Capital rationing involves placing limits on funds allocated to projects to ensure money is used efficiently and shortages are avoided. Risk and return are correlated, with higher risk investments potentially offering higher returns. Techniques for decision-making under uncertainty include single point estimates, scenario analysis, break-even analysis, and decision trees. Scenario analysis considers best, worst, and most likely outcomes. Decision trees graph decisions and uncertainties over time.

1. Capital Rationing
Capital rationing is undertaken by a firm in
order to place limits or restrictions on the
amount of money and other resources
earmarked for a particular project or investment.
The goal of capital rationing is to ensure that
money is allocated to its best use and to ensure
that the enterprise will not run short of cash.

2. The relationship between risk and return is a fundamental
investment concept. The concept states that an increased
probability for return is highly correlated with the increase
in the level of risk taken.
The return is expressed as a percentage and refers to the
gains or losses made from an investment, whereas the risk
element is associated with the volatility of that return. In
theory, an investor could expect higher return on
investment only if willing to accept a higher level of risk.
Concept of Risk and return

3. Techniques of decision making under Risk and
Uncertainty
•Single point estimates
•Scenario analysis
•Break even analysis
•Decision trees

4. Single Point Estimates
 The single point estimate uses a single estimate of each
unknown to determine our performance measure. This
often is similar to using averages to make decisions.
 When using single point estimates we only get one
output value for each alternative.
 If we want to maximize the performance measure, such
as net present value (NPV), we simply choose the
alternative with the highest NPV.

5. Scenario Analysis
This method takes the single point estimate and goes a few
steps beyond. Instead of using a single point estimate for each
unknown, the model is calculated many times while changing the
input variables. When deciding under uncertainty, usually we are
looking at worst case, most likely, and best case scenarios.
 For decision making under risk, we determine several discrete
outcomes from the model and assign a probability to each
outcome.

6. Break Even Analysis
 Break even analysis reverses the modeling
process. We start by setting output to our break
even point, and solve for the input variables. This
method doesn't tell us anything about the
probability of risk, or success. It merely gives the
decision maker a sense of where they stand
relative to downside/upside.

7. Decision Trees
Decision Trees are best for projects that involve decisions over
time. These result in many possible outcomes. Decision trees are
inherently for decision making under risk since we must assign
probabilities for each node emanating from a chance node. Decision
trees also can incorporate the alternatives into one graphic showing the
decisions to be made.
n a decision tree, squares represent decision points. Circles represent
uncertainty, hence they are called chance nodes. The outcomes
emanating from a chance node are uncertain so we assign probabilities to
each outcome. End nodes are final outcomes, and are represented by
triangles.