means-ends analysis

means-ends analysis, heuristic, or trial-and-error, problem-solving strategy in which an end goal is identified and then fulfilled via the generation of subgoals and action plans that help overcome obstacles encountered along the way. Solving a problem with means-ends analysis typically begins by examining the end goal and breaking it down into subgoals. Actions needed to achieve each subgoal are then developed. In some cases, subgoals are further broken down into sub-subgoals. When all of the subgoals have been achieved (or obstacles eliminated), the end goal has been met.

History

The idea of problem solving by means-ends analysis was introduced in 1972 by American computer scientists Allen Newell and Herbert A. Simon in their book Human Problem Solving. They developed the theory in the late 1950s and early ’60s while generating a computer model capable of simulating human problem solving, working with John Clifford Shaw, a scientist and computer expert at the RAND Corporation, where beginning in 1950 Newell also worked as a researcher. The scientists called their model the General Problem Solver (GPS). GPS would recursively apply heuristic techniques in solving a given problem and conduct a means-ends assessment after each subproblem was solved to determine whether it was closer to the intended solution. Through this process, GPS could find solutions to mathematical theorems, logical proofs, word problems, and a wide variety of other well-defined problems. (Newell and Simon received the 1975 Turing Award for their research pertaining to human cognition and artificial intelligence.)

Characteristics and process

Means-ends analysis is unique among problem-solving algorithms in that it emphasizes the generation of subgoals that directly contribute to reaching the end goal. The subgoals are not necessarily of the same type. In other approaches, namely divide-and-conquer, subproblems are created that are then solved recursively and are finally combined to solve the end problem; with divide-and-conquer, the subproblems are always of the same type.

An example of the process of carrying out means-ends analysis can be illustrated by using the end goal of having a well-designed, well-functioning website. Possible subgoals and sub-subgoals include:

• technical setup, such as choosing a web hosting service, registering a domain name, and setting up the hosting environment and linking the domain;

• design, involving the creation of a layout for the homepage, the creation of landing pages and interior pages, the selection of a colour scheme and typography, and the design of menus, buttons, and other interactive elements;

• coding, with a need to learn coding languages and the coding and implementation of interactive elements;

• content development, such as writing content and gathering images and videos;

• testing browser compatibility, with testing of the website on different browsers and on different devices; and

• testing and debugging to make sure the website functions properly, test interactive elements, and fix formatting issues, bugs, or inconsistencies.

Applications

Means-ends analysis is frequently used in artificial intelligence (AI) systems. As a goal-based problem-solving technique, it plays a significant role in creating AI systems that exhibit humanlike behaviour, because the algorithmic steps involved in the analysis simulate aspects of human cognition and problem-solving skills. AI systems also use means-ends analysis for limiting searches in programs by evaluating the difference between the current state of a problem and the goal state, while using a combination of backward and forward search techniques.

Businesses and organizations use means-ends analysis for planning, project management, and transformation projects. In project management, for example, means-end analysis can be used to break down complex projects into subprojects and then to track the progress of those subprojects. It is used in transformation projects to implement changes to business processes by splitting new processes into subprocesses.

Research has been conducted on applying means-ends analysis to product marketing campaigns for brand persuasion purposes. For example, in the 1990s, researchers applied means-ends analysis to study how consumers link a product’s attributes with the consequences (benefits) of using the product and how the attributes and consequences align with personal values. Such studies supported the effectiveness of means-ends analysis in brand persuasion. Later research confirmed the effectiveness of means-ends analysis and its suitability for a wide range of marketing applications and suggested the development of additional methodologies for analyzing observations.