known as multi-agent systems or multi-agent simulations MAS. MAS are systems in which agents are distributed in an artificial environment and are able
to interact with each other andor with the environment in a parallel fashion Bailón 2004; Bousquet and Le Page 2004. In the model, agents are created,
which are entities of the artificial world. On the other hand, actors are people in the real world Bousquet et al. 1999.
MAS take a bottom up approach to generating data comparable to that observable in the real system. This bottom up approach gives attention to writing
instructions to specify the behavior of the individual components parts of the real world system that is being studied. There are two basic components of the
intelligent agents, namely a model of agents and a model of their environment. A model of agents contains the instructions for generating the behavior of the agent
under different situation. On the other hand, a model of the environment is the world in which the agents exist. The overall behavior emerges as a result of the
actions and interactions of the individual agents. At this point, observations of the results can be assessed Deadman 1999.
9.2 Simulation Experiment
This simulation experiment is a preliminary approach to understand the dynamics underlying social capital. In particular, its aim is to test the assumptions
made in the literature about how social capital influences collective action and natural resource management. These assumptions can be summarized in the
following: Individuals overcome the temptation present to overuse the common-pool
resources by communicating their desires to reach acceptable sharing agreements. They build trust in these agreements by extending reciprocity
and impose sanctions on those who violate agreements Ostrom et al. 1994: 327.
Social capital offers an impact on transaction costs. “Social interactions can affect the level of transaction costs associated with many market exchanges.
When agents frequently and regularly interact in social settings, they establish patterns of expected behavior and build bonds of trust. Combined
with the possibility of sanctions, this lowers the likelihood of opportunistic behavior by agents that are in the same social structure. By contrast, the lack
of cooperative norms within social structures can lead to higher transaction costs and more inefficient markets” Isham 2001.
One of the important factors that contribute to well being is trust. Informal institutions regulate trust through social mechanisms, rewarding trustworthy
behavior and applying social sanctions. Formal institutions strengthen the overall environment of trust through equitable access to justice, the rule of
law, enforceable contracts, and a police force that protects citizens Narayan 1999.
In brief, trust and norms of reciprocity are assumed to be basic building blocks for the construction of social capital. Norms of reciprocity and bonds of
trust are accumulated through regular interactions and successful cooperation Putnam 1993; Isham 2001. Moreover, the possibility of sustained cooperation
can only be materialized if there are mutual assurances. These assurances usually come from regulatory measures, namely norms and sanctioning
mechanisms Dasgupta 1988: 50-51. Reciprocity alone is frequently insufficient to cope with the temptations to cheat or to conduct opportunistic behavior. In this
way, sanctions may become constraints, thus incentives, for fishers to participate in mutually-beneficial collective action Ostrom et al. 1994; Isham 2001.
The Model of Destructive Fishery and Social Capital
The model consists of two basic components of the intelligent agents, namely a model of agents and a model of their environment. A model of agents
contains the instructions for generating the behavior of the agent. The agents’ behavior consists of ‘DELIBERATE’, ‘CATCH-FISH’, ‘SELL-FISH’, ‘ENFORCE-
SANCTION’, and ‘LEARN’. Each fisher has options whether to become a ‘cooperator’ or a ‘defector’. A
cooperator means that fisher utilizes non-destructive fishing gears, which symbolized with green color. In contrast, a defector practices destructive fishing
and symbolized with red color. Fisher’s decision to conduct destructive fishing or not depends on their expectations Anderson 1995; van Noordwijk and Suyamto
2006 that expressed in the behavior of ‘DELIBERATE’. The attractiveness of
destructive fishing is calculated by the following formula:
1 1
1 1
E C A
E C E C
= +
10
where: A
1
is attractiveness of destructive fishing, E
1
is expected payoffs of destructive fishing, C is consideration degree. Similarly, the attractiveness of non-
destructive fishing is calculated as follows:
2 2
2 2
E C A
E C E C
= +
11 where: A
1
is attractiveness of non-destructive fishing, E
2
is expected payoffs of non-destructive fishing. If the attractiveness of non-destructive A
1
is higher than the attractiveness of destructive A
2
, the fisher will decide to cooperate or using the non-destructive fishing.
Fisher moves to patches where fish biomass is higher compared with their existing patches. This is similar to the condition in the real system that fishers are
moving to fishing grounds that are productive. This denotes the behavior of ‘CATCH-FISH’.
In the behavior of ‘SELL-FISH’, fisher gets revenue from selling the fish. The income equals to the economic rent, which is the difference between total
revenue subtracted by total costs Fauzi 2004: 107.
