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eLearning Decision Making eLearning sites on: Multiple Criteria Decision Analysis Decision Making Under Uncertainty Negotiation Analysis Prof. Raimo P. Hämäläinen Systems Analysis Laboratory Helsinki University of Technology The OR-World project

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eLearning Decision MakingeLearning sites on:Multiple Criteria Decision AnalysisDecision Making Under UncertaintyNegotiation Analysis

Prof. Raimo P. Hämäläinen

Systems Analysis Laboratory

Helsinki University of Technology


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The OR-World project

  • Funded by the European Commission, IST Programme

  • Partners form industry and university

  • University of Paderborn (coordinator)

  • Helsinki University of Technology

  • Delft University of Technology

  • Lufthansa Systems Berlin

  • Regioworld

  • Dual-Zentrum


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ORWorld

  • WWW based framework for sharing learning material for Operations Research

  • Interdisciplinary subjectMethods, applications, case studies

  • Well suited for hypermediaVisualization, simulation, animation

  • Joint effort to develop a modular study programme

  • To be used in universities and companies worldwide


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SAL e-learning resources in decision making

Value Tree Analysis

Group Decisions and Voting

Negotiation Analysis

Uncertainty & Risk


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Internet standards

  • Today’s standard HTML is unstructured

    • No clear separation between

      • Content,

      • Structure,

      • Representation

    • Reuse of the existing material problematic

      • Multilingual versions

    • No inherent possibility to add specific metadata

    • Need for XML (extensible markup language)


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Split of content, structureand representation in XML

Structure

Contents

Instructions

Complex, confusing decision problems with multiple objectives have been

made since the start of the civilisation.

The history of the decision analysis is

not that long, however. In 1730s

Daniel Bernoulli (1738) first used the

concept of utility when explaining the

evaluation of a particular uncertain gable known as St Petersburg paradox.

...

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dflglkdjdgjlk jdlkgj

reotiuoert dfgdf dfg fg fgd

eteroituertfg fgdg fgryko

ertuertiueryituyertret

LMML

LOM

XSL

XML

Visual representation

HTML

PDF

ljflkj

sdlkfjlsd

dksjflkj

sdflksdjf

ljflkj

sdlkfjlsd

dksjflkj

sdflksdjf


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Content module 2

Learning objects

  • Wrapping material on a higher level

  • Reusable components formulated in XML

  • Classification by meta tags

HypermediaNetwork

Thematic meta structure

Content module 1

XSL

Learning element

TextPDFHTML

Media Element

-Text, animation,simulation, video, audio

Course


Xml elements l.jpg

CASE

1

1

1…*

MODELLING

PROBLEM

SYNTHESIS

1…*

1...*

Content elements

Content elements

1…*

1…*

?

1…*

1…*

*

ANALYSIS

Editing elements

Editing elements

STRUCTURING

METHOD

Partition elements

1…*

1…*

Editing elements

Partition elements

Partition elements

1…*

1…*

1…*

1…*

Editing elements

Partition elements

Editing elements

1…*

*

*

Editing elements

OUTPUT

INPUT

1…*

1…*

Partition elements

Partition elements

1…*

1…*

Editing elements

Editing elements

XML elements


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System architecture

Client Web browser

HUT SAL server

Software:

Web-HIPRE

Prime Decisions

Joint Gains

Opinions Online (voting version)

Self Assessment & Grading

Quiz Star

Q&A Tool set

OR-World server

Learning material

Evaluations:

Opinions Online


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Learning

Paths

Introduction to Value Tree Analysis

Quizzes

Videos

Assignments

Theory

Cases

Evaluation

Module 2

Module 3

Value Tree Analysis

Learning paths and modules

Learning path: guided route through the learning material

Learning module: represents 2-4 h of traditional lectures and exercises


Learning modules l.jpg

Evaluation

Value Tree Analysis

Learning

Paths

Quizzes

Videos

Assignments

Cases

Theory

Learning modules

Introduction to Value Tree Analysis

Module 2

Module 3

  • motivation, detailed instructions, 2 to 4 hour sessions

  • Theory

  • HTML

  • pages

  • Case

  • slide shows

  • video clips

  • Web software

  • Web-HIPRE

  • video clips

  • Assignments

  • online quizzes

  • software tasks

  • report templates

  • Evaluation

  • Opinions

  • Online


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Value Tree Analysis

Theory

introduces concepts and theory

  • XML documents

  • divided in sections

  • colourful graphics and animations

  • pages in HTML format


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Value Tree Analysis

Cases

illustrates theoretical aspects, complements theory

alternative learning route, learning by doing

  • Slide presentations

  • summary of theory

  • case specific material, problem description, methods, analysis,…

  • in Power Point and HTML formats

  • tests with XML + XSLT visualisation

  • Video clips

  • how to apply and use Web-HIPRE, Prime Decisions

  • an easy way to learn software use

  • help in practical issues


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Value Tree Analysis

Quizzes

for revising, self assessment, online exams

  • Online Quizzes in Quiz Star

  • multiple choice, true/false, short answer questions

  • grouped in sections

  • correct answers or references to MCDA material

  • results available for the instructor


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Value Tree Analysis

Video clips

  • Recorded software use with voice explanations (1-4min)

