Can We Reverse the Atmospheric CO
Concentration Trend Using Cooperation?:
Model-based Management for Effective
Cooperation.
Jorge Andrick Parra Valencia
Universidad Aut6noma de Bucaramanga
Systemic Thinking Research Group
japarra@unab.edu.co
Isaac Dyner Rezonzew
Universidad Nacional de Colombia
Systems and Informatics Research Group
idyner@unalmed.edu.co
July 6, 2012
Abstract
The reversibility of the effects of Green-house gases is highly discussed by schol-
ars nowadays. We tested whether cooperation mechanisms are efficient at overturning
the current CO2 concentration trend in the atmosphere. We approach the problem as a
large-scale social dilemma. We developed a system dynamics model to find the condi-
tions required to achieve effective cooperation that may contribute to reverse the current
COz concentration trend. Simulation experiments show that initial conditions of trust
and information delays are determinant to attain cooperation in the direction of reducing
the CO concentration trend.
Key words: CO, Crisis, Green House Gases Crisis, Cooperation, Social Dilem-
mas, Mechanism, Large-Scale Situations, Dependence to Initial Conditions, Dynamic
Complexity.
1 Introduction
Social dilemmas are conflicts between collective best interests and individual rationality
(Kollock, 1998). Such conflicts may affect the performance of groups to manage shared
resources (Ostrom, 2000) and the capability of humanity to keep a sustainable use of
large-scale resources like the atmosphere (Buck, 1998). A short-term individualistic
rationality could produce over-exploitation, pollution and reduce the availability of com-
mon resources. However, people can mitigate and avoid this situation. The availability
of common resources depends on the way that people resolve such dilemmas.
Human groups face the problem of common-resource conservation. The conser-
vation of common resources offers situations of social dilemma (Hardin, 2009, 1243)
that sometimes lead to over-exploitation and pollution (Ostrom, 1990, 27). There are
three possible ways to solve social dilemmas: cooperation (Ostrom, 1990), private rights
(Smith, 1981), markets (Voogt et al., 2000; Morthorst, 2000), and the enforcement by an
external agent (the state) (Hardin, 2009, 1243). Private rights and markets have shown
problems when they are used in situations of large-scale dilemmas (Ostrom, 1990, 33).
Because these alternatives are not always feasible options in large-scale social dilemmas,
we are going to focus our work on cooperation. Over-pollution, as a consequence of a
social dilemma, can be avoided if individuals cooperate to reduce their pollution (Ostrom
et al., 2002, 3). However, individuals could decide not cooperate and reduce polluting
at the level required to sustain the common resources. If individuals follow their own
interest in this situation, they will drive the situation collectively,and they will deplete
common resources (Schlager, 2002, 801).
1.1 Cooperation as an alternative to solve large-social dilem-
mas
Cooperation is then an alternative feasible solution to confront small-scale social dilem-
mas (Ostrom and Walker, 2005; Ostrom et al., 2002; Ostrom, 2000). In both laboratory
(Ostrom et al., 1994) and field (Cardenas and Ostrom, 2004) settings, cooperation is
promoted and sustained using a mechanism based on trust (Ostrom, 2000). Contem-
porary theories of collective action consider that cooperation is possible because of the
possibility of communication. Individuals develop a reputation of cooperation from past
encounters that enable new cooperation (Ostrom, 2000, 12). In the principles of ratio-
nal decision making for collective action on commons resources, Elinor Ostrom offers a
frame to explain cooperation based on trust. This frame of reputation, reciprocity, and
trust is built around causal relationships which define the core of cooperation. This core
drives the change of cooperation according to initial conditions defined by situational
variables (Ostrom, 2000, 13).
Contemporary cooperation theories suggest auto-regulation as a way to deal with
social dilemmas. Ostrom (2000, 11) developed her theory of cooperation for small-scale
resource social dilemmas to specific conditions:
e There is face to face communication.
e Agreements are possible and enforceable.
e Groups have few members.
e¢ Members have similar characteristics.
e There is perfect information about the state of the resource and the results of others’
actions.
These conditions are met by small-scale social dilemmas, but are not met by large-
scale ones.
