Not Only the Tragedy of the Commons:
Misperceptions of Bioeconomics
Erling Moxnes
Foundation for Research in Economics and Business Administration, SNF, Breiviken 2, N-5035 Bergen-Sandviken, Norway
A
n exploratory search for explanations of mismanagement of renewable resources, other
than the theory of the commons, was performed by an experiment. Eighty three subjects,
mostly recruited from the fisheries sector in Norway, were asked to manage the same simulated
virgin fish stock, one subject at a time. Exclusive property rights were granted to rule out the
commons problem. Despite perfect property rights, subjects consistently overinvested, leading
to an average overcapacity of 60%. The resource was reduced by an average of 15% below its
optimal level. Overcapacity and tough ‘‘quotas’’ resemble the situation in Norwegian and other
fisheries during the past few decades. The likely explanation of the observed behaviour is misperception of feedback, a phenomenon that occurs in many experimental studies of dynamically
complex systems. Such misperceptions add a new and important dimension to the problem of
renewable resource management, beyond the commons problem.
(Commons Problem; Bioeconomics; Decision Making; Experimental Economics; Misperception of Feedback)
1. Introduction
There are innumerable cases of mismanagement—particularly over-exploitation—of renewable resources
such as fish, pastures, forests, and groundwater, as well
as of resources that serve as regenerative sinks for pollution such as SO2 , NOx , pesticides, and nutrients. There
are at least two distinct classes of explanation, the second of which is studied in this paper:
(1) The commons problem: With open access to a
common resource, the benefits of over-exploitation accrue to the individual while the costs are borne by all.
The inappropriate incentives lead to the ‘‘tragedy of the
commons,’’ see Gordon (1954) and Hardin (1968). Earlier references such as Aristotle, Lloyd (1833/1977),
Warming (1911) and Pigou (1932) indicate that the basic
idea is not new. Ostrom (1990) uses the term ‘‘appropriation problem’’ indicating the need to design rules
and institutions to allocate rights and responsibilities.
The commons problem is widely held to be the cause of
mismanagement of common renewable resources.
(2) The resource management problem, or ‘‘provision problem’’ in Ostrom’s terminology: Even if the
commons problem is absent or has been solved, a complicated management problem remains. This problem
can be classified as a dynamic, nonlinear optimization
problem under uncertainty and ambiguity. Only approximate solutions to these types of problems exist,
and state-of-the-art results are not necessarily known by
interest groups and decision makers. Clark (1985, p. 11)
points out this problem when he states that: ‘‘. . . the
fishing industry’s apparent lack of concern over its own
long-term welfare remains hard to explain, except perhaps on the basis of a real misunderstanding of the bioeconomic system.’’
In this study, an experimental method has been used
to investigate the management problem. The case is a
fish resource, cod (Gadus morhua). Three questions are
central: first, are there reasons to believe that actors in
the fisheries sector misperceive the bioeconomics of
their resource? Given a positive answer to this question,
the next question turns on the mental models and decision rules that the actors apply, and how these differ
from the perfect models and optimal rules. Thirdly, how
can misperceptions be counteracted?
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The first section of this paper describes the choice of
methodology. The tradition to which the experiment belongs is presented, and relevant earlier results are discussed. While the main reason for using a laboratory
experiment is the lack of real data, some anecdotal evidence of misperception is also introduced. Next, the
experimental task is presented. Then a benchmark for
normative behaviour is discussed together with details
of the experimental design. The results in the following
section show a significant tendency towards overinvestment in capacity, excessive exploitation of the resource, and low capacity utilization. The subjects seem
to use an investment rule which is intendedly rational
for a ‘‘flow resource’’ (e.g., surface water or grass).
However, the rule does not work well for a ‘‘stock resource’’ (e.g., groundwater basins or fish); feedback is
misperceived. The conclusions point towards interactions with the commons problem and to policy implications.
2. Real Data and Experimental
Evidence of Mismanagement
Examples of overexploitation (type I according to
Munro and Scott 1985) of renewable resources abound.
At the same time, persistent overcapacity (type II) is
often observed. Clark (1985, p. 7) writes: ‘‘In practice,
however, it often appears that the effort capacity of
fishing fleets is much larger than twice the optimum
level . . . .’’
