primates’ knowledge of tools. Animal Cognition, 8 (4), 236–246. https://doi.org/10.1007/s10071-004-0246-7 .

Schmidt, G. F., & Cook, R. G. (2006). Mind the gap: Means – End discrimination by pigeons. Animal Behaviour, 71 , 599–608. https://doi.org/10.1016/j.anbehav.2005.06.010 .

Shettleworth, S. J. (2012). Do animals have insight, and what is insight anyway? Canadian Journal of Experimental Psychology/Revue Canadienne de Psychologie Expérimentale, 66 (4), 217–226. https://doi.org/10.1037/a0030674 .

Taylor, A. H., Medina, F. S., Holzhaider, J. C., Hearne, L. J., Hunt, G. R., & Gray, R. D. (2010). An investigation into the cognition behind spontaneous string pulling in new caledonian crows. PLoS One, 5 (2), e9345. https://doi.org/10.1371/journal.pone.0009345 .

Whitt, E., Douglas, M., Osthaus, B., & Hocking, I. (2009). Domestic cats (Felis catus) do not show causal understanding in a string-pulling task. Animal Cognition, 12 (5), 739–743. https://doi.org/10.1007/s10071-009-0228-x .

Willatts, P. (1999). Development of means-end behavior in young infants: Pulling a support to retrieve a distant object. Developmental Psychology, 35 (3), 651–667. https://doi.org/10.1037/0012-1649.35.3.651 .

Wimpenny, J. H., Weir, A. A. S., Clayton, L., Rutz, C., & Kacelnik, A. (2009). Cognitive processes associated with sequential tool use in new Caledonian crows. PLoS One, 4 (8), e6471. https://doi.org/10.1371/journal.pone.0006471 .

Woodward, J. (2011). A philosopher looks at tool use and causal understanding. In T. McCormack, C. Hoerl, & S. Butterfill (Eds.), Tool use and causal cognition (pp. 1–47). Oxford: Oxford University Press.

Download references

Author information

Authors and affiliations.

Department of Behavioural Neurobiology, Max-Planck-Institute for Ornithology, Seewiesen, Germany

Anastasia Krasheninnikova

Max-Planck Comparative Cognition Research Group, Tenerife, Spain

Department of Behavioural Ecology and Evolutionary Genetics, Max-Planck-Institute for Ornithology, Seewiesen, Germany

You can also search for this author in PubMed   Google Scholar

Corresponding author

Correspondence to Anastasia Krasheninnikova .

Editor information

Editors and affiliations.

Department of Psychology, Oakland University, Rochester, MI, USA

Jennifer Vonk

Todd K. Shackelford

Section Editor information

University of Manitoba, Winnipeg, MB, Canada

Dawson Clary

Rights and permissions

Reprints and permissions

Copyright information

© 2022 Springer Nature Switzerland AG

About this entry

Cite this entry.

Krasheninnikova, A. (2022). Means-End Reasoning. In: Vonk, J., Shackelford, T.K. (eds) Encyclopedia of Animal Cognition and Behavior. Springer, Cham. https://doi.org/10.1007/978-3-319-55065-7_1539

Download citation

DOI : https://doi.org/10.1007/978-3-319-55065-7_1539

Published : 20 May 2022

Publisher Name : Springer, Cham

Print ISBN : 978-3-319-55064-0

Online ISBN : 978-3-319-55065-7

eBook Packages : Behavioral Science and Psychology Reference Module Humanities and Social Sciences Reference Module Business, Economics and Social Sciences

Share this entry

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Publish with us

Policies and ethics

• Find a journal
• Track your research

7.3 Problem-Solving

Learning objectives.

By the end of this section, you will be able to:

• Describe problem solving strategies
• Define algorithm and heuristic
• Explain some common roadblocks to effective problem solving

   People face problems every day—usually, multiple problems throughout the day. Sometimes these problems are straightforward: To double a recipe for pizza dough, for example, all that is required is that each ingredient in the recipe be doubled. Sometimes, however, the problems we encounter are more complex. For example, say you have a work deadline, and you must mail a printed copy of a report to your supervisor by the end of the business day. The report is time-sensitive and must be sent overnight. You finished the report last night, but your printer will not work today. What should you do? First, you need to identify the problem and then apply a strategy for solving the problem.

The study of human and animal problem solving processes has provided much insight toward the understanding of our conscious experience and led to advancements in computer science and artificial intelligence. Essentially much of cognitive science today represents studies of how we consciously and unconsciously make decisions and solve problems. For instance, when encountered with a large amount of information, how do we go about making decisions about the most efficient way of sorting and analyzing all the information in order to find what you are looking for as in visual search paradigms in cognitive psychology. Or in a situation where a piece of machinery is not working properly, how do we go about organizing how to address the issue and understand what the cause of the problem might be. How do we sort the procedures that will be needed and focus attention on what is important in order to solve problems efficiently. Within this section we will discuss some of these issues and examine processes related to human, animal and computer problem solving.

PROBLEM-SOLVING STRATEGIES

   When people are presented with a problem—whether it is a complex mathematical problem or a broken printer, how do you solve it? Before finding a solution to the problem, the problem must first be clearly identified. After that, one of many problem solving strategies can be applied, hopefully resulting in a solution.

Problems themselves can be classified into two different categories known as ill-defined and well-defined problems (Schacter, 2009). Ill-defined problems represent issues that do not have clear goals, solution paths, or expected solutions whereas well-defined problems have specific goals, clearly defined solutions, and clear expected solutions. Problem solving often incorporates pragmatics (logical reasoning) and semantics (interpretation of meanings behind the problem), and also in many cases require abstract thinking and creativity in order to find novel solutions. Within psychology, problem solving refers to a motivational drive for reading a definite “goal” from a present situation or condition that is either not moving toward that goal, is distant from it, or requires more complex logical analysis for finding a missing description of conditions or steps toward that goal. Processes relating to problem solving include problem finding also known as problem analysis, problem shaping where the organization of the problem occurs, generating alternative strategies, implementation of attempted solutions, and verification of the selected solution. Various methods of studying problem solving exist within the field of psychology including introspection, behavior analysis and behaviorism, simulation, computer modeling, and experimentation.

A problem-solving strategy is a plan of action used to find a solution. Different strategies have different action plans associated with them (table below). For example, a well-known strategy is trial and error. The old adage, “If at first you don’t succeed, try, try again” describes trial and error. In terms of your broken printer, you could try checking the ink levels, and if that doesn’t work, you could check to make sure the paper tray isn’t jammed. Or maybe the printer isn’t actually connected to your laptop. When using trial and error, you would continue to try different solutions until you solved your problem. Although trial and error is not typically one of the most time-efficient strategies, it is a commonly used one.

   Another type of strategy is an algorithm. An algorithm is a problem-solving formula that provides you with step-by-step instructions used to achieve a desired outcome (Kahneman, 2011). You can think of an algorithm as a recipe with highly detailed instructions that produce the same result every time they are performed. Algorithms are used frequently in our everyday lives, especially in computer science. When you run a search on the Internet, search engines like Google use algorithms to decide which entries will appear first in your list of results. Facebook also uses algorithms to decide which posts to display on your newsfeed. Can you identify other situations in which algorithms are used?

A heuristic is another type of problem solving strategy. While an algorithm must be followed exactly to produce a correct result, a heuristic is a general problem-solving framework (Tversky & Kahneman, 1974). You can think of these as mental shortcuts that are used to solve problems. A “rule of thumb” is an example of a heuristic. Such a rule saves the person time and energy when making a decision, but despite its time-saving characteristics, it is not always the best method for making a rational decision. Different types of heuristics are used in different types of situations, but the impulse to use a heuristic occurs when one of five conditions is met (Pratkanis, 1989):

• When one is faced with too much information
• When the time to make a decision is limited
• When the decision to be made is unimportant
• When there is access to very little information to use in making the decision
• When an appropriate heuristic happens to come to mind in the same moment

Working backwards is a useful heuristic in which you begin solving the problem by focusing on the end result. Consider this example: You live in Washington, D.C. and have been invited to a wedding at 4 PM on Saturday in Philadelphia. Knowing that Interstate 95 tends to back up any day of the week, you need to plan your route and time your departure accordingly. If you want to be at the wedding service by 3:30 PM, and it takes 2.5 hours to get to Philadelphia without traffic, what time should you leave your house? You use the working backwards heuristic to plan the events of your day on a regular basis, probably without even thinking about it.

Another useful heuristic is the practice of accomplishing a large goal or task by breaking it into a series of smaller steps. Students often use this common method to complete a large research project or long essay for school. For example, students typically brainstorm, develop a thesis or main topic, research the chosen topic, organize their information into an outline, write a rough draft, revise and edit the rough draft, develop a final draft, organize the references list, and proofread their work before turning in the project. The large task becomes less overwhelming when it is broken down into a series of small steps.

