System Dynamics And the Nexus Modeling

Iran University of Science and Technology
Fall 2020
Image Sources:
www.nu.nl/economie/6070198/
https://www.ecowatch.com/how-green-is-hydropower-1919539525.html
https://www.techexplorist.com/subsidies-on-irrigation-efficiency-may-have-a-negative-impact-on-water-use/16769/
https://ssir.org/articles/entry/cutting_through_the_complexity_a_roadmap_for_effective_collaboration
System Dynamics
And the Nexus Modeling

Herbert A. Simon (Nobel Laureate)
“The capacity of the human mind for formulating and solving complex
problems is very small compared with the size of the problem whose
solution is required for objectively rational behavior in the real world or
even for a reasonable approximation to such objective rationality.”
2 2020-11-15 System Dynamics And the Nexus Modeling

Systems Thinking
• The tools we have been using have not only failed to solve the persistent problems we face, but
may in fact be causing them.
• Systems Thinking:
• The ability to see the world as a complex system
• Characteristics of Systems Thinking:
• Inherent inter- and trans-disciplinary
• From silo to holistic
• From objects to relationships
• From linear to nonlinear
• From complicated to complex
• From quantitative to qualitative
• From structures to processes
• From normal science to post-normal science
• From Cartesian certainty to approximate knowledge
Source: https://agsystemsthinking.net/about/

Systems Thinking Continuum [6]

Models
• Mental:
• ambiguous, inaccessible, limited, momentary, unreliable, fast, cheap, foundation of decisions
• Descriptive:
• ambiguous, hard interpretation, unreliable, inconsistent, available, lasting
• Physical:
• experimental, unambiguous
• Mathematical:
• computationally difficult, oversimplifying assumptions, available, lasting, reliable, unlimited,
unambiguous
• Dynamical:
• simple but not easy, available, unlimited, unambiguous, lasting, reliable

System Dynamics [7]:
• is created at MIT in the 1950s by Jay Forrester
• help us learn about the structure and dynamics of the complex systems
• can be used for mid-term and long-term simulation and prediction of the system and its
future development trends
• is fundamentally interdisciplinary
• is grounded in the theory of nonlinear dynamics and feedback control
Dynamic complexity arises because systems are [7]:
• Constantly changing
• tightly coupled
• governed by feedback
• nonlinear
• history-dependent
• self-organizing
• adaptive
• characterized by trade-offs
• counterintuitive
• policy resistant

The Event-oriented, Open-loop View [7]
• We assess the state of affairs and compare it to our goals.
• The gap between the situation we desire and the situation we perceive defines
our problem.

The Feedback View [7]
• Our decisions alter our environment, leading to new decisions, but also
triggering side effects, delayed reactions, changes in goals and interventions
by others. These feedbacks may lead to unanticipated results and ineffective
policies.

Key Concepts [7]
• Stocks (Integrals, State/Level Variables): Stocks are accumulations. They characterize the state of the
system and generate the information upon which decisions and actions are based. Stocks change only through
their rates.
• Flows (Derivatives, Rate Variables): Illustrate the rates at which level variables change
• Delays: A delay is a process whose output lags behind its input in some fashion. There are two types of delay:
Information Delays and Material Delays.
• Auxiliary Variable: Auxiliaries consist of functions of stocks (and constants or exogenous inputs).
• Path Dependence: Path dependence is a pattern of behavior in which the ultimate equilibrium depends on
the initial conditions and random shocks as the system evolves.
• Dynamic System: A system which has a state vector that changes over time
• System Dynamics: A top-down modeling approach developed by Forrester (1961) that models complex
systems over time.
Inflow Rate
Outflow RateStock
The stock of water in the tub is filled by the
inflow and drained by the outflow there are no
feedbacks, time delays, nonlinearities, or other
complexities
Source: [7]

Correlation vs Causation [7]

Reinforcing Loop & Balancing Loop [1]
• Reinforcing loop means a self-reinforcing activity while a balancing loop means a self-
correcting activity.

