Introduction to System Dynamics
Model-based policy formulation Models for national development planning (T21)
Main Objectives Analytical knowledge and skills: • SD method: Basic knowledge of the System Dynamics method; • Behavioral analysis: Ability to relate a system’s behavior to the underlying structure; • Understanding complexity: basic elements of complexity in common social, economic and environmental issues.
Main Objectives (2) Technical knowledge and skills: • Software: Knowledge of basic modeling techniques with Vensim (www.vensim.com) • Modeling: Ability of representing economic, social and environmental issues through simple simulation models; • Simulation techniques: Ability to run and compare alternative simulation scenarios.
Model-Based Policy Formulation Challenges and Guidelines
Outline
1. Setting the context – Public policy and formal models
2. The Process of Policy Formulation – Steps, content and context
3. Model-based policy formulation – Methodologies – Challenges – Guidelines
3. Model-based policy formulation 3.1 3.2
Why are models useful Methodologies • Scenarios • Mental models • Formal models – Optimization – Econometrics – Simulation
3.3 3.4
Challenges Guidelines
3.1. Why are models useful • Accurate predictions about the future would be nice to have, but can we really get them? “Essentially, all models are wrong, but some are useful.” “Remember that all models are wrong; the practical question is how wrong do they have to be to not be useful.” Box, George E. P.; Norman R. Draper (1987). Empirical Model-Building and Response Surfaces. Wiley.
3.1. Why are models useful (2) • Models can help policymaking in various ways: – Improving understanding of the possible consequences of policy choices, – Deepening policymakers’ comprehension of the underlying problems and issues, – Clarifying decision-makers’ assumptions and values helping to build understandable narratives (“stories”) in support of policy proposals, – Informing dialogue among stakeholders and policymakers, – Providing a framework for negotiation and consensus building.
• Policymaking is about trying to affect the future: to maintain or improve on the status quo of public wellbeing.
3.2. Methodologies: scenarios and mental models • Scenarios: exploration of a wide range of possible futures. – No attempt to identify the most or least probable among them, aimed at finding resiliency.
• Mental models: someone's explanation of how something works in the real world. – Psychological biases and cognitive limitations may undermine the logical application of the model.
3.2. Methodologies: Formal models • Optimization models, which generate “a statement of the best way to accomplish some goal” (Sterman, 1998), are normative, or prescriptive, models. • Econometrics measures economic relations, running statistical analysis of economic data and finding correlation between specific selected variables. • Simulation models aim at representing what the main drivers for the behavior of the system are.
3.2. Why Use a Formal Model? Model Real World
Alternative scenarios
(reality)
Decision
Strategy, structure, decision rules
Simulation
Perfect Information Interpretation of information
Mental models
Represented in a model
3.3. Challenges: barriers to learning • Complexity of dynamic systems (descriptive model); • Bounded rationality and misperceptions of feedbacks and delays (descriptive model); • Limited information (simulation); • Wrong deductions re. the dynamic behavior of systems (model validation and analysis); • Defensive routines and personal emotional involvement (alternative scenarios). Reference: Sterman, 2000
3.3. Challenges: Methodologies • Optimization: correct definition of an objective function, the extensive use of linearity, the lack of feedback and lack of dynamics. • Econometrics: full rationality of human behavior, availability of perfect information and market equilibrium. • Simulation: correct definition of boundaries and a realistic identification of the causal relations.
3.3. Challenges: Model-based policy formulation • There is a need for integrated tools that could serve as a mean to close the gap between dynamic and all embracing thinking and conventional methodologies and models. • Methodologies should be combined to: – Set targets (optimization) – Define a proposal (econometrics) – Refine a bill (system dynamics)
Models for National Development Planning
Overview 1. What is National Development Planning (NDP)? 2. From strategy to implementation 3. How can models help? 4. Why System Dynamics? 5. Example: The Threshold21 (T21) model 6. Summary
1. What is NDP?
Cascade Planning System National Vision
National Development Plan
Mid Term Strategic Plans
Yearly Budgets
1. What is NDP?
A definition National Development Planning is a: 1. Planning process at the central government level (e.g. Min. Finance) 2. Defines the strategic axes for the country’s medium/long-term development 3. Based on the long-term objectives and forms the basis for short-term strategic plans.