Further problem solving strategies have been identified (listed below) that incorporate flexible and creative thinking in order to reach solutions efficiently.

Additional Problem Solving Strategies :

• Abstraction – refers to solving the problem within a model of the situation before applying it to reality.
• Analogy – is using a solution that solves a similar problem.
• Brainstorming – refers to collecting an analyzing a large amount of solutions, especially within a group of people, to combine the solutions and developing them until an optimal solution is reached.
• Divide and conquer – breaking down large complex problems into smaller more manageable problems.
• Hypothesis testing – method used in experimentation where an assumption about what would happen in response to manipulating an independent variable is made, and analysis of the affects of the manipulation are made and compared to the original hypothesis.
• Lateral thinking – approaching problems indirectly and creatively by viewing the problem in a new and unusual light.
• Means-ends analysis – choosing and analyzing an action at a series of smaller steps to move closer to the goal.
• Method of focal objects – putting seemingly non-matching characteristics of different procedures together to make something new that will get you closer to the goal.
• Morphological analysis – analyzing the outputs of and interactions of many pieces that together make up a whole system.
• Proof – trying to prove that a problem cannot be solved. Where the proof fails becomes the starting point or solving the problem.
• Reduction – adapting the problem to be as similar problems where a solution exists.
• Research – using existing knowledge or solutions to similar problems to solve the problem.
• Root cause analysis – trying to identify the cause of the problem.

The strategies listed above outline a short summary of methods we use in working toward solutions and also demonstrate how the mind works when being faced with barriers preventing goals to be reached.

One example of means-end analysis can be found by using the Tower of Hanoi paradigm . This paradigm can be modeled as a word problems as demonstrated by the Missionary-Cannibal Problem :

Missionary-Cannibal Problem

Three missionaries and three cannibals are on one side of a river and need to cross to the other side. The only means of crossing is a boat, and the boat can only hold two people at a time. Your goal is to devise a set of moves that will transport all six of the people across the river, being in mind the following constraint: The number of cannibals can never exceed the number of missionaries in any location. Remember that someone will have to also row that boat back across each time.

Hint : At one point in your solution, you will have to send more people back to the original side than you just sent to the destination.

The actual Tower of Hanoi problem consists of three rods sitting vertically on a base with a number of disks of different sizes that can slide onto any rod. The puzzle starts with the disks in a neat stack in ascending order of size on one rod, the smallest at the top making a conical shape. The objective of the puzzle is to move the entire stack to another rod obeying the following rules:

• 1. Only one disk can be moved at a time.
• 2. Each move consists of taking the upper disk from one of the stacks and placing it on top of another stack or on an empty rod.
• 3. No disc may be placed on top of a smaller disk.

  Figure 7.02. Steps for solving the Tower of Hanoi in the minimum number of moves when there are 3 disks.

Figure 7.03. Graphical representation of nodes (circles) and moves (lines) of Tower of Hanoi.

The Tower of Hanoi is a frequently used psychological technique to study problem solving and procedure analysis. A variation of the Tower of Hanoi known as the Tower of London has been developed which has been an important tool in the neuropsychological diagnosis of executive function disorders and their treatment.

GESTALT PSYCHOLOGY AND PROBLEM SOLVING

As you may recall from the sensation and perception chapter, Gestalt psychology describes whole patterns, forms and configurations of perception and cognition such as closure, good continuation, and figure-ground. In addition to patterns of perception, Wolfgang Kohler, a German Gestalt psychologist traveled to the Spanish island of Tenerife in order to study animals behavior and problem solving in the anthropoid ape.

As an interesting side note to Kohler’s studies of chimp problem solving, Dr. Ronald Ley, professor of psychology at State University of New York provides evidence in his book A Whisper of Espionage  (1990) suggesting that while collecting data for what would later be his book  The Mentality of Apes (1925) on Tenerife in the Canary Islands between 1914 and 1920, Kohler was additionally an active spy for the German government alerting Germany to ships that were sailing around the Canary Islands. Ley suggests his investigations in England, Germany and elsewhere in Europe confirm that Kohler had served in the German military by building, maintaining and operating a concealed radio that contributed to Germany’s war effort acting as a strategic outpost in the Canary Islands that could monitor naval military activity approaching the north African coast.

While trapped on the island over the course of World War 1, Kohler applied Gestalt principles to animal perception in order to understand how they solve problems. He recognized that the apes on the islands also perceive relations between stimuli and the environment in Gestalt patterns and understand these patterns as wholes as opposed to pieces that make up a whole. Kohler based his theories of animal intelligence on the ability to understand relations between stimuli, and spent much of his time while trapped on the island investigation what he described as  insight , the sudden perception of useful or proper relations. In order to study insight in animals, Kohler would present problems to chimpanzee’s by hanging some banana’s or some kind of food so it was suspended higher than the apes could reach. Within the room, Kohler would arrange a variety of boxes, sticks or other tools the chimpanzees could use by combining in patterns or organizing in a way that would allow them to obtain the food (Kohler & Winter, 1925).

While viewing the chimpanzee’s, Kohler noticed one chimp that was more efficient at solving problems than some of the others. The chimp, named Sultan, was able to use long poles to reach through bars and organize objects in specific patterns to obtain food or other desirables that were originally out of reach. In order to study insight within these chimps, Kohler would remove objects from the room to systematically make the food more difficult to obtain. As the story goes, after removing many of the objects Sultan was used to using to obtain the food, he sat down ad sulked for a while, and then suddenly got up going over to two poles lying on the ground. Without hesitation Sultan put one pole inside the end of the other creating a longer pole that he could use to obtain the food demonstrating an ideal example of what Kohler described as insight. In another situation, Sultan discovered how to stand on a box to reach a banana that was suspended from the rafters illustrating Sultan’s perception of relations and the importance of insight in problem solving.

Grande (another chimp in the group studied by Kohler) builds a three-box structure to reach the bananas, while Sultan watches from the ground.  Insight , sometimes referred to as an “Ah-ha” experience, was the term Kohler used for the sudden perception of useful relations among objects during problem solving (Kohler, 1927; Radvansky & Ashcraft, 2013).

Solving puzzles.

   Problem-solving abilities can improve with practice. Many people challenge themselves every day with puzzles and other mental exercises to sharpen their problem-solving skills. Sudoku puzzles appear daily in most newspapers. Typically, a sudoku puzzle is a 9×9 grid. The simple sudoku below (see figure) is a 4×4 grid. To solve the puzzle, fill in the empty boxes with a single digit: 1, 2, 3, or 4. Here are the rules: The numbers must total 10 in each bolded box, each row, and each column; however, each digit can only appear once in a bolded box, row, and column. Time yourself as you solve this puzzle and compare your time with a classmate.

How long did it take you to solve this sudoku puzzle? (You can see the answer at the end of this section.)

   Here is another popular type of puzzle (figure below) that challenges your spatial reasoning skills. Connect all nine dots with four connecting straight lines without lifting your pencil from the paper:

Did you figure it out? (The answer is at the end of this section.) Once you understand how to crack this puzzle, you won’t forget.

   Take a look at the “Puzzling Scales” logic puzzle below (figure below). Sam Loyd, a well-known puzzle master, created and refined countless puzzles throughout his lifetime (Cyclopedia of Puzzles, n.d.).

What steps did you take to solve this puzzle? You can read the solution at the end of this section.

Pitfalls to problem solving.

   Not all problems are successfully solved, however. What challenges stop us from successfully solving a problem? Albert Einstein once said, “Insanity is doing the same thing over and over again and expecting a different result.” Imagine a person in a room that has four doorways. One doorway that has always been open in the past is now locked. The person, accustomed to exiting the room by that particular doorway, keeps trying to get out through the same doorway even though the other three doorways are open. The person is stuck—but she just needs to go to another doorway, instead of trying to get out through the locked doorway. A mental set is where you persist in approaching a problem in a way that has worked in the past but is clearly not working now.

Functional fixedness is a type of mental set where you cannot perceive an object being used for something other than what it was designed for. During the Apollo 13 mission to the moon, NASA engineers at Mission Control had to overcome functional fixedness to save the lives of the astronauts aboard the spacecraft. An explosion in a module of the spacecraft damaged multiple systems. The astronauts were in danger of being poisoned by rising levels of carbon dioxide because of problems with the carbon dioxide filters. The engineers found a way for the astronauts to use spare plastic bags, tape, and air hoses to create a makeshift air filter, which saved the lives of the astronauts.

