Monte Carlo Simulation

Correct Answer: To enhance the precision of the probability estimations

In Monte Carlo simulation for project risk analysis, doubling the quantity of random variables serves primarily to enhance the precision of probability estimations. This is based on fundamental statistical principles where larger sample sizes lead to more accurate results. Here's why this is correct: - Increased sample size reduces sampling error - Provides more data points for statistical analysis - Improves confidence intervals and reliability of results - Follows the law of large numbers for better convergence to true probabilities Why other options are incorrect: - Reducing computational time is incorrect because more variables actually increase computation time - Establishing a comprehensive framework is not related to quantity of variables but to the model structure - While minimizing standard error is related, it's a consequence of enhanced precision, not the primary strategic purpose

Correct Answer: A significant smoothing effect from multiple interacting variables

In Monte Carlo simulation, when we observe a uniform distribution in outputs despite having highly varied input distributions, this indicates a smoothing effect caused by the interaction of multiple variables. This occurs because: 1. Multiple random variables interact with each other during simulation iterations 2. The Central Limit Theorem comes into play, causing the combined effect of multiple variables to tend toward a normal distribution 3. The interactions between variables create averaging effects that smooth out extreme variations The other options are incorrect because: - Feedback loops typically create more complexity and variation rather than uniformity - Natural tendency to stabilize would show a normal distribution, not necessarily uniform - Compensating factors would need to be specifically designed into the system and would be unlikely to create perfect uniformity across all outcomes The smoothing effect from multiple interacting variables is a well-documented phenomenon in Monte Carlo simulations and best explains the observed pattern of uniform outputs despite varied inputs.

Correct Answer: The overlap between the distributions indicates potential resource conflicts

In Monte Carlo simulation for sequential activities, the overlap between two distributions is the most crucial aspect for decision making because: The first activity has a triangular distribution of (10,15,25) The second activity has a triangular distribution of (20,30,35) There is a clear overlap between these distributions (between 20-25) This overlap is critical because: 1. It indicates potential resource conflicts when one activity is running late while the next needs to start 2. It affects resource allocation and scheduling decisions 3. It helps identify potential schedule risks and dependencies The other options are incorrect because: - Multiple modes are not present in triangular distributions - The triangular shape is a standard distribution choice and does not indicate a need for more consultation - The ranges between min and max values are typical for construction estimates and do not indicate poor technique The overlap between distributions provides valuable information for project planning and risk management, making it the most relevant aspect for decision-making.