People confront large-scale resource problems that could be considered social dilem-
mas. The gains of the selfish are higher than those of the non-selfish. However, every-
body is worse off if the majority acts selfishly. Traffic jams, electricity crises, Internet
congestion, climate change, and many others can be explained as social dilemmas (Buck,
1998, 8).
Large-scale resource social dilemmas have special conditions. We can offer some
characteristics based on Mark6czy (2007, 1931):
e There is no face to face communication, but there is some kind of information
about the state of the resource and others’ actions.
e Agreements are possible and enforceable.
e Groups have a lot of members.
e Members do not have similar characteristics.
e There is not perfect information about the state of the resource and the results of
others’ actions.
e There is dynamic complexity.
1.2 CO, crisis
Most documented explanations about Climate Change claim that the Green-house ef-
fect strongly influences temperature (Intergovernmental Panel on Climate Change-IPCC,
2007, 93). Greenhouse gases (GHGs) like C'Oz have been increased mainly because of
industrial activity. This causes the atmosphere to retain heat (Intergovernmental Panel on
Climate Change-IPCC, 2007, 95). As a globally shared resource, the climate is vulnera-
ble to social dilemmas. Although people can immediately benefit from increasing CO,
gas emissions, the delayed effects will be more damaging (Ostrom et al., 2002; Buck,
1998). To reduce the concentration of greenhouse gases, emissions must fall below the
rate at which GHGs are removed from the atmosphere. However, people do not under-
stand the dynamics of the climate change (Sterman and Sweeney, 2002, 207). Figure 1
shows data for CO measured at Manua Loa, Hawaii (Tans, 2010). This behavior is ex-
plained as a consequence of the accumulation of C'O2 in the atmosphere, which occurs
because emissions are higher than the system’s ability to capture COp.
1.3. Reversibility of the Atmospheric CO, Concentration Trend.
Current scholarly debate about the reversibility of climate change has many uncertain-
ties (Stern et al., 2006). Solomon et al. (2009) show that the climate change due to
xo
C02 _ppmv
Figure 1: CO, Data measured at Manua Loa, Hawaii (Tans, 2010)
increases in carbon dioxide concentration will be irreversible for 1,000 years after emis-
sions stop. They also suggest that atmospheric temperatures will not drop significantly
for at least 1,000 years (Solomon et al., 2009). Some other common irreversible effects
will be dry-season rainfall reductions in several regions and sea level elevation (Solomon
et al., 2009). Some other impacts will be the melting of permafrost, which could release
huge quantities of methane and promote feedback that could lead to a global warming
much greater than current projections (Stern et al., 2006). However, we did not find
any research exploring the capacity of cooperation to reverse the carbon dioxide con-
centration. Literature suggests 350 ppmv is the highest level to preserve the planetary
conditions necessary to support life, but they do not suggest how to stabilize this level
(Hansen et al., 2008).
1.4 Preliminary Obstacles to Cooperation for Reversing the
COz Crisis
We found two problems that a manager should solve to reverse the CO crisis using
cooperation:
e The initial trust will be insufficient to promote and sustain cooperation (Castillo
and Saysel, 2005).
e The delayed effect of past cooperative actions will discourage future cooperation
(Sterman, 2000).
The insufficient initial trust to promote and sustain cooperation is a problem be-
cause of the kind of feedback loop that defines the core relationship of the mechanism
based on cooperation and trust. A dynamic version of the mechanism based on trust
is presented in Figure 2. Trust promotes reciprocity. Later, reputation is affected by
4
_Initial Trust
af eee
Trust“
Reputation
q As)
5
7 . Or Cooperati on
Reciprocity +
Figure 2: Dynamic mechanism of cooperation based on trust. This is our interpretation based
in Ostrom (2000) and Castillo and Saysel (2005).
reciprocity. More reciprocity strengthens reputation and incre
§ cooperation. Finally,
reputation improves trust. In terms of dynamics, the initial conditions for trust affect the
performance of cooperation as explained by these core variables (trust, reputation, and
cooperation). These variables are joined in a feedback loop that reinforces any initial
condition (Sterman, 2000). This is the case with the mechanism of cooperation based on
trust which exhibits dependence on initial conditions (Castillo and Saysel, 2005).
