Module 5 Bayesian belief network modelling

BAYESIAN BELIEF NETWORK MODELING

Professor Ockie Bosch

Dr Nam Nguyen

4. IDENTIFY LEVERAGE/ SYSTEMIC

INTERVENTIONS

3. DEVELOP OR REFINE SYSTEMS

MAPS OR MODELS

1. IDENTIFY ISSUES

7. REFLECTION

Environmental

Eco

no

mic So

cial

New Levels of Learning and Performance at Local Level

A Reminder: Where does BBN modeling fit into the

Evolutionary Learning Laboratory for Managing Complex Issues?

5. INTEGRATED

SYSTEMIC

MANAGEMENT

PLAN

2. BUILD

CAPACITY

6. IMPLEMENT

STRATEGIES,

POLICIESStakeholders’

Mental Models

Systems

Structure

Patterns &

Relationships

Cultural Values

Pollution

Temporary

immigration

Empoloyment

opportunity

Land required

for tourism

Naturalbeauty

Availability of

underground water

Fresh water

consumption

Employment

opportunity for local

people

+

+

Socialissues

Infrastruture

& facility

+

+

Number of

tourists

Attractiveness

of Cat Ba Island

+-

-

+

Wealth of

local people

-

+

Waste+

+

+

+

Illegal forest

exploitaion--

Total

population

+

Agricultural

Production

Livingcost

-

+

+

-

+

-

+

+

Conservation and

agricultural land

-

+

-

B2

B1R1

R3

R2

B3

B4

B5

B6

Studentpopulation

Assess toeducation

+

-

-

-

R5

R4

Investment+

+

+

Resident

population

+

+

BAYESIAN NETWORK MODELS

© Professor Ockie Bosch and Dr Nam Nguyen

Useful for Participatory Systems Analysis

Easy to construct for non-modellers - Visual

Ideal mechanism for integrating mental models, different types and forms of knowledge and data

Can link qualitative and quantitative factors in one model

They represent complex systems (i.e. link data, information and processes)

Quantify uncertainties through the use of probabilities

Easily updateable – through adaptive management/learning by doing

Determine what to do systemically (not just trial and error)

Can rapidly perform diagnostic and sensitivity analysis; Scenario testing

Useful for developing strategic and operational plans


Why using Bayesian Belief Network Models?

Nodes (Variables) TempRain

GrowthRepresentation of

• Graph theory (structures)

• Probability theory (strength of relationships

between variables

Inputs:

• Expert opinion (technical / non-technical)

• Literature findings

• Monitoring & research data

• Other models

Links (Representing causal relationships)


What is a Bayesian Network?

IMPROVEMENT OF …..

Probability that systemic intervention will achieve the goal


A



B

Things You Can Do – Management Interventions


1 2 3

A



B

Effects or Outcomes ofManagement Interventions


X

Other ControllingFactors

Y

1 2 3

A



B

C D FE


X

Other ControllingFactors

Y

1 2 3

A




B

C D FEC D FE

X YA B

PlanningG H JI

What needs to be in place?

Preparation

INTEGRATED STRATEGIC MANAGEMENT PLAN

Systemic Interventions; Strategies; Activities;

Projects; Timing

Probability to improve SDM into a world class institution


Work_Pressure

ReducedIncreased

48.351.7

Qual_of_Life_WISSA

HighLow

37.962.1

Knowledge_Skills

ImprovedUnchanged

47.352.7

Family_Support

YesNo

36.963.1

Infrastructure

ModernPoor

45.854.3

Gov_Support

EffectiveIneffective

50.050.0

Produce_Quality

HighPoor

29.970.1

Market_actors_Linkages

StrongWeak

36.064.0

Producer_Groups

StrongWeak

15.085.0

Branding

YesNo

23.976.1

Market_Access

GoodPoor

36.863.2

Satisfied_Consumers

YesNo

33.466.6

Stable_Markets

YesNo

37.462.6

Income

HighLow

32.767.3

Quality_control


15.085.0

Secondary_Jobs

AvailableUnavailable

18.082.0

Produce_Prices

HighLow

28.771.3

Eco_friendly_Practices

YesNo

30.070.0

Toxic_Chemicals_Use

ReducedIncreased

29.770.3

Rural_hygience

CleanPoor

25.075.0

Health

GoodNot_good

26.973.1

Service_groups


66.533.5

Implements


25.374.8

Prod_efficiency

HighLow

51.548.5

Input_prices_quality

SatisfactoryUnsatisfactory

30.869.2

Access_Healthcare

YesNo

30.969.1

Healthcare_Services

EffectivePoor

35.065.0

Training_content_relevance

YesNo

40.060.0

Capital

AvailableInsufficient

10.090.0

Qual_Training

YesNo

39.860.2

Old_Customs

YesNo

10.090.0

Willingness_to_learn

YesNo

70.030.0

Social_Engagement

HighLow

13.087.0

Learning_Capacity

HighPoor

60.040.0

Qual_Trainers

GoodPoor

55.045.0

Production_Cost

ReducedExpesive

35.864.2

Product_Diversification

YesNo

40.060.0

Work_sharing

YesNo

38.761.3


Quality of Life of Ghana women in agriculture - Current situation

Work_Pressure

ReducedIncreased

78.221.8

Qual_of_Life_WISSA

HighLow

75.824.2

Knowledge_Skills

ImprovedUnchanged

65.734.3

Family_Support

YesNo

83.017.0

Infrastructure

ModernPoor

90.010.0

Gov_Support


100 0

Produce_Quality

HighPoor

82.317.7

Market_actors_Linkages

StrongWeak

100 0

Producer_Groups

StrongWeak

100 0

Branding

YesNo

65.834.2

Market_Access

GoodPoor

76.823.2

Satisfied_Consumers

YesNo

64.535.5

Stable_Markets

YesNo

95.05.00

Income

HighLow

78.521.5

Quality_control


15.085.0

Secondary_Jobs


100 0

Produce_Prices

HighLow

73.426.6

Eco_friendly_Practices

YesNo

100 0

Toxic_Chemicals_Use

ReducedIncreased

82.217.7

Rural_hygience

CleanPoor

25.075.0

Health

GoodNot_good

63.136.9

Service_groups


73.027.0

Implements


100 0

Prod_efficiency

HighLow

87.512.5

Input_prices_quality

SatisfactoryUnsatisfactory

95.05.00

Access_Healthcare

YesNo

67.132.9

Healthcare_Services

EffectivePoor

100 0

Training_content_relevance

YesNo

40.060.0

Capital

AvailableInsufficient

100 0

Qual_Training

YesNo

56.044.0

Old_Customs

YesNo

10.090.0

Willingness_to_learn

YesNo

100 0

Social_Engagement

HighLow

100 0

Learning_Capacity

HighPoor

60.040.0

Qual_Trainers

GoodPoor

100 0

Production_Cost

ReducedExpesive

83.916.1

Product_Diversification

YesNo

100 0

Work_sharing

YesNo

78.421.6


Systemic interventions identified

Follow steps on you computers

Demonstration on

How to populate the model

I. What can be done to achieve the goal (address the goal)? (3 Max. per group)

II. Why would you take these actions (what will be the outcomes that will lead to achieving the goal)? (2-3 Max. per action)

III. What factors could influence the outcomes mentioned under point 2?

IV. What need to be in place for the actions to happen? (2 – 3 Max. per action)


Live Demonstration with Netica & Hands-on

Module 5 Bayesian belief network modelling

Education

Transcript of Module 5 Bayesian belief network modelling