  • Screen capturing with Camtasia

  • AVI format for video players

    • e.g. Windows Media Player, RealPlayer

  • GIF format for common browsers - no sound


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Intro

Quiz 1

Step 1

Assignments

Quizzes

Theory

Cases

Theoretical

foundations

Step 2

Quiz 2

Videos

Step 3

Quiz 3

Problem

structuring

Quiz 4

Preference

elicitation

Step 4

Learning material onValue Tree Analysis

  • Divided in sections

  • Theory and cases closely linked, but independent entities



Theory19 l.jpg

Value tree analysis in brief

Bullet points to reduce reading time

Simple animations, figures

Links to case part

Quizzes

Videos

Assignments

Cases

Theory

Learning

Paths

Intro

Theoretical foundations

Problem structuring

Preference elicitation

Sensitivity analysis

Behavioural issues

Communicating the results

Group decision making

Software

Value Tree Analysis

Theory

Systems Analysis Laboratory

Helsinki University of Technology


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Quizzes

Videos

Assignments

Cases

Theory

Learning

Paths

Case X

Value Tree Analysis

Cases

  • Job selection case

  • basics of value tree analysis

  • how to use Web-HIPRE

  • Car selection case

  • imprecise preference statements, interval value trees

  • basics of Prime Decisions software

  • Family selecting a car

  • group decision-making with Web-HIPRE

  • weighted arithmetic mean method

Theory

Evaluation

Assignments

Intro

Theoreticalfoundations

Problemstructuring

Preferenceelicitation


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Value Tree Analysis

Family selecting a car

Cases

Job selection case

  • group decision making with Web-HIPRE

  • weighted arithmetic mean method

  • basics of value tree analysis

  • how to use Web-HIPRE

Car selection case

Money versus design

  • imprecise preference statements, interval value trees

  • basics of PRIME Decisions


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Assignments

Learning

Paths

Videos

Cases

Theory

Quizzes


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Assignments

Learning

Paths

Quizzes

Videos

Cases

Theory

  • Report templates

  • detailed instructions in a word document

  • to be returned in printed format

Value Tree Analysis

Value Tree Analysis

testing the knowledge on the subject, learning by doing, individual and group reports

  • Software use

  • value tree analysis and group decisions with Web-HIPRE

Systems Analysis Laboratory

Helsinki University of Technology

eLearning / MCDA





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Value Tree Analysis

Learning material


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Value Tree Analysis

Working with Web-HIPRE


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Learning

Paths

Assignments

Quizzes

Videos

Cases

Theory

Videos

Working with Web-HIPRE

Structuring a value tree

Entering consequences of ...

Assessing the form of value...

Direct rating

SMART

SMART

SWING

AHP

Viewing the results

Sensitivity analysis

Group decision making

PRIME method

Value Tree Analysis

Video clips


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Value Tree Analysis

Student evaluation

  • Value Tree learning module

  • Helsinki University of Technology

    • 59 students

    • mainly second and third year

  • The University of Paderborn

    • 95 students

    • mainly first and second year

  • Assisted and non assisted groups

  • Opinions-Online


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Value Tree Analysis

Summary of student evaluation

  • Students enjoyed the session

  • Only little difficulties

  • They would like to work in similar environments

  • Recommend the session to fellow students

  • No major gender differences

Interactive results available at:

http://www.orworld.hut.fi/mcdm/Learning-modules/Short-intro/evaluation-results.htm


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Group Decisions and Voting module

Evaluation

Assignment

Quiz

Theory

Learning material on Group Decisions and Voting

  • One learning module

  • All material included in the module


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Group Decisions and Voting module

Evaluation

Quiz

Assignment

Theory

Group Decisions and Voting

Theory

Group characteristics

Brainstorming

Nominal group technique

Delphi technique

Voting procedures

Value aggregation

Slide show in HTML + GIF format


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Group Decisions and Voting

Group Decisions and Voting module

Evaluation

Quiz

Assignment

Theory

Quiz

for revising, self assessment, online exams

  • Group Decisions and Voting quiz

  • in Quiz Star server

  • 10 multiple choice and 3 true/false questions

  • correct answers or references to MCDA material

  • results available for the instructor


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Group Decisions and Voting

Group Decisions and Voting module

Evaluation

Quiz

Assignment

Theory

Assignment

testing the knowledge on the subject, learning by doing, distributed decision making team, voting online over the Web