Observing mismanagement in the field, it is often difficult to say whether it is the commons problem or misperception of bioeconomics that is the cause. Misperceptions seem important in the following two situations:
Mismanagement is known to have occurred when access and harvest have been controlled by regulatory
agencies. Excessive quotas for fish, and the overexpansion of national fleets after the extension of economic
zones to 200 miles are examples. Governmental subsidies of capital and guarantees for loans in times of excess capacity are other indications of misperception in
this case, see, e.g., Flåm (1987).
Misperceptions are also indicated by radical changes
in policies enacted in the wake of crises. Given correct
forecasts, such policies could and should have been in-
troduced in time to prevent crises. Dasgupta and Heal
(1979, p. 75) write: ‘‘But on occasion the problem is
sighted a bit too late,’’ and refer to the extinction of
whooping cranes, bowhead and right whales. According to Ostrom (1990, p. 106): ‘‘Over-extraction threatened [through salt water intrusion] all of the ground
water basins in this region [Los Angeles area] until institutional changes were initiated by those affected.’’
One might speculate that crises are needed to convince
the majority about faulty assumptions. However, one
might also argue that the resource problem is well understood, and that it is the time delays involved in policy design that are misperceived. For instance, the decimation of North Sea herring in the mid-seventies was
expected, even while policy-makers were awaiting the
conclusions of the Law of the Sea negotiations.
The scarcity of data and potential difficulties in interpreting data lead towards an experimental approach.
Experiments have revealed a number of anomalies or
biases in human judgement (Kahneman and Tversky
1974, Thaler 1987). The subjects will have to deal with
the complexity of a dynamic bioeconomic system. Recent experimental studies of problems in this category
show, with few exceptions, considerable deviations
from normative standards, see Brehmer (1990), Brehmer (1992), Diehl and Sterman (1995), Funke (1991),
Paich and Sterman (1993), Smith et al. (1988), Sterman
(1989a, b).
Since in most of these studies subjects receive sufficient information to make appropriate decisions, it
seems correct to argue, as many of the authors do, that
mismanagement follows from deficient mental models, or ‘‘misperceptions of feedback’’ in the terminology of Sterman. Deficiencies pertain to problem formulations and perceptions of relevant system structures, not only misperception of information cues.
The complexities of these tasks should be experienced
by every scientist working with problem formulations. After problem formulation, subjects are supposed to intuitively solve optimization problems involving nonlinear differential equations and uncertainty. Typically people will have to resort to
simplifications and the use of heuristics. Hence, it
does not seem too surprising that people at times
choose heuristics resulting in systematically suboptimal behaviour (Forrester 1971, Simon 1982).
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The following findings are of particular importance
for the management of a renewable fish resource and
for learning about system structure. First, there is a tendency to underestimate the length or importance of delays, Sterman (1989b) uses the term ‘‘misperception of
delays.’’ According to Brehmer (1989): ‘‘. . . the same
effects are obtained when subjects are told about the
possibility of delays beforehand as when they are not
. . . .’’ Consistent with this, poor performance was observed in Sterman (1989a) and Diehl and Sterman
(1995) in spite of fully visible delays. These findings indicate that the problem arises from deficient mental
models where information about delays does not fit in.
It seems that behaviour improves significantly only
when information about delays influence underlying
mental models. In this connection Brigham and Laios
(1975) find that subjects are able to control a hydrodynamic system when they are allowed to actually see the
liquid. When they have to infer the flows from instruments they are not. In the fishery case there is a oneyear delivery delay of new ships, and results are reported only once a year. Random variations in catch and
measurements imply that information should be
smoothed to enable reliable conclusions to be drawn.
This introduces a further delay of information. Only the
delivery delay is highly ‘‘visible.’’
Second, there is the possibility of misperception of
stocks and flows. For example, a person has a fortune
of one million dollars and no income. After one year’s
consumption the fortune is reduced to 34 million. In this
case it is quite easy to calculate that his current rate of
consumption will deplete the fortune in four years. It
should not be necessary to experience all four years to
see this. However, in the case of fishery management
the corresponding calculation is complicated by recruitment to the stock (income) and by the fact that harvesting (consumption) increases with increasing fleet sizes
and decreases when fish stocks are decimated. In this
case it is difficult to forecast the future stock. If managers choose to wait and see, the stock and flow problem adds considerably to the information delays inherent in the system.
