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27-6 Handbook of Dynamic System Modeling

a still larger sales force. This positive feedback loop serves the same general function as the positive loop

in the ﬂower model. If the positive feedback is sufﬁciently strong, the sales company will be able to grow.

However, the positive feedback in the sales company is different in that the company has a clearly identiﬁed

goal associated with the budgeting process. (Such a goal is not apparent in the ﬂowers model.) Figure 27.7

shows a negative feedback loop associated with the company’s attempt to bring the size of the sales force

into balance with the budgeted goal. Such loops are to be expected whenever a system is designed with

goal-seeking behavior. An additional negative loop is associated with the departures of sales persons. This

loop is controlled by the exit rate and is similar in function to the decay loop in the previous model.

The fourth loop in Figure 27.7 provides the density-dependent feedback that we would expect to see if

the sales company is to eventually come into dynamic equilibrium. To understand the impact of the loop,

imagine there is a major increase in the size of the sales force. This would reduce the effectiveness of each

sales person which tends to reduce the widget sales. Lower sales lead to lower revenues, a reduction in the

budgeted size of the sales force, fewer new hires, and a smaller size of the sales force. By working through

the loop in a step-by-step fashion, we see that the loop acts to negate the effect of the original change. This

is a characteristic effect of negative feedback.

27.3 Background on Daisy World

Daisy World is an imaginary world invented by Watson and Lovelock (1983) to illustrate a system with close

coupling between the biota and the global environment. This imaginary world is inhabited by white daisies

and black daisies. The white daisies have a high albedo, which means they are highly reﬂective. When the

planet surface is covered by a large area of white daisies, the planet will tend to reﬂect much of the incoming

solar luminosity. The black daisies have a low albedo, so their surface tends to absorb much of the incoming

luminosity. Consequently, the mix of daisies on the planet inﬂuences the absorbed luminosity and the

planet’s temperature. Now imagine that the planet’s temperature inﬂuences the rate of growth of the

daisies. If the temperature is close to the optimum value for ﬂower growth, the ﬂowered areas will spread

across the planet. But if the temperature is too high or too low, the ﬂowered areas will recede over time.

This imaginary world was created as a concrete example of system with homeostatic properties, like

those described for the human body by Cannon (1932). According to Levine (1993, p. 89), Lovelock’s

conclusion on a living planet followed from the principle of physiology: “that the living body strives to

maintain the constancy of its internal environment.” Lovelock believed that “Earth behaves in the same

way: its living and nonliving parts collaborate to hold temperature and other conditions at reasonably

constant levels.” The idea of a “living earth” needed a name, and Lovelock was reluctant to invent a

“barbarous acronym such as Biocybernetic Universal Systems Tendency/Homeostasis.” Instead, he chose

the name “Gaia Hypothesis” with the hope that Gaia would remind us of The Greek Earth goddess, and

“hypothesis” would encourage scientists to ask a different set of questions about the world.

The Gaia Hypothesis is a subject of much discussion (Capra, 1996; Dawkins, 1982; Joseph, 1990;

Levine, 1993; Lovelock, 1988, 1991, 1995), as is the example of Daisy World (Capra, 1996; Joscourt, 1992;

Kirchner, 1989; Lovelock, 1991). My purpose in this chapter is to use Daisy World as a third example of

system dynamics modeling. This new model builds from the simple ﬂower model shown previously so that

the reader can appreciate how system dynamics models can be created by building on previous work. The

Daisy World model will also illustrate how causal loop diagrams can help one understand and anticipate

the dynamic behavior. Finally, the model is used to illustrate a “management ﬂight simulator” a model

designed to promote learning through interactive experimentation.

27.4 The Daisy World Model

The Daisy World model was created by adapting the original differential equations by Watson and Lovelock

(1983) into a system dynamics model implemented in Stella (Ford, 1999, Chapter 21). Figure 27.8 shows

part of the model in the form of an equilibrium diagram. (An equilibrium diagram is a stock-and-ﬂow

System Dynamics Modeling of Environmental Systems 27-7

Total area 1000 acres

White daisy area

Black daisy area

Empty area

Decay rate

Black growth

81 acres/year

Actual white growth rate

Black decay

White decay

121 acres/year

Growth reduction multiplier

White growth

Indicated white growth rate

Indicated black growth rate

0.30/year

271 acres

403 acres

326 acres

0.326

121 acres/year

81 acres/year

0.30/year

Actual black growth rate

0.92/year

17.46°C

Temperature near the white daisies

27.46°C

Temperature near the black daisies

FIGURE 27.8 Equilibrium diagram for the daisy areas in the Daisy World model (from Island Press, 1999).