Causal Loop Diagram (CLD) Example:
The Invisible Hand [7]

GPA [7]

System Dynamics Modelling Process [8]
Boundary
Definition
Formulating a
Dynamic
Hypothesis
CLD
Construction
SFD
Construction
Testing,
Verification and
Validation
Simulation
Scenario and
Policy Testing,
Sensitivity
Analysis
CLD: Casual Loop diagram
SFD: Stock-Flow Diagram
Iterative
and
reciprocal
process

System Dynamics Software
Software License Monte Carlo
Simulation
Optimization Website Last Update
Analytica Subscription based ✓ ✗ https://lumina.com/ 2018
Gold Sim commercial ✓ ✓ www.goldsim.com 2020
LOOPY free ✗ ✗ https://ncase.me/loopy/v1.1/ 2019
Ture free ✓ ✓ https://www.true-world.com/htm/en/index.html 2019
Simulink (MATLAB) commercial ✓ ✓ mathworks.com 2020
Simcad Pro commercial ✓ ✓ www.createasoft.co 2019
Vensim commercial ✓ ✓ https://vensim.com/ 2020
Stella/iThink commercial ✓ ✓ https://www.iseesystems.com/ 2020
Insight Maker free ✗ ✓ https://insightmaker.com/ 2020
AnyLogic commercial ✓ ✓ https://www.anylogic.com 2020
Powersim commercial ✓ ✓ http://www.powersim.com/ 2018
Comparison of SD simulation software
(Source: [3] and https://en.wikipedia.org/wiki/Comparison_of_system_dynamics_software , last visit: October 2020)

Vensim Modelling

Stock and Flow Diagrams (SFD) Example:
COVID-19 cases
• Initial Population:
• 100000 (person)
• Population Birth Rate and Death Rate:
• 40000 and 15000 (person/year)
• 5% of the population gets COVID-19
• 10% of COVID cases die
• Initial Available Masks:
• 10000
• Masks Production and Consumption Rate Before
COVID:
• 5000 and 2000
• Public Participation:
• Full Cooperation after the 2nd month (wearing all
available masks)
• Mask Import:
• 5000 in the 6th month
• Fist Vaccine:
• 75% efficiency, released after the 12th month
• Second Vaccine:
• 95% efficiency, released after the 16th month

COVID-19 cases

Successful SD Modelling [7]:
• Develop a model to solve a particular problem, not to model the system
• Modeling should be integrated into a project from the beginning
• Be skeptical about the value of modeling and force the “why do we need it” discussion at the start
of the project
• System dynamics does not stand alone. Use other tools and methods as appropriate
• Focus on implementation from the start of the project
• Modeling works best as an iterative process of joint inquiry between client and consultant
• Avoid black box modeling (garbage in garbage out)
• Get a preliminary model working as soon as possible. Add detail only as necessary
• A broad model boundary is more important than a great deal of detail
• Use expert modelers, not novices
• Implementation does not end with a single project

Validation and Verification Are Impossible
• Webster’s defines “verify” as “to establish the truth, accuracy, or reality of.”
• “Valid” is defined as “having a conclusion correctly derived from premises . . .
Valid implies being supported by objective truth.”
• By these definitions, no model can ever be verified or validated. Why?
• Because all models are wrong.
• All models, mental or formal, are limited, simplified representations of the real world.

Tests for Assessment of Dynamic Models [7]
• Boundary Adequacy:
• Are the important concepts for addressing the problem endogenous to the model?
• Does the behavior of the model change significantly when boundary assumptions are relaxed?
• Do the policy recommendations change when the model boundary is extended?
• Structure Assessment:
• Is the model structure consistent with relevant descriptive knowledge of the system?
• Is the level of aggregation appropriate?
• Does the model conform to basic physical laws such as conservation laws?
• Do the decision rules capture the behavior of the actors in the system?
• Dimensional Consistency:
• Is each equation dimensionally consistent without the use of parameters having no real world meaning?
• Parameter Assessment:
• Are the parameter values consistent with relevant descriptive and numerical knowledge of the system?
• Do all parameters have real world counterparts?
• Extreme Conditions:
• Does each equation make sense even when its inputs take on extreme values?
• Does the model respond plausibly when subjected to extreme policies, shocks, and parameters?