Third, subjects tend to be ‘‘. . . insensitive to nonlinearities which may alter the strengths of different feedback loops as a system evolves,’’ Paich and Sterman
(1993). Two such nonlinearities in the fishery example
are represented by the catch per unit effort (CPUE) and
the recruitment function. These nonlinearities make it
even harder to forecast how the stocks and flows will
develop. This is another reason to resort to outcome
feedback in order to simplify the decision problem. An
extensive body of data is required to learn about the
nonlinearities, introducing still more delays in perception of system structure.
Fourth, if misperceptions are severe, learning might
be slow during an experiment (one trial) and even over
repeated trials (Brehmer 1990, Dörner 1989, Paich and
Sterman 1993). The focus in our study is the first trial,
and the complexity implies slow learning. On the other
hand, most participants in this study are experienced
fishing industry professionals and should have had ample opportunity to learn from the ongoing debate about
overfishing and overinvestment during the past two
decades.
Although several experimental investigations into
different commons problems have been carried out
(e.g., Walker and Gardner 1992, Andreoni 1988, and
Walker et al. 1990), this author has found no reports on
experiments dealing explicitly with the management
problem of stock resources. Prior commons experiments
were performed with flow resources (resource availability in one period is independent of past exploitation), so none of the attributes of dynamic complexity
discussed above were present. The design of the present
experiment is inspired by a group simulation game, including the commons problem (Meadows et al. 1993).
3. The Task: Managing a Fishing
Fleet with Property Rights
The experimental task is to manage a ‘‘flight simulator’’
portraying a fishing firm over a 20-year period. The fish
resource is a cod population in an isolated fjord to
which the subject has exclusive property rights, and
where nobody else is fishing. Each year the subject
makes decisions concerning ordering of new vessels
and the percentage utilization of the fleet (lay-ups).
Each year the subject receives information about the
economics of his company as well as about catch, fish
weight, and resource estimates (see Figure 1). To assist
in their task, subjects are advised to take notes.
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Figure 1
Decision and Information Panel on Computer Screen (with Initial Values). The Experiment Was Programmed in an Early Version of POWERSIM
and Is Available as a Simulator
Ordering of vessels
----
Lay-ups [%]
Revenue
1800
Year
Operating costs
1200
Depreciation
120
Interest
100
Net income
380
76
Tax
Net income after tax
304
---0
Balance
No. of vessels this year
1
No. of vessels next year
1
Total catch (tonnes/year)
300
Debt
Catch/vessel (tonnes/year)
300
2880
1576
Av. fish weight in catch (kg)
4.7
Bank deposits
Equity
Estimate of MSY* (tonnes/year)
934
1304
Resource estimate. (tonnes)
Fleet value
0
13443
All values in 1000 NOK.
* Maximum sustainable yield.
The underlying simulation model is a standard cohort model of cod with fixed mortality rates, nonlinear
stock recruitment and a CPUE relationship, and no interaction with other species, (see Moxnes 1993 for details). Ships are assumed to have a one-year delivery
delay and an average lifetime of 25 years. Net income
after tax and depreciation is used to repay debt and accumulate in bank deposits. The price of fish is held constant (the player is a small seller in a large market).
No formal pre-tests of the subjects were performed.
However, they were given a guided tour of the computer
screen with comments about important assumptions concerning the resource and the economics. They were encouraged to ask questions. They also received written instructions including basic numerical information. The following general features, as well as the treatment
conditions in the next section, were particularly stressed.
The reasons for the assumptions were not revealed:
• Property rights; a crucial assumption in that it removes the commons problem. It also makes the role of
a fleet owner similar to that of a regulator such that
both fishermen and bureaucrats should feel somewhat
aquainted with the design.
• Not allowed to sell vessels; an assumption that helps
mimic the conditions of a region in which it is difficult
to sell specialized vessels in times of overcapacity.
• Virgin initial cod population; ensures that subjects are
faced with the problem of approaching resource limits.
• Maximize the sum of company equity and the value of remaining fish in the fjord in year 20; i.e., infinite horizon
optimization. NOK 1000 ($150) was paid to the subject
with the highest score.
