PERSPECTIVES

Environment models and decisions

Jeffrey Keisler • Igor Linkov

Published online: 28 August 2014

� Springer Science+Business Media New York (outside the USA) 2014

Environmental models are of crucial importance for

undertaking environmental assessment and management.

Even though the term ‘‘environmental model’’ is used

frequently, it is not well defined; even Wikipedia does not

have a definition. Our experience shows that environmental

models can range from simple qualitative cartoons

reflecting major processes, to quantitative process-based

mathematical models, all the way to miniaturized recre-

ations of major physical environments. What is important

about environmental models is their use. Rarely are envi-

ronmental models developed in the abstract; more fre-

quently, they are built to address specific decision needs.

Nevertheless, the linkage of environmental models and

decisions is defined with even less clarity than the taxon-

omy of environmental models itself. Some key questions

are rarely addressed regarding environmental models and

decision processes, some examples of which include the

following: How should information generated by environ-

mental models (both physical and analytical) be integrated

to address specific decision needs? What confidence is

there for a proposed course of action given all the data and

model uncertainty? Which tests must be conducted to

reduce uncertainty and increase confidence of selected

decisions? The decision maker with her/his values, goals,

and mission (within the context of a limited funding stream

and deadline) is often isolated from modeling efforts and

has very little control over the modeling process and

structure (Fig. 1). This is where mediated modeling efforts

have seen fruition. This Editorial recommends a way that

decision models can be added to the environmental mod-

eling toolset to increase the usefulness of the totality of

efforts in the field of environmental assessment and

management.

The discussion here will be limited to quantitative

analytical models—even though decision models can be

used to integrate results of physical and qualitative models

as well. Our simplified taxonomy model (Fig. 1) starts with

mechanistic models. These that require significant under-

standing of environmental and biological processes and

data in order to quantify parameters. Statistical models

require less understanding of biological mechanisms, but

require a significant volume of observations to correlate

data and conduct statistical testing. As with statistical

models, Bayesian models require understanding of con-

nections in environment, but they allow integration of

limited observational data with expert judgment expressed

in forms of probabilities. Of course, data need decrease

when one moves from mechanistic to Bayesian models and

the reliance on expert judgment increases. Most of the

work done in environmental science and engineering relies

on mechanistic and statistical models, while Bayesian

models have only recently begun to attract attention, e.g.,

Aguilera et al. (2011). In some cases, the nature of the

problem, the resources available, and the support of deci-

sion makers are sufficient to allow construction of such

state-of-the-art Bayesian decision models (Uusitalo 2007),

but these conditions are far from the norm.

There is much potential for bridging the substantial

distance from mechanistic models to state-of-the-art deci-

sion models, i.e., between ‘‘higher tech’’ decision analysis

and no decision analysis. Multi-criteria decision analysis

(MCDA, e.g., Belton and Stewart 2002) refers to a range of

J. Keisler

University of Massachusetts Boston, Boston, MA, USA

I. Linkov (&)

US Army Engineer Research and Development Center, Concord,

MA, USA

e-mail: Igor.Linkov@usace.army.mil

123

Environ Syst Decis (2014) 34:369–372

DOI 10.1007/s10669-014-9515-4

distinct methods with some common elements. As long as

the limitations associated with its simplifying assumptions

are accounted for, user-friendly strands of MCDA can play

this in-between role. This utility was already identified in

Kiker et al. (2005). Now some years later, with our

growing experience with MCDA, its potential is becoming

apparent. It can yield many of the benefits of the state-of-

the-art decision practices while still being practical for the

larger community of environmental modelers to assimilate

and understand. We shall now introduce key aspects of

such approaches and describe in detail the role of MCDA,

which is the focus of this article.

MCDA can involve rather simple mathematics: Typi-

cally, the analyst first identifies a number of categories,

then assigns weights to them, scores alternatives with

respect to each of them, and adds up the weighted scores

for each alternative to get a total score. Such linear additive

scoring models are also used to construct, for example,

habitat suitability indices, which may have implicit weights

and which may be used in conjunction with MCDA or in a

similar manner to MCDA (Linkov and Moberg 2012). Note

that such analyses can make use of little more than the

operations of addition, multiplication, and, possibly, divi-

sion. There are somewhat more sophisticated MCDA

methods that build off of these basic steps to get a richer

process and greater insight, but first it is important to be

confident that the base is worth building on.

What is the goal of MCDA, and what reason is there to

think it will work? The ultimate goal for decision makers is

to identify the most desirable course of action. Most often,

they are trying to select from among a set of alternatives.

Sometimes analysis can also play a role in creating alter-

natives that are likely to be desirable, and sometimes what

is needed is a tool to measure and discuss the desirability of

hypothetical alternatives or whatever alternatives may

appear in the future. Note, there are other approaches such

as mediated modeling (van den Belt 2004), whose goals

and methods have some overlap with MCDA, e.g., utilizing

expert judgments and integrating knowns and unknowns.

