1

Decision Making: An Introduction

2

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.

3

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

4

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.

5

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.

6

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.

7

• 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.

8

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.

9

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.

10

Development of a model

Feedback

11

Problem identification

Model formulation

Development of algorithms

Implementation and testing

Development of a model

Feedback

12

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

13

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.

14

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

15

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.

16

In this module ….

Optimization models

Predictive models

Pattern recognition and machine learning models

17

References.

Business Intelligence, C. Vercellis, Wiley, 2009. Chapters 2 and 4.

Decision Support and Business Intelligence Systems, Pearson International,

8th Ed. Chapter 2.

18