   Researchers have investigated whether functional fixedness is affected by culture. In one experiment, individuals from the Shuar group in Ecuador were asked to use an object for a purpose other than that for which the object was originally intended. For example, the participants were told a story about a bear and a rabbit that were separated by a river and asked to select among various objects, including a spoon, a cup, erasers, and so on, to help the animals. The spoon was the only object long enough to span the imaginary river, but if the spoon was presented in a way that reflected its normal usage, it took participants longer to choose the spoon to solve the problem. (German & Barrett, 2005). The researchers wanted to know if exposure to highly specialized tools, as occurs with individuals in industrialized nations, affects their ability to transcend functional fixedness. It was determined that functional fixedness is experienced in both industrialized and nonindustrialized cultures (German & Barrett, 2005).

In order to make good decisions, we use our knowledge and our reasoning. Often, this knowledge and reasoning is sound and solid. Sometimes, however, we are swayed by biases or by others manipulating a situation. For example, let’s say you and three friends wanted to rent a house and had a combined target budget of $1,600. The realtor shows you only very run-down houses for $1,600 and then shows you a very nice house for $2,000. Might you ask each person to pay more in rent to get the $2,000 home? Why would the realtor show you the run-down houses and the nice house? The realtor may be challenging your anchoring bias. An anchoring bias occurs when you focus on one piece of information when making a decision or solving a problem. In this case, you’re so focused on the amount of money you are willing to spend that you may not recognize what kinds of houses are available at that price point.

The confirmation bias is the tendency to focus on information that confirms your existing beliefs. For example, if you think that your professor is not very nice, you notice all of the instances of rude behavior exhibited by the professor while ignoring the countless pleasant interactions he is involved in on a daily basis. Hindsight bias leads you to believe that the event you just experienced was predictable, even though it really wasn’t. In other words, you knew all along that things would turn out the way they did. Representative bias describes a faulty way of thinking, in which you unintentionally stereotype someone or something; for example, you may assume that your professors spend their free time reading books and engaging in intellectual conversation, because the idea of them spending their time playing volleyball or visiting an amusement park does not fit in with your stereotypes of professors.

Finally, the availability heuristic is a heuristic in which you make a decision based on an example, information, or recent experience that is that readily available to you, even though it may not be the best example to inform your decision . Biases tend to “preserve that which is already established—to maintain our preexisting knowledge, beliefs, attitudes, and hypotheses” (Aronson, 1995; Kahneman, 2011). These biases are summarized in the table below.

Were you able to determine how many marbles are needed to balance the scales in the figure below? You need nine. Were you able to solve the problems in the figures above? Here are the answers.

   Many different strategies exist for solving problems. Typical strategies include trial and error, applying algorithms, and using heuristics. To solve a large, complicated problem, it often helps to break the problem into smaller steps that can be accomplished individually, leading to an overall solution. Roadblocks to problem solving include a mental set, functional fixedness, and various biases that can cloud decision making skills.

References:

Openstax Psychology text by Kathryn Dumper, William Jenkins, Arlene Lacombe, Marilyn Lovett and Marion Perlmutter licensed under CC BY v4.0. https://openstax.org/details/books/psychology

Review Questions:

1. A specific formula for solving a problem is called ________.

a. an algorithm

b. a heuristic

c. a mental set

d. trial and error

2. Solving the Tower of Hanoi problem tends to utilize a  ________ strategy of problem solving.

a. divide and conquer

b. means-end analysis

d. experiment

3. A mental shortcut in the form of a general problem-solving framework is called ________.

4. Which type of bias involves becoming fixated on a single trait of a problem?

a. anchoring bias

b. confirmation bias

c. representative bias

d. availability bias

5. Which type of bias involves relying on a false stereotype to make a decision?

6. Wolfgang Kohler analyzed behavior of chimpanzees by applying Gestalt principles to describe ________.

a. social adjustment

b. student load payment options

c. emotional learning

d. insight learning

7. ________ is a type of mental set where you cannot perceive an object being used for something other than what it was designed for.

a. functional fixedness

c. working memory

Critical Thinking Questions:

1. What is functional fixedness and how can overcoming it help you solve problems?

2. How does an algorithm save you time and energy when solving a problem?

Personal Application Question:

1. Which type of bias do you recognize in your own decision making processes? How has this bias affected how you’ve made decisions in the past and how can you use your awareness of it to improve your decisions making skills in the future?

anchoring bias

availability heuristic

confirmation bias

functional fixedness

hindsight bias

problem-solving strategy

representative bias

trial and error

working backwards

Answers to Exercises

algorithm:  problem-solving strategy characterized by a specific set of instructions

anchoring bias:  faulty heuristic in which you fixate on a single aspect of a problem to find a solution

availability heuristic:  faulty heuristic in which you make a decision based on information readily available to you

confirmation bias:  faulty heuristic in which you focus on information that confirms your beliefs

functional fixedness:  inability to see an object as useful for any other use other than the one for which it was intended

heuristic:  mental shortcut that saves time when solving a problem

hindsight bias:  belief that the event just experienced was predictable, even though it really wasn’t

mental set:  continually using an old solution to a problem without results

problem-solving strategy:  method for solving problems

representative bias:  faulty heuristic in which you stereotype someone or something without a valid basis for your judgment

trial and error:  problem-solving strategy in which multiple solutions are attempted until the correct one is found

working backwards:  heuristic in which you begin to solve a problem by focusing on the end result

Share This Book

• Increase Font Size

• The Art of Effective Problem Solving: A Step-by-Step Guide

Daniel Croft

Daniel Croft is an experienced continuous improvement manager with a Lean Six Sigma Black Belt and a Bachelor's degree in Business Management. With more than ten years of experience applying his skills across various industries, Daniel specializes in optimizing processes and improving efficiency. His approach combines practical experience with a deep understanding of business fundamentals to drive meaningful change.

• Last Updated: February 6, 2023
• Learn Lean Sigma
• Problem Solving

Whether we realise it or not, problem solving skills are an important part of our daily lives. From resolving a minor annoyance at home to tackling complex business challenges at work, our ability to solve problems has a significant impact on our success and happiness. However, not everyone is naturally gifted at problem-solving, and even those who are can always improve their skills. In this blog post, we will go over the art of effective problem-solving step by step.

You will learn how to define a problem, gather information, assess alternatives, and implement a solution, all while honing your critical thinking and creative problem-solving skills. Whether you’re a seasoned problem solver or just getting started, this guide will arm you with the knowledge and tools you need to face any challenge with confidence. So let’s get started!

Problem Solving Methodologies

Individuals and organisations can use a variety of problem-solving methodologies to address complex challenges. 8D and A3 problem solving techniques are two popular methodologies in the Lean Six Sigma framework.

Methodology of 8D (Eight Discipline) Problem Solving:

The 8D problem solving methodology is a systematic, team-based approach to problem solving. It is a method that guides a team through eight distinct steps to solve a problem in a systematic and comprehensive manner.

The 8D process consists of the following steps:

• Form a team: Assemble a group of people who have the necessary expertise to work on the problem.
• Define the issue: Clearly identify and define the problem, including the root cause and the customer impact.
• Create a temporary containment plan: Put in place a plan to lessen the impact of the problem until a permanent solution can be found.
• Identify the root cause: To identify the underlying causes of the problem, use root cause analysis techniques such as Fishbone diagrams and Pareto charts.
• Create and test long-term corrective actions: Create and test a long-term solution to eliminate the root cause of the problem.
• Implement and validate the permanent solution: Implement and validate the permanent solution’s effectiveness.
• Prevent recurrence: Put in place measures to keep the problem from recurring.
• Recognize and reward the team: Recognize and reward the team for its efforts.

Download the 8D Problem Solving Template

A3 Problem Solving Method:

The A3 problem solving technique is a visual, team-based problem-solving approach that is frequently used in Lean Six Sigma projects. The A3 report is a one-page document that clearly and concisely outlines the problem, root cause analysis, and proposed solution.

The A3 problem-solving procedure consists of the following steps:

• Determine the issue: Define the issue clearly, including its impact on the customer.
• Perform root cause analysis: Identify the underlying causes of the problem using root cause analysis techniques.
• Create and implement a solution: Create and implement a solution that addresses the problem’s root cause.
• Monitor and improve the solution: Keep an eye on the solution’s effectiveness and make any necessary changes.

Subsequently, in the Lean Six Sigma framework, the 8D and A3 problem solving methodologies are two popular approaches to problem solving. Both methodologies provide a structured, team-based problem-solving approach that guides individuals through a comprehensive and systematic process of identifying, analysing, and resolving problems in an effective and efficient manner.

