Management Support Systems and Decision-Making

Management Support Systems and Decision-Making

Supporting Managers with Information Systems

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.

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

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

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

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.

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

Management Roles Interpersonal Roles: Figurehead, Leader, Liaison
Informational Roles: Monitor, Disseminator, Spokesman Decisional Roles: Entrepreneur, Disturbance Handler, Resource Allocator, Negotiator

Mintzberg: The Nature of Managerial Work
Formal Authority and Status Interpersonal Roles Informational Roles Decisional Roles

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.

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.

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.

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.

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%)

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.

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.

Simon’s Model Flowchart of Decision Process
Intelligence Design Choice

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

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.

Summary: Intelligence Phase
Intelligence activities result in dissatisfaction with the current state or identification of potential rewards from a new state.

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

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)

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:

Alter Textbook Model Decision-making is represented as a problem-solving 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.

An Alternative Model: Rubenstein and Haberstroh’s
1. Recognition of problem or need for decision 2. Analysis and statement of alternatives 3. Choice among the alternatives 4. Communication and implementation 5. Follow-up and feedback of results

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

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..

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

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

The role of models in decision-making
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.

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.

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.

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 two-dimensional 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

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.

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.

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

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.

III. Alternative Generation
A significant part of the process of decision-making 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.

Decision-Making Concepts

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

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

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

Decision-Making Under Conditions of Certainty: Rationality
If the outcomes are known and the values of the outcomes are certain, the task of the decision-maker 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

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

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

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

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

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.

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 pre-specified by a set of rules or decision procedures Non-programmed Decisions - those that do not have any pre-established decision rule or procedures

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.

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.

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?

Management Support Systems and Decision-Making Part II

Views or Models of Individual and Organizational Decision-Making

Views or Models of Decision-Making
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

I - 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.

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 (I.e. what you should do), and has little descriptive support in true form. It is impractical and over-idealized. Influenced all other views of decision-making.

II. -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.

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.

Satisficing View - 3 Simon also recognized the relationship between problem-solving strategies and the nature of the task, I.e. different tasks require different approaches. This is apparent in his characterization of programmed and non-programmed decisions. The ‘Rational Manager’ or ‘Economic Man’ viewpoint thinks basically all problems can use the same strategy. Interestingly, some researchers look at decision-making in terms of personality or ‘cognitive style’. We discuss this as a separate viewpoint.

Sidebar: 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.

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

III. - 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.

Organizational Process View - 2
Organizational decision-making is essentially a bargaining process among coalitions that 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.

Organizational Process View - 3
Question – Can you provide an example in an organizational setting that supports this viewpoint of decision-making? This viewpoint is significantly influenced by separate functional areas of the participants. For example, accounting and marketing participants will view a problem in terms of their own functional area. If a functional area has little to do with a decision-making situation, there may be little interest from that functional area.

IV. - 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.

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”.

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.

IV. The Individual Differences View
This view focuses on the individual decision-maker and his/her personalized strategies and abilities or ‘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. The outcome of the decision is substantially influenced by these characteristics, and any analytic aid (I.e. a DSS) must be consistent with the user’s style. Such as DSS tool could be very valuable in complementing or extending the user’s style. However, DSS incompatibility with the user’s problem-solving habits, strategies, and abilities will normally result in the DSS not being used.

Individual Differences - 2
Personal rationality is subjective, and behavior is determined by the manner in which individuals process information. 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).

Contrast: Analytic, systematic, methodological approach vs. Intuitive, divergent,more global strategy To problem solving. Systematic thinkers tend to approach a problem by structuring it inn terms of some method which when followed through, leads to a likely solution. This is really what model building is: making casual relationships explicit, articulating formal criteria, and then sequences of analysis. Intuitive thinkers generally avoid committing themselves in this way. Their strategy is often one of hypothesis testing and trial-and-error.

