10.4 Sensitivity Analysis and Risk Assessment
3 min read•august 7, 2024
isn't always clear-cut. and help us understand how changes in key variables might affect our results. By tweaking inputs and running different scenarios, we can get a better grip on the uncertainties.
These techniques give us a range of possible outcomes instead of just one answer. They help decision-makers understand potential risks and rewards, making it easier to choose the best course of action in an uncertain world.
Sensitivity and Scenario Analysis
Evaluating Uncertainty in Cost-Benefit Analysis
Top images from around the web for Evaluating Uncertainty in Cost-Benefit Analysis
Risk assessment template - tools4dev View original
Is this image relevant?
The SARM Process View original
Is this image relevant?
Making Sense of Uncertainty: Nonprofit Scenario Planning During a Crisis | Bridgespan View original
Is this image relevant?
Risk assessment template - tools4dev View original
Is this image relevant?
The SARM Process View original
Is this image relevant?
1 of 3
Top images from around the web for Evaluating Uncertainty in Cost-Benefit Analysis
Risk assessment template - tools4dev View original
Is this image relevant?
The SARM Process View original
Is this image relevant?
Making Sense of Uncertainty: Nonprofit Scenario Planning During a Crisis | Bridgespan View original
Is this image relevant?
Risk assessment template - tools4dev View original
Is this image relevant?
The SARM Process View original
Is this image relevant?
1 of 3
- Sensitivity analysis systematically changes variables in a model to determine the effects of such changes and identify the most sensitive variables
- Involves changing one variable at a time while holding others constant to see how sensitive the outcome is to each variable (discount rate, costs, benefits)
- creates different scenarios by changing multiple variables simultaneously to assess their combined impact on the project's net present value or other outcome measures
- Considers best-case, worst-case, and most likely scenarios to provide a range of possible outcomes and help decision-makers understand potential risks and rewards (optimistic, pessimistic, baseline scenarios)
Break-Even Analysis and Uncertainty
- determines the point at which total costs equal total benefits, indicating the minimum level of benefits needed for the project to be worthwhile
- Calculates the break-even point by setting net present value to zero and solving for the unknown variable, such as the minimum number of users for a public service
- refers to the lack of precise knowledge about the values of costs and benefits in a cost-benefit analysis
- Arises from incomplete information, measurement errors, and the inherent variability of key parameters (discount rates, project lifespan, externalities)
- Sensitivity analysis, scenario analysis, and break-even analysis help address uncertainty by exploring how changes in assumptions affect the results and inform decision-making under uncertainty
Risk Assessment Techniques
Quantifying Risk through Monte Carlo Simulation
- Risk assessment involves identifying, analyzing, and evaluating potential risks that could impact the costs, benefits, or overall success of a project or policy
- is a computerized mathematical technique that accounts for risk in quantitative analysis and decision-making
- Involves defining a range of possible values for uncertain variables and randomly sampling from those ranges to generate a large number of scenarios (thousands of iterations)
- Produces a of potential outcomes, allowing analysts to quantify the likelihood of different results and identify key sources of risk (, )
Expected Value and Probability Distributions
- Probability distribution shows the likelihood of different values occurring for an uncertain variable, based on historical data, expert judgment, or statistical analysis
- Common probability distributions include normal (bell-shaped curve), uniform (equal probability for all values), and triangular (minimum, most likely, maximum values)
- is the sum of all possible values of a variable, weighted by their respective probabilities
- Represents the average outcome over a large number of trials or scenarios, helping decision-makers compare and rank alternatives based on their expected costs and benefits
- Combining Monte Carlo simulation with probability distributions provides a comprehensive risk assessment by generating a range of potential outcomes and their associated probabilities (5th percentile, 95th percentile)
Key Terms to Review (14)
Baseline scenario: A baseline scenario is a reference point used in forecasting and analysis to evaluate the effects of various interventions, policies, or changes over time. It represents the expected future state of a system or situation if current trends continue without any significant disruptions or policy changes. This term is essential in understanding how different factors might impact outcomes in sensitivity analysis and risk assessment, providing a clear context for comparison.