Step 1 – Define the Problem

The definition of the problem is the first step in effective problem solving. This may appear to be a simple task, but it is actually quite difficult. This is because problems are frequently complex and multi-layered, making it easy to confuse symptoms with the underlying cause. To avoid this pitfall, it is critical to thoroughly understand the problem.

To begin, ask yourself some clarifying questions:

• What exactly is the issue?
• What are the problem’s symptoms or consequences?
• Who or what is impacted by the issue?
• When and where does the issue arise?

Answering these questions will assist you in determining the scope of the problem. However, simply describing the problem is not always sufficient; you must also identify the root cause. The root cause is the underlying cause of the problem and is usually the key to resolving it permanently.

Try asking “why” questions to find the root cause:

• What causes the problem?
• Why does it continue?
• Why does it have the effects that it does?

By repeatedly asking “ why ,” you’ll eventually get to the bottom of the problem. This is an important step in the problem-solving process because it ensures that you’re dealing with the root cause rather than just the symptoms.

Once you have a firm grasp on the issue, it is time to divide it into smaller, more manageable chunks. This makes tackling the problem easier and reduces the risk of becoming overwhelmed. For example, if you’re attempting to solve a complex business problem, you might divide it into smaller components like market research, product development, and sales strategies.

To summarise step 1, defining the problem is an important first step in effective problem-solving. You will be able to identify the root cause and break it down into manageable parts if you take the time to thoroughly understand the problem. This will prepare you for the next step in the problem-solving process, which is gathering information and brainstorming ideas.

Step 2 – Gather Information and Brainstorm Ideas

Gathering information and brainstorming ideas is the next step in effective problem solving. This entails researching the problem and relevant information, collaborating with others, and coming up with a variety of potential solutions. This increases your chances of finding the best solution to the problem.

Begin by researching the problem and relevant information. This could include reading articles, conducting surveys, or consulting with experts. The goal is to collect as much information as possible in order to better understand the problem and possible solutions.

Next, work with others to gather a variety of perspectives. Brainstorming with others can be an excellent way to come up with new and creative ideas. Encourage everyone to share their thoughts and ideas when working in a group, and make an effort to actively listen to what others have to say. Be open to new and unconventional ideas and resist the urge to dismiss them too quickly.

Finally, use brainstorming to generate a wide range of potential solutions. This is the place where you can let your imagination run wild. At this stage, don’t worry about the feasibility or practicality of the solutions; instead, focus on generating as many ideas as possible. Write down everything that comes to mind, no matter how ridiculous or unusual it may appear. This can be done individually or in groups.

Once you’ve compiled a list of potential solutions, it’s time to assess them and select the best one. This is the next step in the problem-solving process, which we’ll go over in greater detail in the following section.

Step 3 – Evaluate Options and Choose the Best Solution

Once you’ve compiled a list of potential solutions, it’s time to assess them and select the best one. This is the third step in effective problem solving, and it entails weighing the advantages and disadvantages of each solution, considering their feasibility and practicability, and selecting the solution that is most likely to solve the problem effectively.

To begin, weigh the advantages and disadvantages of each solution. This will assist you in determining the potential outcomes of each solution and deciding which is the best option. For example, a quick and easy solution may not be the most effective in the long run, whereas a more complex and time-consuming solution may be more effective in solving the problem in the long run.

Consider each solution’s feasibility and practicability. Consider the following:

• Can the solution be implemented within the available resources, time, and budget?
• What are the possible barriers to implementing the solution?
• Is the solution feasible in today’s political, economic, and social environment?

You’ll be able to tell which solutions are likely to succeed and which aren’t by assessing their feasibility and practicability.

Finally, choose the solution that is most likely to effectively solve the problem. This solution should be based on the criteria you’ve established, such as the advantages and disadvantages of each solution, their feasibility and practicability, and your overall goals.

It is critical to remember that there is no one-size-fits-all solution to problems. What is effective for one person or situation may not be effective for another. This is why it is critical to consider a wide range of solutions and evaluate each one based on its ability to effectively solve the problem.

Step 4 – Implement and Monitor the Solution

When you’ve decided on the best solution, it’s time to put it into action. The fourth and final step in effective problem solving is to put the solution into action, monitor its progress, and make any necessary adjustments.

To begin, implement the solution. This may entail delegating tasks, developing a strategy, and allocating resources. Ascertain that everyone involved understands their role and responsibilities in the solution’s implementation.

Next, keep an eye on the solution’s progress. This may entail scheduling regular check-ins, tracking metrics, and soliciting feedback from others. You will be able to identify any potential roadblocks and make any necessary adjustments in a timely manner if you monitor the progress of the solution.

Finally, make any necessary modifications to the solution. This could entail changing the solution, altering the plan of action, or delegating different tasks. Be willing to make changes if they will improve the solution or help it solve the problem more effectively.

It’s important to remember that problem solving is an iterative process, and there may be times when you need to start from scratch. This is especially true if the initial solution does not effectively solve the problem. In these situations, it’s critical to be adaptable and flexible and to keep trying new solutions until you find the one that works best.

To summarise, effective problem solving is a critical skill that can assist individuals and organisations in overcoming challenges and achieving their objectives. Effective problem solving consists of four key steps: defining the problem, generating potential solutions, evaluating alternatives and selecting the best solution, and implementing the solution.

You can increase your chances of success in problem solving by following these steps and considering factors such as the pros and cons of each solution, their feasibility and practicability, and making any necessary adjustments. Furthermore, keep in mind that problem solving is an iterative process, and there may be times when you need to go back to the beginning and restart. Maintain your adaptability and try new solutions until you find the one that works best for you.

• Novick, L.R. and Bassok, M., 2005.  Problem Solving . Cambridge University Press.

Was this helpful?

Daniel Croft is a seasoned continuous improvement manager with a Black Belt in Lean Six Sigma. With over 10 years of real-world application experience across diverse sectors, Daniel has a passion for optimizing processes and fostering a culture of efficiency. He's not just a practitioner but also an avid learner, constantly seeking to expand his knowledge. Outside of his professional life, Daniel has a keen Investing, statistics and knowledge-sharing, which led him to create the website learnleansigma.com, a platform dedicated to Lean Six Sigma and process improvement insights.

5S Floor Marking Best Practices

The Kaizen Event: A Powerful Approach for Rapid Process Improvement

Free lean six sigma templates.

Improve your Lean Six Sigma projects with our free templates. They're designed to make implementation and management easier, helping you achieve better results.

In lean manufacturing, the 5S System is a foundational tool, involving the steps: Sort, Set…

How to Measure the ROI of Continuous Improvement Initiatives

When it comes to business, knowing the value you’re getting for your money is crucial,…

8D Problem-Solving: Common Mistakes to Avoid

In today’s competitive business landscape, effective problem-solving is the cornerstone of organizational success. The 8D…

The Evolution of 8D Problem-Solving: From Basics to Excellence

In a world where efficiency and effectiveness are more than just buzzwords, the need for…

8D: Tools and Techniques

Are you grappling with recurring problems in your organization and searching for a structured way…

How to Select the Right Lean Six Sigma Projects: A Comprehensive Guide

Going on a Lean Six Sigma journey is an invigorating experience filled with opportunities for…

Psych 256: Cognitive Psychology SP15

Making connections between theory and reality, problem solving using means-end analysis.

Everyone has had a long list of to dos or personal things to take care of.  Looking at the whole it can seem a little overwhelming and most of us get lost, depressed, or even frustrated.  Using a means-end analysis is basically looking at a goal, starting point, and the best way to get from point A to point B by breaking down the list or problems to make them more doable in our lives (Newell & Simon, 1972).  For this blog, I will pass on some tips on how to manage your homework load that you have each semester to lessen the stress.  Keeping in mind, that these tips have worked for me but can be modified to individual needs, because let’s face it, each of us have different lifestyles and means-end analysis will not necessarily work for everyone problem.

Each of us when we start a new semester, whether it is at a new school or the current one, can feel overwhelmed by the work load that looms ahead of us.  What I do to lessen the stress is I take each class syllabus and course calendar and a full size desk top calendar, the ones with the large squares for the dates, and I write down the class and what is due each week including test and quizzes.  This way I can see clearly what I have to complete. This is the process of setting subgoals which helps to move toward the overall goal of course completion (Goldstein, 2011). Once classes are taken care of I will then insert any social or work related items. With all of this in place I have a black and white picture of my life each month.  Once this is in place I can set up a realistic schedule to complete the semester and how it is broken down weekly, allows me to manage it properly without over stressing out.  I take one day at a time.  As each day passes you can cross out what has been completed and feel a sense of accomplishment.