Peter Keen notes the Intuitive Strategy should be respected: “ Each mode of evaluation has advantages and risks. In tasks such as production management, the Systematic thinker can develop a method or procedure that utilizes all his experience and that economizes on effort. An intuitive thinker in such a task may reinvent the wheel each time he deals with a particular problem. However, the Intuitive thinker is better able to approach ill-structured problems where the volume of data, the criteria for a solution or the nature of the problem itself do not allow the use of any pre-determined method.”

Individuals validate information and perceive reality in different ways: sensing vs. intuition; thinking vs. feeling; judging vs. perceiving. What is information for one type definitely will not be information for another. The job of a DSS designer is not to force all types of individuals to conform to one system, but to give each type the kind of information he is psychologically attuned to and will use most effectively.

Relate this viewpoint back to Mintzberg’s study of Managerial work. He said the work was characterized by: Brevity – of time available for one task Fragmentation – tasks often addressed in pieces over time Variety - of problems What does this tell us? Simply providing access to raw data for managers in not enough. Designers should not assume their users are like themselves.

Cognitive Style Dimensions
Left brain words analytic sequential active realistic planned Right brain images intuitive simultaneous receptive imaginative impulsive

Sidebar: Empirical Research
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).

VI. - 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.

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.

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.

Individual Aspects of Decision-Making

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

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

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

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

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.

Behavioral Aspects of Organizational Decision-Making

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

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

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”

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

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.

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

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

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

Decision Making Under Psychological Stress

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

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.

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?

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

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.

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.

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.

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

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.

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.

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.

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.

Deciding Among Alternatives

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.

Optimization Techniques Under Certainty
All alternatives and their outcomes are known. The computational problem is to choose which one is optimal for a particular objective function Use optimization techniques systems of equations linear programming integer programming dynamic programming queuing models inventory models, etc. Capital budgeting analysis Break-even Analysis

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

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?

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).

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.

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.

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, perhaps 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.

General Payoff Matrix States of Nature Strategies n1 n2 n3 n4 S1 S2 S3

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?

Anniversary Problem Payoff Matrix
Possible Outcomes (States of Nature) Decision Alternatives (Strategies) It IS Your Anniversary It IS NOT Your Anniversary Buy Flowers SPOUSE SUSPICIOUS AND YOU ARE OUT $50 DOMESTIC BLISS Do Not Buy Flowers SPOUSE IN TEARS AND YOU IN DOGHOUSE STATUS QUO Anniversary Problem Payoff Matrix

$50 LOSS AND SUSPICIOUS WIFE
Decision Tree for Anniversary Problem DOMESTIC BLISS Anniversary Buy Flowers NOT Anniversary $50 LOSS AND SUSPICIOUS WIFE DOGHOUSE Anniversary Do Not Buy Flowers NOT Anniversary Decision Point 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?

Payoff Matrix (in Thousands of $)
New Competitor 0.20 Highway Rerouting 0.30 Same - 0.5 Strategies Do Nothing 2 -1 Refurbish 4 -3 3 7 2 Rebuild -10

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) = r $900 Therefore, refurbishment is the best choice. Remember here that the probabilities of various conditions or states of nature are assumed to be known with reasonable exactness in the above example.

Statistical Decision Theory: Imperfect Knowledge of Consequences
What is the decision maker is very uncertain about the probabilities of the various conditions that may occur. There are some rules that can be used for deciding among the alternatives, based on the individual preferences (‘cognitive style’) of the decision maker. Define: Regrets=the differences between the best payoff for a state of nature,and the other outcomes. Consider three separate strategies: Minimize regret Maximin Rule MaxiMax Rule

Statistical Decision Theory: Imperfect Knowledge of Consequences
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.

