Advances in Applied Sociology
2012. Vol.2, No.4, 344-349
Published Online December 2012 in SciRes (
Copyright © 2012 SciRes .
The Advantages of Using a Computer-Based Integrated
Assessment to Promote Cooperative Behavior in Groundwater
Oliver López Corona1, Pablo Padilla2, Octavio Pérez Maqueo3, Oscar Escolero4
1Posgrado en Ciencias de la Tierra, Instituto de Geología, Universidad Nacional Autónoma de México,
Mexico City, México
2IIMAS, Universidad Nacional Autónoma de Méxic o, Mexico City, México
3Red Ambiente y Sustentabilidad, Instituto de E cología A.C., Xalapa, México
4Instituto de Geología, Universid a d Nacional Autónoma de México, Mexico City, México
Email: oliverlc@geologí
Received October 17th, 2012; revised November 20th, 2012; acc ep te d N ov ember 30th, 2012
The ultimate goal of environmental impact assessment is to guarantee that benefits generated by a devel-
opment project will not cause highly negative effects on the environment or public health. The fulfillment
of this goal depends on the willingness of proponents and society to cooperate. The information manage-
ment, its accessibility to community and the educational level of participants are of great relevancy too.
Cooperation is not always attainable due to conflicts between individual and community interests. Con-
flict leads to a variety of cooperative and non-cooperative responses, depending on the information avail-
able to the actors. In order to capture the tendency in which a community perceives the proposals, we in-
troduced an information index. We prove that computer models have a direct impact on this information
index. This computer-based approach, leads the EIA to the paradigm of adaptive environmental assess-
ment and management. To implement this, a system based on artificial intelligence and game theory was
used to resolve a study case of conflict in groundwater management.
Keywords: Environmental Sociology; Environmental Management; Artificial Intelligence; Optimal
Management; Game Theory
Societies have become more participate and aware of the ef-
fects that the environment suffers as a consequence of devel-
opment projects. As a result, the question of how development
should be conducted to assure sustainability and society coop-
eration arose. In order to anticipate and avoid negative conse-
quences derived from any development project, environmental
impact assessment tools, EIA, were created (Pérez-Maqueo,
The main objective of the EIA is the evaluation and predic-
tion of the positive and negative effects that a project may have
on the environment. For this purpose, its proponent is com-
pelled to assess the possible environmental consequences it
may cause. Although society may participate directly in the
evaluation of these assessments, the decision to approve the
project rests, in most cases, with authorities. But EIA is much
more than a predictive tool. Given the intrinsic quality of EIA
as a forum for public participation, and as a consensus tool for
decision making, it is regarded as a valuable route to sustain-
ability (Lawrence, 1997). Furthermore, it is considered the best
control option for projects that cannot be easily regulated
through legal standards or land use plans (Pérez-Maqueo,
A cooperative behavior between proponents and society
members is an indispensable condition to reach the optimal
benefit for every part. In the best case, this agreement should
not only consider the interests of directly involved parties, but
also those of other sectors of society, including future genera-
tions. Developers must carry out a reliable EIA, and society
must develop confidence in it, otherwise, a non-cooperative
behavior could emerge due to distrust among them (Pérez-
Maqueo, 2004).
Unfortunately, cooperation is not always possible because
individual and community interests may be in conflict. For
instance, in order to save money, a proponent could hide or fail
to acknowledge the negative effects of a project. Other sectors
of the society could exaggerate the importance of the environ-
mental impacts of it to obtain overcompensation (Pérez-Ma-
queo, 2004).
If these non-cooperative behaviors occur, short and medium
terms conflicts come about. Decisions may be made outside the
EIA framework, and influenced by external interests. If this
non-cooperative situation prevails, the investment could plunge
into uncertainty, the development could be constrained and the
confidence in EIA lost (Pérez-Maqueo, 2004).
As the complexity of interactions in socio-ecological systems
grows, the successful management becomes a more difficult
task. Traditional approach of EIA’s concentrates on techno-
logical development (hard path) as the only solution to envi-
ronmental problems. On the other hand, adaptive management
which addresses directly the links between social and ecologi-
cal systems, is now recognized as a promising alternative ap-
proach. This emerging approach incorporates the stakeholders
in the decision process, making this so called “soft path” re-
quire special tools to facilitate collaboration between experts
and stakeholders (Magnuszewski et al., 2005).