1. What is NDP?
Type of issues at stake Some examples of mid-long term issues: • Poverty • Economic growth • Access to social services – –
• • • •
Education Health
Environmental sustainability Quality of institutions Urban planning, land use planning Disaster risk management
2. From Strategy to Implementation
Implementation - Generic Strategic planning and Policy Development
Policy Review and Revision
Budget Preparation
Reporting and External Audit
Budget Execution
Accounting, Monitoring and Internal Audit
3. How Can Models Help?
A Learning Process objectives strategy
information feedback
decisions
current situation
3. How Can Models Help?
Role of Planning Models objectives strategy
information feedback
simulated results
decisions planning models
current situation
Necessary characteristics Formal models provide the possibility to test policies beforehand and accelerate learning Necessary characteristics for medium - long term planning models: 1. Endogenously represent key variables (E) 2. Comprehensive (C) 3. Properly represent dynamic complexity (D) 4. Transparent (T)
Endogenous Key Variables (E) real gdp at factor cost 4e+012 Short Term
Mid-Long Term
3e+012 I
2e+012
A
F
1e+012 0 1990
TI R NE
1995
2000
real gdp at factor cost : MODEL real gdp at factor cost : DATA
2005 2010 Time (Year)
ME A D UN
A NT
2015
L
ES G AN H C
2020
2025
cfa87/Year cfa87/Year
Comprehensive (C) GDP
Gov. Revenue
Society
Economy
Environment
Gov. Expenditure
Dynamic Complexity (D) Gov. Revenue
GDP
Gov. Expenditure
Non-Linearity 1 0.9 0.8
OR J MA
0.7 0.6 0.5 0.4 0.3 0.2
YS A L DE
Society 1 0.9 0.8
Economy 0.1
0.2
0.3
Non-Linearity
AJ O
R
0.7 0.6 0.5
0.1 0 0
M
0.4
0.5
0.6
0.7
0.8
0.9
1
DE
0.4 0.3 0.2
LA
YS
0.1 0 0
Environment
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Transparency (T) Input
???
Output
Existing approaches to NDP
Approach Disaggregated consistency Macro-econometrics
Software
E
C
D
T
MS-EXCEL
NO
NO
NO
YES
EVIEWS
YES
NO
YES
NO
Computable general eq.
GAMS
YES
NO
YES
NO
Integrated simulation
VENSIM
YES
YES
YES
YES
Strengths of SD approach Why SD models? 1. 2. 3. 4.
Focus on endogenous explanation Support multidisciplinary approach Proper representation of complexity Transparent – User friendly
Brief History of Economic Models
• • • • • •
Political Economy and conceptual models Introduction of quantification to Economics Linear models and Econometrics Linear accounting frameworks: RMSM Matrix models: I-O, SAM, and CGE Broader Systemic models: T21
Linear Models and Econometrics • Look at limited set of relations and variables GDP = a*INV + c INV = 1/a * GDP - c
• Help understand importance of relationships in sub-sector mode • Limited range of variables considered • No relation to other economic variables
Linear Accounting Frameworks • Address “whole” economy in a single framework
GDP = CONS + INV + XP - IMP = CONS + SAV INV - SAV = XP - IMP GOV BAL = REV + AID + BOR - EXP - DSER BoP = XP - IMP + TRANS + NetCap
• Help see how “whole” economy balances • Dominated by exogenous assumptions and accounting balances • Few internal links and fewer links to other important factors in development
Matrix Models: Input-Output • Incorporate links among production activities Input-Output Table Agriculture Agriculture 25 Industry 45 Services 26 Total 96
Industry 53 87 23 163
Services
Total 18 31 12 61
96 163 61
• Help understand how production sectors interact • Linear structure with many exogenous factors • Lack links with other economic factors or the rest of society
Matrix Models: SAMs • Incorporate the rest of the economy with SAM Agriculture Industry Agriculture 25 53 Industry 45 87 Services 26 23 Households 48 55 Government 15 34 11 44 RoW Total 170 296
Services 18 31 12 40 12 12 125
Households Government 43 12 65 28 24 27 13 15 21 20 168 100
RoW 19 40 13 12 24
Total 170 296 125 168 100 108
