Transcript Management Support Systems and Decision

Management Support Systems and
Decision-Making
CIS 465 - Management Support and Decision-Making
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Supporting Managers with
Information Systems
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Models and Methods for
Management Support
• To understand how computers support
managers, it is necessary to understand what
managers do.
• It is difficult to produce a standard job description
for all managers.
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Fundamental Functions of
Management
• The traditional description of what managers do
was first characterized by French industrialist
Henri Fayol in his 1916 classic, Administration
Industrielle et Generale. Fayol considered the
manager's job as a composite of four separate
functions:
• Planning
• Controlling
• Leading
• Organizing
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Fundamental Functions of
Management - defined
• Planning - establishing goals and selecting the
actions needed to achieve them over a specific
period of time.
• Controlling - measuring performance against the
planned objectives and initiating corrective
action.
• Leading - inducing the people in the organization
to contribute to its goals
• Organizing - establishing and staffing an
organizational structure for performing business
activities
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Mintzberg’s Studies of Managers
Myth #1: The manager is a reflective systematic
planner.
Fact:
Study after study shows managers
work at an unrelenting pace, that their activities
are characterized by brevity, variety, and
discontinuity, they are strongly oriented toward
action, and dislike reflective activities.
Myth #2: The effective manager has no regular
duties to perform.
Fact:
Managerial work involves performing a
number of regular duties, including ritual and
ceremony, negotiations, and processing of soft
information that links the organization with its
environment
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Mintzberg’s Studies of Managers
Myth #3: The senior manager needs aggregated
information, which a formal management
information system best provides.
Fact:
Managers strongly favor verbal media,
telephone calls, and meetings over documents.
Myth #4: Management is, or at least is quickly
becoming, a science and a profession.
Fact:
The managers' programs - to schedule
time, process information, make decisions, and
so on-remain locked deep inside their brains.
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Classic Study of Managerial
Work
• The classic study of managerial work was done
by Mintzberg, who divided the manager’s roles
into three categories:
– 1. Interpersonal roles
– 2. Informational roles
– 3. Decisional roles
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Management Roles
• Interpersonal Roles:
– Figurehead, Leader, Liaison
• Informational Roles:
– Monitor, Disseminator, Spokesman
• Decisional Roles:
– Entrepreneur, Disturbance Handler, Resource
Allocator, Negotiator
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Mintzberg: The Nature of
Managerial Work
Formal
Authority
and
Status
Interpersonal
Roles
Informational
Roles
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Decisional
Roles
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Mintzberg’s Management Roles
Interpersonal Roles
• Figurehead - Carries out a symbolic role as head
of the organization, performing duties of a legal
or social nature.
• Leader - In the most widely recognized
managerial duty, the executive is responsible for
motivating and "activation" of subordinates, as
well as staffing, training, promoting.
• Liaison - Develops and maintains a personal
network of external contacts who provide
information and favors.
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Mintzberg’s Management Roles
Informational Roles:
• Monitor - Seeks and receives a wide variety of
special information (much of it current) to develop a
thorough understanding of the organization and the
environment. In this role, the executive serves as the
nerve center of internal and external information
about the organization.
• Disseminator - Transmits information received from
outsiders or subordinates to other members of the
organization. Some information is factual, some
involves interpretation and integration of diverse value
positions of organizational influencers. All information
is to guide subordinates in decision making.
• Spokesman - Communicates information to outsiders
on the organization's plans, policies, actions, results,
etc. serves as the expert on the organization's
industry.
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Mintzberg’s Management Roles
• Entrepreneur - Searches the organization and
environment for opportunities and initiates
"improvement projects" to bring about change;
supervises design of certain projects as well.
• Disturbance Handler - Responsible for
corrective action when the organization faces
important, unexpected disturbances.
• Resource Allocator - Allocates organizational
resources of all kinds-in effect the making or
approval of all significant organizational
decisions.
• Negotiator - Represents the organization in
major negotiations.
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IS and Mintzberg’s Roles
Information Support for
Management
• Early information systems mainly supported the
informational roles.
• The purpose of recent information systems is to
support all three roles.
• We will explore the information support required
for all roles, beginning with the decisional roles.
• The success of management depends on the
execution of managerial functions such as
planning, organizing, leading, and controlling. To
carry out these functions, managers engage in
the continuous process of making decisions.
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Executive Activities and
Information Support
• Handling Disturbances (42%) - A disturbance is
something that happens unexpectedly and
demands immediate attention, but it might take
weeks or months to resolve.
• Entrepreneurial Activity (32%) - activities
intended to make improvements that will increase
performance levels. Improvements are strategic
and long term in nature.
• Resource Allocation (17%) - Allocating
resources within the framework of the annual and
monthly planning tasks and budgets
• Negotiations (3%) resolve conflicts and
disputes, either internal or external.
• Other Activities (6%)
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Introduction to Decision-Making
• A basic understanding of decision making is
essential because most information systems are
designed to support decision making in one way
or another.
• We will survey some models and concepts of
decision making and methods for deciding
among alternatives.
• We will look at their relevance to information
systems design.
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Decision Making:
Phases
• Herbert A. Simon (1960) proposed the most
famous model of the Decision-Making process.
– 1. Intelligence
– 2. Design
– 3. Choice
• Some models of decision making include a 4th
step: Implementation.
• There is a flow activities from one phase, to the
next. At any time there may be a return to a
previous phase.
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Simon’s Model
Flowchart of Decision Process
Intelligence
Design
Choice
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Intelligence Phase
• Searching the environment for conditions calling
for decisions
• Data inputs obtained, processed, examined for
clues to identify problems or opportunities
• Identify problems for opportunity situations
requiring design and choice.
• Scanning the environment, intermittently or
continuously, is important.