diagram with the equilibrium values for each variable on display.) The model represents a world with

1000 acres. When the system reaches equilibrium, the white daisies occupy 403 acres, and the black daisies

occupy 271 acres. Both ﬂowered areas are subject to a ﬁxed decay rate of 30%/year, so the system ﬁnds

its way to an equilibrium with both growth rates at 30%/year. With this mix of areas, the average albedo

is 0.53 which means the plant would absorb 47% of the incoming solar luminosity. Lovelock and Watson

set the incoming luminosity at 1.0, but their main interest was how the world would respond to changes

in the solar luminosity. They calculated the planet’s average temperature as a nonlinear function of the

absorbed luminosity. In this case, the average temperature turns out to be 21.8

◦

C. They then assumed

that local effects would lead to somewhat higher temperatures in the area covered by black daisies and

somewhat lower temperatures in the area covered by white daisies. Figure 27.8 shows the equilibrium

temperatures at 17.46

◦

C near the white daisies and 27.46

◦

C near the black daisies. Both ﬂowers grow

best (at 100%/year) when their local temperature is 22.5

◦

C. Figure 27.8 shows that the white ﬂowers are

experiencing temperatures cooler than the optimum, and their indicated rate of growth would be 92% if

there were no limits based on the empty area. The black ﬂowers are experiencing temperatures hotter than

the optimum, and their indicated rate of growth is also 92%/year (if there were no limits based on the

empty area). Both ﬂowers experience a reduction in the actual growth rates from the space limitations. By

the time all of these effects are sorted out, the two areas grow at the rate of 30%/year, just what is required

to counter the decay rate of 30%/year. The preceding description skips over some of the equations, but

the interested reader can learn the missing details from Modeling the Environment or by downloading the

model (http://www.wsu.edu/∼forda/GuidetoRest.html).

It is useful to now turn to the feedback loops that will help us anticipate how this world would respond

to a change in solar luminosity. Figure 27.9 shows a collection of eight feedback loops. This is a complex

diagram, so it is helpful to ﬁnd a familiar place to start. Let us begin with the black area in the upper right

corner of the diagram and look for the feedback loops that remind us of the three loops shown previously

in Figure 27.6. This comparison shows that the spread of the black daisies is controlled by the same set of

loops. There is a positive loop to power the growth in a ﬂowered area; a negative loop based on a ﬁxed rate

of decay, and a negative loop whose strength changes with the amount of empty area. If we turn to the

white area in the upper left part of the diagram, we see a similar set of three loops that control the spread

of the white daisies.

The remaining two loops in Figure 27.9 involve the plant’s absorbed luminosity and average temperature.

Each of these loops provides negative feedback, and it is these feedback effects that give the world the

27-8 Handbook of Dynamic System Modeling

homeostatic properties to illustrate the Gaia Hypothesis. To understand these loops, let us trace the

world’s reaction to a major change in solar luminosity. To begin, let us assume a large increase in incoming

luminosity and trace the reaction of the left-side loop involving the growth of the white daisies. With an

increase in solar luminosity, we would see an increase in the absorbed luminosity, an increase in the planet’s

average temperature, and an increase in the local temperature for the white daisies. The white ﬂowers would

then experience temperatures that are closer to their optimum growing conditions. This increases the white

growth rate and the area covered by white ﬂowers. With a greater area covered by white daisies, the planet

has a higher albedo and a lower absorbed luminosity. By tracing the cause and effect reactions around the

loop, we learn that the loop acts to negate the impact of the assumed increase in solar luminosity. We have

learned that this loop acts to protect the world against the threat of increases in the solar luminosity.

The remaining loop in Figure 27.9 operates through the black daisy area and the average temperature. To

understand the effect of this loop, let us imagine that the world experiences a major drop in the incoming

solar luminosity. This would lower the absorbed luminosity and the planet’s average temperature. The

local temperature near the black daisies would be lowered, so the black ﬂowers would then experience

temperatures closer to their optimum growing conditions. This increases the black growth rate and the area

covered by black ﬂowers. With a greater black area, the planet has a lower albedo and a greater absorbed

luminosity. The overall reaction of this loop is to negate the impact of the assumed drop in solar luminosity.

