Exploring the Potential of

Systems Dynamics Modelling

Impact Innovation and Learning: Towards a Research and

Practice Agenda for the Future

Brighton 26/27 March 2013

Peter Loewe

Content

• Impact paths of enterprise development projects

• Linear causal chains vs closed causal loops

• Causal loop diagrams: 3 investment strategies

• System Dynamics past and present

• The UNIDO demonstration tool

• Two simulations

• Status, lessons, way forward

Causal chain model of PSD poverty impact

Source: OECD Donor Committee for Enterprise Development

How Private Sector Development leads to Pro-Poor Impacts: A Framework for Evidence

Poverty impact paths under scrutiny

Employment

reduction

IU In

terv

en

tio

n

Competitiveness

impact

Industrial

Development/

Economic Growth

Impact

Employment

impact Skill content impact Additional Effects Poverty impact

Crowding out of less

competitive SMEs

Business decline

Job-less growth

(productivity path)

High skill jobs

Low skill jobs

Non-poor growth

Increase in poverty

Reduction in local

purchasing power,

consumption,

production, SMEs,

and employment

increase in poverty

Market

access &

partnership

Cluster &

networking

Pro-poor skill

development

& training

Pro-poor

social

protection

Pro-poor

targeting

(regions, sectors,

firms, services)

Pro-poor

entrepreneur &

SME

development

Business

maintenance

High skill jobs

Competitive SMEs

(high road/costs &I U)

Business expansion

of local suppliers &

subcontractors

Business expansion

in export markets

Business expansion

in local market

Employment creation

/ job-rich growth

(expansion path) Low skill jobs Pro-poor

growth

Increase in local

purchasing power,

consumption,

production, SMEs,

and employment

poverty reduction

Employment

maintenance

Business decline of

less competitive local

suppliers &

subcontractors

Competitive SMEs

(low road/costs)

Possible impact drivers are shown in red.

Causal Chains vs. Systems Dynamics

• Linear causal chain modeling (logframe) – state-of-the-art in development cooperation

– too simplistic for complex cases

• System Dynamics Modelling (SDM) – Causal loops instead of causal chains

– Negative feed-back loops (“goal seeking”)

– Positive feed-back loops (“exponential growth”)

– Interconnected loops

– Strong / weak coupling

– Variables can be “stocks” or “flows”

– Short term / long term behavior

– Non-linear behavior(small causes – big effects)

6

Causal loop example (1)

Profits

Investment

Expansion

Competitivenes

on price

+

+

-

+

Negative feed back

loop ("goal seeking")

7

Causal loop example (2)

Profits

Investment

Rationa

-lizationExpansion

Competitiveness

on price

+

+

-

+

+

+

Exponentialgrowth(buttoothebottom)

Positive feed back loop(but jobless "low road"))

8

Causal loop example (3)

Profits

Investment

Rationa

-lizationExpansion

Competitiveness

on price

+

+

-

+

+

+

Exponential growth (but too the bottom) Positive feed back loop(but jobless "low road")

Competitiveness on

quality & new products

Innovation

+

-+

Positive feed backloop ("high road")

Negative feed backloop (equilibrium)

9

Entry points for interventions

Profits

Investment

Rationa

-lization

Expansion

Competitiveness

on price

+

+

-

+

+

+

Competitiveness on

quality & new products

Innovation

+

-

+

Business

climate

Access to

finance

Trade

liberalization

Improve national

quality and innovation

system

System Dynamics: Past & present

• Early applications

– Engineering: Technological feed-back systems

– Biology: Ecological systems (predator – prey systems)

• 1961: Jay Forrester (MIT) application to management :

“Industrial Dynamics”

• 1972: Forrester/Meadows: “World Model” – Limits of Growth

• 1980s: System Dynamics tools for PC (Ithink; Vensim)

• Current applications: management; traffic/city/regional

planning; energy and environment; economic development

• World System Dynamics Society: www.systemdynamics.org

• 2005: Millennium Institute “Threshold 21” simulation tool

11

I believe we are proposing the “Process” of modeling rather

than particular frozen and final models. … It seems to me that

the average person will be greatly concerned if he feels that

the future and alternatives are being frozen once and for all

into a particular model, instead we are suggesting that

models will help to clarify our processes of thought; they will

help to make explicit the assumptions we are already

making; and they will show the consequences of the

assumptions. But as our understanding, our assumptions,

and our goals change, so can the models.