To meet the new challenges of sustainability, assessment
must be able to integrate: multi-objective and multi-agent
problems, social and natural sciences, multiple scales of analy-
sis, models of the system components and the use of multiple
decentralized databases (Jakeman & Letcher, 2003). In the
particular case of groundwater highly sophisticated models are
required and for most multi-objective optimization problems,
there are no satisfactory deterministic algorithm available. By
contrast, genetic algorithms (GA) have been proved to be
highly suitable for this task (Back, 1995; Fogel, 2006). There-
fore the main objective of this paper is to explore under what
conditions cooperation emerges and how the combination of
resource modelling and optimization would be used to improve
this emergence. We also highlight the importance of informa-
tion in the decision making processes.
When EIA’s Fails
In recent years, the federal government in México was plan-
ning to build a new international airport in the vicinities of
México e City. Authorities announced that after analyzing all
sites suited to build the airport, they had concluded that only
two satisfied the technical requirements. First, the problem was
perceived as a stag hunt game, while for environmental asso-
ciations it became a matter of concern for the probable envi-
ronmental impacts it could generate. The government decided
to conduct a comparative study between both locations, to im-
prove their credibility in the decision making process. Aca-
demic institutions were invited to perform an environmental
diagnostic evaluation (not an EIA). Experts reported that both
sites would be subjected to similar environmental costs. The
final site was chosen then, considering the technical, aeronauti-
cal and economic viability of the project. Nevertheless, as so-
cial aspects were neglected in the analysis, once the results of
the study were made public, local inhabitants protested against
the project arguing land propriety and low compensation prices.
They stated they would not move from the site. The conflict
generated different ways of protests: blocking highways, deci-
sion-makers kidnapping, violent and armed confrontations,
wounded people and even the death of one of the project’s op-
ponents. Although the government increased the compensation
payments, no agreements were reached. Finally an alternative
location to get on the project was looked for.
It has been stated (Wathern, 2001) that EIA possess several
flaws which render it to fail. They were becoming increasingly
lengthy and unwieldy as a result of some kind of “measure
everything” syndrome. They are deficient as an impact predict-
tion tool because of the highly dynamic disposition of natural
systems, not to mention that the technical nature of EIA’s re-
ports break down communication between EIA’s personnel and
decision makers or society.
As discussed by Alshuwaikhat (2005), despite the existence
of good EIA guidelines and legislation, environmental degrada-
tion continues to be a major concern in developing countries. In
many cases, EIA has not been effective due to legislation, or-
ganizational capacity, training, environmental information,
participation, diffusion of experience, donor policy and political
will. EIAs have not been able to provide environmental sus-
tainability assurance (ESA) for these countries (Sadler, 1999).
This failure and the inherent limitations of EIA lead to the con-
sideration of strategic environmental assessment (SEA). It is
the proactive assessment of alternatives to proposed, in the
context of a broader vision, set of goals or objectives to assess
the likely outcomes of various means to select the best alterna-
tive(s) to reach desired ends (Noble, 2000).
As a response to these alleged weaknesses, much effort has
been made to achieve an integrated assessment (IA) such as the
adaptive environmental assessment and management approach
(Holling, 1978). It combines different academic disciplines to
obtain concise data based predictive knowledge that provides
useful input for decision makers as noted by (Rotmans & Dow-
latabadi, 1997; Rotmans, 1998; Toth & Hizsnyik, 1998) and
(Sluijs et al., 2001; Van Asselt & Rijkens-Klomp, 2002).
In this IA framework, small workshops can be used to get
together scientists, decision makers, society representatives and
computer modeling experts. The goal is that participants reach a
consensus on the important features and relationships that
characterized the system under study. This must be achieved in
such a way that the fundamental interrelations of social and
natural processes appear transparent to all, scientists and
non-scientists (Siebenhner, 2004).