We have learned that this loop acts to protect the world against the threat of reduced solar luminosity.

Some readers may look at the temperature-dependent loops in Figure 27.9 and think of the actions

of a heating/cooling system that protects our home against changes in the external temperature. But

this analogy was not what Watson and Lovelock had in mind for Daisy World. They intentionally avoided

creatinga world withanythingresemblingthe target temperature on our home thermostat. Giventheir view

of the natural world, a better analogy is the human body which has evolved over the centuries to achieve

homeostatic regulation of body temperature. This is accomplished through involuntary physiological

(⫺)

(⫹)(⫹)

(⫺)

(⫺)(⫺)

Local heating near blacks

Black local temperature

Indicated black growth rate

Black growth rate

Black growth

Black area

Black decay

Solar luminosity

Planet average temperature

Absorbed luminosity

Average albedo of

the planet

Growth reduction multiplier

Empty area

Local cooling near whites

White local temperature

Indicated white growth rate

White growth rate

White growth

White decay

White area

⫹

⫹⫹

⫹

⫽⫽

⫹⫹

⫹

⫹⫺

⫺

⫹

FIGURE 27.9 Feedback loop control of Daisy World temperatures (from Island Press, 1999).

System Dynamics Modeling of Environmental Systems 27-9

reactions such as shivering and sweating. Shivering acts to protect the body’s core temperature under cold

conditions, so one might think of shivering as analogous to the spread of black daisies on Daisy World.

Sweating acts to protect the body’s core temperature under hot conditions, so one might think of sweating

as analogous to the spread of white daisies on Daisy World.

27.5 The Daisy World Management Flight Simulator

Figure 27.10 concludes the Daisy World example with a view of an interface to make it easy to experiment

with the model in interactive fashion. Such models are sometimes called “management ﬂight simulators”

because they remind us of a pilot ﬂight simulator, an electromechanical model of an airplane. A pilot ﬂight

simulator is a simpliﬁed representation of an airplane equipped with a sophisticated interface resembling

the cockpit. Pilots experiment with the simulated airplane to improve their ﬂying instincts. A management

ﬂight simulator has a similar purpose. It is designed to help managers improve their business instincts.

Such simulations have proven useful in several business systems including health care, airlines, insurance,

real estate, and project management (Morecroft, 1988; Sterman, 1992; Senge, 1990). Environmental

examples include simulators for management of fees and rebates (to promote sale of cleaner vehicles) and

the management of a salmon ﬁshery (Ford, 1999).

The simulator in Figure 27.10 provides information buttons to explain that the user is to experiment

with strategies to help Daisy World support life in the face of massive uncertainties in the solar luminosity.

In the example shown in Figure 27.10, the user has elected to test the simulated world’s response to a heat

shock scenario in which the solar luminosity jumps from 1.0 to 1.25 in the year 2010. The user may select

from a variety of daisies with different albedos and optimum temperatures. In this example, the user has

selected the 5th variety (which corresponds to the ﬂowers described by Watson and Lovelock). The graph

in Figure 27.10 shows the ﬁrst 30 years of a 40-year simulation. (The simulation is advanced 2 years at

a time when the user clicks on the “Run for 2 years” button.) The graph shows that the planet’s average

temperature has declined to around 22

◦

C by the year 2010. This is the year of the sudden increase in solar

FIGURE 27.10 Part way through a simulation of a heat shock scenario for Daisy World.

27-10 Handbook of Dynamic System Modeling

luminosity, and the planet’s average temperature jumps immediately to around 37

◦

C. But the world is not

ﬁxed at 37

◦

C even though the solar luminosity is ﬁxed at 1.25. Figure 27.10 shows that the planet’s average

temperature declines to around 22

◦

C within a decade after the onset of the heat shock conditions. This

decline is made possible by the spread of white daisies and the retreat of the black daisies, both of which

act to increase the planet’s average albedo. By the 30th year of the simulation, the world appears to have

found a new equilibrium condition with the average temperature close to the value well suited for the

ﬂowers. This is the response that Lovelock and Watson were looking for since it shows the biota reacting

with the nonliving part of the system to maintain conditions suitable for life—the central feature of the

Gaia Hypothesis.