108
• Better view of “whole” economy with interaction among agents and equilibrium • Heavy data requirements • Limited relations beyond SAM
Matrix Models: CGEs • Convert SAM entries into equations Agriculture Industry Services Households Government RoW Total
Agriculture Industry A1=f(K,L,ais) I1=f(K,L,ais) A2=f(K,L,ais) I2=f(K,L,ais) A3=f(K,L,ais) I3=f(K,L,ais) AW=f(K,L,ais) IW=f(K,L,ais) Atax=f(Ag) Itax=f(In) AgM=f(AG) InM=f(In) Sum Ag Sum In
Services Households S1=f(K,L,ais) Dag=f(Inc,Pref) S2=f(K,L,ais) Din=f(Inc,Pref) S3=f(K,L,ais) Dse=f(Inc,Pref) SW=f(K,L,ais) Stax=f(Se) Hhtax=f(Inc) SeM=f(Se) DM=f(Inc,Exr) Sum Se Sum HH
Government Dgag=F(bud) Dgin=F(bud) Dgse=F(bud) Trans=f(bud)
RoW AgX=f(ForD) InX=f(ForD) SeX=f(ForD) Rem=f(Emig) Aid=f(ForAid)
Gpay=f(debt) Sum Gov Sum RoW
Total Sum Ag Sum In Sum Se Sum HH Sum Gov Sum RoW
• Non-linear relations, but ‘static’ solutions from ‘black box’ • Require all markets to clear in equilibrium • Lack links with social and environmental factors which affect economy, e.g. MDGs
Broader Systemic Models: T21 •
Addresses the WHOLE system, including, economic, social and environmental factors Consumption
Loans/debt
Health, Edu., Fam. Planning Income
Investment Population Capital Labor force Production
Education level
Labor productivity
Resource conservation
• • •
Live expenctancy
Pollution control
Takes account of interactions across the WHOLE system Generates long-term scenarios to show effects over time Helps users analyze and understand how national systems function
Architecture
37
Implementation process outline Step 1: Refinement of focus issues Step 2: Discussion on key elements to be considered, via a series of open sessions Step 3: Elaboration of results from open sessions into a simulation model Step 4: Testing and validating the model Step 5: Analysis and discussion of results
38
Key Success Factors 1. Solid Model a) Data b) Participation
2. Local Modeling Capacity a) Training b) Practical use
3. Local Ownership a) Commitment b) Ongoing Development and Use 39
Looking More Closely at T21 • Original systemic model applied to sustainable development • More applications and experience adapting transparently to countries • Includes deeper coverage of important noneconomic social factors, environment, MDGs, poverty accounting • Easier to use and less expensive
T21 Fits into Planning Toolkits • Macro models Provide Macro Balances, MTEF, IFI discussions Short term -- need longer-term, x-sector validation
• CGE Models SAM, Detailed relations, Optimum effects Comparative static -- need more transparent paths
• Threshold 21 Long term, Cross sector links, Transparent results Not as detailed, builds on local data and input from other tools
5. Example: T21
Focus of the Threshold 21 The model was originally built for serving three purposes: (1) Studying mid-long term development issues (2) Testing alternative policies (3) Enhancing learning about system => Support mid-long term planning through understanding of the system
5. Example: T21
Benefits from using T21 1. Consistency check of data and assumptions 2. Identification of future potential issues 3. Identification of alternative strategies 4. Basis for monitoring and evaluation
5. Example: T21
Limitations of T21 approach • Mid-long term approach: does not focus on shortterm dynamics • National perspective: does not consider diversity among different regions • Medium-high level of aggregation: parameters are averaged by sector • Requires active involvement of client in definition of model’s structure
6. Summary
Key Messages (1) • • •
NDP is a medium to long term planning activity NDP needs formal models to speed-up learning process NDP models should: – – – –
• •
endogenously represent key variables; be comprehensive; properly represent dynamic complexity; be transparent.