4. Hypothesis and Experimental
Design
The main hypotheses are that various misperceptions of
feedback lead to overinvestments in and overutilization
of fishing fleets even when there is no commons property problem. To test these hypotheses, measures of capacity and utilization are compared to benchmark values. The following discussion of benchmarks pertains
to the basic treatment, named U.
The task is one of infinite horizon optimization of a
nonlinear, dynamic system under uncertainty and ambiguity. The complexity implies that an optimal solution
has not been found. However, certain properties of the
problem make it possible to deduce a benchmark to be
compared with the results. (A more elaborate approach
yielding similar results is discussed by Moxnes 1993.)
The subjects know that the initial biomass estimate,
13443 tonnes, measures harvestable cod ranging in age
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Table 1
N
W
B
F
O
Sum
Sample Sizes (i.e., Number of Subjects) for Individual Cells
U
C
M
R
I
Sum
5
2
4
3
5
5
2
4
3
4
3
2
3
1
6
4
0
3
3
4
3
2
4
3
4
20
8
18
13
23
19
18
15
14
16
82
N–Northern Fishermen, B-Bureaucrats, W-Western Fishermen, O-Others,
F-Researchers, U-Uncertainty, I-No Information, R-Random Recruitment, CCertain Information, M-MSY Information.
from 3 to 15 years; an age span of 12 years. Thus, one
can easily calculate that on average there are 1120
tonnes per annual cohort. If we could catch all the fish
as they entered a given age class, we could harvest 1120
tonnes per year. This is a first indication of the maximum sustainable yield (MSY). The observed average
weight of fish in the catch, 4.7 kg, indicates that the biomass is not concentrated in the lower age classes. A
more elaborate use of the given data would show that
the biomass is likely to peak in the middle age classes.
However this should not lead to any substantial increase in the estimate of the MSY since the subjects are
also told that the fishing technology, Danish seine, harvests fairly uniformly over all age classes.
Dividing the MSY estimate by the initial catch of one
vessel, 300 tonnes per year, results in a fleet estimate of
3.7 vessels. This fleet will produce the maximum economic yield per year (MEY) only if the schooling tendency is perfect (constant catch per unit effort, CPUE).
However the instructions say there is only a certain
schooling tendency. At the other extreme with no
schooling, CPUE is proportional to stock size. In this
case one could argue that the fleet should be around 1.5
vessels. Thus, a range for the optimal fleet size of 1.5 to
3.7 vessels is established. Several factors may modify
this result somewhat. However, neither uncertainty,
ambiguity, nor irreversibility is likely to cause major
modifications (Moxnes 1993).
On the basis of the above considerations, four vessels
or more will be considered an overinvestment for an
individual. For groups of subjects, an average maximum fleet size above 3.2 vessels will be considered an
overinvestment. For instance, this number would allow
35% of the subjects to have fleets with four vessels,
when 50% have three and 15% have only two. The preceding analysis implies that adjustments over time
should take place only one vessel at a time, and that
initial ordering should probably not exceed one vessel
in addition to the one already in place. Furthermore, one
should allow a good deal of time to filter the outcome
feedback after a new vessel has been ordered. This is
because of the previously mentioned information delays (mainly due to filtering), stock and flow complexities, and nonlinearities.
By using simulations not available to the subjects, we
find that three vessels give a slightly higher criterion
value than two vessels, NOK 28.0 million versus NOK
27.1 million. Allowing for divisible vessels, the optimal
fleet size is 2.6. To catch the MSY, 4.2 vessels are needed
in equilibrium. Hence, there is a considerable difference
between the fleet that maximizes the criterion and the
one that catches the MSY (1.6 vessels).
In order to judge the amount of overfishing, we will
compare minimum resource levels attained by the subjects with the minimum resource level that follows from
investing in two new vessels from the very beginning.
This level lies at 8130 tonnes. To judge utilization, a
nearly optimal strategy has been found by trial and error, utilizing more information than is available to the
subjects. This strategy will be used as a benchmark to
see if the subjects behave differently from a wellinformed manager.
uÅ
H
100
S
min 100; 100
3
N
D
S ú 8000 tonnes
S ° 8000 tonnes.
