Cognitive models emphasize the role of mental representations, such as schemas, scripts, and images, in problem solving.

Information processing models view problem solving as a sequence of cognitive operations, such as encoding, retrieval, and transformation.

Expected utility theory is a widely used decision-making model that assumes individuals choose the option with the highest expected value.

Problem formulation involves clearly defining the problem's goal and constraints, which is a crucial step in the problem-solving process.

Means-ends analysis is a heuristic that involves identifying the difference between the current state and the goal state and then taking steps to reduce that difference.

Mathematical models of problem solving aim to provide insights into the cognitive processes and mechanisms underlying human problem-solving behavior.

The information processing approach assumes that problem solving involves the manipulation of mental representations, such as symbols, images, and concepts.

Divergent thinking refers to the process of generating multiple and varied solutions to a problem.

Cost-benefit analysis is a method used to evaluate the potential benefits and costs of different solutions to a problem.

Decision-making refers to the process of selecting the best course of action from a set of alternatives.

State space representation is a common approach used to represent the problem space in mathematical models of problem solving.

Implementation refers to the process of applying a solution to a problem in the real world.

Linear programming is a common technique used in mathematical models of problem solving to find an optimal solution to a problem with linear constraints.

Evaluation refers to the process of assessing the effectiveness of a solution after it has been implemented.

Decision tree is a common approach used in mathematical models of problem solving to represent the decision-making process.