Figure 3 presents how initial conditions drive trust. We performed a sensitivity anal-
ysis for initial conditions of trust. This analysis consisted of 200 simulations using initial
values of trust between 0 and 10 based on the uniform probability distribution. Figure
3 shows how initial conditions of trust affect the resulting performance of trust because
this mechanism is constituted of a reinforced feedback loop. To be effective, cooperation
requires a minimum value of initial trust.
confianzainicialalta
Current
50% 75% (NN 95° 10006 I
confianza de cooperacién
20
15
10
a 25 50 75 100
Time (dia)
Figure 3: Dependence on initial conditions of trust. This sensitivity analysis presents confi-
dence spaces for the mechanism of cooperation based on trust in a model for cooperation in
energy provision for households.
Dependence on initial conditions of trust is a problem for managers because they do
not know if the initial trust is enough to promote cooperation. Then they are not able to
assure the effectiveness of cooperating to reverse the C'O> crisis. Therefore, the mech-
anism of trust is insufficient to promote, assure, and sustain cooperation for all possible
initial conditions. Additionally, there is disagreement about whether managers can apply
cooperation based on trust to large-scale social situations (McGinnis and Ostrom, 2008)
or not (Biel et al., 1999). The mechanism of cooperation based on trust was developed
to meet the conditions of small-scale social dilemmas!.
The core relationship of the mechanism based on trust supposes another problem for
reversing the C'O2 crisis: the delayed effect of past cooperative actions will discourage
future cooperation. According this mechanism, people require information about past
cooperation effects to decide whether to make new cooperative actions. Because of the
long life-time of COy in the atmosphere, we will not see the results of cooperation in the
short term. There is a debate about the residence time of CO2 in the atmosphere with
possible implications in policy design. The debate is defined by whether the residence
time is above or below 10 years (Essenhigh, 2009). A larger residence time supposes
more difficulties to enhance and sustain cooperation for reversing the C'O2 crisis.
In this paper, we test the effectiveness of cooperation to reverse the CO) crisis in
a large-scale social dilemma using additional mechanisms combined with cooperation
based on trust. To test this claim, firstly we developed a simulation model of the CO
crisis. Secondly, we designed and tested a construct to assure effective cooperation for
reversing that C’'O2 crisis. Finally, we developed a simulation model that integrates a
'We assume a small-scale social dilemma is a conflict between individual and collective rationality which is
faced by no more than 10 people. This definition is compatible with the definitions proposed by Kollock (1998)
and Ostrom (2000).
representation of accumulation of C'O2 in the atmosphere. The model explains how
the construct could reduce the accumulation of CO2. We are going to explain how our
mechanism is able to deal with both initial dependence on trust and the delayed results
of cooperation to assure the possibility for reversing the increasing trend of the CO.
2 Method
The steps we followed to develop the construct and model the case were:
e To develop a dynamic hypothesis that explains how mechanisms can promote and
sustain cooperation.
e To model the COz crisis as a large-scale social dilemma.
e To simulate experiments to test the reversibility of the concentration of GHGs
through cooperation.
We use system dynamics guidelines (Sterman, 2000; Forrester, 1961) to develop our
construct as a dynamic hypothesis and to apply it for modeling the concentration of
COz in the atmosphere and the effect of the mechanisms for promoting cooperation. We
developed the model using Vensim 5.7 for Windows.
Sustainable
Cooperative Action in = Resource
Resource Management Management
Le
Ae)
Cooperation
based on Trust
Trust in Resource
Management
Figure 4: Mechanism of cooperation based on trust as a component of the proposed construct.
3 Results
Firstly, we present the construct that defines our claim about how cooperation mecha-
nisms can reverse the C’O2 crisis. Then, we explain a model that represents the CO2
's. Finally, we present simulation experiments that support our dynamic hypothesis.
3.1 The Construct
We define a construct as a structure that combined mechanisms to promote a social ob-
jective (Elster, 1989; Maskin, 2008). Our construct integrates three mechanisms: coop-
eration based on trust, cooperation as a norm, and cooperation as perception of damage.
Figure 4 presents the mechanism of cooperation based on trust. This mechanism is de-
fined by a reinforced feedback loop as explained before. This means that every change
in a variable present in this kind of feedback loop is also reinforced. This feedback loop
presents dependence on initial conditions. An increase of trust in resource management
promotes cooperation in resource management, therefore achieving a sustainable use of
the resource. This feedback loop is based on Ostrom (2000).