SD is well suited to develop models in accordance to the above criteria. T21 is built using the SD approach, and it is rapidly diffusing worldwide.
6. Summary
Key Messages (2) • • •
T21 is the results synthesis of best models and internal T21 is innovative in the way sectors are linked together T21 is useful at four levels in the planning process: – – – –
Check of data and assumptions Identification of future potential issues Identification of alternative strategies Basis for monitoring and evaluation
Introduction to System Dynamics
Introduction to System Dynamics The objective of System Dynamics is: • To improve our understanding of the interdependencies existing between the structure of a system and its behavior and the extent to which various policies influence its functioning mechanisms. Such policies can then eventually be used as levers for future development.
Models and Methodologies Type of models: • • • • • •
Econometric; Geographical maps; Behavioral; Language; Mental; …
Mental Models
Within System Dynamics, a “mental model” is defined as: • Our beliefs and theories on causes and effects that define and underlie the structure and behavior of a system, with the limitations/boundaries of the model.
Foundations of System Dynamics and T21: Stocks and Flows Stock and Flows • Stock: accumulations ruled by a flow; • Flow: the rate of change of a stock. Examples Population (stock); Fertility rate (positive flow); Mortality rate (negative flow). Money in a bank account (stock); Interest rate on the same account (flow).
Stocks and flows The stock describes the actual situation
The flow changes the stock and the actual state of the system
Applications Worldwide
T 21 Countries MI Partner MI Head Office MEG Countries
Why Take a Systemic View?
To Avoid Unexpected Results! Velocity
Strength Delays
Vensim interface
Vensim interface
Vensim interface
Motivation for this study • There is a need for integrated tools that could serve as a mean to close the gap between dynamic and all embracing thinking and static available models; • These tools are required when facing critical issues such as the upcoming energy transition and climate change, because conventional modeling tools do not examine their broader causes and impacts.
Contextualizing issues The approach proposed includes: • (1) the analysis of the context in which energy issues arise, whether they are global, regional and national, and • (2) the study of various policy options that are being considered for solving energy, environmental and national security issues (which are normally implemented at the national level and have narrow boundaries).
Core Capabilities • Illustration of the synergies and implications of different options across a broad framework • Provision of a basis for productive long-term planning and unite various parties around consistent policies • Deeper understanding of the interrelations existing among critical issues • Support for the creation of cooperation among stakeholders at the planning and technical level
Some results • Emergence of various unexpected side effects is likely; • Elements of policy resistance arise over the medium and longer term due to the interrelations existing between energy and society, economy and environment; • Side effects or unintended consequences may arise both within the energy sector and in the other spheres of the model; nevertheless, these behavioral changes influence all society, economy and environment spheres.
T21-Ecuador - Sectors
Concept – Energy Sector
Macro Feedbacks – Energy Sector
Policies Analyzed • Subsidizing electricity prices – To reduce households’ costs
• Investing 1% of GDP in energy efficiency – To reduce electricity demand and energy costs
• Investing in Renewable Energy – To reduce thermal electricity generation and GHG emissions from the power sector and export more oil
• Increasing Electricity Imports – To further reduce domestic thermal generation and export oil, also to reduce natural gas trafficking
Some Results • A: Investing 1% of GDP in energy efficiency – – – –
Higher income Higher gov. revenues (from oil) and GDP Lower energy demand, but increasing Lower emissions
• B: A + Investing in renewable energy – Same income and GDP – Higher employment and lower emissions
• Avoided costs and added gov. revenues are reinvested in social services (edu and infrastructure)
GHG Emissions
Why new insights? • Side effects or unintended consequences arise from within the energy sector and influencing both the same sector as well as society, economy and environment. • Results emerge from a combination of: – Four integrated “spheres”; – The representation of feedback, nonlinearity and delays; – A participatory and transparent approach.
The approach used contributes to the representation and understanding of the context (social, economic, environmental and political) in which issues arise and within policies are formulated and implemented.