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 or strategy (assuming the payoff matrix has columns for states and rows to show strategies)

Sample Calculation of Regret Matrix
N1 N2 N3 S1 2 -1 Original Payoff Matrix S2 3 -3 4 S3 7 2 -10 N1 N2 N3 S1 5 3 = 8 Regret Matrix S2 2 3 = 5 = 10 S3 1 9

Analysis with Imperfect Knowledge
Minimize Regret: The action which minimizes regret is REFURBISH. Do Nothing: = 8 Refurbish: = 5 Rebuild: = 10 This assumes equal probabilities for outcomes. An expected regret for each strategy can also be computed by multiplying each regret by its probability. N1-0.5 N2-0.2 N3-0.3 S1 2.5 .6 = 3.1 Expected Regret Matrix S2 .6 1.5 = 2.1 S3 .2 2.7 = 2.9

Original Payoff Matrix (again) (in Thousands of $)
New Competitor 0.20 Highway Rerouting 0.30 Same - 0.5 Strategies Do Nothing 2 -1 Refurbish 4 -3 3 7 2 Rebuild -10

Analysis with Imperfect Knowledge - 2
Maximin Rule – Select the strategy which will have the highest utility payoff (max) if the worst state of nature (min) occurs. In other words, identify the state of nature with the worst payoff and choose the strategy with the least unfavorable payoff, given that state. Essentially a pessimistic view, this results in choosing to do nothing because the worst case occurs with rerouting and do nothing is the best strategy for this worst case.

Analysis with Imperfect Knowledge - 3
Maximax Rule – Select the strategy or alternative which provides greatest utility payoff (max) if the most favorable state of nature (max) occurs. In other words, identify the state of nature with the best payoff and choose the strategy with the best payoff, given that state. Essentially an optimistic view, this results in choosing the strategy of rebuilding because the payoff of 7 is best.

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

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.

Utility vs. Money UTILITY MONEY
The Linear Assumption of Money for Utility in this Narrow Range

Utility vs. Money - 2 For example, the utility of getting a fairly large sum is larger than the utility computed from a set of small amounts. In other words, $1=1utile, however, $100,000 in one payment is larger than 100,000 utiles for $1. After rising steeply, the curve flattens out because the utility of substantially more money is not great; For the average individual, $20 million has not much more utility than $10 million. The loss side of the curve behaves in an opposite fashion. A large loss has significantly greater negative utility than merely the sum of disutilities for smaller losses.

Utility vs. Money - 3 This helps explain attitudes toward insurance. Assume the following payoff matrix for an insurance problem: FIRE NO FIRE E/V (0.003) (0.997) INSURANCE -$ $ $240 NO INSURANCE - $50, $150 Looking at strictly dollars, the rational person assumes insurance is not a good value. However, the insurance loss of $50,000 may have a disasterous consequence (say -150,000 utiles) while the insurance has –240 utiles.

Utility vs. Money - 4 Consider the same payoff matrix measuring utiles instead of dollars: FIRE NO FIRE E/V (0.003) (0.997) INSURANCE ut ut ut NO INSURANCE -150,000 ut ut This example looks at only one value property (“money”). In many cases there is more than one value property and various combinations of the properties yield the same utility. These differences can be represented by indifference curves.

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 value-properties. (e.g. leisure time vs. money).

Indifference Curves Money I3 I2 I1 Leisure Time

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

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.

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.

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.

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.

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.

Sample Planning Models

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

Sample Profit Plan Sales $100,000 Less cost of Sales ( 40,000)
Gross Profit $60,000 Less Operating Expenses: ( 52,000) Profit Before Taxes: $ 8,000 Less Taxes ( 3,840) Net Profit $4,160

Use of Planning Models Selling Expense = 0.10 x sales
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 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.

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.

Summary and Relevance of Decision-Making Concepts for Information Systems Design

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

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

Description: Believing too strongly in one’s own knowledge
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

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

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).

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.

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.

Support for the Choice Phase
An information system is most effective if the results of design are presented in a decision-impelling 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.

Relevance for Information System Design
Provide support for decision-makers in semi-structured 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.

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: “what-if 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.

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.

Management Support Systems and Decision-Making

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