Figure 5 presents the mechanism of cooperation based on trust integrated with the
mechanism of cooperation as a norm. This part of the construct suggests that people
can learn to cooperate in the long term because they learn to cooperate in the short term.
An increase in cooperative actions promotes learning about resource management that
improves the resource’s sustainability. This learning allows us to assume cooperation as
anorm. This mechanism is inspired by Biel et al. (1999).
Figure 6 presents the mechanism of cooperation as a perception of damage incorpo-
rated into the construct. A perception of a lack of sustainable resource management leads
to an expectancy of scarcity. This expectancy creates an improvement in the sustainabil-
ity of resource management. This mechanism consists of a balance feedback loop. This
means that a change in one variable in the feedback loop is compensated by an opposite
9
Cooperative Learning in
Resource Management
ey
R
Cooperation as
Mores
Sustainable
Cooperative Action in Resource
Resource Managernent Management
Cooperation
based on Trust
Trust im Resource
Management
Figure 5: Mechanism of cooperation as a norm with cooperation based on trust.
change in the other variable. This mechanism is inspired by Schelling (1958).
Figure 7 presents the construct united configuration of mechanisms which pro-
mote and sustain cooperation in large-scale social dilemmas. This construct is based on a
general structure proposed by Parra (2010). All mechanisms allow community members
to confront the temptation to free ride. Free riding is represented by a feedback loop
of balance. An increase in the availability of the resource produces free riding which
feeds back to decrease the sustainable resource management. Our construct suggests a
configuration of mechanisms which are able to face social dilemmas with effective co-
operation. Finally, we present the model which was developed to test the ability of these
mechanisms to promote cooperation in the CO2 crisis.
We proposed our dynamic hypothesis as an expression of the mechanism for coop-
eration for large-scale resource social dilemmas. In Figure 8 we claim that people will
only recognize a threat of damage about climate and the emission of GHG if they find
a strong relationship between the emission of GHG and the extreme effects of global
warming. Only this recognition will produce enough pressure to reduce emissions in the
short term.
3.2 The Simulation Model
We developed a simulation model to test the proposed mechanisms. The model is a
system of differential equations. The general structure of the model is presented in Figure
9. This structure is formed by the mechanisms presented previously.
We present a separate configuration for each mechanism. Figure 10 presents the
differential equation for recognition of danger which is accumulated by the awareness of
an increase in the concentration of CO). This recognition suffers depreciation because of
its defined lifetime. The longer the lifetime, the better we are able to sustain cooperation
with this mechanism.
10
Cooperative Learning In
Resource Managernent
A
R
Cooperation as +
‘Norm
Sustainable
Cooperative Action in + Resource
Resource Managernent Management
.
ae)
coonarauen
based on Trust
Gs
Benen atsHas
Perception of Damage
Trust in Resource
Management Expectancy of
Scarcity
Figure 6: Mechanism of cooperation based on a perception of damage.
Cooperative Learning in
Resource Management Free Riding +
Resource
Gy Availability
Temptation
~Y to Free Ride
Norm
Sustainable
Cooperative Action in Resource
Resource Management Manegement
bis
R
Cooperation
based on Trust
Gs
ee
Perception of Damage
Trust in Resource
Management Expectancy of
Scarcity
Figure 7: Free riding and mechanisms of cooperation.
11
Cooperation’
Learning
temptation to free
ride
R
Social Learning
COZ in
ion —_—
restriction 3 atmosphere
cooperative action
B
Expectation
+ R
Cooperation
expectation of damage
‘by climate change
Ko
trust.
Figure 8: Dynamic hypothesis about how cooperation could contribute to reduce C'O con-
centration in the atmosphere.
Cooperation as a Norm:
&e)
aogier am Gs
rote
Temptation
to Free Ride
Free Riding
Resource
1)
‘Cooperation Gs
eee wo
oes
Cooperation as
Perception of
Cooperation based in
Trust
Damage
Figure 9: General structure for the model.
adjuotaent tice sig tii
so nt aS. inresng option dept ention
a tognition c02 ae danger
accumusied weopution of danger
\ ineoaring ni Grpmnieon
rend oo?
vege tine 20g rial
tieneod
retire secognition
sitialoo2 trend SS
ection
inion danger
vege 19 retion
retionshp danger
redrton
Figure 10: Structure for perception of danger.
