Decision trees are diagrammatic representations used to map sequential decisions and chance events, allowing for the calculation of expected values at each node.

Simulation (specifically Monte Carlo simulation) involves building a model and running it through many iterations using random variables to predict the probability distribution of outcomes.

Sensitivity analysis assesses risk by changing one key variable at a time (ceteris paribus) to observe the magnitude of change in the final result (e.g., NPV), identifying which variables are most critical.

Pay-off tables (or matrices) organize data by charting the outcomes (payoffs) for every possible combination of decision alternatives and uncertain states of nature.

Drury, C. (2018). Management and Cost Accounting. 10th ed. Cengage Learning.

Chapter 12: Risk and Uncertainty.

Section 12.3 (Decision Trees): Describes the sequential nature of decisions and events.

Section 12.4 (Sensitivity Analysis): Defines it as analyzing the "responsiveness of the outcome" to changes in input variables.

Horngren, C. T., Datar, S. M., & Rajan, M. V. (2015). Cost Accounting: A Managerial Emphasis. 15th ed. Pearson.

Chapter 3, Section "Sensitivity Analysis": Defines it as a "what-if" technique examining how a result changes if predicted data is not achieved.

Chapter 21 (Capital Budgeting): Discusses simulation as a tool for risk analysis involving probabilities and iterations.

Luce, R. D., & Raiffa, H. (1957). Games and Decisions: Introduction and Critical Survey. Wiley.

Chapter 2: Discusses the formal structure of decision making under uncertainty, including the use of payoff matrices (tables) to map acts (decisions) against states.