Now we move into supplies to fulfill the semester.  I will look at each class and see if any will need something other than the usual, such as art supplies, software, etc.  Take a list to your local store and remember it is ok to over buy on paper, printer ink, and writing tools because you never can estimate what you might use up.  I find that having all the items for each class available will help keep stress levels down by not having to run to the store at the last minute.  Being well stocked will help prepare for the semester.

Next, let’s look at textbooks.  Most of us will buy them and throw them in the back of our cars.  The best thing to do is to look them over, read the index, and maybe a few chapters to get acclimated to the class.  I use colored tabs to mark out each chapter, so I can easily flip to it.  Being familiar with the classes you are taking and what is required in readings, online work, extra supplies, etc. will help you plan your weeks to come plus lower your stress levels.

Now the important thing is your study area.  Some people will study in their bedroom, but this is too tempting of a place to take a nap or simply fall asleep and then your day is over.  If all you have is your bedroom then I would suggest going to the school library or a quiet area in a coffee shop or 24 hour restaurant.  I have a desk in a room with little to no distractions.  I have everything I need in this room to complete my homework, readings, and school projects.  I have set times to do my school work according to my calendar that I discussed earlier.  Another thing that might help is turn off cell phones and any other communication or social media devices.  Let people know that you will be studying at set times so that they will not text or call to disturb you.  These types of disruptions can derail you from what you are reading or writing.  If you have children, you can do what a friend of mine does and put a sign on the door letting your children know that it is mommy/daddy study time.  Of course this doesn’t always work because children are children and that just doesn’t happen.  In that case, perhaps work it out with your partner that they handle the kids while you are studying or have a friend or relative watch the children for that bit of time.  Having a place to work just for you will increase productivity and or course lower some stress.

With some of the tips I have given, you can lower stress levels of the new semester by setting smaller goals so you can reach the finish line at the end of the semester.  This is all made possible by using a means-end analysis solution.  Keep in mind this may not work for you or you may have to modify the ideas that I have given to make it work in your life.  Whatever will work by planning out your semester so you can achieve the grades is great.  I hope you will be able to add some of or all of my tips to get you to your semester goals by breaking down the whole to more manageable pieces.

Goldstein, E. (2011). Cognitive psychology: Connecting mind, research, and everyday experience (3rd ed.). Australia: Wadsworth Cengage Learning.

Newell, A., & Simon, H. A. (1972).  Human problem solving  (Vol. 104, No. 9). Englewood Cliffs, NJ: Prentice-Hall.

Leave a Reply

You must be logged in to post a comment.

How to analyze a problem

May 7, 2023 Companies that harness the power of data have the upper hand when it comes to problem solving. Rather than defaulting to solving problems by developing lengthy—sometimes multiyear—road maps, they’re empowered to ask how innovative data techniques could resolve challenges in hours, days or weeks, write  senior partner Kayvaun Rowshankish  and coauthors. But when organizations have more data than ever at their disposal, which data should they leverage to analyze a problem? Before jumping in, it’s crucial to plan the analysis, decide which analytical tools to use, and ensure rigor. Check out these insights to uncover ways data can take your problem-solving techniques to the next level, and stay tuned for an upcoming post on the potential power of generative AI in problem-solving.

The data-driven enterprise of 2025

How data can help tech companies thrive amid economic uncertainty

How to unlock the full value of data? Manage it like a product

Data ethics: What it means and what it takes

Author Talks: Think digital

Five insights about harnessing data and AI from leaders at the frontier

Real-world data quality: What are the opportunities and challenges?

How a tech company went from moving data to using data: An interview with Ericsson’s Sonia Boije

Harnessing the power of external data

7.3 Analytical Thinking

Questions to Consider:

• How can you best establish component parts in thinking?
• How can you use analysis to improve efficiency?

Thinking helps in many situations, as we’ve discussed throughout this chapter. When we work out a problem or situation systematically, breaking the whole into its component parts for separate analysis, to come to a solution or a variety of possible solutions, we call that analytical thinking. Characteristics of analytical thinking include setting up the parts, using information literacy, and verifying the validity of any sources you reference. While the phrase analytical thinking may sound daunting, we actually do this sort of thinking in our everyday lives when we brainstorm, budget, detect patterns, plan, compare, work puzzles, and make decisions based on multiple sources of information. Think of all the thinking that goes into the logistics of a dinner-and-a-movie date—where to eat, what to watch, who to invite, what to wear, popcorn or candy—when choices and decisions are rapid-fire, but we do it relatively successfully all the time.

Employers specifically look for candidates with analytical skills because they need to know employees can use clear and logical thinking to resolve conflicts that cause work to slow down or may even put the company in jeopardy of not complying with state or national requirements. If everything always went smoothly on the shop floor or in the office, we wouldn’t need front-line managers, but everything doesn’t always go according to plan or company policy. Your ability to think analytically could be the difference between getting a good job and being passed over by others who prove they are stronger thinkers. A mechanic who takes each car apart piece by piece to see what might be wrong instead of investigating the entire car, gathering customer information, assessing the symptoms, and focusing on a narrow set of possible problems is not an effective member of the team. Some career fields even have set, formulaic analyses that professionals in those fields need to know how to conduct and understand, such as a cost analysis, a statistical analysis, or a return on investment (ROI) analysis. You can learn more about these in Chapters 4 and 12.

Generate a list of at least two courses you are taking now that you think would routinely practice analytical thinking. Now, think of the profession you are interested in joining. How could the deliberate use of analytical thinking processes be beneficial for that career field? What are you currently learning about in your courses that apply directly to your chosen career path? Think of at least two ways analytical thinking would be used in the career field you are pursuing.

Establishing Component Parts

Component parts refer to the separate elements of a situation or problem. It might include the people involved, the locations of the people, the weather, market fluctuations, or any number of other characteristics of the situation you’re examining. If you don’t identify all parts of a problem, you run the risk of ignoring a critical element when you offer the solution. For example, if you have a scheduling problem at home and seem to never see your loved ones, the first step in thinking through this problem analytically would be to decide what is contributing to this unfavorable result. To begin, you may examine the family members’ individual work, school, and personal schedules, and then create a group calendar to determine if pockets of time exist that are not taken by outside commitments. Perhaps rather than reading your homework assignments at the college library, you could plan to one day a week read with other members of your family who are doing quiet work. You may also need to determine how time is spent to better understand the family’s use of time, perhaps using categories such as work/school, recreation, exercise, sleep, and meals. Once you sort the categories for all the family members, you may see blocks of time spent that would lend themselves to combining with other categories—if you and your significant other both exercise three times a week for an hour each time but at separate locations, one possible solution may be to work out together. You could alternate locations if both people have favorite places to run, or you could compromise and decide on one location for both of you—one week at the park, one week at the campus rec center. This may not ultimately be the solution, but after establishing the component parts and thinking analytically, you have provided at least one viable solution.

What if you look at the situation and decide you have too many component parts? Consider, for instance, how Amazon delivers packages every day. That’s a lot of items going to and from seemingly countless locations within a relatively short time—sometimes within just one day. An organization such as Amazon must use a great deal of thinking and organizing to deliver goods and services.

One way to maintain clear thinking with so many parts is hyper organization. Proper labeling (for Amazon to ship it uses the foundation of our mailing system, unique ZIP codes that each address must contain to be delivered) as well as a strong sense of categorization (fulfillment warehouses, customer return warehouses, grocery item warehouses, etc.) are necessary for Amazon to do business. If you were faced with a major research paper your freshman composition professor expects to be polished by the end of the semester, where do you start? What are the component parts of a high-quality research paper? What tasks do you need to finish and how quickly to accomplish the overall goals? A partial list might include generating ideas, selecting a topic, researching, reviewing the available literature, outlining, drafting, and reviewing. What if you encounter setbacks in any of the steps? Do you have a contingency plan? In the construction industry, engineers called this float, and they deliberately build in extra time and money in case problems arise on the project. This allows them to avoid getting off schedule, for instance if a severe storm makes access to the worksite impossible.

Forging a Revolution

While most problems require a variety of thinking types, analytical thinking is arguably required in solving all. There was a time when manufacturing was completed by a few people who moved around a workspace to complete their projects. As companies grew, this became more and more inefficient, leading to the need for automation. Henry Ford, the early-20th-century American auto inventor, used analytical thinking to revolutionize the way companies increase production by inventing the assembly line. He perceived the problem in his own factory. When the demand for cars increased but his workers continued their work at the same pace, he analyzed their process to create something more efficient in the assembly line. This invention allowed one person to perform the same role over and over before sending the car chassis to another person who also performed the same role over and over as the evolving car moved down a sort of conveyor-belt system. The workers on Ford’s assembly lines still had to think and make sure that the task for which they were responsible was properly constructed, free of defects, and ready to move to the next station; they just did this thinking about their one area of expertise. Instead of various skilled workers wasting time and energy moving themselves and their tools around the factory from one incomplete car to the next, possibly getting in the way of each other’s work, the cars came to the workers. Ford vastly improved production rates and decreased manufacturing time by thinking about this then-new way of doing things.