–
–
–
–
–
–
Organizational objectives
search and scanning procedures
data collection
problem identification
problem classification
problem statement
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Examples of the Intelligence
Phase
• Air traffic controller continuously scanning to
detect problems in air space.
• Each time you start your car, there is a conscious
or unconscious scanning (listening, checking
gauges, etc.).
• Marketing executive makes periodic visits to key
customers to review possible problems and
identify new customer needs.
• A plant manager reviews daily scrap report to
check for quality control problems.
• An executive reads the industry trade paper to be
aware of events and changes in the environment.
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Summary: Intelligence Phase
• Intelligence activities result in dissatisfaction with
the current state or identification of potential
rewards from a new state.
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Design Phase
• Inventing, developing, and analyzing possible
courses of action
• This involves processes to understand the
problem, to generate solutions and test solutions
for feasibility:
– Formulate a model.
– Set criteria for choice.
– Search for alternatives
– Predict and measure outcomes
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Choice Phase
• Select an alternative from those available
• Select and implement a choice:
– Solution to the model
– sensitivity analysis
– selection of best (good) alternatives(s)
– plan for implementation (action)
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Comment on Simon’s Model
• Simon’s Model does not go beyond the choice
phase.
• There are no steps for implementation, or
feedback from the results of the decision.
• Although Simon’s model is the most famous,
others have adapted it.
• Our textbook provides a similar model:
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Alter Textbook Model
• Decision-making is represented as a problemsolving process preceded by a separate
problem-finding process.
• Problem-solving is the use of information,
knowledge, and intuition to solve a problem that
ha previously been defined.
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An Alternative Model:
Rubenstein and Haberstroh’s
•
•
•
•
•
1.
2.
3.
4.
5.
Recognition of problem or need for decision
Analysis and statement of alternatives
Choice among the alternatives
Communication and implementation
Follow-up and feedback of results
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Slade’s Model of Decision
Making
Identify
Problem
Identify
Alternatives
Choose Usual
Action
Evaluate
Alternatives
Choose Among
Alternatives
Generate New
Alternatives
Effect Choice
Abandon
Problem
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Summary - I
All models indicate the same basic ideas:
1. Problem finding - Identify situations where
problems need to be solved.
2 Problem formulation - clearly state the problem.
3. Alternative Generation
4. Evaluate Outcomes.
5. Choice
6. Implement
7. Evaluate..
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Summary -II
• In the models of decision-making, the most
important aspects of the intelligence and design
phases are:
– I - Problem Finding
– II - Problem Formulation
– III - Alternative Generation
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I. - Problem Finding
• It is the difference between existing state and the
desired state
• The problem finder usually has an idea of the
desired state ( a model)
• Compared with the reality and differences noted
• A Problem exists when there is a major
difference
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The role of models in decisionmaking
• A major characteristic of decision-making is the
use of models.
• A model is a simplified representation or
abstraction of reality.
• It is usually simplified because reality is too
complex to copy.
• Basis idea is that analysis is performed on a
model rather than on reality itself.
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Pounds’ Categories of Models Expectations against which
reality is measured
• Historical - expectation based on extrapolation of
past experience.
• Planning - the plan is the expectation
• Inter-organizational - Models of other people in
the organization (e.g. superiors, subordinates,
other departments, etc.)
• Extra-organizational - models where the
expectations are derived from competition,
customers, professional organizations, etc.
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Another classification of models
•
•
•
•
Iconic Models
Analog Models
Mathematical Models
Mental Models
• These four types are distinguished according to
their degree of abstraction, with iconic being the
least abstract, and mental models being the most
abstract.
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Iconic and Analog Models
• Iconic (scale) models - the least abstract model,
is a physical replica of a system, usually based
on a different scale from the original. Iconic
models can scale in two or three dimensions.
• Analog Models - Does not look like the real
system, but behaves like it. Usually twodimensional charts or diagrams. Examples:
organizational charts depict structure, authority,
and responsibility relationships; maps where
different colors represent water or mountains;
stock market charts; blueprints of a machine;
speedometer; thermometer
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Mathematical Models
• Mathematical (quantitative) models - the
complexity of relationships sometimes can not be
represented iconically or analogically, or such
representations may be cumbersome or time
consuming.A more abstract model is built with
mathematics.
• Note: recent advances in computer graphics use
iconic and analog models to complement
mathematical modeling.
• Visual simulation combines the three types of
models.
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Mental Models
• People often use a behavioral mental model.
• A mental model is an unworded description of
how people think about a situation.
• The model can use the beliefs, assumptions,
relationships, and flows of work as perceived by
an individual.
• Mental models are a conceptual, internal
representation, used to generate descriptions of
problem structure, and make future predications
of future related variables.
• Support for mental models are an important
aspect of Executive Information Systems. We
will discuss this in depth later.
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II. - Problem Formulation
• There is always the danger of solving the wrong
problem.
• Here, you try to clarify the problem so that you
work on the “right” problem
• Frequently, the process of clearly stating the
problem is sufficient; in other cases, reduction of
complexity is needed.
• Some strategies to use for reducing complexity
and formulating a manageable problem are
shown in the next slide
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Problem Formulation Strategies
• Determine problem boundaries (I.e. what is
clearly part of the problem)
• Examine changes that precipitated the problem
• Break it down into smaller sub-problems
• Focus on controllable elements
• Relate to a previously solved class of problems,
an analogy situation.
– For example, recognizing that a problem is really an
“allocation” problem allows the problem solver to look at other
“allocation” problems and see what was done previously. The
idea is to reduce complexity and rely on past experiences.
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III. Alternative Generation
• A significant part of the process of decisionmaking is the generation of alternatives to be
considered in the choice phase.
