decision making: an introduction
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Decision Making: An Introduction. 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. - PowerPoint PPT PresentationTRANSCRIPT
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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
Environment
Alternatives
Criteria
Decision
Problem•Alternatives: possible actions aimed at solving the given problem
•Criteria: measurements of effectiveness of the various alternatives
and correspond to system performances such as oProfitabilityoOverall costoProductivityoQualityoDependabilityoRiskoServiceoFlexibility
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Logical structure of a decision processAlternative options
ConstraintsOperationalTechnicalProceduralLegalSocialPolitical
Feasible options
CriteriaProfitabilityOverall costQualityDependabilityFlexibilityService
Decision
Ruled-out decisions
Excl
usio
nEv
alua
tion
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.
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Real world systems
Systems idealized by assumptions
Model
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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• 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 characteristics
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
Feedback
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Problem identification
Model formulation
Development of algorithms
Implementation and testing
Development of a model
Feedback
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Problem identification
Model formulation
Development of algorithms
Implementation and testing
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
2. Evaluation criteria:o monetary costs and payoffs
o effectiveness and level of service
o quality of products and services
o flexibility of operating conditions
o reliability in achieving objectives
3. Decision variables, eg. production volumes.
4. Numerical parameters, eg. production capacity
5. Mathematical relationship
Development of a model
Feedback
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Problem identification
Model formulation
Development of algorithms
Implementation and testing
o A solution algorithm is identified
o A software tool that incorporates the solution method should be developed or acquired
o Analyst should have thorough knowledge of current solution methods and their characteristics o Assess correctness of data and
parameters
o Model validation by experts:
• plausibility and likelihood of the
conclusions achieved
• consistency of results at extreme
values of parameters
• stability of results with minor changes
in the parameters
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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