Tests for Assessment of Dynamic Models [7]
• Integration Error:
• Are the results sensitive to the choice of time step or numerical integration method?
• Behavior Reproduction:
• Does the model reproduce the Reproduction behavior of interest in the system (qualitatively and quantitatively)?
• Does it endogenously generate the symptoms of difficulty motivating the study?
• Does the model generate the various modes of behavior observed in the real system?
• Do the frequencies and phase relationships among the variables match the data?
• Behavior Anomaly:
• Do anomalous behaviors result when assumptions of the model are changed or deleted?
• Family Member:
• Can the model generate the behavior observed in other instances of the same system?
• Supervise Behavior:
• Does the ,model generate previously unobserved or unrecognized behavior?
• Does the model successfully anticipate the response of the system to novel conditions?
• Sensitivity Analysis:
• Numerical Sensitivity: Do the numerical values change significantly. . .
• Behavioral sensitivity: Do the modes of behavior generated by the model change significantly . . .
• Policy sensitivity: Do the policy implications change significantly. . .
• . . . when assumptions about parameters, boundary, and aggregation are varied over the plausible range of uncertainty?
• System Improvement:
• Did the modeling process help change the system for the better?

Fundamental Modes of Dynamic Behavior [7]
+ Stasis, or Equilibrium
+ Randomness
+ Chaos

Dynamic Behavior:
Exponential Growth [7]
• Arises from positive (self-reinforcing) feedback.
• In pure exponential growth the state of the system
doubles in a fixed period of time.
• Common example: compound interest, population
growth

Dynamic Behavior:
Goal Seeking [7]
• Negative loops seek balance, and equilibrium, and
try to bring the system to a desired state (goal).
• Negative loops counteract change or disturbances.
• Negative loops have a process to compare desired
state to current state and take corrective action.
• Pure exponential decay is characterized by its half
life – the time it takes for half the remaining gap to
be eliminated.

Dynamic Behavior:
Oscillation [7]
• It is caused by goal-seeking behavior, but results
from constant ‘overshoots’ and ‘under-shoots’
• The over-shoots and under-shoots result due to
time delays- the corrective action continues to
execute even when system reaches desired state
giving rise to the oscillations

Dynamic Behavior:
S-shaped growth [7]
• Growth is exponential at first, but then gradually slows until the
state of the system reaches an equilibrium level.
• Resembles ecological concept of carrying capacity: the carrying
capacity of any habitat is the number of organisms of a particular
type it can support and is determined by the resources available
in the environment and the resource requirements of the
population. As a population approaches its carrying capacity,
resources per capita diminish thereby reducing the fractional net
increase rate until there are just enough resources per capita to
balance births and deaths, at which point the net increase rate is
zero and the population reaches equilibrium.
• A system generates S-shaped growth only if two critical
conditions are met:
• First, the negative loops must not include any significant time
delays
• Second, the carrying capacity must be fixed

Dynamic Behavior:
S-Shaped Growth with Overshoot [7]
• S-shaped growth requires the negative feedbacks
that constrain growth to act swiftly as the carrying
capacity is approached. Often, however, there are
significant time delays in these negative loops.
Time delays in the negative loops lead to the
possibility that the state of the system will
overshoot and oscillate around the carrying
capacity

Dynamic Behavior:
Overshoot and Collapse [7]
• The second critical assumption underlying S-
shaped growth is that the carrying capacity is
fixed. Often, however, the ability of the
environment to support a growing population is
eroded or consumed by the population itself.
• Consumption or erosion of the carrying capacity
by the population creates a second negative
feedback limiting growth.

Systems Archetypes [5]:
• These "systems archetypes“ or "generic structures" embody the key to learning to see structures
in our personal and organizational Jives. The systems archetypes— of which there are only a
relatively small number'—suggest that not all management problems are unique, something that
experienced managers know intuitively.
• The systems archetypes reveal an elegant simplicity underlying the complexity of management
issues.
• The purpose of the systems archetypes is to recondition our perceptions, so as to be more able to
see structures at play, and to see the leverage in those structures.