• This is a creative task and creativity can be
taught
• Can be enhanced by aids such as
–
–
–
–
scenarios
brainstorming
analogies
checklists, etc
• Requires Knowledge of the problem and its
boundaries (domain knowledge), as well as
motivation to solve the problem.
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Decision-Making Concepts
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Decision Making Concepts
• Decisions differ in a number of ways.
• The differences affect the alternative generation
process, and how a final choice will be made.
• The differences can also affect how information
systems and information technology can support
the process at any one of the stages.
• Four dimensions of decision types::
– I. Knowledge of Outcomes
– II. level of structure/programmability
– III. criteria for the decision
– IV. level of decision impact
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Decision Making Concepts I:
Knowledge of Outcomes
• Outcome - what will happen if a particular
alternative or course of action is chosen
• Knowledge of outcomes is important with multiple
alternatives
• Three types of knowledge with respect to
outcomes are usually distinguished:
– Certainty
– Risk
– Uncertainty
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Knowledge of Outcomes
Three Types
• Certainty
– Complete and accurate knowledge of outcome
of each alternative. There is only one
outcome for each alternative.
• Risk
– Multiple possible outcomes for each
alternative and a probability can be assigned
to each
• Uncertainty
– Multiple outcomes for each alternative and a
probability cannot be assigned to each
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Decision-Making Under
Conditions of Certainty:
Rationality
• If the outcomes are known and the values of the
outcomes are certain, the task of the decisionmaker is to compute the optimal alternative or
outcome.
• Are we rational decision makers?
• There is ongoing argument pro and con
• People are said to be limited rationalists
• We might look for a limited number of alternatives
and decide
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Rationality:
Example
• A rational decision maker is expected to decide
on the “optimal alternative” or outcome
• The optimal alternative is one that is related to
some optimization criteria such as minimize cost,
for example
• Thus the rational decision maker chooses the
one that has the minimum cost
• Consider purchasing two products that are
identical in all respects and appear equal in value
• All other things being equal, the rational decision
maker chooses the one with the lower cost
• Rare, since all things are rarely equal
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Decision Making under Risk
• Risk is when multiple outcomes of each
alternative is possible and a probability of
occurrence can be associated with each
• In such cases, the general rule is to pick the one
that has the highest expected value
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Risk
Expected Value
• Which would you choose?
• Action 1 offers 1% probability of a gain of
$15,000, or
• Action 2 that offers 50% probability of a gain of
$400
• Solution: use Expected Value
• Expected value is defined as the product of the
outcome and the probability of the outcome
• Expected value = outcome x probability
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Risk
Expected Value (contd.)
•
•
•
•
Action 1 : Expected Value = 0.01 x 15,000 = $150
Action 2 - Expected Value. = 0.5 x 400 = $200
Action 2 has the higher expected value
The rational decision maker chooses the strategy
that has the higher expected value
• OK strategy if the probability is known
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Decision Making Under
Uncertainty
• Uncertainty is the situation where the outcomes
are known, but the probabilities are unknown
• One solution is to somehow assign the
probabilities and then convert it to a problem
under risk.
• Other decision rules are to minimize regret and to
use the maximum and minimum criteria. We will
look at these later.
• Uses Bayesian decision theory which
recommends maximizing subjective expected
utility, and on decision analysis which uses
decision trees, payoff matrices, and influence
diagrams to implement Bayesian Decision
Theory.
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Decision-Making Concepts II:
Programmed vs. Non Programmed
Decisions
• We have reviewed this with the Gorry and Scott
Morton Paper discussed earlier
• Programmed Decisions - those that can be prespecified by a set of rules or decision procedures
• Non-programmed Decisions - those that do not
have any pre-established decision rule or
procedures
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Criteria for Decision-Making III:
Normative vs. Descriptive Models
• Normative or Prescriptive - a model of decision
making that tells the decision maker how to make
a class of decisions. These have been developed
by economists, management scientists, etc.
Examples: Linear programming, game theory,
capital budgeting, statistical decision theory.
• In normative models the criterion for selecting
among alternatives is maximization or
optimization of either utility or expected value.
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Criteria for Decision-Making III:
Normative vs. Descriptive Models
• Descriptive - a model of decision making that
describes how decision makers actually make
decisions. They are used primarily by behavioral
scientists.
• Descriptive models introduce the concept of
satisficing.
• These two models introduce the Rational
Approach as well as behavioral approaches.
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Criteria for Decision-Making IV:
Level of Decision Impact
• What are the consequences of the Decision?
• Will the consequences affect choice?
• What are the consequences under conditions of
certainty, risk, or uncertainty?
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Management Support Systems and
Decision-Making
Part II
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Views or Models
of
Individual and Organizational
Decision-Making
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Views or Models of DecisionMaking
•
•
•
•
•
•
The Rational Manager View
The Satisficing, or Process-Oriented View
The Organizational Procedures View
The Political View
The Individual Differences View
The Garbage Can Model
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The Rational Manager View
• Oldest Theory to be proposed and studied in
detail - it is a normative model.
• Based Heavily on Theory of Economic Man
developed in economics and applied to
management.
• Assumes organizational actors have complete
knowledge of a decision scenario, and complete
knowledge of their preferences.
• An exhaustive search is made of all possible
alternatives.
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Rational Manager - 2
• Consequences of alternatives are evaluated in
terms of known preferences.
• An optimal choice can be selected.
• Proponents of cost-benefit analysis adopt this
view.
• Model is highly normative, and has little
descriptive support in true form.
• It is impractical and over-idealized.
• Influenced all other views of decision-making.
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The Satisficing Viewpoint
• Simon was among first to attack the Rational
Viewpoint.
• Most decision situations provide limited
knowledge on some aspect of the problem.
• Impractical to think of generating all possible
relevant alternatives for a situation.
• The bounded rationality of the human mind would
make all of this information unassimable.
