Decision under Certainty, Uncertainty,
and Risk
Decision-making under Certainty
A condition of certainty exists when the decision-maker knows with reasonable certainty
what the alternatives are, what conditions are associated with each alternative, and the
outcome of each alternative. Under conditions of certainty, accurate, measurable, and reliable
information on which to base decisions is available.
The cause and effect relationships are known and the future is highly predictable under
conditions of certainty. Such conditions exist in case of routine and repetitive decisions
concerning the day-to-day operations of the business.
Decision-making under Risk:
When a manager lacks perfect information or whenever an information asymmetry exists,
risk arises. Under a state of risk, the decision maker has incomplete information about
available alternatives but has a good idea of the probability of outcomes for each alternative.
While making decisions under a state of risk, managers must determine the probability
associated with each alternative on the basis of the available information and his experience.
Decision-making under Uncertainty:
Most significant decisions made in today’s complex environment are formulated under a state
of uncertainty. Conditions of uncertainty exist when the future environment is unpredictable
and everything is in a state of flux. The decision-maker is not aware of all available
alternatives, the risks associated with each, and the consequences of each alternative or their
probabilities.
The manager does not possess complete information about the alternatives and whatever
information is available, may not be completely reliable. In the face of such uncertainty,
managers need to make certain assumptions about the situation in order to provide a
reasonable framework for decision-making. They have to depend upon their judgment and
experience for making decisions.
Modern Approaches to Decision-making under Uncertainty:
There are several modern techniques to improve the quality of decision-making under
conditions of uncertainty.
The most important among these are:
,(1) Risk analysis,
(2) Decision trees and
(3) Preference theory.
Risk Analysis:
Managers who follow this approach analyze the size and nature of the risk involved in
choosing a particular course of action.
For instance, while launching a new product, a manager has to carefully analyze each of the
following variables the cost of launching the product, its production cost, the capital
investment required, the price that can be set for the product, the potential market size and
what percent of the total market it will represent.
Risk analysis involves quantitative and qualitative risk assessment, risk management and risk
communication and provides managers with a better understanding of the risk and the
benefits associated with a proposed course of action. The decision represents a trade-off
between the risks and the benefits associated with a particular course of action under
conditions of uncertainty.
Decision Trees:
These are considered to be one of the best ways to analyze a decision. A decision-tree
approach involves a graphic representation of alternative courses of action and the possible
outcomes and risks associated with each action.
By means of a “tree” diagram depicting the decision points, chance events and probabilities
involved in various courses of action, this technique of decision-making allows the decision-
maker to trace the optimum path or course of action.
Preference or Utility Theory:
This is another approach to decision-making under conditions of uncertainty. This approach
is based on the notion that individual attitudes towards risk vary. Some individuals are
willing to take only smaller risks (“risk averters”), while others are willing to take greater
risks (“gamblers”). Statistical probabilities associated with the various courses of action are
based on the assumption that decision-makers will follow them.
3For instance, if there were a 60 percent chance of a decision being right, it might seem
reasonable that a person would take the risk. This may not be necessarily true as the
individual might not wish to take the risk, since the chances of the decision being wrong are
40 percent. The attitudes towards risk vary with events, with people and positions.
, Top-level managers usually take the largest amount of risk. However, the same managers
who make a decision that risks millions of rupees of the company in a given program with a
75 percent chance of success are not likely to do the same with their own money.
Moreover, a manager willing to take a 75 percent risk in one situation may not be willing to
do so in another. Similarly, a top executive might launch an advertising campaign having a
70 percent chance of success but might decide against investing in plant and machinery
unless it involves a higher probability of success.
Though personal attitudes towards risk vary, two things are certain.
Firstly, attitudes towards risk vary with situations, i.e. some people are risk averters in some
situations and gamblers in others.
Secondly, some people have a high aversion to risk, while others have a low aversion.
Most managers prefer to be risk averters to a certain extent, and may thus also forego
opportunities. When the stakes are high, most managers tend to be risk averters; when the
stakes are small, they tend to be gambler.
Decision Tree Analysis
Decision Tree may be understood as the logical tree, is a range of conditions (premises) and
actions (conclusions), which are depicted as nodes and the branches of the tree which link the
premises with conclusions. It is a decision support tool, having a tree-like representation of
decisions and the consequences thereof. It uses ‘AND’ and ‘OR’ operators, to recreate the
structure of if-then rules.
A decision tree is helpful in reaching the ideal decision for intricate processes, especially
when the decision problems are interconnected and chronological in nature.
A decision tree does not constitute a decision but assists in making one, by graphically
representing the material information related to the given problem, in the form of a tree. It
diagrammatically depicts various courses of action, likely outcomes, states of nature, etc, as
nodes, branches or sub-branches of a horizontal tree.
Nodes
There are two types of Nodes:
Decision Node: Represented as square, wherein different courses of action arise from
decision node in main branches.