Break-even analysis: Break-even analysis is a financial assessment used to determine the point at which total revenues equal total costs, resulting in neither profit nor loss. This analysis helps organizations understand how many units of a product must be sold to cover costs and can inform decision-making about pricing, budgeting, and investment. By analyzing the break-even point, organizations can also assess the impact of changes in costs or sales volume on profitability.
Cost-benefit analysis: Cost-benefit analysis is a systematic approach used to evaluate the economic pros and cons of different policy options by comparing the total expected costs against the total expected benefits. This method helps policymakers decide whether a proposed action is worthwhile, guiding the allocation of resources in a way that maximizes societal benefits.
Expected Value: Expected value is a key concept in probability and statistics that represents the average outcome of a random variable when considering all possible values it can take, each weighted by its probability of occurrence. This measure helps to evaluate the potential benefits or risks associated with different decisions and outcomes, making it crucial for effective analysis and decision-making in uncertain situations.
Monte Carlo Simulation: Monte Carlo Simulation is a statistical technique that uses random sampling and repeated simulations to estimate the likelihood of different outcomes in a process that cannot easily be predicted due to the intervention of random variables. This method is widely used in fields such as finance, engineering, and public policy for risk assessment and sensitivity analysis, as it allows analysts to account for uncertainty and variability in their models.
Normal Distribution: Normal distribution is a probability distribution that is symmetric around the mean, showing that data near the mean are more frequent in occurrence than data far from the mean. This bell-shaped curve is crucial for understanding various statistical concepts, particularly in assessing risks and conducting sensitivity analysis, as it allows for the estimation of probabilities and the modeling of real-world phenomena.
Optimistic Scenario: An optimistic scenario is a projection or forecast that anticipates the most favorable outcomes in a given situation, often highlighting the best possible results based on certain assumptions. This approach is commonly used in sensitivity analysis and risk assessment to understand how favorable conditions could impact decision-making and planning.
Pessimistic scenario: A pessimistic scenario is a projection or forecast that outlines the most unfavorable outcomes based on certain assumptions or variables. This type of scenario is often utilized in risk assessment and sensitivity analysis to understand potential adverse impacts and prepare for worst-case situations. It helps policymakers and analysts evaluate risks, identify vulnerabilities, and develop strategies to mitigate negative consequences.
Probability Distribution: A probability distribution is a mathematical function that describes the likelihood of different outcomes in a random experiment. It provides a systematic way to represent all possible values of a variable and their associated probabilities, allowing for analysis and interpretation of uncertainty in decision-making processes. This concept is crucial for understanding how different factors influence outcomes and assessing risks in various scenarios.
Risk Assessment: Risk assessment is the process of identifying, analyzing, and evaluating potential risks that may negatively impact a project or policy. This process helps policymakers understand uncertainties and make informed decisions by weighing the likelihood and consequences of adverse outcomes. It connects closely to policy design, cost-benefit analysis, and understanding value conflicts when making trade-offs.
Scenario analysis: Scenario analysis is a strategic planning tool used to analyze and evaluate possible future events or outcomes by considering alternative scenarios. It helps decision-makers understand how different factors might influence the results of their choices, enabling them to prepare for various contingencies and uncertainties that may arise.
Sensitivity analysis: Sensitivity analysis is a technique used to determine how different values of an independent variable affect a particular dependent variable under a given set of assumptions. This method helps in understanding the impact of uncertainty in input variables on outcomes, making it crucial for decision-making and evaluating risks and trade-offs.
Skewed distribution: A skewed distribution is a probability distribution in which the values are not symmetrically distributed around the mean, resulting in a tail that is longer on one side than the other. This asymmetry can indicate the presence of outliers or influential observations, and it plays a critical role in understanding data behavior and conducting analyses such as sensitivity analysis and risk assessment.
Uncertainty: Uncertainty refers to the lack of definite knowledge or predictability regarding outcomes, which can arise from incomplete information, variability in data, or inherent unpredictability in complex systems. In decision-making contexts, such as evaluating policies or projects, understanding uncertainty is crucial as it influences how costs and benefits are assessed, impacting the reliability of the analysis and the confidence in recommendations made based on that analysis.