In the 1960s, companies did not have a fast, reliable, and cost-effective way to deliver urgent documents or packages to each other. The standard mail system was slow but inexpensive, and the only alternative was a private courier, which, while faster, was prohibitively expensive. That’s when Frederick W. Smith came up with the idea of a national, overnight delivery service as a part of an assignment in his undergraduate economics class at Yale University. As the story goes, Smith received only an average grade because evidently his professor wasn’t all that impressed with the concept, but after analyzing the problems with the current system, thinking through his original ideas more fully, and refining his business plan, Smith launched FedEx, the largest, now global, overnight delivery service in the world. 1 This isn’t a parable about ignoring your professors, but a testimony to thinking through ideas others may not initially support or even understand; thinking can create change and always has. As with Ford’s assembly line and Smith’s overnight delivery service, any service we now use and any problem we may still face provides thinkers with opportunities to generate solutions and viable options for improvement. Your thinking may result in a new personal service, a cure for cancer, or a revolutionary way to deliver water to developing countries.

• 1 “Online Extra: Fred Smith on the BIrth of FedEx.” Bloomberg Business Week. 2004. Retrieved 1/28/20. https://www.bloomberg.com/news/articles/2004-09-19/online-extra-fred-smith-on-the-birth-of-fedex

As an Amazon Associate we earn from qualifying purchases.

This book may not be used in the training of large language models or otherwise be ingested into large language models or generative AI offerings without OpenStax's permission.

Want to cite, share, or modify this book? This book uses the Creative Commons Attribution License and you must attribute OpenStax.

Access for free at https://openstax.org/books/college-success/pages/1-introduction

• Authors: Amy Baldwin
• Publisher/website: OpenStax
• Book title: College Success
• Publication date: Mar 27, 2020
• Location: Houston, Texas
• Book URL: https://openstax.org/books/college-success/pages/1-introduction
• Section URL: https://openstax.org/books/college-success/pages/7-3-analytical-thinking

© Sep 20, 2023 OpenStax. Textbook content produced by OpenStax is licensed under a Creative Commons Attribution License . The OpenStax name, OpenStax logo, OpenStax book covers, OpenStax CNX name, and OpenStax CNX logo are not subject to the Creative Commons license and may not be reproduced without the prior and express written consent of Rice University.

Navigation Menu

Search code, repositories, users, issues, pull requests..., provide feedback.

We read every piece of feedback, and take your input very seriously.

Saved searches

Use saved searches to filter your results more quickly.

To see all available qualifiers, see our documentation .

• Notifications You must be signed in to change notification settings

05---means-end-analysis.rst

Latest commit, file metadata and controls, 05 - means-end analysis.

01 - Preview

Today we will discuss two other very general AR methods of problem solving called means end analysis and problem reduction.

Like [INAUDIBLE] these two methods, means analysis and problem reduction, are really useful for very well-formed problems.

Not all problems are well-formed. But some problems are. And then these methods are very useful. These three methods, generate and test, means-ends analysis, form reduction, together with semantic networks as a knowledge representation, form the basic unit of all fundamental topics in this course.

We'll begin with the notion of state spaces. Then talk about means-end analysis.

Then we'll illustrate means-end analysis as a matter for solving problems and then we'll move onto the method of problem reduction.

02 - Exercise The Block Problem

To understand a method of means and analysis. Let us look at this blocks word problem. This is a very famous problem in AI. It has occurred again and again. And almost every textbook in AI has this problem. You're given a table on which there are three blocks. And A is on table, B is on table, and

C is on A. This is the initial state. And you want to move these blocks, to the gold state. On this configuration, so that C is on table, B is on C and

A is on B. The problem looks very simple listen, doesn't it?

Let's introduce a couple of constraints.

You may move only one block at a time, so you can't pick both A and B together.

And second, you may only move a block that has nothing on top of it. So, you cannot move block A in this configuration, because it has C on top of it.

Let us also suppose that we're given some operators in this world.

These operators essentially move some object to some location. For example, we could move C to the table, or C onto B, or C onto A.

Not all the operators may be applicable in the current state. C is already on A, but in principle, all these, all of these operators are available. Given these operators, and this initial state and this goal state, write a sequence of operations that will move the blocks from the initial state to the goal state.

03 - Exercise The Block Problem

>> That's a good answer, David, that's a correct answer.

Now the question becomes how can we make in AI agent that will come up with the similar sequence of operations? In particular, how does the matter of means-end analysis work on this problem and come up with a particular sequence of operations?

04 - State Spaces

So, we can imagine problem solving as occurring in a state space.

Here is the initial state, here is the goal state. And the state space consists of all of the states that could be potentially produced from the initial state by iterative application of the various operators in this micro world. I want to come up with a path in the state space, takes me from initial state to the goal state. There is one path, this is not the only path, but this is one path to go from the initial state to the goal state. The question then becomes, how might an AI agent derive this path that may take it from the initial state to the goal state.

Let us see how this notion of path finding applies to our blocks world problem.

>From the initial state, here it is one path of going to the goal state. First, we put C on the table. Then we put B on top C. And then we put A on top of B.

Which is exactly the answer that David had given. This is one sequence, one path from the initial state to the goal state. The question then becomes, how does AI method know what operation to select in a given state?

Consider this state, for example. There are several operations possible here.

One could put C on top of B or B on top of A.

How does the AI agent know which operation to select at this particular state?

05 - Differences in State Spaces

One way of thinking about this is to talk in terms of differences. This chart illustrates the differences between different states and the goal state. So, for example, if the current state was this one then this red line illustrates the difference from the goal state. So we should pick an operator that will help reduce the difference between the current state and the goal state.

So the reduction between the difference with the current state and the goal state is the end. The application of the operator is the means.

That's why it's called the means-ends analysis. At any given state,

I'm going to pick an operator that will help you deduce the difference between the current state and the goal state. Note in a way this problem is similar to the problem of part finding in robotics, where we have to design a robot that could go from one point to another point in some navigation space.

>From my office to your office, for example, if all our offices were in the same building. There too we would use the notion of distances between offices. Here we using the notion of distance in a metaphorical sense, in a figurative sense, not in a physical sense. So I'll sometimes use the word difference instead of distance but it's the same idea. We are trying to deduce the distance or the difference but in an abstract space. So going back to an example of going from this initial state to this goal state. I can look at initial state and see that there are three differences between the initial state and the goal state. First, A is on table here, but A should be on B.

B is on table here, but B should be on C. And third, C is on top of A here, the C should be on top, on table there. So three differences. Here the number of operations are available to us. Nine operations in particular. Let us do a means-end analysis. We can apply an operator that would put C on table.

In which case the difference between the new state and the goal state will be two. We could apply an operator that will put C on top of B, in that case the difference between the current state and the goal state will still be three. Or we can apply the operator putting B on top of C, in which case the distance between the current state and the goal state will be 2. Notice that the notion of reducing differences now leads to two possible choices. One could go with this state or with this one.

Means-end analysis by itself does not help an AI agent decide between this course of action and that course of action. This is something that we will return to, both a little bit later in this lesson and even much more in detail when we come to planning in this course. For now, let us resume that we choose the top course of action just like they had done already there. So this chart illustrates the pot taken from the initial state to the goal state.

And the important thing to notice here is that with each different move the distance between the current state and the goal state is decreasing, from three to two to one to zero. This is why means-end analysis comes up with this path because at each time it reduces a difference

06 - Process of Means End Analysis

We can summarize the means-ends analysis method like this.

Compare the current state and the goal state. Find the differences between them. For each difference, look at what operators might be applicable. Select that operator that gets you closest to the goal state from the current state. We did this for the blocks and worlds problem. We also did this with regards to the business problem. But throughout those states in regards to business problem, which we're not getting us close to the goal state. This is the means-ends analysis method in summary.

07 - Exercise Block Problem I

To understand more deeply the properties of means and analysis, let us look at another, slightly more complicated example. In this example, there are four blocks instead of the three in the previous example. A, B,

C, D. In the initial state, the blocks are arranged as shown here.

The goal state is shown here on the right. The four blocks are arranged in a particular order. Now if you compare the configuration of blocks on the left with the configuration of blocks on the right, in the goal state, you can see there are three differences. First, A is on Table, where A is on B here. B is on C. That's not a difference. C is on Table.

C is on D here, D's on B, D's on Table here. So there are three differences.