If these are the things MCDA is supposed to do, what does

it mean to do them well? Here, classical decision analysis

(CDA, e.g., Raiffa 1968) provides some guidance. CDA uses

a set of axioms to formulate utility functions with the useful

property that, whenever the utility (or expected utility) of the

outcome (or probabilistic lottery over outcomes) associated

with one course of action is higher than that of another, it

means exactly that the decision maker prefers the results of

first course of action to those of the second. The aforemen-

tioned preference is not because of the utility scores; decision

makers have preferences for outcomes whether or not any-

one has created a mathematical utility function. Rather, the

fact that it is possible to formulate such a utility function

means that by determining the decision maker’s preferences

in a small number of reference situations, it is possible

exploit the relationship thereby derived to perform calcula-

tions to predict what the decision maker would prefer in other

situations that may arise.

CDA can formulate single-attribute utility functions,

most commonly utility of wealth, that are used to compare

gambles over that attribute. Multi-attribute utility theory

(MAUT, e.g., Keeney and Raiffa 1976) extends this idea,

so that it is possible to compare uncertain outcomes, and

the related concepts of multi-attribute value theory

(MAVT, e.g., Dyer and Sarin 1979) allow comparison of

deterministic outcomes with dimensions that are not natu-

rally expressed in a single common unit such as dollars.

MAUT composes a single large function from simpler

functions associated with each attribute, and it does so

mostly through weighting and addition, as is usually done

with other types of MCDA; we can think of MAUT as a

special type of MCDA method, one that conforms to the

axioms of decision theory (part of CDA) and initially

imposes few other assumptions about the form of the

scoring function. If there is a unitary decision maker who

has well-defined preferences for particular outcomes, and

these outcomes can be mapped to numerical values for a set

of parameters; then, MAUT is the gold standard for rational

decision making (in that the axioms of decision theory are

commonly taken to be axioms of rationality).

Note the axioms of decision theory, drawn from prob-

ability theory, logic, and von Neumann and Morgenstern’s

(1947) formulation of game theory: orderability (which for

our purposes means that preferences over states exist),

transitivity (preferences are consistent), continuity (so that

utilities can be calibrated), substitutability and monoto-

nicity (expected utility works), and decomposability

(decision trees work).

Other MCDA methods serve as either simpler or more

flexible approximations for MAUT. If there is not time,

Fig. 1 Decision analysis modeling in environmental science for an

age of data abundance, complexity, and accountability

370 Environ Syst Decis (2014) 34:369–372

123

attention, or ability to articulate preferences over so many

states, MCDA methods can be efficient. If there are multiple

stakeholders, MCDA methods can support a decision pro-

cess that comprehends diverse preferences. In all cases, the

goal is to be able to score alternatives in order to identify,

understand, and act upon their relative desirability.

Simple MCDA methods may be especially useful when

there are decision implications for issues whose nature is

still developing and emerging. Consider the case of

deciding which new chemicals are safe enough and bene-

ficial enough to approve. Decision makers do not know all

the chemicals that might be developed from a new tech-

nological area (e.g., nanoparticles); they do not know how

these might differ from each other (i.e., how to characterize

them); they do not know what all the possible effects are;

and they may not have even thought about how good or bad

these effects would be. Still, it is necessary to act. It is

necessary to act early and often because there are many

decisions to make about each of many possible products.

Since decisions taken affect lives and livelihoods, [as the

precautionary principle would support (Johnson 2012)]

these decisions should be made as sensibly as possible

given the information, knowledge, information, and con-

cerns we do have.

It is possible, in effect, to brainstorm concerns and ideas

relating to the chemicals. What is already known from

other chemicals can be incorporated into the structure, e.g.,

that there will be health, environmental, and economic

impacts. From this, it is possible to work toward more

detailed descriptions of impacts that will be incorporated in

an MCDA model at various appropriate levels of a value

hierarchy. No one would presume to understand all the

potential physical properties and interactions for a newly

emerging technology, and yet it is reasonable to expect that

with enough experience and research these will be dis-

covered, so some of the scores assigned in the model

represent the best guess at what relationships will ulti-

mately be revealed. The elements of the model are defined

such that human beings can make these judgments; this

requires definitions that are not only clear, but also have

some connection to reality. It is not yet practical to think

explicitly about all the impacts, but again, decision makers

have an idea of what types of impacts there will be and it is

possible to create functional categories for them. Knowing

in advance that the aim is not to structure complex math-

ematical relationships (about which one could only make

wild guesses), analysts can instead use simple linear

additive relationships. In doing so, it is critical that not to

take too many shortcuts, but rather to define elements

accordingly (e.g., as suggested by Belton and Stewart), so

that this is a logically correct way to work with the vari-

ables. In order to complete calculations, it is necessary to

populate models with numerical scores on alternatives and

numerical weights on factors. It is critical here to be cog-

nizant of the meaning of those scores and weights. One

may define scales with numerical values that will be

obtained from objective data, or use qualitative language

refined, so that it has as common as possible a meaning to

different audiences; here, analysts often use standardized

language associated with a given methodology.