• Simon argues we tend to satisfice, or settle for a
choice after a moderate amount of search.
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Satisficing View - 2
• Since search is not exhaustive, heuristics or rules
of thumb are used to identify solutions that are
good enough most of the time.
• Heuristics reflect bounded rationality, i.e. a
compromise between the demands of the
problem, and the capabilities and commitment of
the decision-maker.
• Simon’s Model has had wide discussion.
• His model, called Administrative Man, is a
rejection of the Economic Man theory.
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Empirical Research
• Empirical research has shown the importance of
rationality and bounded rationality in
organizational decision-making.
• Rationality and bounded rationality may be
viewed at opposite ends of a continuum with the
decision setting playing a contingency role.
• Threatening environments, high uncertainty,
external control decreased rationality.
• The more complex or turbulent the environment,
the less rationality used.
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Empirical Research - 2
• Comprehensiveness - a desire to be rational,
reflects how exhaustive and inclusive the
decision process is in seeking alternatives.
HIGH
Comprehensiveness
LOW
STABLE
UNSTABLE
Environment
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Organizational Process View
• Cyert and March extended Simon’s concept of
bounded rationality to the organizational setting.
• Organizational Decision-Making in terms of:
– formal and informal structure of the organization
– standard operating procedures
– channels of communication
• Choice is made in terms of goals, on the basis of
expectations.
• The organization is a coalition of participants with
disparate demands, focus of attention, and
limited ability to attend to all problems
simultaneously.
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Organizational Process View - 2
• Bargaining among coalitions produces
agreements which are the organization’s goals.
• Organizational expectations arise from
inferences from available information.
• Choice emerges as the selection of the first
alternative that expectations identify as
acceptable in terms of goals.
• Choice in the short-run is driven by standard
operating procedures.
• Choice in the long-run driven by organizational
goals.
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The Political View
• Here decision-making is a personalized
bargaining process among organizational units.
• Power and Influence determine the outcome of
any situation.
• The players act in terms of no consistent set of
strategic objectives, but rather according to their
personal goals, stakes, interests.
• Organizational choice is the result of the pulling
and hauling that is organizational politics.
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The Political View - 2
• One must understand the realities of power and
the compromises and strategies necessary to
mesh interests and constraints of the players.
• Decisions are made to enhance the “winner's
conception of organizational, group, or personal
interests.
• Allison argued that “Politics is a process or
conflict and consensus Building”.
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Empirical Research - 3 (1992)
• Procedural Rationality, or the desire to follow a
rational approach to decision-making, is
independent of the political process
• Decisions can be both procedurally rational and
political, or neither.
• They can be independent dimensions of the
organizational decision-making process.
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The Political View - 3
• An important sub-model is the concept of
incremental change - because there are so many
actors involved in an organizational decision
setting, clear, rapid progress is rarely possible
• The result of political bargaining and compromise
is incremental change, i.e. decision-makers move
to situations which are only slightly different from
the current situation.
• Lindbloom talked of this in The Science of
Muddling Through (1959).
• Muddling through is explicitly anti-utopian - it is
the best we can do.
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The Individual Differences View
• This view focuses on the individual decisionmaker and his/her cognitive style, information
processing and problem-solving behavior.
• Some individuals have specialized styles of
decision-making which are effective in some
contexts, and less so in others.
• Personal rationality is subjective, and behavior is
determined by the manner in which individuals
process information.
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Individual Differences - 2
• In the organizational context, managers develop
their own mental models of problems and issues.
• Decision-making can be a mixture of rationality
and intuition, based heavily of experience and
style.
• MS and OR are attractive to an analytic,
systematic style. They may be less attractive to
managers with a more intuitive style.
• Personality (what a person thinks) vs. Cognitive
Style (how a person thinks).
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Cognitive Style Dimensions
Left brain
Right brain
•
•
•
•
•
•
•
•
•
•
•
•
words
analytic
sequential
active
realistic
planned
images
intuitive
simultaneous
receptive
imaginative
impulsive
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Empirical Research - 4
• Bobbit and Ford (1980) saw that executives had
firm pre-dispositions about how the process of
looking for ideas should unfold.
• Executive attitudes are influences by belief
structures and past experience - pragmatic.
• Those with a low tolerance for ambiguity and
high need for structure will adopt a decision
process that has a narrow search zone.
• Risk propensity and risk perception also play
roles, with risk propensity dominating decision
situations. (Sitkin and Pablo, 1992).
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The Garbage Can Model
• Proposed by Cohen, March, and Olsen in 1972.
• Appropriate for highly complex, unstable, and
ambiguous environments called organized
anarchies.
• Decisions result from a complex interaction
between four independent streams of events:
• problems, solutions, participants, and choice
opportunities.
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Garbage Can Model - 2
• The interaction of these events creates a
collection of:
– choices looking for problems
– issues and feelings looking for decision situations in
which they might be aired
– solutions looking for issues to which they might be the
answer
– decision-makers looking for work.
• The four streams are independent in nature and
interact in a random fashion.
• A decision is “made” only when the four streams
happen to interact.
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Garbage Can Model - contd.
• Good decisions are made when this happens at
the right time.
• Solutions represent the ideas constantly flowing
through an organization.
• Solutions are used to formulate problems.
• Note that managers often do not know what they
want until they have some idea of what they can
get.
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Individual Aspects
of Decision-Making
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Human Expectations
• Humans display a variety of responses in
decision making. Some are related to individual
differences such as cognitive style, others are
related to expectations.