John Forrester (1985), “The” model versus a modeling process, in: System

Dynamics Review, 1, S. 133-134.

Importance of the modeling process

The UNIDO experimental simulation tool

• Part of an evaluation of “Industrial Upgrading” projects

• Concrete case: Leather project in Ethiopia

• Programmer: Sebastian Derwisch (University of Bergen)

• Consultant: Cornelia Staritz (Austrian Development Foundation)

• Group model building workshop in December 2011

• Computer model using VENSIM software: – About 200 internal variables / equations

– Three external factors

– Eight input variables (development interventions)

– Eleven output variables

• Presentations to project managers and management

• Recommendation to pursue

Structure of the UNIDO model

3 external factors

• Import tariffs (increased imports)

• Cost of raw materials

• Increased competition on export markets

8 interventions (input variables)

• Investment in equipment

• Labor intensity

• Investment in skills

• Access to credit

• Strengthening of National Quality Infrastructure

• Logistics and customs infrastructure

• “Buy local” campaign

• Promotion of labor standards

11 effects (output variables)

• Equipment

• Skills

• Productivity

• Costs

• Quality

• Price

• Local demand

• International demand

• Production

• Jobs

• Wages

17

"Imports increasing (Tariff reduction)"

0 1

Increase of imports

0.6

0.45

0.3

0.15

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

"Imports increasing (Tariff reduction)" : Baseline with trade liberalization

Represents an increase in competition on the nationalmarket. The parameter can be varied between 0 (nocompetition - everything produced for the domesticmarket can be sold) and 1 (full competition, nothing

produced for the domestic market can be sold)

Simulation 1

External change: trade liberalization

in 2011 - 2014

Slider to change the parameter between 0 and 1

Simulation period: 2010 to 2035

18

Simulation 1:

Effects of the external change Costs

2

1.4

0.8

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".expected costs." : Baseline with trade liberalization

Production

1

0.75

0.5

0.25

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".production." : Baseline with trade liberalization

Price

1

0.8

0.6

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".expected price." : Baseline with trade liberalization

International Demand

0.2

0.1

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".international demand." : Baseline with trade liberalization

Local Demand

1

0.5

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".local demand." : Baseline with trade liberalization

Equipment

1

0.7

0.4

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".equipment." : Baseline with trade liberalization

Productivity

2

1.7

1.4

1.1

0.8

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

relative productivity : Baseline with trade liberalization

Quality

1

0.8

0.6

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

score quality : Baseline with trade liberalization

Jobs

1

0.7

0.4

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".jobs." : Baseline with trade liberalization

Wages

1

0.9

0.8

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

wages : Baseline with trade liberalization

Skill per worker

2

1.7

1.4

1.1

0.8

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

relative average skills per worker : Baseline with trade liberalization

Promotion of labour standards

0 5

logistics and customs upgrading program

0 5

Equipment upgrading program

0 5

Skills upgrading program

0 5

buy local campaign

0 5

access to credit

0 5

NQS upgrading program

0 5

Investment in Equipment

2

1

0

2010 2015 2020 2025 2030 2035

Time (Year)

dmnl

Equipment upgrading program : 8 Liberalisation response

Logistics and customs upgrading

2

1.4

0.8

2010 2015 2020 2025 2030 2035

Time (Year)

dmnl

logistics and customs upgrading program : 8 Liberalisation response

Investment in skills

4

3

2

2010 2015 2020 2025 2030 2035

Time (Year)

dmnl

Skills upgrading program : 8 Liberalisation response

NQS upgrading

2

1.4

0.8

2010 2015 2020 2025 2030 2035

Time (Year)

dmnl

NQS upgrading program : 8 Liberalisation response

Buy local campaign

2

1.7

1.4

1.1

0.8

2010 2015 2020 2025 2030 2035

Time (Year)

dmnl

buy local campaign : 8 Liberalisation response

Labour standards

2

1.5

1

0.5

0

2010 2015 2020 2025 2030 2035

Time (Year)

dmnl

Promotion of labour standards : 8 Liberalisation response

Access to credit

2

1.75

1.5

1.25

1

2010 2015 2020 2025 2030 2035

Time (Year)

dmnl

access to credit : 8 Liberalisation response

Interventions reduces labor requirements - Saveson workforce and maintains production. The

parameter can be varied between -1 (fullautomatization, no workers needed) and 1

(labour intensity increased by 100%)

Interventions increases labor productivity byinvestments into skill building - increases

productivity of workers. The parameter can bevaried between 0 (no additional investment inskills) and 5 (investment in skills increased by

500%)

Interventions affecting productivity

labor intensity intervention

-1 1

Intervention increases investment into equipment -Maintains woker and increases stock of equipment.

