Decision Making: An Introduction
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Decision Making
• Decision Making is a process of choosing among two or more alternative
courses of action for the purpose of attaining a goal or goals.
• It is influenced by several major disciplines which are behavioral and
scientific in nature.
• Behavioral disciplines include anthropology, law, philosophy, political
science, psychology, social psychology, and sociology.
• Scientific disciplines include computer science, decision analysis, economics,
engineering, management science/operations research, mathematics and
statistics.
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Logical flow of a decision making/problem solving process
Criteria
Environment
Alternatives
Decision
• Alternatives: possible actions aimed at solving the given problem
Problem
• Criteria: measurements of effectiveness of the various alternatives
and correspond to system performances such as
o
o
o
o
o
o
o
o
Profitability
Overall cost
Productivity
Quality
Dependability
Risk
Service
Flexibility
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Logical structure of a decision process
Constraints
Operational
Technical
Procedural
Legal
Social
Political
Ruled-out decisions
Exclusion
Alternative options
Feasible options
Evaluation
Criteria
Profitability
Overall cost
Quality
Dependability
Flexibility
Service
Decision
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Models
• A main characteristic of a Decision Support System is the inclusion of at
least one model.
• Model is a selective abstraction of a real system designed to analyze and
understand from an abstract point of view the operating behavior of a real
system
• Includes only elements deemed relevant for the purpose of the investigation
being carried out.
Real world systems
Systems
idealized by
assumptions
Model
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Different types of models according to their characteristics
• Iconic: material representation of a real system, whose
behavior is imitated for the purpose of the analysis.
Example: a miniaturized model of a new city neighborhood.
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Different types of models according to their characteristics
• Analogical: imitates real behavior by analogy rather than by
replication.
Example: a wind tunnel built to investigate the aerodynamic
properties of a motor vehicle.
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Different types of models according to their characteristics
• Symbolic: abstract representation of a real system.
It describes the behavior of the system through a series of
symbolic variables, numerical parameters and mathematical
relationship.
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Different types of models according to their probabilistic nature
 Stochastic: some input information represents random events,
characterized by probability distribution.
 Deterministic: all input data are supposed to be known a priori and
with certainty.
o When it is not possible to know the data with absolute certainty,
sensitivity and scenario analyses allow one to test the robustness of
the decisions to variations in the input parameters.
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Different types of models according to their temporal dimension
 Static: considers a given system and related decision-making
process within a single temporal stage.
 Dynamic: considers a given systems through several temporal
stages, corresponding to a sequence of decisions.
o Discrete-time dynamic models observe the status of a system
only at the start or the end of discrete intervals.
o Continuous-time dynamic models consider a continuous
sequence of periods on the time axis.
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Development of a model
Problem identification
Model
formulation
Feedback
Development of algorithms
Implementation and
testing
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Development of a model
Problem identification
Model
formulation
Observed critical symptoms must be analyzed and
interpreted to formulate hypotheses for investigation.
Define a mathematical model to represent the
system.
Important factors:
1. Time horizon
Feedback
2. Evaluation criteria:
o
monetary costs and payoffs
Development of algorithms
o
effectiveness and level of service
o
quality of products and services
o
reliability in achieving objectives
Implementation and
testing o flexibility of operating conditions
3. Decision variables, eg. production volumes.
4. Numerical parameters, eg. production capacity
5. Mathematical relationship
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Development of a model
Problem identification
o A solution algorithm is identified
o A software tool that incorporates the solution
Modelor acquired
method should be developed
formulation
o Analyst should have thorough knowledge of
current solution methods and their characteristics
o Assess correctness of data and
Feedback
parameters
Development of algorithms
o Model validation by experts:
•
Implementation and
testing
plausibility and likelihood of the
conclusions achieved
•
consistency of results at extreme
values of parameters
•
stability of results with minor
changes in the parameters
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Classes of models
 Predictive models: input data used to predict future events/outcomes.
o Regression: a set of independent variables used to predict a continuous
dependent variable value, e.g. salary
o
Classification: a set of independent variables used to predict a discrete
dependent variable value, e.g. approve/not approve.
 Pattern recognition and machine learning models: efficient algorithms that learn
from past observations and derive new rules for the future.
o
Interpretation models: identify regular patterns, express them as understandable
rules and criteria.
o
Prediction models: forecast future value.
o
Supervised learning: target is known, e.g. classification, regression.
o
Unsupervised learning: target does not exist, e.g. clustering.
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Classes of models
 Optimization models: given a set of feasible decisions, identify
the optimal one according to the chosen evaluation criterion
 Different forms of optimization models:
o Linear optimization
o Integer optimization
o Convex optimization
o Network optimization
o Multiple-objective optimization
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Classes of models
 Project management models: a set of interrelated activities carried out in
pursuit of a specific goal, e.g. a new product line. Network models are
often used to represent the component activities of a project and the
precedence relationships among them.
 Risk analysis models: choose among a number of available alternatives,
having uncertain information regarding the effects that these options may
have in the future.
 Waiting line models: to investigate congestion phenomena occurring
when the demand for and the provision of a service are stochastic in
nature.
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In this module ….
 Optimization models
 Predictive models
 Pattern recognition and machine learning models
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References.
 Business Intelligence, C. Vercellis, Wiley, 2009. Chapters 2 and 4.
 Decision Support and Business Intelligence Systems, Pearson International,
8th Ed. Chapter 2.
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