Figure 27.10 shows two distinctive features that are crucial to the use of system dynamics models for

interactive experimentation as management ﬂight simulators. Since system dynamics focuses on dynamic

behavior, the ﬁrst and most important feature is the display of simulated behavior over time. In this

case, the time graph shows the jump in temperature in 2010 followed by the decline in temperature as

the planet’s feedback mechanisms negate the effect of the heat shock scenario. The display of dynamic

behavior over time is present in all system dynamics models and is often a key feature that distinguishes the

approach from other modeling methods. (In the case of Watson and Lovelock’s Daisy World, for example,

the original set of differential equations was not simulated over time. Rather, the authors found the

equilibrium properties of the world through analytical methods.) The second distinguishing feature is the

use of models in a highly interactive fashion to promote learning. Interactive simulation is encouraged by

a“score keeper”which gives a quantitative measure of the success in maintaining a world with homeostatic

properties that are conducive to life. The score keeper is a convenient way to promote friendly competition

and increased discussion within a group of students or managers.

Further Reading

The preceding examples provide the reader with simple, concrete examples of system dynamics modeling

of environmental systems. To learn more, the reader may turn to a variety of texts and articles. Starting

with texts, two of the early books are Forrester’s (1961) Industrial Dynamics and Richardson and Pugh’s

(1981) Introduction to System Dynamics Modeling with Dynamo. In my view, the most comprehensive and

useful textbook is Business Dynamics by Sterman (2000). Readers interested in environmental systems can

learn from Modeling the Environment (Ford, 1999).

System dynamics has been applied to a wide variety of environmental and resources systems. The most

widely known application is probably the collection of models associated with the study of population

growth in a world with ﬁnite resources (Forrester, 1971; Meadows et al., 1972, 1973, 1974, 1992). Some

of the recent applications are described in special issues of the System Dynamics Review. A special edition

edited by Sterman (2002) presents historical reﬂections and recent applications, all organized to cele-

brate the life of Dana Meadows. A special issue edited by Ford and Cavana (2004) presents the articles

summarized in Table 27.2.

Readers may learn of other applications from the citations listed in the bibliography of the System

Dynamics Society (2003). Figure 27.11 reveals the distribution of citations with keywords “environmental”

or “resource” over the time period from 1960 to 2002. There are 635 citations, approximately 10% of the

total publications in the 2003 bibliography. The number of publications increased in the early 1970s,

probably as a result of the tremendous interest in the Limits to Growth (Meadows et al., 1972). There were

around 10 citations per year during the 1980s and early 1990s followed by a dramatic increase at the turn

of the century. The appearance of 70–80 annual citations in the past few years is certainly a dramatic

development that might be attributed to growth in the System Dynamics Society and a growing awareness

of environmental problems.

Figure 27.12 provides further analysis of the number of environmental and resource publications during

the past four decades. This chart shows that energy and resource applications dominate the frequency of

citations. Resource applications cover a lot of ground, so it is reasonable for this keyword to extract a large

System Dynamics Modeling of Environmental Systems 27-11

TABLE 27.2

Summary and Comparison of Papers in the Special Issue of the

System Dynamics Review

(Ford and Cavana, 2004)

Author(s)

Dudley

Martinez Fernandez and Moxnes

Faust, Jackson, Ford, Earnhardt Arquitt and Johnstone

Esteve Selma

and Thompson

Model

Log export ban Irrigated landscapes Misper

ception of fee

dback Wildlife population management Blue-green algae

blooms

(spectacled and grizzly bears)

Geographic location Forests in Indonesia Irrigated

lands in SE Spain Reindeer rangelands AZA zoos, and

Greater Yellowstone Coastal waters of Queens

land,

of Norway

Ecosystem, USA

Australia

Issue(s)

Substantial increases New irrigated lands leading Over

use of renewable Improved management and Algae

blooms threatens water

in illegal logging to overexploitation of resour

ces owing

to conservation of wildlife

quality, coastal ecosystems, and

and deforestation available water resources misper

ceptions of populations in captivity and har

mful to humans

the dynamics

natural habitats

Model purpose To understand the To analyz

e the key

Simulators to test decision To assess the impact of

Scoping and consensus model

effects of a log export socioeconomic and making

of reindeer management actions, help guide t

o understand the dynamics of

ban on the forestry environmental factors management

with limiting data

collection and allocation

algae blooms and for developing

sector

leading to

resources

of conservation resource

research directions

overexploitation.