  • Software use

  • voting with Opinions-Online.vote

  • precompleted voting template

  • two voting rounds

  • Report template

  • detailed instructions in a word document

  • analysis of the voting results

  • to be returned in printed format


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Group Decisions and Voting

  • Opinions-Online.vote:

  • voting

  • surveys

  • group decisions

  • advanced voting rules


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Learning material onRisk and Uncertainty

  • Theory slides only

  • Used in decision making course at HUT


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Group Decisions and Voting

Theory

Slideshow 1

Meanings of uncertainty

Interpretations of probability

Estimation of probabilities

Biases in probability elicitation

Calibration of experts

Updating of probabilities

Slideshow 2

Decision criteria

On the concept of risk

Risk measures

Utility function

Risk attitudes

Stochastic dominance

Decision trees

Influence diagrams



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Learning material onNegotiation Analysis

  • Mathematical modelling approach

  • Game and bargaining theory

  • Cases

    • e-Commerce: Buyer – Seller Negotiations

    • Resource Management: Problem of Commons

  • Joint Gains web software


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Negotiation Analysis

Main concepts in brief

Colourful graphics

Intro

Multiple criteria decision analysis

Game theory

Axiomatic bargaining

Negotiation analysis

Method of improving directions

Value Tree Analysis

Quizzes

Videos

Assignments

Cases

Theory

Theory

Systems Analysis Laboratory

Helsinki University of Technology


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Negotiation Analysis

Value Tree Analysis

Quizzes

Videos

Assignments

Cases

Theory

Cases

  • Buyer – Seller Negotiations

  • basics of a negotiation problem

  • solving a negotiation problem interactively

  • how to use Joint Gains

  • Problem of Commons

  • solving a negotiation problem by

  • value functions

Theory

Evaluation

Assignments

Intro

MCDA

Game THeory

Axiomatic Bargaining


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Negotiation Analysis

Value Tree Analysis

Quizzes

Videos

Assignments

Cases

Theory

Quizzes

  • 4-6 questions per theory section

  • the student is asked to

  • interpret graphs


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Negotiation Analysis

  • Report templates

  • detailed instructions available in MS word and HTML format

  • to be returned electronically

Value Tree Analysis

Quizzes

Videos

Assignments

Cases

Theory

Assignments

testing the knowledge on the subject, learning by doing

  • Theoretical part

  • Software use

  • buyer - seller negotiations with Joint Gains

Systems Analysis Laboratory

Helsinki University of Technology


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Negotiation Analysis

Joint Gains negotiation

  • user can create his own case

  • 2 to N participants (negotiating parties, DM’s)

  • 2 to M continuous decision variables

  • linear inequality constraints

  • participants distributed in the web


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Negotiation Analysis

WWW Browser

SERVER

Mediator software

WWW Browser

WWW Browser

WWW Browser

Joint Gains negotiation support system

Case Administrator

World Wide Web

. . .

Participant 1

Participant N

Participant 2


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Negotiation Analysis

Videos illustrating the use of Joint Gains:

  • Creating a negotiation case

  • Negotiating with Joint Gains

  • Viewing the results

Value Tree Analysis

Quizzes

Videos

Assignments

Cases

Theory

Video clips






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Negotiation Analysis

Student evaluation

  • Introduction to game theory and negotiation learning module

  • Virtual university of Finland: Advanced web course on mathematical modelling

  • Students worked unassisted in Espoo, Tampere, Jyväskylä, Lappeenranta, Oulu and Geneve in one or two person groups

    • 9 groups and 13 students


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Negotiation Analysis

Summary of student evaluation

  • Enjoyed the session even if this module requires advanced skills

  • Willing to work in similar environments

  • Found quizzes and the theory section useful

  • Prefer this format to video lectures


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Work package status - completed

  • Learning modules on

    1. Value Tree Analysis

    2. Group Decisions and Voting

    3. Game Theory and Negotiation

  • Also material on Decision Making under Uncertainty

  • Value tree analysis theory in XML and HTML formats

    • Description of new XML elements

  • Video clips

    • modules 1 and 3

  • Slides in Power Point and GIF + HTML formats

  • Evaluations of the first and third learning module


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Dissemination

  • All modules

    • Permanent use in SAL courses on decision making and computational assignments in applied mathematics + national web course on mathematical modelling

    • OR-World partners

  • Facilitator training in EU Rodos project

  • Other universities in the future

  • Available at www.decisionarium.hut.fi

    • DSS software used by different universities


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Web sites

  • www.dm.hut.fi

    • Decision making resources at Systems Analysis Laboratory

    • Links to all evaluations

  • www.mcda.hut.fi

    • eLearning in Multiple Criteria Decision Analysis

  • www.negotiation.hut.fi

    • eLearning in Negotiation Analysis

  • www.decisionarium.hut.fi

    • Decision support tools and resources at Systems Analysis Laboratory

  • OR-World project site: www.or-world.com


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