As long as the resource, S, is greater than 8000 tonnes,
the fleet of vessels is fully utilized, u Å 100 percent.
When the resource is equal to or less than 8000 tonnes,
a maximum of three vessels are utilized. With this utilization, the resource will not fall much below 8000
tonnes. The maximum number of utilized vessels, i.e.
three, and the target, 8000 tonnes, are good approximations for all fleet sizes, N. The formula yields ‘‘optimal’’ utilizations for each subject from recorded values
of N and S.
To shed light on what motivates behaviour, five different treatments and five different groups of subjects
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were used (see Table 1). Subjects denoted ‘‘Fishermen’’
were almost exclusively owners of one or more fishing
vessels and worked professionally in fishing. ‘‘Researchers’’ and ‘‘Bureaucrats’’ were selected from people professionally involved in resource management,
mostly recruited from the institutions responsible for
Norwegian fishery policy. All groups represent more
than one institution. The subjects were randomly assigned to treatments. A couple of computer breakdowns
explain the two cells with fewer than two subjects. One
outlier has been removed, and one subject had no reported criterion value due to computer problems.
Treatment U (uncertainty) is used as a reference. It is
characterized by uncertainty in observations of the fish
stock ( h Å 0.3, smoothing time 3 years) and the weight
of the fish ( h Å 0.3), while ‘‘fishing luck’’ varies from
year to year ( h Å 0.1). To increase the probability of
detecting differences, recruitment is not random except
in one treatment, and resource estimates are reported
with correct values in the first year. The subjects were
not informed of the latter fact.
Treatment C (certainty) differs from U in that information and catch are not corrupted by randomness. According to theory, certainty should lead to a more rapid
fleet adjustment. Treatment R (recruitment) differs from
U in that recruitment is random ( h Å 1.0). Theory, as
well as debate, indicates higher fleet levels (Flåm 1990).
Treatment I (information) differs from U in that the resource information is removed. Comparisons will reveal the effects of resource information on results and
behaviour. Treatment M (MSY) replaces the resource
estimate with uncertain, yearly estimates of the maximum sustainable yield ( h Å 0.3, smoothing time 3
years). The treatment also includes a description of the
MSY concept. The treatment removes much of the complexity pertaining to the estimation of the MSY from the
resource estimate. The challenge of maximizing MEY
remains.
Lifetimes of vessels are distributed around the average lifetime of 25 years. When ordering a minimum of
ships at an early stage, some of them will be scrapped
before the 20-year horizon is reached. Thus it is possible
to test for a learning effect. If reinvestment takes place
after previous overinvestment (a fleet size of four or
more vessels), this signals a deficiency of learning in
spite of considerable outcome feedback.
Some of the effects hypothesized above could vary
with background. We test for differences between researchers, bureaucrats, northern and western fishermen, and a control group not familiar with fisheries
management.
Concerning the overall design of the experiment, all
the precepts suggested by Smith (1982) should be met.
Most subjects used between 45 minutes and 1.5 hours
on the task and they seemed very interested. The control
of risk aversion is not a settled issue in the literature
(Forsythe 1986). Awarding a prize to the subject with
the highest score means that the expected criterion does
not saturate, unless the subject is sure to win or lose.
Bolle (1990) finds that using a prize is a cost-effective
way to control incentives.
Though the simulated task does not correspond exactly to a real fishery, the biological model is adapted
from one developed by biologists at the Institute of Marine Research in Norway. The economic part of the
model is similar to standard economic models.
5. Results
5.1. Average Fleet Sizes, Utilization, Intervals
Between Investments, and Reinvestments
The main finding is that most participants build a larger
fleet than the benchmark, with 74% of the participants
building a fleet of four or more vessels. Only 16%
choose fleets of one and two vessels. The median is five.
The average size fleet of all participants is 5.13 vessels,
i.e., an overexpansion of 60% compared to the benchmark of 3.2 vessels. The difference is highly significant
(p õ 0.00001). At the 5% level, the average is significantly higher than 4.5 vessels, i.e., an overinvestment of
41%. Hence, the average fleet size is also significantly
higher than the fleet size needed to catch the MSY, 4.2
vessels.
Sixteen percent of the subjects overinvest during the
first year, in that they order three or more ships in addition to their initial one. On average 5.0 vessels are
ordered during the first year by this minority group.