Client/sponsor/ Centre for International Public policy

makers Public and

Population biologists,

Committee est. by Environmental

decision maker Forestry Research, (Spanish

National Water private managers

zoo managers, national

Protection Agency, Queensland

Public and private Plan), local decision of r

enewable

park administrators

from Government departments,

forestry managers makers, general public, resour

ce systems

and advisors

academics and technical people,

and technical people

and community organizations

27-12 Handbook of Dynamic System Modeling

102

Year

Number

FIGURE 27.11 Number of publications listed in the System Dynamics Society bibliography extracted using the

keywords “environmental” or “resource” (from Cavana and Ford, 2004).

108

112

146

178

372

486

0 100 200 300 400 500

Wildlife

Climate

Earth

Waste

Fish

Pollution

Agriculture

Ecology

Forest

Natural resource

Sustainable

Water

Population

Environmental

Energy

Resource

Frequency

FIGURE 27.12 Analysis of System Dynamics Society 2003 bibliography by keyword frequency (from Cavana and

Ford, 2004).

number of citations. Energy issues have received major attention of system dynamics practitioners, as

explained by Ford (1999) and by Bunn and Larsen (1997). Figure 27.12 shows a signiﬁcant number of

citations for more narrowly deﬁned keywords such as ﬁsheries and earth systems.

The citation analysis shows a tremendous quantity of work, and these citations are mostly limited to

those who elect to publish their models in the system dynamics literature. The number of citations would

be even greater if we were to count the many modeling applications using system dynamics software

System Dynamics Modeling of Environmental Systems 27-13

such as Stella. These citations would include work by Costanza and Ruth (1997), Grant et al. (1997),

Costanza (1998), Deaton and Winebrake (1999) and the recent book on Mediated Modeling by Van Den

Belt (2004). These researchers do not place the same emphasis on feedback structure that is common in

system dynamics studies advocated by Forrester (1961), Ford (1999), and Sterman (2000). Nevertheless,

their use of computer simulation helps one understand the reasons for dynamic behavior. Counting such

applications would reinforce the upward trends in the citation analysis. Clearly, there is a major surge in

interest in the application of system dynamics methods to environmental systems.

References

Bunn, D.W. and Larsen, E.R., Systems Modeling for Energy Policy, Wiley, Chichester, 1997.

Cannon, W., The Wisdom of the Body, W.W. Norton, New York, 1932.

Capra, F., The Web of Life, Doubleday, New York, 1996.

Cavana, R. and Ford, A., Environmental and resource systems: editors’ introduction, Sys. Dyn. Rev., 20(2),

2004.

Costanza, R., Ed., Ecological Modeling (special issue on modeling in Stella), Vol. 112, Elsevier Science,

1998.

Costanza, R. and Ruth, M., Dynamic systems modeling for scoping and consensus building, in Sustain-

ability and Global Environmental Policy: New Perspectives, Dragun, A.K. and Jakonson, K.M., Eds.,

Edward Elgan, Cheltenham, UK, 1997.

Dawkins, R., The Extended Phenotype. Oxford University Press, New York, 1982.

Deaton, M. and Winebreake, J., Dynamic Modeling of Environmental Systems, Springer, New York, 1999.

Ford, A., Modeling the Environment: An Introduction to System Dynamics Modeling of Environmental

Systems, Island Press, Washington, DC, 1999.

Ford, A., and Cavana, R., Eds., Special issue on environmental and resource systems, Syst. Dyn. Rev.,

20(2), 2004.

Forrester, J., Industrial Dynamics, Pegasus Communications, Waltham, MA, 1961.

Forrester, J., World Dynamics, Pegasus Communications, Waltham, MA, 1971.

Grant, W., Pedersen, E., Marin, S., Ecology & Natural Resource Management: Systems Analysis and

Simulation, Wiley, New York, 1997.

Joscourt, S., Comments on ‘Chaos in Daisyworld’, Tellus, 44B, 243–346, 1992.

Joseph, L., Gaia: The Growth of an Idea, St. Martin’s Press, New York, 1990.

Kirchner, J., The Gaia hypothesis: can it be tested? Rev. Geophys., 27(2), 223–235, 1989.

Levine, L., GAIA: goddess and idea, BioSystems, 31, 85–92, 1993.

Lovelock, J., The Ages of Gaia: A Biography of Our Living Earth, W.W. Norton, New York, 1988.

Lovelock, J., Healing Gaia, Harmony Books, New York, 1991.

Lovelock, J., Gaia: A New Look at Life on Earth, Oxford University Press, New York, 1995.