Particularly in the IA of very complex systems, such as cli-
mate change or groundwater, computer models are the domi-
nant means of scientific knowledge production. They have
demonstrated suitability to accomplish a common understand-
ing of environmental-social problems, analyze the causes and
impacts of the problems, explore and examine management
options and support the formulation of objectives and restrict-
tions (Tuinstra et al., 1999; Hisschemller et al., 2001). But even
in more simple systems, compared to human experts, computer
models are often reckoned as more comprehensive and reliable,
which usually improve the perception that society has of a pro-
ject (Siebenhner, 2004).
A Computer-Based IA for Groundwater
Groundwater is the most intensive extracted natural resource
nowadays, it provides around 70% of drinkable water in the
European Union and more than 50% in the rest of the world. It
is the corner stone of the Asian’s “green revolution”, sustain
wide rural areas in the subsaharian zone and more than 1200
million people living depend on it in cities all over the world
(Zektser & Margat, 1997; Stephen et al., 1998; Burke & Monch,
Let us picture that the groundwater management authority in
México’s National Water Commission (CNA), desired to de-
sign a sustainable policy plan, in order to avoid overexploita-
tion, for an aquifer with considerable extraction and for which
society’s demand is expected to grow in the near future.
Since a couple of decades ago, long term planning for
groundwater management has been carried out with computa-
tional modeling (Routh, 1877; Bennet, 1979; Jones et al., 1987)
in which optimization techniques have become more common
as time goes by. Nevertheless, as the underground flow is gov-
erned by second order partial differential equations, its control
is hard to calculate and management issues are even harder to
respond to because of their multi-objective and multi-con-
straints nature. For this real world problem, no deterministic
algorithm seems to be fast and robust enough to be used; in-
stead, genetic algorithms or more generally, evolutionary com-
putation, have proved to be most adequate (Andrei, 2004b;
Bellman, 1957; Das & Datta, 1999a).
Copyright © 2012 SciRe s . 345
Considering the above, we coupled a standard open source
groundwater modeling software MODFLOW (USGS, 2008)
with a free software genetic algorithm optimization tool GA-
toolbox (Sastry, 2006) and a game theory analysis free software
Gambit (McKelvey et al., 2007). We call this implementation
as Natural Resources Optimal Management System: SMORN.
It can resolve a multi-objective optimal control problem, with
m constraints for groundwater flow. To illustrate the SMORN
capabilities, we used real data from Duero’s river basin in Mi-
choacan México. We aimed a three objective function problem:
maximize the total water extraction, minimize the mean draw-
down and minimize the mean drawdown velocity. The first
objective function is clearly designed to obtain the maximum
benefit from the aquifer, meanwhile the second and third seek
to lower aquifer impacts and possible subsidence problems.
SMORN optimization converges to four different types of op-
timal solution: the first one corresponds to an extraction privi-
leged type of solution; the second, privileges the aquifer con-
servation and the two others offer an intermediate solution
where extraction and conservation are in equilibrium.
These normalized values could be interpreted as payoffs for a
hypothetical player that pursues to take the most profit from a
specific objective function. Considering the results presented, a
CNA’s authority could increase the possibility for aquifer co-
operation, but even in that case, what type of solution should be
implemented? Since genetic algorithms provide Pareto front
solutions indistinguishable from the optimization point of view,
the best answer to the question could seem choosing the one
that privileges extraction. However, the authorities have also to
consider those sectors of society that could protest against that
posture, so what should be done? Consider this as a game of
four players, being: a management authority (admin); aquifer
users (user); a sector of the society concerned mostly with aq-
uifer conservation (aquifer); and a player that personifies
chance (chance). Bearing this in mind we construct payoffs as
follows: User’s payoffs are conceptualized as the proportion of
water extraction permitted by the authority taken from the
maximum normalized water extraction rate; Aquifer’s payoffs
are calculated by the grade of conservation contemplated in the
management policy adopted by the authority, considering the
mean drawdown and mean drawdown velocity control;
Admin’s payoffs are conceptualized as the image perception
from each part of this hypothetical society.