• Role of expectations can be partially explained
by
– theory of cognitive dissonance
– commitment theory
– theory of anticipatory regret
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Theory of Cognitive Dissonance
• propagated by Leon Festinger
• explains behavior after a choice is made
• Selected alternative has some negative features
and rejected ones have some positive features
• Decision maker has feelings of mental discomfort
following a decision because of recognition of
above
• “Second-Guessing”
• Ex.: Purchase of car
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Cognitive Dissonance - 2
• Customers might need to be bolstered about
their decision
• Hence, sales procedures follow up a sale with a
congratulatory letter to bolster the effect of
cognitive dissonance reduction
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Theory of commitment
• If the person knows the decision is not revocable
(firm commitment to decision), then decision time
increases and processes will be more careful
• Having spent time making decision, the decision
maker is reluctant to change it
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Theory of anticipatory regret
• The decision maker anticipates the regrets that
might occur
• This inhibits the decision maker from making a
decision without contemplating the
consequences
• Can be used to lessen post-decision regret;
thinking about consequences before they happen
reduces the psychological impact when hey
happen.
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Behavioral Aspects of
Organizational
Decision-Making
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Behavioral Aspects of
Organizational Decision Making
• Many Issues Related to the Organizational
Procedures Viewpoint and the Political Viewpoint
– quasi-resolution of conflict
– uncertainty avoidance
– problemistic search
– organizational learning
– incremental decision making
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Quasi-Resolution of Conflict
• An organization can be considered as coalition of
members having different goals and unequal
power to influence organizational objectives.
• There are conflicts among the goals of the
various members (e.g. production, sales,
inventory).
• Conflicts need to be resolved thru:
– local rationality
– acceptable-level decision rules
– sequential attention to goals
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Uncertainty Avoidance
• Organizations live in uncertain environments
• This theory assumes that organizations will seek
to avoid risk and uncertainty at the expense of
expected value
• “A decision maker will be willing to accept a
reduction in the expected value in exchange for
an increase in the certainty of the outcome”
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Uncertainty Avoidance - 2
• Thus, a decision maker will choose a 90%
chance of making $10 over a 12% chance of
making $100
• The second alternative has a higher expected
value
• The decision though, is the first alternative
• Major benefit - reduction in uncertainty
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Uncertainty Avoidance - 3
Legal Methods
• Short-run feedback and reaction cycle
– short feedback cycle allows frequent new
decisions and thus reduce need to be
concerned about future uncertainty.
• Negotiated environment
– organization seeks to control its environment
through industry-wide conventional practices
(sometimes just as restrictive or collusive
behavior)
– long-term supply contracts, etc.
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Problemistic Search
• Search for solutions is problem-stimulated
• Little planned search for solutions not motivated
by problems
• Simple rules
– search locally close to present symptom
– if this fails, expand search to vulnerable areas
before moving to other areas
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Organizational Learning
• Organizations exhibit adaptive behavior over time
• They change their goals and revise problem
search procedures on the basis of experience
• Aspiration levels for goals are assumed to
change in response to results obtained
• Plans tend to reflect aspiration levels
• Information systems are an important factor in
reconciling achievement level and aspiration
level
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Incremental Decision Making
• Decision making in organizations is confined to
small changes from existing policy and
procedures
• Emphasis is on correcting or improving existing
policies and actions
• Emphasis on consensus
• Called “muddling through” by Lindbloom
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Decision Making Under
Psychological Stress
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Decision Making under Stress
• based on the conflict-theory model of Janis and
Mann (1977)
• Decision making causes stress but here the
characteristic is that all the alternative courses of
action appear to have serious undesirable
outcomes.
• Symptoms of such conflict are
–
–
–
–
apprehensiveness
hesitation
vacillation
distress
• Decisions are made using coping patterns
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Coping Patterns
• Used in emergency situations such as a flood or
a fire
• Can be extended to situations where there exist
serious threats
• Four Questions that determine the typical coping
pattern.
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Coping Patterns - Questions
• Q1: Are the risks serious in the absence of
change?
• Q2: Are the risks serious if change is made?
• Q3: Is it realistic to hope for a better solution?
• Q4: Is there sufficient time to search and
deliberate?
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Coping Patterns - 3
• If answer to Q. 1 is yes, then next is relevant
• If answer to Q. 2 is yes, then go to Q. 3
• If answer to Q. 3 is no, then the coping pattern
may be defensive avoidance
• If answer to Q. 3 and Q. 4 is yes, then the coping
strategy can be a vigilant process of search,
appraisal, and contingency planning.
• If answer to Q. 4 is no, (e.g. a fire) then the
coping pattern may be hypervigilance
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Hypervigilance
• Typical response to disasters
• The decision maker focuses on the expected
unfavorable consequences and fails to process
information indicating that they may not happen.
• Pressure is felt to take immediate action.
• Hastily choose without considering the overall
result or other possible actions.
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Defensive Avoidance
• This coping pattern is most appropriate for the
design of information systems and decision
support systems.
• Marked by decision maker avoiding exposure to
disturbing information, wishful thinking, distortion
of information received and selective inattention.
• If risk of postponing decision is low,
procrastination is chosen.
• If not, buck passing is tried.
• Bolstering is used beforehand in the lack of
complete information
• After decision, bolstering is used to reduce
cognitive dissonance.
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Defensive Avoidance - 2
• Some example bolstering tactics:
– Exaggeration of favorable consequences
– minimizing unfavorable consequences
– Denial of adverse feelings
– Exaggeration of remoteness of action that will
be required following decision
– Assuming lack of concern by society (iit is a
private decision).
– Minimizing of personal responsibility.
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Defensive Avoidance - 3
• This pattern can also be observed in a group
• Janis coined the term groupthink for collective
defensive avoidance
• E.g. industry that fails to react to vigorous price,
quality and design competition by foreign
competitors
• Symptoms of groupthink - see next slide
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Groupthink Symptoms
• Illusion of invulnerability - The company is
large and powerful and has customer loyalty.
• Collective Rationalization - No one can match
our research.
• Belief in the inherent morality of the group The managers are the best trained and preserve
“traditional values”.