Systems Archetypes:
Limits to Growth [5]
• A process feeds on itself to produce a period of accelerating growth or expansion. Then the
growth begins to slow (often inexplicably to the participants in the system) and eventually comes
to a halt, and may even reverse itself and begin an accelerating collapse.
• The growth phase is caused by a reinforcing feedback process (or by several reinforcing feedback
processes). The slowing arises due to a balancing process brought into play as a "limit" is
approached. The limit can be a resource constraint, or an external or internal response to growth.
The accelerating collapse (when it occurs) arises from the reinforcing process operating in
reverse, to generate more and more contraction.

Systems Archetypes:
Success to Successful [5]
• Two activities compete for limited support or resources. The more successful one becomes, the
more support it gains, thereby starving the other.

Systems Archetypes:
Tragedy of the Commons [5]
• Individuals use a commonly available but limited resource solely on the basis of individual need.
At first they are rewarded for using it; eventually, they get diminishing returns, which causes
them to intensify their efforts. Eventually, the resource is either significantly depleted, eroded, or
entirely used up.

Systems Archetypes:
Fixes that Backfire [5]
• A fix, effective in the short term, has unforeseen long-term consequences which may require even
more use of the same fix.

Systems Archetypes:
Shifting the Burden [5]
• A short-term "solution" is used to correct a problem, with seemingly positive immediate results.
As this correction is used more and more, more fundamental long-term corrective measures are
used less and less. Over time, the capabilities for the fundamental solution may atrophy or
become disabled, leading to even greater reliance on the symptomatic solution.

Systems Archetypes:
Eroding Goals [5]
• A shifting the burden type of structure in which the short-term solution involves letting a long-
term, fundamental goal decline.

Systems Archetypes:
Escalation [5]
• Two people or organizations each see their welfare as depending on a relative advantage over the
other. Whenever one side gets ahead, the other is more threatened, leading it to act more
aggressively to reestablish its advantage, which threatens the first, increasing its aggressiveness,
and so on. Often each side sees its own aggressive behavior as a defensive response to the other's
aggression; but each side acting "in defense" results in a buildup that goes far beyond either side's
desires.

Systems Archetypes:
Growth and Underinvestment [5]
• Growth approaches a limit which can be eliminated or pushed into the future if the firm, or
individual, invests in additional "capacity." But the investment must be aggressive and
sufficiently rapid to forestall reduced growth, or else it will never get made. Oftentimes, key goals
or performance standards are lowered to justify underinvestment. When this happens, there is a
self-fulfilling prophecy where lower goals lead to lower expectations, which are then borne out
by poor performance caused by underinvestment.

2020-11-15 System Dynamics And the Nexus Modeling39
Water-Energy-Food (WEF) Linkage
Synergy and Trade-off

Traditional view
business-as-usual approach
single-silo thinking
Vs.
Integrated view
holistic approach
nexus thinking
Source: https://magic-nexus.eu/nexus-times

• WFE nexus approach was proposed by the World
Economic Forum for the first time in 2011 (Hoff,
2011) with the intention of confronting problems
such as scarcity of resources. It identifies the
interrelation between WFE resources temporally and
spatially and aims at enhancement of the WFE
resources security and determination of the
interrelations between WFE systems in order to
facilitate inter-sector and holistic decision making,
which can eventually lead nations toward
sustainability [4].

• The WEF Nexus is a novel concept in resources
management that integrates and considers feedback
connections of water, energy, and food production
and consumption in a single framework [9].
• The discussion of WEF Nexus commonly involves
several parties with different backgrounds and
expertise to decide sustainable management plan [9].
• There is a close and intricate relationship among
water, energy, and food; they coordinate with each
other to form a multi-variable coupling, reciprocal,
dynamic system, and the coordinated development of
them will play a positive role in human survival and
development [9].

