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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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slide1

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

Prof. Raimo P. Hämäläinen

Systems Analysis Laboratory

Helsinki University of Technology

the or world project
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
orworld
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
sal e learning resources in decision making
SAL e-learning resources in decision making

Value Tree Analysis

Group Decisions and Voting

Negotiation Analysis

Uncertainty & Risk

internet standards
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)
split of content structure and representation in xml
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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reotiuoert dfgdf dfg fg fgd

eteroituertfg fgdg fgryko

ertuertiueryituyertret

LMML

LOM

XSL

XML

Visual representation

HTML

PDF

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ljflkj

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learning objects
Content module 2Learning 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
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
system architecture
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

learning paths and modules
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
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
theory
Value Tree AnalysisTheory

introduces concepts and theory

  • XML documents
  • divided in sections
  • colourful graphics and animations
  • pages in HTML format
cases
Value Tree AnalysisCases

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
quizzes
Value Tree AnalysisQuizzes

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
video clips
Value Tree AnalysisVideo 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
learning material on value tree analysis
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
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

cases21
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

cases22
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
slide23
Assignments

Learning

Paths

Videos

Cases

Theory

Quizzes

slide25
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

video clips36
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
student evaluation
Value Tree AnalysisStudent 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
summary of student evaluation
Value Tree AnalysisSummary 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

learning material on group decisions and voting
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
theory45
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

slide46
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
assignment
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
slide48
Group Decisions and Voting
  • Opinions-Online.vote:
  • voting
  • surveys
  • group decisions
  • advanced voting rules
learning material on risk and uncertainty
Learning material onRisk and Uncertainty
  • Theory slides only
  • Used in decision making course at HUT
theory50
Group Decisions and VotingTheory

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

learning material on negotiation analysis
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
theory53
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

cases55
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

quizzes57
Negotiation Analysis

Value Tree Analysis

Quizzes

Videos

Assignments

Cases

Theory

Quizzes
  • 4-6 questions per theory section
  • the student is asked to
  • interpret graphs
slide58
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

joint gains negotiation
Negotiation AnalysisJoint 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
joint gains negotiation support system
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

video clips61
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
student evaluation66
Negotiation AnalysisStudent 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
summary of student evaluation69
Negotiation AnalysisSummary 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
work package status completed
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
dissemination
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
web sites
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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