• Stereotypes of outgroups - The competitor’s
products are inferior. They can not provide
service.
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Groupthink Symptoms - contd.
• Direct pressure on dissenters -demotion or
firing of managers who disagree on a subject.
• Self-censorship - The subject of foreign
competition is never put on the table by anyone
in the group.
• Illusion of unanimity - No one is objecting, so
everyone must agree that foreign competition is
not serious.
• Self-appointed mind guards - evidence that
contradicts the thinking of the group is removed
as it moves up the organization.
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Groupthink Example: PATCO
Strike of 1981
• Illusion of Vulnerability: - “The air system can
not survive long without air traffic controllers
(ATC’s). Plans to replace them will not work.
• Collective Rationalization - The oath not to
strike wasn’t binding in this case, even though a
strike was illegal.
• Belief in Inherent morality of the group - The
strike for higher pay is morally justified because
ATC’s are responsible for more lives now.
• Stereotypes of Outgroups - The government is
a typical bureaucracy. Reagan is just bluffing on
a threat to fire us. No one has listened to our
complaints.
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Groupthink Example: PATCO
Strike of 1981 - contd.
• Direct Pressure of Dissenters - John Feydon,
President of PATCO until 1980, forced to resign
because he did not support strike.
• Self-censorship - Quotes such as “Doubts
seemed in the minority”...”The union is tight,
almost like a family.” 20 % of strike force
returned to work.
• Illusion of Unanimity - Other unions offered
token support for PATCO. AFL/CIO privately was
critical of PATCO’s strike.
• Self-Appointed Mind Guards - Negotiators
claimed there was no alternative but to strike.
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Deciding Among Alternatives
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Introduction to Methods
• Numerous method help one decide among
alternatives.
• They generally assume that all alternatives are
known or can be know, even though the search
process often stops well before all feasible
alternatives have been examined.
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Optimization Techniques Under
Certainty
• All alternatives and their outcomes are known.
The computational problem is to choose which
one is optimal.
• Use optimization techniques
–
–
–
–
–
–
–
–
systems of equations
linear programming
integer programming
dynamic programming
queuing models
inventory models, etc.
Capital budgeting analysis
Break-even Analysis
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Mathematical Programming
• Mathematical Programming is the name for a
family of tools designed to solve managerial
problems in which the decision maker must
allocate scarce (or limited) resources among
various activities to optimize a measurable goal.
• Example: Distribution of machine time (the
resource) among various products (the activities)
is a typical allocation problem
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Sample Linear Programming
• XYZ corporation makes servers. A decision must
be made. How many servers should be
produced next month in the Boston plant? Two
types of servers are considered: S-7 requires 30
hours of labor and $10,000 in materials; S-8
requires 50 hours of labor and $15,000 in
materials. The profit contribution of S-7 is $8,000
whereas that of S-8 is $12,000. The plant has a
capacity of 20,000 hours per month while the
material budget is $8,000,000 per month.
Marketing requires that at least 100 units of S-8
be produced.
• Problem: How many units of S-7 and S-8
should be produced?
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The Model
• Decision Variables: X = units of S-7 to be
produced; Y = units of S-8.
• Result Variable: The total profit. The objective is
to maximize total profit.
• Objective function:
– Total Profit = 8,000X + 12,000Y
• Constraints:
– Labor Constraint: 30X + 50Y <= 20,000 (in
hours)
– Budget Constraint: 10,000X + 15,000Y <=
8,000,000 (in dollars)
– Marketing Requirement: X >= 100 (in units).
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Optimization Techniques
• Computer algorithms and programs are readily
available to handle many problems of this class.
• The major problem is to construct the model
correctly.
• Reference other books on Optimization,
Mathematical Programming, or Operations
Research, or Management Science for a further
discussion of these models and their application.
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Statistical Decision Theory
• Decision Theory provides a rational framework
for choosing between alternative courses of
action when the consequences resulting from
choice are imperfectly known.
• The necessity of making decisions in the face of
uncertainty is an integral part of our lives.
• The theory provides techniques for
mathematically evaluating potential outcomes of
alternative actions in a given decision situation.
• In all cases, the decision-Maker has an objective
(e.g. maximize profit).
• Two methods: Payoff Matrix and Decision Tree.
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Statistical Decision Theory:
Payoff Matrix
• The payoff matrix consists of rows for the
alternatives or strategies available and columns
for the conditions that affect the outcomes
• Each cell contains the payoff (the consequences)
in dollars if that strategy is chosen and that state
occurs
• If it is known with certainty which state will
prevail, then choose the strategy that has the
highest payoff for that state
• This is simply the strategy of maximizing
expected utility.
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General Payoff Matrix
States of Nature
Strategies
n1
n2
n3
n4
S1
S2
S3
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Example 1: The Anniversary
Problem
You are suddenly driving home from work in the evening
when you suddenly recall that your wedding anniversary
comes about this time of year. In fact, it seems quite probable,
(but not certain), that it is today. You can still stop at
the local florist and buy a dozen roses, or you may go home
empty-handed and hope the anniversary lies in the future.
What do you do?
Possible Outcomes (States of Nature)
Decision
Alternatives
(Strategies)
Buy Flowers
Do Not
Buy Flowers
It IS Your
Anniversary
DOMESTIC
BLISS
SPOUSE IN TEARS
AND
YOU IN DOGHOUSE
It IS NOT Your
Anniversary
SPOUSE SUSPICIOUS
AND YOU ARE OUT
$50
STATUS QUO
Anniversary Problem Payoff Matrix
Decision Tree for Anniversary Problem
DOMESTIC
BLISS
Anniversary
Buy Flowers
NOT
Anniversary
$50 LOSS AND
SUSPICIOUS WIFE
DOGHOUSE
Do Not
Buy Flowers
Decision Point
Anniversary
NOT
Anniversary
STATUS QUO
Resolution of
Uncertainty
Example 2: Fast Service
Restaurant
An entrepreneur is deciding among three alternatives
for a fast-service restaurant that she owns:
(1) leave as is;
(2) refurbish it to improve layout
(3) or re-build completely to add capacity and improve layout.