The parameter can be varied between 0 (noadditional investment in equipment) and 5

(investment in equipment increased by 500%)

Labor intensity

-0.4

-0.7

-1

2010 2015 2020 2025 2030 2035

Time (Year)

dmnl

labor intensity intervention : 8 Liberalisation response

Other Interventions

Represents a campaign that stimulates localdemand - whats inserted is the assumed increase oflocal demand by the campaign. The parameter can

be varied between 0 (additional increase indemand) and 5 (local demand increased by 500%)

Represents an upgrading of wages - thevalue inserted represents the increase of thewages by promoting better pabor standards,higher wages have an effect on the skill per

worker

Access to credit lifts the overall investmentby the value inserted. The parameter can bevaried between 0 (no additional investment)

and 5 (investment increased by 500%)

NQS upgrading represents investment in NQSfacilities. The parameter canbe varied between

0 (no additional investment into NQSupgrading) and 5 (investment into NQS

upgrading increased by 500%)

Represents investments to improve logisticswhich reduces fluctuations in the delivery delay.

The parameter can be varied between 0 (noadditional investment into logistics) and 5

(investment into logistics increased by 500%)

Simulation 2:

Interventions of a possible development program

Set parameters

for year n Observe effects

for year n+1

Adapt parameters

for year n+1

Observe effects

for year n+2

Adapt parameters

for year n+3

20

Simulation 2: How the interventions of the response

program overcome the effects of the external change

Costs

2

1

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".expected costs." : 8 Liberalisation response

Production

20

15

10

5

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".production." : 8 Liberalisation response

Price

2

1.4

0.8

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".expected price." : 8 Liberalisation response

International Demand

20

10

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".international demand." : 8 Liberalisation response

Local Demand

10

5

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".local demand." : 8 Liberalisation response

Equipment

6

3

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".equipment." : 8 Liberalisation response

Productivity

4

3

2

1

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

relative productivity : 8 Liberalisation response

Quality

8

4

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

score quality : 8 Liberalisation response

Jobs

2

1.4

0.8

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

".jobs." : 8 Liberalisation response

Wages

2

1.4

0.8

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

wages : 8 Liberalisation response

Skill per worker

8

6

4

2

0

2010 2015 2020 2025 2030 2035

Time (Year)

dm

nl

relative average skills per worker : 8 Liberalisation response

Work in progress - some preliminary conclusions

• SD: an appropriate approach to cope with complexity

• A “meta language” - alternative to linear causal chains

• “Qualitative modeling” through “Group Model Workshops”:

– Consensus building on parameters & dynamics of complex settings

– Making implicit assumptions explicit

– Feeding “lessons learned” from evaluation into model structure

• Computer simulation (“quantitative modeling”)

– Not a must - “qualitative modeling” is useful exercise in itself

– Requires experienced programmer

– Rather time consuming

• Useful for generic types of interventions (project families)

• Towards an “Artificial Intelligence” tool for program design?

22

Omitting structures or variables

known to be important because

numerical data are unavailable is

actually less scientific and less

accurate than using your best

judgment to estimate their

values.

To omit such variables is

equivalent to saying they have

zero effect - probably the only

value that is known to be wrong! John Sterman (2002), All models are wrong:

reflections on becoming a systems scientist, System

Dynamics Review, Vol. 18, p. 523

The simple is false

- but the complex

is unusable

Paul Valéry

Also known as

“Bonini’s paradox”

The complexity of our mental models

vastly exceeds our capacity to

understand their implications. …

Formalizing qualitative models and

testing them via simulation often leads

to radical changes in the way we

understand reality.

John Sterman (2000), Business Dynamics, p. 29

„All

models

are

wrong“