The end result is a model that represents what people

understand, what they believe, and what they care about.

The model should contain sufficiently rich detail for deci-

sion makers and stakeholders to see results regarding all

sorts of issues large and small; it should be transparent, so

that the way in which some new input affects the relative

value (i.e., desirability) of alternatives is easily traced,

tested, and trusted. It should represent different viewpoints

in a manner that allows for neutral discussion of the dif-

ferences, their nature, and their implications. That is, the

model serves not only to recommend a high-score alter-

native, but the challenge creating it serves as the focus of a

process in which viewpoints are articulated, discussed, and

debated, while the application of the model focuses a social

process that aims to end in decision. If the model is created

with care, it does in fact indicate how attractive each

alternative is and to whom, and thus, it is reasonable to

expect that the decisions produced will actually lead to the

most desirable outcomes possible based on what is known

at the time.

As computational technology improves, data availability

grows, and the pace of activity increases, the demand for

analytic approaches to decision making is generally rising.

This is especially true in the environmental field, where

scientific understanding is growing but the number of

questions keeps increasing as the stakes keep growing.

High-quality MCDA and other decision analytic modeling

are the key to keeping up with this growing need. In the

authors’ first attempt at a formal literature review focusing

Fig. 2 Ratio of multicriteria decision analysis to total environmental

publications in World of Science database normalized to 1990 value.

Search methodology is described in Huang et al. (2011)

Environ Syst Decis (2014) 34:369–372 371

123

on MCDA applications (Linkov et al. 2014), there was so

little out there with regard to MCDA that it was difficult to

even construct a database of environmentally relevant

publications and we had to expand the search to include

tangential areas. By the time of a second review (Huang

et al. 2011), there were so many applications it was nec-

essary to screen out papers from several thousand hits. The

field has continued to grow rapidly over the last 4 years

(Fig. 2). Currently, 2 % of all environmental papers

indexed by Web of Science utilize MCDA. The number of

MCDA papers published during the last 4 years (1,106

papers in 2010–2013) is larger than the total number of

MCDA papers published in the preceding decade (1,059

papers published in 2000–2009). Let this trend continue.

Acknowledgments Drs. Greg Kiker and Thomas Seager and Mr.

Arun Varghese were early supporters of our efforts to incorporate

MCDA into environmental assessment and management. Ivy Huang

was instrumental in our second literature review and was kind enough

to re-generate temporal trend presented in Fig. 2. Our current effort

and this paper would not be possible without Matthew Bates, Zach

Collier, and Cate Fox-Lent. Finally, we wish to thank Dr. Todd

Bridges whose vision and enthusiasm increased MCDA methods in

USA the Army Corps research enterprise from a single review paper

in 2003 to today a multimillion dollar enterprise covering all USACE

business lines, including navigation, flood management, climate

change, assets management. Permission was granted by the USACE

Chief of Engineers to publish this material. The views and opinions

expressed in this paper are those of the individual authors and not

those of the US Army, or other sponsor organizations.

References

Aguilera PA, Fernandez A, Fernandez R, Rumı R, Salmeron A (2011)

Bayesian networks in environmental modelling. Environ Model

Softw 26(12):1376–1388

Belton V, Stewart T (2002) Multiple criteria decision analysis: an

integrated approach. Springer, Berlin

Dyer JS, Sarin RK (1979) Measurable multiattribute value functions.

Oper Res 27:810–822

Huang I, Keisler J, Linkov I (2011) Multi-criteria decision analysis in

environmental sciences: ten years of applications and trends. Sci

Total Environ 409(19):3578–3594

Johnson AR (2012) Avoiding environmental catastrophes: varieties of

principled precaution. Ecol Soc 17:9

Keeney R, Raiffa H (1976) Decisions with multiple objectives. Wiley,

New York

Kiker G, Bridges T, Varghese AS, Seager TP, Linkov I (2005)

Application of multi-criteria decision analysis in environmental

management. Integr Environ Assess Manag 1:49–58

Linkov I, Moberg E (2012) Multi-criteria decision analysis: environ-

mental applications and case studies. CRC Press, Boca Raton

Linkov I, Anklam E, Collier Z, DiMase D, Renn O (2014) Risk-based

standards: integrating top-down and bottom-up approaches.

Environ Syst Decis 34:134–137

Raiffa Howard (1968) Decision analysis: introductory lectures on

choices under uncertainty. Addison-Wesley, New York

Uusitalo L (2007) Advantages and challenges of Bayesian networks

in environmental modeling. Ecol Model 203:312–318

Van den Belt M (2004) Mediated modeling: a system dynamics

approach to environmental consensus building. Island Press,

Washington

von Neumann J, Morgenstern O (1947) Theory of games and economic

behavior, 2nd edn. Princeton University Press, Princeton

372 Environ Syst Decis (2014) 34:369–372

123