This makes up 78% of all their investments. This group
ends up with an average fleet of 7.4 vessels, which is 2.3
times greater than the benchmark (p õ 0.001). Thus,
when this minority overinvests, this is primarily explained by an erroneous initial analysis. This group also
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shows signs of excess ordering during the adjustment
process, even though these subjects receive early and
convincing feedback about overexpansion.
The remaining 84% of the subjects do not overinvest
during the first year, and they end up with an average
of 4.7 vessels (1.5 times the benchmark, p õ 0.00001).
On average 0.86 vessels are ordered during the first
year, and this is only 23% of total investments for this
group. When members of the majority overinvest, it is
because of errors made in the adjustment process.
On average the subjects reach an actual, minimum
resource level of 6930 tonnes. The median lower stock
level is at 6880 tonnes. The average is 15% below the
benchmark of 8130 tonnes. The difference is highly significant (p õ 0.0005).
The average lay-ups (reduced utilization) in years
with lay-ups average 27% for all subjects. The average
rate of lay-ups over all years, including years with no
lay-ups, is 14%. Utilization is reduced in 52% of the
years. The average lay-up of the first eight years is 6%
and in the last 12 years it is 19%. This reflects that layups are typically used when the consequences of overinvestments are revealed. The average maximum layup for all subjects is 41%.
Overutilization is calculated by subtracting observed
lay-ups from the calculated ‘‘optimal’’ lay-ups. The
overall average shows an underutilization of two percentage points. This underutilization is not significantly
different from zero. Hence, on average there is no evidence of over- or underutilization.
However, for individual subjects, average overutilization, U o , and maximum fleet sizes, M, are correlated,
as revealed in the regression shown in the equation below (both coefficients have p-values below 0.00001, R 2
Å 0.36, N Å 81). The more aggressive the subject is as
an investor, the more aggressive he is as utilizer. According to the formula, overutilization starts when the
maximum fleet size exceeds 5.4 vessels. For M ranging
from one to four vessels, there is considerable underutilization of the fleets.
U o Å 022.6 / 4.2 M.
The costs of overinvestment and over- or underutilization are measured by the average financial performance (infinite horizon NPV) relative to a benchmark.
Ordering one vessel initially and another after 10 years
yields a benchmark of NOK 25.5 million. The average
for all subjects is only 54% of the benchmark. If group
R, which has a bias in the direction of high recruitment,
is excluded, the average drops to 47%. After removing
the six worst cases, with criteria well below NOK 05.0
million, the average result for all groups except R rises
to only 72% of the benchmark.
Average intervals between investments are calculated
for all subjects with investments dispersed in time, ignoring instances of reinvestments after scrapping. The
total average for 67 observations is 4.3 years. The median of each subject’s average is 3.7 years. Note that due
to the one-year delivery delay, investments take place
after only 3.3 years (or 2.7 years) of experience with the
last vessel ordered.
Reinvestments after vessels are (automatically)
scrapped are reported in Table 2. Each subject counts
only once, even when more than one boat is scrapped.
From the results one can see that a larger maximum
number of vessels typically implies that a lower fraction
of subjects reinvest after a vessel is scrapped. The more
vessels, the more likely it is that a subject understands
that an overinvestment has taken place. Using binomial
distributions, quite wide confidence intervals for the
fraction reinvesting follow for each fleet size. More precise estimates are obtained by pooling fleet sizes of four,
five and six vessels. In this case the 95 percent confidence interval ranges from 0.15 to 0.53. Hence a considerable fraction of the subjects choose to maintain their
oversized fleets.
5.2. Differences Between Treatments and Groups
We start by comparing maximum fleet sizes between
treatments and groups.1 The data are shown in Table 3.
Differences between treatments or between groups are
not likely to be significant. A similar comparison of frequencies of overinvestment yields the same negative result (see Moxnes 1996 for details).
A comparison of the maximum fleet sizes for the subjects who do not overinvest during the first year (84%)
shows a difference between pooled treatments, UCR
and treatment I, at the 4.0% level. The average for I is
1
By using the SAS platform in JMP, unequal sample sizes are correctly
dealt with. Results are identical with the results of the method described by Keppel (1982, chapter 15).