There are three significant, independent conditions (assume
only one can occur) that affect the possible profit (payoff)
from each alternative strategies. These conditions are:
(1) a competitor may open on a nearby property
(2) a proposed highway re-routing will change the
traffic passing by
(3) conditions will stay approximately the same as they are.
What should the entrepreneur do?
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Payoff Matrix
(in Thousands of $)
Same - 0.5
Strategies
New
Competitor
0.20
Highway
Rerouting
0.30
Do
Nothing
2
0
-1
Refurbish
4
3
-3
7
2
-10
Rebuild
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Analysis with Knowledge
• If we assume conditions remain the same,
Rebuild is the best strategy. (Payoff $7,000).
• If probabilities are assigned, using a criteria of
maximizing expected value:
– Do Nothing: (0.5)(2) + (.2)(0) + (0.3)(-1) = 0.7 or $700
– Refurbish: (0.5)(4) + (0.2)(3) + (0.3)(-3) = 1.70 or
$1,700
– Rebuild: (0.5)(7) + (0.2)(2) + (0.3)(-10) = 0.90 0r $900
– Therefore, refurbishment is the best choice.
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Statistical Decision Theory:
Imperfect Knowledge of
Consequences
• If there is uncertainty about the probabilities of
the various conditions, then you can use one of
several rules for deciding:
– minimize regret -select strategy which minimize the
sum of regrets for the strategy
– maximin - Select strategy which has highest payoff
if the worst state of nature occurs (pessimistic).
– maximax - Select strategy which has highest payoff
if most favorable state of nature occurs (optimistic).
• Each one of these rules has been criticized in the
literature. They have disadvantages if applied as
a general decision rule. You must decide if the
rule is appropriate for the situation.
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Regret Definition
• The regrets are the differences between the best
payoff for a state of nature and the other
outcomes.
• To compute a matrix of regret, subtract the value
in each entry in a column from the highest value
in the column.
• Sum the rows to compute the regrets for each
action (assuming the payoff matrix has columns
for states and rows to show actions)
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Analysis with Imperfect
Knowledge
• Minimize Regret:
– Do Nothing:
– Refurbish:
– Rebuild:
5
3
0
3
0
1
0
2
9
=8
=5
= 10
– Therefore, action which minimizes regrets is to
Refurbish.
• Maximin Rule: Identify state with worst payoff,
and choose strategy with least unfavorable
payoff. Therefore, choose to Do Nothing.
• Maximax Rule: Rebuild
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Statistical Decision Theory
• When decisions must be made under uncertainty,
the emphasis is on Bayesian decision theory
which recommends maximizing subjective
expected utility.
• Bayesian decision theory provides a framework
in which all available information is used to
deduce which of the decision alternatives is best
according to the decision maker’s
preferences.
• Distinguish between a good decision and a good
outcome
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The Concept of Utility
• Not all outcomes can be compared in terms of
dollars. Dollars and other measures work well in
a narrow range of values, but not at extremes
(e.g. overtime pay).
• Here money is used as a substitute measure of
the outcome’s utility.
• Whereas utility may be linear in a certain range in
comparison to money, it generally is not under all
ranges.
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Utility vs. Money
UTILITY
MONEY
The Linear Assumption of Money for Utility in this Narrow Range
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Indifference Curves
• Any two possible outcomes can be compared,
and generally one can say which one is
preferred.
• In some cases they may be equally desirable, in
which case you are indifferent.
• Example: You may prefer a week’s vacation in
Florida rather than paid double time to work a
week extra.
• There is a tradeoff here between two valueproperties. (e.g. leisure time vs. money).
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Indifference Curves
Money
I3
I2
I1
Leisure Time
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Other Alternative Selection
Techniques
• Ranking, Weighting, or Elimination by aspects often used to evaluate competitive bids.
• Game Theory (for conflict bargaining) - when one
decision unit (player) gains, the other loses.
• Classical Statistical Inference
– sampling
– probability distributions
– regression and correlation analysis
– testing of hypotheses
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Rational Choice and the Framing
of Problems
• Alternative descriptions of a problem often give
rise to different preferences.
• Example: Consider the following statistical
information provided on two alternative
treatments of lung cancer. The same statistics
are presented in terms of survival rates and in
terms of mortality rates to two groups of
respondents.
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Rational Choice and the Framing
of Problems - (contd.)
• Example: Survival Frame:
– Surgery: Of 100 people having surgery, 90 live
through the post-operative period, 68 are alive
at the end of the first year and 34 are alive at
the end of five years.
– Radiation Therapy: Of 100 people having
radiation therapy all live through the treatment,
77 are alive at the end of one year, and 22 are
alive at the end of five years.
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Rational Choice and the Framing
of Problems - (contd.)
• Example: Mortality Frame:
– Surgery: Of 100 people having surgery 10 die
during surgery or the post-operative period, 32
dies by the end of the first year, and 66 die by
the end of five years.
– Radiation Therapy: Of the 100 people having
radiation therapy, none die during the
treatment, 23 die by the end of year one, and
78 die by the end of five years.
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Framing Example: Results
• The inconsequential difference in framing
produced a marked effect. The overall
percentage of respondents who favored radiation
therapy rose from 18% in the survival frame to
44% in the mortality frame.
• Radiation Therapy appears better than surgery
when stated as a reduction of the risk of
immediate death from 10% to 0%, rather than as
an increase from 90% to 100% in rate of survival.