So, this is a heuristic measure of the difference between the initial state and the goal state. Once again, we'll assume that the AI agent can move only one block at a time.

Given the specification of the problem, what states are possible from the initial state? Please write down your answers in these boxes.

08 - Exercise Block Problem I

>> That's good David.

09 - Exercise Block Problem II

Okay now for each of these states that is possible from the initial state what are the differences as compared to the goal state?

Please write down your answers in these boxes.

10 - Exercise Block Problem II

>> Good, David. So in each state David is comparing the state with the goal state and finding differences between them.

11 - Exercise Block Problem III

Given these three choices which operation would means-end analysis choose?

12 - Exercise Block Problem III

>> That's correct, David

13 - Exercise Block Problem IV

Given this current state, we can apply means ends analysis veritably.

Now, if we apply means on some of those to this particular state, the number of choices here is very large, so I will not go through all of them here.

But I'd like you to write down the number of possible next states. As well as, how many of those states reduce the difference to the goal? Which is given here.

14 - Exercise Block Problem IV

>> That's good, David.

15 - Exercise Block Problem V

So, the operation of putting A on B will bring us to this state.

Given this state, we can have, again, apply a means of analysis. Again, I'm not sure that all these states here, but

I'd like you to find out how many possible states are there and how many of those states reduce the difference to the goal described.

16 - Exercise Block Problem V

>> That's right David and that means that means-ends analysis doesn't not always take us to what's the goal. Sometimes it can take us away from the goal.

And sometimes means-end analysis can get caught in loops. Means-end analysis, like genetic and test, is an example of universal error methods.

These universal error methods are applicalbe to very large classes of problems.

However, they can rate few guarantees of success, and they're often very costly. They're costly in terms of computational efficiency.

They neither provide any guarantees of computational efficiency, nor provide any guarantees of the optimality of the solution that they come up with.

Their power lies in the fact that they can be applied to a very large class of problems. Later in this class, we'll discuss problem-solving methods, which are very specialized problem-solving methods.

Those methods are applicable to a smaller class of problems. However, they are more tuned to those problems and often are more efficient and sometimes, also provide guarantees over the optimality of the solution.

Although means-end analysis did not work very for this problem. It in fact works quite well for many other problems and therefore is an important AI method.

Later in this class when we come to planning, we will look at more powerful specialized methods that can in fact address this class of problems quite well.

17 - Assignment Means-Ends Analysis

So how do you use means ends analysis to solve Raven's Progressive Matrices?

What exactly is our goal in this context?

You might think of the goal in different ways. We might think of it as, the goal is to solve the problem or in a different sense we might think of the goal as the transform sum frame into another frame. And then trace back and find what the transformation was? In that context how would you then measure distance? We noticed that distance is important in doing means ends analysis because that helps us decide what to do next. Once you have a measure of how to actually measure distance to your goal what are the individual operators or moves that you can take to actually move closer to your goal and how would you weight them to be able to decide what to do at any given time.

In addition, what are the overall strengths of using means and analysis as a problem solving approach in this context, and what are its limitations. Is it well suited for these problems, or are there perhaps other things that we can be doing that aren't necessarily under this topic that would actually make the problem even easier.

18 - Problem Reduction

Let us now turn to the third problem solving method under this topic called problem reduction. The method of problem reduction actually is quite intuitive.

I'm sure you use it all the time. Given the hard complex problem, reduce it.

Decompose it into multiple easier, smaller, simpler problems. Consider, for example, computer programming or software design that I'm sure many of you do all the time. Given a hard part of the address, you decompose it with a series of smaller problems. How do I read the input? How do I process it?

How do I write the output? That itself is a decomposition. In fact, one of the fundamental roles that knowledge plays is it tells you how to decompose a hard problem into simpler problems.

Then once you have solutions to this simpler smaller problems.

You can think about how to compose the sub-solutions to the sub-problems into a solution of the problem as a whole. That's how problem reduction works.

19 - Problem Reduction in the Block Problem

Let us start from where we left off when we finished [UNKNOWN] analysis.

This was the current state, this was the goal state. As we saw from [UNKNOWN] analysis, achieving this goal state is not a very easy problem.

However, we can think of this goal state as being composed of several sub goals, so D on top of table. C on top of D. B on top of C. A on top of B.

Four sub goals here. Now, we can try to address this problem by looking at one sub goal at a time. Let us suppose that we have picked this sub goal,

C on top of D. Give that sub goal, we can now start from this current state and try to achieve this sub goal. Now of course, one might ask the question, why did we pick the goal C over D, and not the goal, B over C, or the goal A over B?

Well one reason is that, the difference between this state and that state had to do with C over D. But in general, problem reduction by itself does not tell us, what sub-goal to attack first. That is a problem, we'll address later when we come to planning. Well now the major point is, that we can decompose the goal into several subgoals, and attack one subgoal at a time. Now that we have C over

D as a subgoal, we really don't carry about whether A is on B or B is on C. What we are focused on is the other two states, C on table, D on table, because those are the blocks that occur in the goal state. So let us now see how [INAUDIBLE] have been solved this sub problem [INAUDIBLE] goal C on D and D on Table.

20 - Exercise Problem Reduction I

So given this is a current state, what successor states are possible if we were to apply means and analysis? Please fill in these boxes.

21 - Exercise Problem Reduction I

>> That looks right, David.

22 - Exercise Problem Reduction II

Let us now calculate the difference from each of the states to the goal state.

23 - Exercise Problem Reduction II

>> So note that both the state at the top and this state at the bottom have a equal amount of difference compared to goal state. We could've chosen either state to go further. For now, we going to go with the one at the bottom. The reason of course is that if I put A on D that will get in the way of solving the rest of the problem. For now, let us go with this state. Later on we will see how an AI agent will decide that this is not a good path to take and this is the better path to take.

24 - Exercise Problem Reduction III

So if we make the move that we had at the end of the last shot, we'll get this state. So now we need to go from this state to the goal state.

Please write down what is the sequence of operators which might take us from the current state to the goal state.

25 - Exercise Problem Reduction III

>> That was the right answer David, thank you. You will note that we leaving several questions unanswered for now and that is fine, but you will also note that this problem reduction helps us make progress towards solving the problem.

26 - Exercise Problem Reduction III

So the application of the last move in the previous shot will bring us to this state. In this state the the sub-goal C over D has been achieved. Now that we've achieved the first sub-goal, we can worry about achieving the other sub-goals.

The other sub-goals, recall, were B over C and A over B. Given this as the current state and this as the goal state. Please write down the sequence of operations that will take us from the current state to the goal state.

27 - Exercise Problem Reduction III

>> That was correct, David. Now this particular problem might look very simple.

Because for you and me as humans, going from this state to this state is almost trivial. But notice how many different questions arose in trying to analyze this problem. Clearly, you and I as humans must be addressing these issues.

This kind of A.I anaylsis makes explicit what is usually tacit when humans solve this problem. And that is one of the powers of A.I.. Indeed we have left a lot of questions unanswered. But each unanswered question then requires an answer.

Now we know that if you must develop methods that somehow will help to address those questions. Like genetic [x] tests and like [x] dialysis.

Problem reduction is a universal method. It is applicable very large class of problems. Once again, problem reduction does not provide guarantee of successes.

28 - Means-Ends Analysis for Ravens

>> That's good analysis, David. Let's go one step further.

There's also has generation test on it. We are generated solutions, that we can then test against the various choices that were given to us.

So in this particular problem, you can see means-end analysis working, problem reduction working, and direct link test working.

Often, solving a complex problem requires a combination of error techniques.

At one point, one might use problem reduction, at another point, one might use direct link test, at a third point, one might use means-end analysis. Notice also, that the one single knowledge representation of semantic network, supports all three of these strategies.

The coupling between the knowledge representation and semantic network, and any of these three strategies from reduction, means-end analysis, or generate and test, is weak. Late on we'll come across methods, in which knowledge and the problem solving method are closely coupled. The knowledge of folds certain inferences. And inferences, demand certain kinds of knowledge.

This is why these methods are known as weak methods. Because the coupling between these universal methods, and the knowledge representation is weak.

29 - Assignment Problem Reduction

So how would you apply a problem reduction to Raven's Progressive Matrices?

Before we actually talk about how our agents would do it, we can think about how we would do it. When we are solving a matrix, where do the smaller or easier problems that we are actually breaking it down into?

How are we solving those smaller problems, and how are we then combining them into an answer to the problem as a whole? Once we know how we're doing it, how will your agent actually be able to do the same kind of reasoning process?

How will it recognize when to split a problem in to smaller problems?

How will it solve the smaller problems? And how will it then combine those in to an answer to the problem as whole? During this process think about, what exactly is it that makes these smaller problems easier for your agent to answer than just answering the problem as a whole?