Figure 11 presents the structure for the temptation to free ride. If the concentration
of C'Oz is reduced, then the temptation to free ride is increased. The level of temptation
to free ride depends on lifetime.
Figure 12 presents the structure for trust. Cooperation is measured by the improve-
ments made to reduce the CO: concentration. The perception of this reduction is accu-
mulated in the differential equation as trust. This trust is depleted according to a lifetime.
The larger lifetime, the longer trus sustained.
Figure 13 presents the structure for the CO, concentration. We suppose emissions
are accumulated in the atmosphere. Due to nature’s process, the C in CO is captured
according to its lifetime in the atmosphere. The larger the lifetime, the greater the climate
change effects.
3.3 Simulation Experiments
Figure 14 presents the results for a simulation experiment. We defined the value for
the social objective in 315 ppmv for C'O. in the atmosphere. Then, we assumed the
concentration of CO» for 2010 as an initial value for the simulation. Later, we tested
if the mechanisms inside the construct were able to promote and sustain cooperation in
order to achieve the social objective proposed below. The simulated results support our
dynamic hypothesis.
Each mechanism has predominance in a specific period of time. Figure 15 shows the
predominance of cooperation as perception of damage. In this simulation, the perception
of damage solves the exponential growth of COg, because this mechanism allows us to
promote cooperation even if the initial condition for trust is zero. This assures enough
initial trust to develop cooperation based on trust.
Figure 16 presents the zone of predominance for cooperation based on trust. This
kind of cooperation, which will be learned as a norm, will allow us to achieve the goal
13
welationship trend.
202 free ride
trend co2=
geeferptation to fie; Poon)
inereasing leraptation. riding decreasing temptation
‘to fice ride to fie riding
initial ternptation to
it een lative teraptation
pve
tojsedace to five nile eeverage life tire
free riding
relationship free riding fraction increase co2
riding,
fraction increase co2 by fee
Figure 11: Structure for temptation to free ride.
delay to perceive
trend cooperation
\y initial trend
perceived trend
‘cooperation
increasing trust decreasing trust
fF danger>
average trust life
rainiun trust to seas vitive trast
reduce
portion reduction reduction by
cooperation ~*~ eration
Figure 12: Structure for trust.
14
life time
inflow accounts
Pa implementation
initial rate gowth Sele
1
raprox rate growth
mee
eS
tin
Figure 13: Structure for the CO, b
02 accounts outflow accounts
proxy cooperation
£02 goal
adjustment tinne
implementation delay
dynamics.
600
500
co2 ppv
\
300
200
1958 1970 1982 1994 2006 2018 2030 2042 2054 2066 2078 2090 2102 2114 2126 2138 2150
‘Time (Year)
02 goal ; Current
CO2 ppmv : Current
02 accounts : Curent
Figure 14: Simulated behavior for CO, under a treatment based on mechanisms for promot-
ing cooperation in red. C'O2 data by Tans (2010) is presented in blue.
15
600
500
300
200
cozn
Annosphere
Perception of Damage
R
Expectation evectation of damage
by elmate change
1958 1970 1982 1994 2006 2018 2030 2042 2054 2066 «2078 2090 2102 2114 2126 2138 2150
co2
‘Tune (Year)
ppm | Curent, $$ $$ (602 goal = Cargent|§ $$ aa
co2 accounts : Current
Figure 15: Simulated behavior for CO, under a treatment based on mechanisms for promot-
ing cooperation. The predominance for cooperation as perception of damage is presented in
red. CO> data by Tans (2010) is in blue.
of 315 ppmv for COy in the atmosphere.
Figure 17 presents how cooperation as a norm controls C'O2 in the long term.
3.4 Sensitivity Analysis
We performed a sensitivity analysis to test if small changes in the average lifetime for
cooperation as perception of damage could produce more than proportional changes in
cooperation. We made 200 simulations defining lifetime values from 5 to 33 years. Fig-
ure 18 presents the dynamic confidence bounds for the sensitivity analysis for CO2. The
longer the lifetime in cooperation as perception of damage, the more CO, is reduced.