TR TC π =
−
12 where:
π is economic rent or income, TR is total revenue, TC is total cost. The total revenue is obtained from fish price p multiplied by fishing payoffs or
production
h
, as follows:
TR ph
=
13 Furthermore, the fishing production and cost are differentiated between
destructive fishing and non-destructive fishing. The income from destructive fishing is further subtracted by the sanction imposed by the cooperators. In sum,
the formulas for fishing income are as follows:
1 1
1 s
ph c
c π =
− −
14
2 2
2
ph c
π = −
where: π
1
is income for destructive fishing, π
2
is income for non-destructive fishing, p is price, h
1
is production of destructive fishing, h
2
is production of non- destructive fishing, c
1
is cost of destructive fishing, c
2
is cost of non-destructive fishing, c
s
is cost on the subject of sanction. However, c
s
is optional to enter the equation. It enters the equation only when it fulfills a condition that the number of
cooperators is similar or higher than the threshold number of cooperators to
sanction, in which the cooperators will enforce sanction to the defector. If this condition is not fulfilled, it means c
s
equals to zero. Parameter c
s
is related to agent behavior of ‘ENFORCE-SANCTION’. This is the social capital dimension of the interaction between fishers. It is expressed
through parameters of ‘sanction’ and ‘cooperators-to-sanction’. The latter parameter shows that the lower the threshold number of cooperators to sanction
the defectors, the higher the probability of getting caught. The threshold indicates the social capital of a shared norm against the existence of destructive fishing. If
the threshold number of cooperators to sanction is too high, then it is less unlikely that sanction is imposed.
Moreover, in the decision-making process, a fisher is continuously learning. He reflects his current decision of fishing and the information gathered from his
neighboring fishers to decide on the next expectation of the fishing gears to
utilize. This denotes the behavior of ‘LEARN’. The fisher’s expected payoffs of
destructive fishing E
1
and of non-destructive fishing E
2
is calculated using the formula as follows:
1 1
1 1
E E
L I E
= +
−
15
2 2
2 2
E E
L I E
= +
−
16 where: L is learning rate, I
1
is information about destructive fishing, and I
2
is information above non-destructive fishing. The value of learning rate is between 0
and 1. If the learning rate is 0, it means that a fisher does not reflect on current information and will eventually follow the previous mode of fishing. In other
words, fisher’s next expectation will follow the past expectation in using fishing gears, regardless of the actual information he gathers. On the contrary, the
learning rate 1 suggests that fisher’s next expectation will be based on actual information.
The information about destructive fishing or non-destructive fishing I
1
, I
2
is calculated based on the mean of the economic rent or fishing income
π
1
, π
2
of the neighboring fishers:
1 1
1 1
F I
F π
=
∑ ∑
17
2 2
2 2
F I
F π
=
∑ ∑
18
where: F
1
is the number of destructive fishers or defectors in the neighborhood, and F
2
is the number of non-destructive fishers or cooperators in the neighborhood.
Some parameters are normally distributed random based on a mean and a coefficient-per-variance of the respective parameters that are determined when
the model is initialized. They are expected payoffs of destructive fishing E
1
, expected payoffs of non-destructive fishing E
2
, learning rate L, and consideration degree C.
The model of the agents’ environment is characterized by fish biomass. The growth of fish biomass denotes by ‘RECOVER-ECOSYSTEMS’, which is
determined by the logistic growth model. The logistic function is written as:
1 x
x rx
h t
K ∂
= −
−
∂
19 where: r is intrinsic growth rate, x is fish stock, K is carrying capacity, and h
is fish harvest or production.
Figure 37 View, plot, and monitors resulted from a simulation.
The simulation model was built using NetLogo, version 3.1 Wilensky 1999. The appearance of the model is presented in Figure 37. The procedures in
constructing the model can be seen in Appendix 7. After the model is built, a simulation is proceeded, which takes a case study of reef fishery in Taka
Bonerate Marine National Park. Some parameters are added into the model, which are taken from the field surveys done in 2004 and 2005 as well as other
studies Pet-Soede et al. 1999; DFW-Coremap 2003; Sultan 2004.
Sensitivity Analysis
After the model is built and simulated, the overall behavior emerges as a result of the actions and interactions of the individual agents. At this point,
observations of the results can be assessed. The impact of sanction on the numbers of defectors and cooperators are
detected. The ratio between defector and cooperator DC ratio is higher when sanction does not exist, compared with when sanction is in force Table 61. It
means that the number of defectors is higher in a situation without social capital or sanction in place. The charge of the sanction and the probability of getting
caught are also influential. When the charge of sanction is lower, the DC ratio is higher. Similarly, if the probability of sanction is lower, the DC is higher.