And how does that actually help you solve these problems better?

30 - Wrap Up

So let's wrap up what we've talked about today.

We started off today by talking about state spaces and we used this to frame our discussion of mean-ends analysis. Means-ends analysis is a very general purpose problem solving method, that allows us to look at our goal and try to continually move towards it. We then use means-ends analysis to try and address a couple of different kinds of problems. But when we did so, we hit an obstacle. To overcome that obstacle, we used problem reduction.

We can use problem reduction in a lot of other problem solving contexts, but here we use it to specifically to overcome the obstacle we hit during means-ends analysis. Problem reduction occurs and we take a big hard problem and introduce it into smaller easier problems. By solving the smaller easier problems, we solve the big hard problem. Next time we're going to talk about production systems, which are the last part of the fundamental areas of our course. But if you're particularly interested in what we've talked about today, you may wish to jump forward to logic and planning. Those were built specifically on the types of the problems we talked about today. And in fact in planning, we'll see a more robust way of solving the kinds of obstacles that we hit, during our exercise with means and analysis earlier in this lesson.

31 - The Cognitive Connection

Let us examine the connection between methods like means ends analysis and problem reduction on one hand, and human cognition on the other. Methods like means ends analysis, problem reduction and even generate and test, are sometimes called weak methods.

They are weak because they make only little use of knowledge. Later on, we'll look at strong methods that are knowledge intensive. That will demand a lot of knowledge. The good thing about those knowledge intensive methods is, that they will actually use knowledge about the world, to come up with good solutions in an efficient manner. On the other hand, those knowledge intensive methods require knowledge, which is not always available. So humans, when they are working in a domain, in a world at which they are experts, tend to use those knowledge intensive methods because they know a lot about the world. But of course, you and I constantly work in worlds, in domains in which we are not experts. When we're not an expert in our domain, a domain that might be unfamiliar to us, then we might well go with matters that are weak because they don't require a lot of knowledge.

32 - Final Quiz

We're at the end of this lesson.

Please summarize what you learned in this lesson, inside this box.

33 - Final Quiz

And thank you for doing it.

How it works

For Business

Join Mind Tools

Article • 7 min read

Cause and Effect Analysis

Identifying the likely causes of problems.

By the Mind Tools Content Team

(Also known as Cause and Effect Diagrams, Fishbone Diagrams, Ishikawa Diagrams, Herringbone Diagrams, and Fishikawa Diagrams.)

When you have a serious problem, it's important to explore all of the things that could cause it, before you start to think about a solution.

That way you can solve the problem completely, first time round, rather than just addressing part of it and having the problem run on and on.

Cause and Effect Analysis gives you a useful way of doing this. This diagram-based technique, which combines Brainstorming with a type of Mind Map , pushes you to consider all possible causes of a problem, rather than just the ones that are most obvious.

Click here to view a transcript of this video.

About the Tool

Cause and Effect Analysis was devised by professor Kaoru Ishikawa, a pioneer of quality management, in the 1960s. The technique was then published in his 1990 book, " Introduction to Quality Control ." [1]

The diagrams that you create with are known as Ishikawa Diagrams or Fishbone Diagrams (because a completed diagram can look like the skeleton of a fish).

Although it was originally developed as a quality control tool, you can use the technique just as well in other ways. For instance, you can use it to:

• Discover the root cause of a problem.
• Uncover bottlenecks in your processes.
• Identify where and why a process isn't working.

How to Use the Tool

Follow these steps to solve a problem with Cause and Effect Analysis:

Step 1: Identify the Problem

First, write down the exact problem you face. Where appropriate, identify who is involved, what the problem is, and when and where it occurs.

Then, write the problem in a box on the left-hand side of a large sheet of paper, and draw a line across the paper horizontally from the box. This arrangement, looking like the head and spine of a fish, gives you space to develop ideas.

In this simple example, a manager is having problems with an uncooperative branch office.

Figure 1 – Cause and Effect Analysis Example Step 1

(Click image to view full size.)

Some people prefer to write the problem on the right-hand side of the piece of paper, and develop ideas in the space to the left. Use whichever approach you feel most comfortable with.

It's important to define your problem correctly. CATWOE can help you do this – this asks you to look at the problem from the perspective of Customers, Actors in the process, the Transformation process, the overall World view, the process Owner, and Environmental constraints.

By considering all of these, you can develop a comprehensive understanding of the problem.

Step 2: Work Out the Major Factors Involved

Next, identify the factors that may be part of the problem. These may be systems, equipment, materials, external forces, people involved with the problem, and so on.

Try to draw out as many of these as possible. As a starting point, you can use models such as the McKinsey 7S Framework (which offers you Strategy, Structure, Systems, Shared values, Skills, Style and Staff as factors that you can consider) or the 4Ps of Marketing (which offers Product, Place, Price, and Promotion as possible factors).

Brainstorm any other factors that may affect the situation.

Then draw a line off the "spine" of the diagram for each factor, and label each line.

The manager identifies the following factors, and adds these to his diagram:

Figure 2 – Cause and Effect Analysis Example Step 2

Step 3: Identify Possible Causes

Now, for each of the factors you considered in step 2, brainstorm possible causes of the problem that may be related to the factor.

Show these possible causes as shorter lines coming off the "bones" of the diagram. Where a cause is large or complex, then it may be best to break it down into sub-causes. Show these as lines coming off each cause line.

For each of the factors he identified in step 2, the manager brainstorms possible causes of the problem, and adds these to his diagram, as shown in figure 3.

Figure 3 – Cause and Effect Analysis Example Step 3

Step 4: Analyze Your Diagram

By this stage you should have a diagram showing all of the possible causes of the problem that you can think of.

Depending on the complexity and importance of the problem, you can now investigate the most likely causes further. This may involve setting up investigations, carrying out surveys, and so on. These will be designed to test which of these possible causes is actually contributing to the problem.

The manager has now finished his analysis. If he hadn't looked at the problem this way, he might have dealt with it by assuming that people in the branch office were "being difficult."

Instead he thinks that the best approach is to arrange a meeting with the Branch Manager. This would allow him to brief the manager fully on the new strategy, and talk through any problems that she may be experiencing.

A useful way to use this technique with a team is to write all of the possible causes of the problem down on sticky notes. You can then group similar ones together on the diagram.

This approach is sometimes called CEDAC (Cause and Effect Diagram with Additional Cards) and was developed by Dr. Ryuji Fukuda, a Japanese expert on continuous improvement.

Professor Kaoru Ishikawa created Cause and Effect Analysis in the 1960s. The technique uses a diagram-based approach for thinking through all of the possible causes of a problem. This helps you to carry out a thorough analysis of the situation.

There are four steps to using the tool.

• Identify the problem.
• Work out the major factors involved.
• Identify possible causes.
• Analyze your diagram.

You'll find this method is particularly useful when you're trying to solve complicated problems.

[1] Ishikawa, K (1990).  'Introduction to Quality Control ,' Tokyo: 3A Corporation.

You've accessed 1 of your 2 free resources.

Get unlimited access

Discover more content

Solving problems with cause and effect analysis.

How to Use Cause and Effect Diagrams

Problem Solving

How to solve problems using the Cause and Effect Analysis technique

Add comment

Comments (0)

Be the first to comment!

Sign-up to our newsletter

Subscribing to the Mind Tools newsletter will keep you up-to-date with our latest updates and newest resources.

Subscribe now

Business Skills

Personal Development

Leadership and Management

Member Extras

Most Popular

Latest Updates

Using Mediation To Resolve Conflict

Mind Tools Store

About Mind Tools Content

Discover something new today

Resolving workplace conflict through mediation.

Managing Disputes Informally

Personal SWOT Analysis

Making the Most of Your Talents and Opportunities

How Emotionally Intelligent Are You?

Boosting Your People Skills

Self-Assessment

What's Your Leadership Style?

Learn About the Strengths and Weaknesses of the Way You Like to Lead

Recommended for you

Pain points podcast - beating burnout.

Protecting Your Wellbeing at Work

Business Operations and Process Management

Strategy Tools

Customer Service

Business Ethics and Values

Handling Information and Data

Project Management

Knowledge Management

Self-Development and Goal Setting

Time Management

Presentation Skills

Learning Skills

Career Skills

Communication Skills

Negotiation, Persuasion and Influence

Working With Others

Difficult Conversations

Creativity Tools

Self-Management

Work-Life Balance

Stress Management and Wellbeing

Coaching and Mentoring

Change Management

Team Management

Managing Conflict

Delegation and Empowerment

Performance Management

Leadership Skills

Developing Your Team

Talent Management

Decision Making

Member Podcast