16
600
Perception of Damage weeks
500 a x
Cooperation
300 7
Cooperation based on Trust
200
1958 19701982 19942006 go1B 2030 a042__g0s4 9066 2078 a090 2102 aia 212621582150
‘Time (Year)
CO2 ppmv : Current 02 goal: Current
662 accounts » Curent,
Figure 16: Simulated behavior for CO. under a treatment based on mechanisms for promot-
ing cooperation. The predominance for cooperation based on trust is presented in red. CO,
data by Tans (2010) is in blue.
600
Perception of Damage
500
300
Cooperation based on Trust
200
1958 1970 1982 1994 2006 2018 2030 2042 +2054 «2066 +«2078:«2090:«2102,«aN14 «2126 «2138 2150
‘Time (Year)
(CO2 ppmv : Current
sd 02 goal: Current
602 accounts » Current,
Figure 17: Simulated behavior for CO2 under a treatment based of mechanisms for promoting
cooperation. The predominance for cooperation as norm is presented in red. C'O2 data by
Tans (2010) is in blue.
17
50% 75% INOS % I 100%
co2 accounts
800
600,
400
1958 2006 2054 2102
Time (Year)
Figure 18: Sensitivity analysis for cooperation as perception of damage.
18
2150
4 Discussion
We presented a construct as a dynamic hypothesis which explains how mechanisms
could be combined to reverse the COy crisis through cooperation. We explained how
the dependence of initial conditions of trust in the Mechanism of Cooperation Based on
Trust is controlled with our construct using complementary mechanisms such as percep-
tion of damage and cooperation as a norm. We applied system dynamics guidelines to
develop the model and test the construct (Parra, 2010).
Our work suggests how cooperation can be effective to reverse the C’'O2 crisis, and
supposes a new alternative to solve this crisis. This alternative solution could be com-
bined with other institutional designs to solve the C'O2 crisis such as green certificates
(Morthorst, 2000) and emissions permits (Jensen and Rasmussen, 2000). Cooperation is
a possible option to reverse the C'O2 crisis and other crises and its undesired effects.
The construct has limitations which need to be considered. For example, dynamic
complexity, understood as the effect of delays regarding information about the state of
the shared resource and the effect of the cooperation of others, is critical if we want to
succeed using cooperation. This problem could be linked with previous work about the
difficulties of people to make high quality decisions in situations characterized by high
inertia and delays (Sterman and Sweeney, 2007; Diehl and Sterman, 1995; Sterman,
1989). Our results suggest a new application of dynamic complexity studies for solving
large-scale social dilemmas such as the CO, crisis.
19
5 Conclusion
This paper presented a system dynamics model to gain insights about the conditions
required to achieve effective cooperation which allows us to reverse the current CO2
concentration trend in the atmosphere. Simulation experiments show that initial condi-
tions of trust and information delays about the results of cooperation are necessary key
elements to ensure the capability of cooperation to reverse the CO, crisis.
20
References
Biel, A., C. Von Borgstede, and U. Dahlstrand (1999). Norm perception and cooperation
in large scale social dilemmas. Resolving social dilemmas: Dynamic, structural, and
intergroup aspects, 245-252.
Buck, S. (1998). The global commons: an introduction. Island Press.
Cardenas, J. and E. Ostrom (2004). What do people bring into the game? Experiments
in the field about cooperation in the commons. Agricultural Systems 82(3), 307-326.
Castillo, D. and A. Saysel (2005). Simulation of common pool resource field experi-
ments: a behavioral model of collective action. Ecological Economics 55(3), 420-
436.
Diehl, E. and J. Sterman (1995). Effects of feedback complexity on dynamic decision
making. Organizational Behavior and Human Decision Processes 62(2), 198-215.
Elster, J. (1989). Nuts and bolts for the social sciences. Cambridge University Press.
Essenhigh, R. (2009). Potential dependence of global warming on the residence time
(rt) in the atmosphere of anthropogenically sourced carbon dioxide. Energy & Fu-
els 23(5), 2773-2784.