Chance Node: Symbolised as a circle, at the terminal point of decision node, the chance
node is present, where they emerge as sub-branches. These depict probabilities and
outcomes.
and Risk
Decision-making under Certainty
A condition of certainty exists when the decision-maker knows with reasonable certainty
what the alternatives are, what conditions are associated with each alternative, and the
outcome of each alternative. Under conditions of certainty, accurate, measurable, and reliable
information on which to base decisions is available.
The cause and effect relationships are known and the future is highly predictable under
conditions of certainty. Such conditions exist in case of routine and repetitive decisions
concerning the day-to-day operations of the business.
Decision-making under Risk:
When a manager lacks perfect information or whenever an information asymmetry exists,
risk arises. Under a state of risk, the decision maker has incomplete information about
available alternatives but has a good idea of the probability of outcomes for each alternative.
While making decisions under a state of risk, managers must determine the probability
associated with each alternative on the basis of the available information and his experience.
Decision-making under Uncertainty:
Most significant decisions made in today’s complex environment are formulated under a state
of uncertainty. Conditions of uncertainty exist when the future environment is unpredictable
and everything is in a state of flux. The decision-maker is not aware of all available
alternatives, the risks associated with each, and the consequences of each alternative or their
probabilities.
The manager does not possess complete information about the alternatives and whatever
information is available, may not be completely reliable. In the face of such uncertainty,
managers need to make certain assumptions about the situation in order to provide a
reasonable framework for decision-making. They have to depend upon their judgment and
experience for making decisions.
Modern Approaches to Decision-making under Uncertainty:
There are several modern techniques to improve the quality of decision-making under
conditions of uncertainty.
The most important among these are:
,(1) Risk analysis,
(2) Decision trees and
(3) Preference theory.
Risk Analysis:
Managers who follow this approach analyze the size and nature of the risk involved in
choosing a particular course of action.
For instance, while launching a new product, a manager has to carefully analyze each of the
following variables the cost of launching the product, its production cost, the capital
investment required, the price that can be set for the product, the potential market size and
what percent of the total market it will represent.
Risk analysis involves quantitative and qualitative risk assessment, risk management and risk
communication and provides managers with a better understanding of the risk and the
benefits associated with a proposed course of action. The decision represents a trade-off
between the risks and the benefits associated with a particular course of action under
conditions of uncertainty.
Decision Trees:
These are considered to be one of the best ways to analyze a decision. A decision-tree
approach involves a graphic representation of alternative courses of action and the possible
outcomes and risks associated with each action.
By means of a “tree” diagram depicting the decision points, chance events and probabilities
involved in various courses of action, this technique of decision-making allows the decision-
maker to trace the optimum path or course of action.
Preference or Utility Theory:
This is another approach to decision-making under conditions of uncertainty. This approach
is based on the notion that individual attitudes towards risk vary. Some individuals are
willing to take only smaller risks (“risk averters”), while others are willing to take greater
risks (“gamblers”). Statistical probabilities associated with the various courses of action are
based on the assumption that decision-makers will follow them.
3For instance, if there were a 60 percent chance of a decision being right, it might seem
reasonable that a person would take the risk. This may not be necessarily true as the
individual might not wish to take the risk, since the chances of the decision being wrong are
40 percent. The attitudes towards risk vary with events, with people and positions.
, Top-level managers usually take the largest amount of risk. However, the same managers
who make a decision that risks millions of rupees of the company in a given program with a
75 percent chance of success are not likely to do the same with their own money.
Moreover, a manager willing to take a 75 percent risk in one situation may not be willing to
do so in another. Similarly, a top executive might launch an advertising campaign having a
70 percent chance of success but might decide against investing in plant and machinery
unless it involves a higher probability of success.
Though personal attitudes towards risk vary, two things are certain.
Firstly, attitudes towards risk vary with situations, i.e. some people are risk averters in some
situations and gamblers in others.
Secondly, some people have a high aversion to risk, while others have a low aversion.
Most managers prefer to be risk averters to a certain extent, and may thus also forego
opportunities. When the stakes are high, most managers tend to be risk averters; when the
stakes are small, they tend to be gambler.
Decision Tree Analysis
Decision Tree may be understood as the logical tree, is a range of conditions (premises) and
actions (conclusions), which are depicted as nodes and the branches of the tree which link the
premises with conclusions. It is a decision support tool, having a tree-like representation of
decisions and the consequences thereof. It uses ‘AND’ and ‘OR’ operators, to recreate the
structure of if-then rules.
A decision tree is helpful in reaching the ideal decision for intricate processes, especially
when the decision problems are interconnected and chronological in nature.
A decision tree does not constitute a decision but assists in making one, by graphically
representing the material information related to the given problem, in the form of a tree. It
diagrammatically depicts various courses of action, likely outcomes, states of nature, etc, as
nodes, branches or sub-branches of a horizontal tree.
Nodes
There are two types of Nodes:
Decision Node: Represented as square, wherein different courses of action arise from
decision node in main branches.
Chance Node: Symbolised as a circle, at the terminal point of decision node, the chance
node is present, where they emerge as sub-branches. These depict probabilities and
outcomes.