The ratio between defector’s income and cooperator’s income DC income ratio is similar with DC ratio. The DC income ratio tends to lower when the
charge and probability of sanction are higher. The DC income ratio is the highest when there is no sanction. It shows that without sanction, the income from
destructive fishing tends to higher, thus cheating or defector is higher. This result also shows that the DC income ratio is always 1, which
means that the income from destructive fishing is higher than that of non- destructive fishing, even when sanction is present. Even though the fish price is
identical, but fish production of each method is different, more fish catch when using destructive fishing. Nevertheless, DC income ratio is lower when sanction
exists. Table 61 Simulation result to observe the impact of sanction
Sanction Time
simulation DC ratio
DC income ratio
No 10 4,14
8,09 Yes, highest charge and highest
probability of sanction 10 3,13 6,89
Yes, lower charge 10
3,39 6,77
Yes, lower probability of sanction
10 3,26 5,86
The impact of fish price is observed. The DC ratio and DC income ratio are higher when the fish price is high Table 62. It means that the number of
defectors is higher in a situation where fish price is high. The high fish price encourages fishers to obtain more income through destructive fishing.
Table 62 Simulation result to observe the impact of fish price
Sanction Condition Time
simulation Total fish
biomass DC ratio
DC income ratio
No Price Rp
80.000kg 10 7.102 4,14
8,09 Yes Price
Rp 80.000kg
10 8.202 3,13
6,89 No Price
Rp 200.000kg 10
8.291 5,13 9,33
Yes Price Rp
200.000kg 10 10.130 3,17
5,43
Production cost influences the total fish biomass as well as DC ratio and DC income ratio. The DC ratio and DC income ratio are higher when the
production cost is high, which means that destructive fishing is increased. Fishers tend to use destructive fishing that offers more production and revenue in order to
meet the production cost. However, the total fish biomass is higher when the production cost is high Table 63. Thus, with high production cost, the fish
biomass tends to sustain. When it is coupled with the presence of sanction, the fish biomass is more likely to sustain, because the defectors is lower that can be
observed from the lower DC ratio.
Table 63 Simulation result to observe the impact of production cost 1
Sanction Condition Time
simulation Total fish
biomass DC ratio
DC income ratio
No Production cost DF is 2
times higher than non-DF 10 7.102
4,14 8,09 Yes
Production cost DF is 2 times higher than non-DF
10 8.202 3,13 6,89
No Production cost DF and
non-DF are 5 times higher than previously
10 8.762 5,03
11,28 Yes
Production cost DF and non-DF are 5 times higher
than previously 10 11.673
3,02 10,17
One would predict that the production cost affects the way fishers choose fishing gears. This simulation restrains the production cost of destructive fishing
10 times higher than that of non-destructive fishing. The result shows that when the production cost of destructive fishing is so high, the number of defectors
tends to lessen and the total fish biomass is likely to sustain Table 64.
Table 64 Simulation result to observe the impact of production cost 2
Sanction Condition Time
simulation Total fish
biomass DC ratio
DC income ratio
No Production cost DF 2
times higher than non-DF 10 7.102
4,14 8,09 Yes
Production cost DF 2 times higher than non-DF
10 8.202 3,13 6,89
No Production cost DF 10
times higher than non-DF 10 10.892
2,56 4,16 Yes
Production cost DF 10 times higher than non-DF
10 5.547 2,24 3,40
Besides sanction, fish price, and production cost, the driver that encourages fishers to conduct destructive fishing is their expectation on the
payoffs of fish catches that they will get. Here it is considered as the expected fishing payoffs E
1
and E
2
. As in the real world, the expected payoffs of destructive fishing are higher than that of non-destructive fishing. Fishers get
more catches when fishing using destructive fishing gears than when they are not. In a simulation, it is assumed that both expected payoffs are identical. The
result is shown in Table 65.
Table 65 Simulation result to observe the impact of initial expected payoffs
Sanction Condition Time
simulation Total fish
biomass DC ratio
DC income ratio
No Initial expected-payoffs
DF is 2 times than initial expected payoffs
non-DF 10 7.102
4,14 8,09 Yes Initial
expected-payoffs DF is 2 times than
initial expected payoffs non-DF
10 8.202 3,13 6,89
No Initial expected-payoffs
DF and non-DF is identical
10 8.909 1,24 2,23
Yes Initial expected-payoffs
DF and non-DF is identical
10 10.209 0,71 1,40
Compared with a number of previous simulations, this one has lower DC ratio and DC income ratio. This shows that the initial expected payoffs affect the
way fishers harvesting. When fishers consider that destructive fishing is not offering higher payoffs, then the number of defector is more likely to decrease
and the total fish biomass tends to sustain. Similarly, when it is coupled with the presence of sanction, these conditions are much better.
9.3 Sanction, Fish Price, Production Cost, and Destructive Fishing