We use Gambit free software to analyze the game proposed
as shown in Figures 1 and 2. The first move is made by admin,
who mostly decides if a management plan must be imple-
mented or not. If admin decides not to implement a manage-
ment plan, then an arbitrary use of aquifer takes place; situation
in which user can decide whether to continue the actual exploit-
tation of the aquifer or make an undefined change. In the first
case payoffs are calculated from original water extraction rates
data; in the second one no payoff can be assigned due to uncer-
Conversely, admin could decide to implement a management
plan, in which user may cooperate or not, this is represented as
a chance move with a probability of occurrence called con-
vincing index. If user does not cooperate, like in the preceding
case, two options arise: user may continue with the current use
of the aquifer or make an undefined change. In case user coop-
erates, then four types of optimal calculated solutions are
available for selection. Finally it takes place a chance move
with a probability called confidence index.
Figure 1.
First part of the extended game that represents the decision making
process for this problem where conflicted interests compete. The order
in the payoffs is admin, user and aquifer. Color red is for admin, blue
for user and black for chance.
Figure 2.
Second part of the extended game that represents the decision making
process for this problem where conflicted interests compete. The order
in the payoffs is admin, user and aquifer. Color red is for admin, blue
for user and black for chance.
Being the game as described, we calculate the Nash equilib-
ria for the strategic associated game to find the optimal strate-
gies for all players. In game theory, a Nash equilibrium is the
set of strategies obtained when the total payoff of no player
increases unless another one changes his strategy. In this exam-
ple two types of Nash equilibria are found. The first kind is
characterized by admin choosing not to plan and user main-
taining current use of aquifer. In the second type, admin
chooses to plan, while the proportions of users that cooperate
Copyright © 2012 SciR es .
choose the optimal solution number three, and the part that does
not, continues with the current use of the aquifer.
When convincing and confidence indexes are small both
types of Nash equilibria are presented but if one of these is
greater than .5 then only the second type is found. On account
of this, the Information index is defined as the product of the
convincing and confidence indexes that represents the quality
of the project’s information, the way in which it has been dis-
played and the grade of confidence that society has over the
proponent and authorities.
In this way we could evaluate the emergency of cooperation
in function of the Information index. This kind of game theory
analysis seems to be very useful in conflict scenarios, but that it
only provides the best solutions must be recalled. That is a
second benefit of Pareto solutions, for having a wider range of
solutions provides a great opportunity to negotiate in case some
users show themselves reluctant to cooperate with the optimal
In each case EIAs are regulated under norms and rules en-
forced by a central authority who decides whether the imple-
mentation of a project is suitable or not, based on environ-
mental, economic and social terms. In this way the central au-
thority ensures law will be applied if the proponent defects,
increasing users confidence. However cooperation could be at
risk cause, even though coercion could avoid the trust dilemma,
it is important to remember that a coercive force is useful as
long as defectors are efficiently punished by the authority (Os-
trom et al., 1999). Unfortunately there are many cases in which
the institutional capacity to monitor the restrictions incorpo-
rated by the authority is not adequate, and leaves non-fulfill-
ment of conditions without sanction (Pardo, 1997). On the other
hand, sometimes (for example in public projects) government is
perceived as the interested party on top of both, proponent and
society, and not as the referee.
The success of EIAs also depends on how reliable is the
communication between the society and the proponent. In this
sense, communication based on a formal quantitative and scien-
tific analysis that allows an estimation of probable effects, has
several advantages (Porter, 1995). According to Suter (Suter,
1993) amongst these advantages are the ability to establish the
basis to compare and prioritize risks; a greater credibility in
EIAs; the chance to focus on the assumptions and the data on
which the predictions are made; and to separate the scientific
process of estimating magnitude from management decisions
(risk management). Following Sinclair and Diduck (Sinclair &
Diduck, 1995) who emphasize that education is a precondition
to advance public involvement, we consider vital to improve
the understanding society has on the role of EIA’s. In addition,
Schenider (Schneider, 1997) states that society requires literacy
about how scientific and decision making elements interact.
The worst scenario appears when society defects even if the
proponent is willing or compelled to cooperate. Even if, as a
suboptimal payoff, mitigation or compensation measures are
imposed, a cooperative behavior from society cannot be ex-
pected, rendering the above tools limited.