Forrester, J. (1961). Industrial Dynamics. MIT press Cambridge, MA.
Hansen, J., M. Sato, P. Kharecha, D. Beerling, R. Berner, V. Masson-Delmotte, M. Pa-
gani, M. Raymo, D. Royer, and J. Zachos (2008). Target atmospheric co2: Where
should humanity aim? Arxiv.
Hardin, G. (2009). The tragedy of the commons. Journal of Natural Resources Policy
Research 1(3), 243-253.
Intergovernmental Panel on Climate Change-IPCC, . (2007). Climate Change 2007:
Synthesis Report. Ipcc, UN.
Jensen, J. and T. Rasmussen (2000). Allocation of co2 emissions permits: A general
equilibrium analysis of policy instruments* 1. Journal of Environmental Economics
and Management 40(2), 111-136.
Kollock, P. (1998). Social dilemmas: The anatomy of cooperation. Annual Review of
Sociology 24(1), 183-214.
Markéczy, L. (2007). Utilitarians arent always fair & the fair arent always utilitarian:
Distinct motives for cooperation. Journal of Applied Social Psychology 37(9), 1931-
1955.
Maskin, E. (2008). Mechanism design: How to implement social goals. American
Economic Review 98(3), 567-576.
21
McGinnis, M. and E. Ostrom (2008). Will Lessons from Small-Scale Social Dilemmas
Scale Up? New issues and paradigms in research on social dilemmas, 189-211.
Morthorst, P. (2000). The development of a green certificate market. Energy pol-
icy 28(15), 1085-1094.
Ostrom, E. (1990). Governing the commons: The evolution of institutions for collective
action. Cambridge University Press.
Ostrom, E. (2000). A behavioral approach to the rational choice theory of collective
action. In Polycentric games and institutions: readings from the Workshop in Political
Theory and Policy Analysis, pp. 472. University of Michigan Press.
Ostrom, E., T. Dietz, N. Dolsak, P. Stern, S. Stonich, et al. (2002). The drama of the
commons. National Research Council.
Ostrom, E., R. Gardner, and J. Walker (1994). Rules, games, and common-pool re-
sources. University of Michigan Press.
Ostrom, E. and J. Walker (2005). Trust and reciprocity: Interdisciplinary lessons from
experimental research. Russell Sage Foundation Publications.
Parra, J. (2010). Constructo para la evaluacién de la cooperacion en dilemas sociales
de gran escala. Ph. D. thesis, Universidad Nacional de Colombia. Doctorado en
Ingenieria Area Sistemas.
Schelling, T. (1958). The strategy of conflict. Prospectus for a reorientation of game
theory. Journal of Conflict Resolution 2(3), 203.
Schlager, E. (2002). Rationality, cooperation, and common pool resources. American
Behavioral Scientist 45(5), 801.
Smith, R. (1981). Resolving the tragedy of the commons by creating private property
rights in wildlife. Cato Journal 1(2), 439-468.
Solomon, S., G. Plattner, R. Knutti, and P. Friedlingstein (2009). Irreversible climate
change due to carbon dioxide emissions. Proceedings of the national academy of
sciences 106(6), 1704.
Sterman, J. (1989). Misperceptions of feedback in dynamic decision making. Organiza-
tional behavior and Human Decision Processes 43(3), 301-335.
Sterman, J. (2000). Business dynamics: Systems thinking and modeling for a complex
world with CD-ROM. Irwin/McGraw-Hill.
Sterman, J. and L. Sweeney (2002). Cloudy skies: assessing public understanding of
global warming. System Dynamics Review 18(2), 207-240.
Sterman, J. and L. Sweeney (2007). Understanding public complacency about climate
change: Adults mental models of climate change violate conservation of matter. Cli-
matic Change 80(3), 213-238.
22
Stern, N. et al. (2006). The economics of climate change. United Kingdom.
Tans, P. (2010). Data carbon dioxide measured at Manua Loa Observatory, Hawaii.
Earth System Research Laboratory www.esrl.noaa.gov/gmd/ccgg/trends/.
Voogt, M., M. Boots, G. Schaeffer, and J. Martens (2000). Renewable electricity in a
liberalised market-the concept of green certificates. Energy & Environment 11(1),
65-79.
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