Water-Energy-Food (WEF) Nexus
Water Energy Food
Water for -
Hydropower
Cooling Systems
Biofuel Production
Fuel Extraction and Refinery
Mining
Thermal Pollution
Energy Generation from WW (digesters)
Irrigation
Cattle and Livestock Farming
Fish Farming
Fish Hunting
Energy for
Transport Infrastructures
Pumping
Desalination
W/WW Treatment and Reuse
-
Agricultural Activities
Fertilizer Production
Food Industries
(Transport, Packaging and Cooling Systems)
Catering
Food for
Food Export (Virtual Water)
Agricultural Impacts on Groundwater and Flooding
Agricultural WW impacts on Water Quality
Application of Certain Types of Plants in W Treatment
Biomass
Energy generation from Organic Waste
(Anaerobic Digestion)
-

Recent Trends in Literature:

Common Nexus Tools [4]:
Tools/Frameworks Main Components Modeling Approach
WEFSiM-opt Water, Food, Energy, Economy SD
ANEMI 2 Water, Food, Land, Energy, Economy, Climate Change, Carbon Cycle Vensim DSS
Water, Energy, Food Nexus Tool 2.0 Water, Energy, Land, Economy, Carbon Emission -
MuSIASEM 2.0 Water, Food, Energy, Socioeconoic -
CLEW Water, Energy, Land, Econoomy, Carbon Emission AEZ/WEAP-LEAP
WPE Model Water, Energy, Ecosystem MATLAB
ABM Model Water, Food, Energy, Ecosystem Servieces Spatially scalable agent-based model + SWAT
Other Tools:
FAO Nexus Approach, Forseer, GLEW, GLOBIOM, IRENA’s Tool, MARKAL/TIMES, MAXUS ,NEST v1.1, NexSym,
Q-nexus, SPATNEX-WE, WBCSD, WEF Nexus Rapid Appraisal Tool, WEFNI, WESTWeb, WREI

Water-Energy-Food [8]

Water-Energy-Food-Land [1]

Water [2]

Food and Energy [2]

WEF and Population

Systems Archetypes:
Limits to Growth [1]
• Occurs when the growth is bounded by a limited resource.
• Solutions: Remove the barriers, new supply rescore
agricultural production is limited by water availability
industrial development is bounded by water availability
residential growth is bounded by water availability

Systems Archetypes:
Success to Successful [1]
• Occurs when two growing activities compete for the same resources.
• Solutions: proper allocation, apply new and efficient technologies

Potential Scenarios
• Changing crop pattern
• Enhancing crop productivity
• Controlling groundwater withdrawal
• Demographical changes
• ?

References:
1. Bahri, Muhamad. "Analysis of the water, energy, food and land nexus using the system archetypes: A case study in
the Jatiluhur reservoir, West Java, Indonesia." Science of The Total Environment 716 (2020): 137025.
2. Chen, Yan, and Weizhong Chen. "Simulation Study on the Different Policies of Jiangsu Province for a Dynamic
Balance of Water Resources under the Water–Energy–Food Nexus." Water 12, no. 6 (2020): 1666..
3. Honti, Gergely, Gyula Dörgő, and János Abonyi. "Review and structural analysis of system dynamics models in
sustainability science." Journal of Cleaner Production 240 (2019): 118015.
4. Ravar, Zeinab, Banafsheh Zahraie, Ali Sharifinejad, Hamid Gozini, and Samannaz Jafari. "System dynamics
modeling for assessment of water–food–energy resources security and nexus in Gavkhuni basin in Iran." Ecological
Indicators 108 (2020): 105682.
5. Senge, Peter M. The fifth discipline: The art and practice of the learning organization. Currency, 2006.
6. Stave, Krystyna, and Megan Hopper. "What constitutes systems thinking? A proposed taxonomy." In 25th
International Conference of the System Dynamics Society. 2007.
7. Bayer, Steffen. "Business dynamics: Systems thinking and modeling for a complex world." (2004): 324-326.
8. Tan, Andrew Huey Ping, and Eng Hwa Yap. "Energy Security within Malaysia’s Water-Energy-Food Nexus—A
Systems Approach." Systems 7, no. 1 (2019): 14.
9. Wicaksono, Albert, and Doosun Kang. "Nationwide simulation of water, energy, and food nexus: Case study in
South Korea and Indonesia." Journal of Hydro-environment Research 22 (2019): 70-87.

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