Within the theoretical development of game theory, some
solutions have been proposed for situations in which defection
seems the most rational choice. A recommended way to cope
with these cases is to restructure payoffs using transfer pay-
ments or others means, so that the affected sector sees the out-
come as equitable (Lejano & Davos, 1999). However, the major
drawback is when, even if the proponent cooperates, for ex-
ample by compensating the affected sectors, they reject the
proposal in order to gain an overcompensation (Pérez-Maqueo,
2004). Nash (Nash, 1950) suggested a reviewing scheme to
avoid cases in which one of the parties involved tried to get
additional benefits. The aim of this scheme is to maximize the
benefits of each party by means of arbitration. Once parties
reach an agreement, a contract could be celebrated. This con-
tract should contain all concerned issues like mitigation meas-
ures, monitoring programs and compensations discussed pre-
viously. They could also be complemented by environmental
assurance bonds (Pérez-Maqueo, 2004). These bonds should
guarantee the rights of proponent and society under conditions
of uncertainty (Costanza & Cornwell, 1992). Let us consider
that the proponent agrees to make a financial deposit to cover
any damage the project could generate on the environment. If
such damage occurred, then the bond would be used to com-
pensate the affected segment of society. If there were no dam-
age, then the bond would be returned to the proponent, with the
interests accumulated along that period.
Certainly, the above tools and recommendations do not
guarantee that cooperation will emerge in each project (or in
terms of game theory in one shot game). However they can still
be useful if both, cooperation and defection, are behaviors that
could spread or influence other segments of society. In this
sense, one of the main issues is to understand how cooperation
could be achieved in situations where individual interests are at
odds with common welfare. One of the hypotheses is that co-
operation could be attained by convincing the parties of the
benefits of indirect reciprocity (Nowak & Sigmund, 1998).
Simulation models and computerized experiments (Millinski et
al., 2002) show that cooperation pays off by means of indirect
reciprocity because this behavior increases the chance of re-
ceiving a cooperative response from others. Although, in a one
shot game society may not reciprocate the cooperative behavior
from proponents, it would confer them reputation for new pro-
jects. In addition, reputation is an important asset that is posi-
tively correlated with cooperative actions among players in our
society (Millinski et al., 2002). The recent implementation of
environmental management system (EMS) such as Eco-Man-
agement and Audit scheme, ISO 14000 and BS 7750 and vol-
untary environmental compliance audits promoted by the
Mexican’s Environmental enforcement agency (PROFEPA),
are examples of tools that enhance reputation and that operate
independently from the authority enforcement. Society can also
generate reputation, but it depends on how many of the seg-
ments of it are recognized as cooperative players that reach
agreements with proponents. Possibly, in the future, proponents
will endeavor to conduct their projects in sites where society
satisfies this condition. And hopefully, the selective process
between proponents and society will lead a fair development in
the future.
Finally, we used a computer-based IA, which incorporates
evolutionary computation and game theory, to promote coop-
eration. Cooperation dependency to a proposed Information
index was analyzed. The information index is constructed to
contemplate not only the quality of the information, but also the
way in which it is displayed to society, as well as the confi-
dence rapport over the proponent and authorities. We showed
that cooperation can not be ensured unless information index is
Copyright © 2012 SciRe s . 347
complete enough, which can be achieved by considering the
following key points: 1) scientific approach to environmental
problems and the use of computer simulation promotes coop-
eration but only if adequate translation is made to make this
scientific knowledge accessible to all participants; 2) as infor-
mation index includes proponent’s credibility, mechanisms to
track the reputation of participants like the mentioned above are
highly recommended.
This study was supported by a CONACyT fellowship in the
UNAM’s Earth Science Grad School, a grant from CONA-
CYT's program 90219 of the Instituto de Ecología A.C. and by
UNAM'S PAPIIT ptoject: IN229109 “Modelación espacial y
manejo de recursos naturales en la región de Chamela-Cuix-
mala en la costa de Jalisco”. We thank Miguel Equihua, Fer-
nando Salmerón, Jose Luis Palacio, Mría Luisa Martínez and
Bianca Delfosse for constructive comments on the manuscript.
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