• Framing effect was similar for physicians,
business students, and a group of clinic patients.
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What do We learn from Framing?
• Normative models of choice, which assume
invariance of preferences, can not provide an
adequate descriptive account of choice behavior.
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Sample Planning Models
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Planning Models
• A planning model is a method for structuring,
manipulating, and communicating future plans.
• Simple Profit Model:
–
–
–
–
–
Sales = input variable
Cost of Sales = 0.4 x sales
Gross Margin = sales - cost of sales
Operating expenses = input variable
Profit before taxes = gross margin - operating
expenses
– Taxes = 0.48 x profit before taxes
– Net Profit = profit before taxes - taxes
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Sample Profit Plan
Sales
Less cost of Sales
$100,000
( 40,000)
Gross Profit
$60,000
Less Operating Expenses: ( 52,000)
Profit Before Taxes:
Less Taxes
Net Profit
$ 8,000
( 3,840)
$4,160
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Use of Planning Models
• Model building can begin with simple models
calling for inputs of major, high-level items.
• Subsequent model development can expand the
details f the model to calculate the high-level
items from more basic input. Example:
• Selling Expense = 0.10 x sales
– Advertising expenses = 0.05 x sales
– Interest expense = 0.10 x average long-term debt +
0.12 x average short term loans
– bad debt expense = 0.01 x accounts receivable
balance at beginning of period
– administrative expense = input variable
– operating expense = selling + advertising + interest
+ bad debt + administrative expense.
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Use of Planning Models - contd.
• These models are are characteristic of
Managerial Accounting.
• Individual terms can be estimated using
techniques of statistics based on past history.
• Planning Models provide opportunities for “what
if” scenerios.
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Summary and Relevance of
Decision-Making Concepts for
Information Systems Design
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How Information Systems Might Help Counteract
Common Flaws in Decision Making
POOR FRAMING
Description: Allowing a decision to be influenced
excessively by the language used for describing
the decision
How an information system might help: Provide
information encouraging different ways to think
about the definition of the issue
RECENCY EFFECTS
Description: Giving undue weight to the most
recent information
How an information system might help: Provide
information showing how the most recent
information might not be representative
How Information Systems Might Help
Counteract Common Flaws in Decision Making
PRIMACY EFFECTS
Description: Giving undue weight to the first
information received
How an information system might help: Show
how some information is inconsistent with the first
information received
POOR PROBABILITY ESTIMATION
Description: Overestimating the probability of
familiar or dramatic events; underestimating the
probability of negative events
How an information system might help: Make it
easier to estimate probabilities based on
pertinent data
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How Information Systems Might Help Counteract
Common Flaws in Decision Making
OVERCONFIDENCE
Description: Believing too strongly in one’s own
knowledge
How an information system might help: Provide
counterexamples or models showing that other
conclusions might also make sense
ESCALATION PHENOMENA
Description: Unwillingness to abandon courses
of action decided upon previously
How an information system might help: Provide
information or models showing how the current
approach might give poor results
How Information Systems Might Help
Counteract Common Flaws in Decision Making
ASSOCIATION BIAS
Description: Reusing strategies that were
successful in the past, regardless of whether they
fit the current situation
How an information system might help: Provide
information showing how the current situation
differs from past situatioins
GROUPTHINK
Description: Bowing to group consensus and
cohesiveness instead of bringing out unpopular
bias
How an information system might help: Provide
information inconsistent with the current
consensus and prove its relevance
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Support for the Intelligence
Phase
• The search process involves an examination of
data both in predefined and in ad hoc ways.
Information systems support should provide both
capabilities.
• Scanning of internal and external databases for
opportunities and problems.
• Filtering should be used to avoid information
overload.
• Information system should scan all data and
trigger a request for human examination of
situations apparently calling for attention (e.g.
examination of key indicators and critical success
factors).
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Support for the Intelligence
Phase - contd.
• Routine and ad-hoc reports can aid in the
intelligence phase (e.g. summarization,
comparison, prediction, confirmation).
• Various models should be included in the
scanning and report layouts (e.g. historical,
planning, etc.).
• Either the system of the organization should
provide communication channels for perceived
problems to be moved up the organization until
they can be acted upon.
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Support for the Design Phase
• The information system should contain decision
models to process data and generate alternative
solutions.
• It should assist with checklists, templates of
decision processes, scenarios, etc.
• The models should assist in analyzing the
alternatives.
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Support for the Choice Phase
• An information system is most effective if the
results of design are presented in a decisionimpelling format.
• Presentation of alternatives. Use of appropriate
methods depending on presence of certainty,
risk, uncertainty.
• When the choice is made, the role of the system
changes to the collection of data for further
feedback and assessment.
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Relevance for Information
System Design
• Provide support for decision-makers in semistructured and unstructured situations by bringing
together human judgment and computerized
information. Structured problems are easily
handled by methods of management science and
operations research.
• Provide support for model development, whether
formal models or mental models.
• Provide tailorability to style of the decision-maker.
• Recognize that organizations place constraints
on the decision-maker. Rationality is not always
an option.
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Relevance for Information
System Design - (contd.)
• Recognize that uncertainty is a part of life.
• Recognize that stress is a part of life.
• Looks at methods for sensitivity analysis: “whatif analysis” and “goal-seeking analysis”, and
other methods for deciding among alternatives.
• Promote organizational learning.
• Provide knowledge components for very difficult
problems.
• Specific examples will be discussed when we
look at specific examples for compute-based
support of decision-making.
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Final Note
• The previous discussion of models has
concentrated on individual decision making.
• Many complex decisions in organizations are
made by groups of people.
• Groups can often produce inadequate solutions
to problems - “A camel is a horse designed by a
committee”.
• We will look at models for group decision making
and the development of group decision support
systems later.
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