Meadows, D.H., Meadows, D.L., and Randers, J., Beyond the Limits: Confronting Global Collapse,

Envisioning a Sustainable Future, Chelsea Green, Post Mills, VT, 1992.

Meadows, D.H., Meadows, D.L., Randers, J., and Behrens, W.W., III, The Limits to Growth, Universe Books,

New York, 1972.

Meadows, D.L., Behrens, W.W., III, Meadows, D.H., Naill, R.F., Randers, J., and Zahn, E., Dynamics of

Growth in a Finite World, Pegasus Communications, Waltham, MA, 1974.

Meadows, D.L. and Meadows, D.H., Eds., Toward Global Equilibrium: Collected Papers, Pegasus

Communications, Waltham, MA, 1973.

Morecroft, J., System dynamics and microworlds for policy makers, Eur. J. Oper. Res., 59, 9–27, 1988.

Richardson, G., and Pugh, A., Introduction to System Dynamics Modeling with Dynamo, Pegasus

Communications, Waltham, MA, 1981.

Senge, P., The Fifth Discipline, Doubleday Currency, New York, 1990.

Sterman, J.D., Teaching takes off: ﬂight simulators for management education, OR/MS Today,

October, 1992.

27-14 Handbook of Dynamic System Modeling

Sterman, J.D., Business Dynamics: SystemsThinking and Modeling for a Complex World, Irwin McGraw-Hill,

Boston, 2000.

Sterman, J.D., Ed., Special issue. The global citizen: celebrating the life of Dana Meadows. Syst. Dyn. Rev.,

18(2), 2002.

System Dynamics Society, System Dynamics Bibliography (User Notes Version 03B, July 2003), System

Dynamics Society, Albany, USA (available from web site: http://www.albany.edu/cpr/sds/biblio/

sdbib.html), 2003.

Van Den Belt, M., A System Dynamics Approach to Environmental Consensus Building, Island Press,

Washington, DC, 2004.

Watson, A.J. and Lovelock, J.E., Biological homeostasis of the global environment: the parable of daisy

world. Tellus Series B, 35, 284–289, 1983.

Dynamic Simulation with

Energy Systems Language

Clay L. Montague

University of Florida

28.1 Introduction .................................................................28-1

28.2 Reading an Energy Systems Language Diagram .........28-4

Layout of a Diagram and its Connection to Energy

Theory

•

Symbols within the System Boundary

•

Stylistic Diagramming Features not Directly Involved

in Equation Formulation

28.3 Translating a Diagram to Dynamic Equations ...........28-8

Equation Naming Convention Used in this Chapter

•

Basic Equation Forms Indicated in Energy Systems

Language Diagrams

28.4 Calibration of Model Constants ..................................28-21

28.5 Preparation for Simulation ..........................................28-22

Simulation Software and Energy Systems Language

•

Timescales and Numerical Integration

28.6 Dynamic Output of the Marsh Sector Model.............28-27

Model Output Analysis

•

Model Validation

28.7 A Brief Comparison with Forrester’s Systems

Dynamics Approach.....................................................28-29

28.8 Conclusions...................................................................28-31

28.1 Introduction

Energy Systems Language is a tool for identifying self-organized environmental systems and exploring

their development and possible responses to environmental change. It is a diagrammatic language not a

computer language. The meaning of the resulting diagrams is so speciﬁc, however, that a simulation model

can be directly derived from a diagram. The process of reading a diagram and deriving the simulation

model is the main focus of this chapter. In addition, some background history and theory of the method will

be mentioned along with some general guidelines for system identiﬁcation through diagramming. With

these tools, the reader may begin to ﬁnd appropriate uses of Energy Systems Language in their own work.

At its core, Energy Systems Language incorporates both an energy-based philosophy about natural orga-

nization of systems, and a methodology for developing and testing complex hypotheses about ecosystem

organization and response to change. The philosophy and methodology were developed by H.T. Odum

(1924–2002), one of the most inﬂuential intellectual leaders in the ﬁeld of ecology and a primary developer

of the discipline of systems ecology. The philosophy includes a broad theory of self-organization of natural

systems such as ecosystems and other open energy systems of humanity and nature.

No plans can be consulted for the design of self-organized systems, however, basic laws of nature must

be followed. Energy is the primary natural resource for self-organized systems. A constant supply of energy

is essential both to maintain each living individual as well as to obtain all other resources needed for life.

28-1