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Table 2
Frequency of Reinvestment for Different Fleet Sizes; 52
Observations. Scrappings in the Final Year are Not Included
Maximum Number
of Vessels
3
4
5
6
7
¢8
Cases
Fraction
Reinvesting
¢95% Confidence
Interval
4
11
13
8
7
9
0.50
0.55
0.23
0.25
0.29
0.11
0.00–1.00
0.18–0.91
0.00–0.54
0.00–0.63
0.00–0.71
0.00–0.44
5.94 compared with 4.40 for UCR. For investments during the first year (Table 4) we find no differences between treatments. The variation in first-year investments varies between pooled treatments with resource
information, UCR, and treatments M and I, with pvalues of 3 and 0.2%, respectively.
Intervals between investments, minimum resource
levels, and net present values (fortunes) are not found
to differ between groups or treatments. Lay-ups in years
with reduced utilization differ between both treatments
M and I and a pooled treatment with resource information, UCR. Differences are at the 0.03 and 1.1% levels,
respectively. Overutilization differs between treatment
I and the pooled treatment UCR at the 6% level.
5.3. Investment Behaviour
Investment behaviour is analysed by a logistic model,
using time-series data from the experiment. According
Table 3
N
W
B
F
O
Means
Mean Maximum Fleet Sizes for Different Groups and
Treatments. Asterisk Denotes Means Significantly Higher
Than 3.2 at the 5% Level
U
C
M
R
I
Means
4.6
6.5
4.8
12.3*
4.8
3.8
4.0
5.5*
3.3
6.0
2.7
6.0
4.3
4.0
4.3
3.5
—
7.0
4.3
4.3
5.3
4.5
3.0
9.7
7.3
4.0*
5.2*
4.8*
7.2*
5.2*
6.1*
4.6*
4.2
4.7*
5.9*
5.1*
Table 4
N
W
B
F
O
Means
Investments First Year, 82 Observations
U
C
M
R
I
Means
0.8
1.5
2.3
10
2.2
1.0
1.5
1.0
1.0
2.0
0.3
1.5
1.0
3.0
1.0
1.3
—
1.0
0.7
1.3
0.3
0.0
1.0
1.3
1.0
0.8
1.1
1.6
3.2
1.5
3.0
1.3
1.1
1.1
0.8
1.5
to the logistic model, the probability of ordering, Ot , a
given number of vessels, Oi , is given by the formula:
p(Ot Å Oi ) Å
1
J
1 / exp( 0ai / ( jÅ1
bj xjt )
0
1
.
1 / exp( 0ai01 / ( JjÅ1 bj xjt )
All probabilities depend on the same set of explanatory
variables xjt. For the case with one explanatory variable,
xt, the ratio ai /b has the interpretation of the value of xt
for which the probability of ordering Oi or fewer boats
equals 0.5. We expect ai to be greater than ai01. Parameters
ai and bj are estimated by a maximum likelihood method.
Data for each treatment are pooled in order to obtain sufficient degrees of freedom, and to enable comparisons between treatments. Data for year one are excluded.
HYPOTHESIS A. Our basic hypothesis is that the subjects order new vessels as long as they perceive that profits (income after tax) are improving, i.e., we consider
only one explanatory variable, improvement xIt . This
rule is intuitively appealing, resembling a hill-climbing
procedure. Its relevance is suggested by repeated comments by the subjects of the type: ‘‘Things seem to be
working well; I’ll order another vessel.’’ It is not obvious
from these comments that net income after tax was their
prime indicator of improvement. However, a priori we
know that net income is important for the present value
criterion, and it is correlated with other likely measures
such as catch and weight. When testing Hypothesis A,
xIt is calculated as the difference between perceived 2
2
N–Northern Fishermen, B-Bureaucrats, W-Western Fishermen, O-Others,
F-Researchers, U-Uncertainty, I-No Information, R-Variable Recruitment, CCertain Information, M-MSY Information.
Perceived profits is a moving average of profits, with fixed weights
of 0.7 on current profits and 0.3 on last years profits, corresponding to
an averaging time of nearly one year. The smoothing is motivated by
both theory and empirical investigations.
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current profits and perceived profits at the time when
the last investment was made. The upper half of Table
5 shows the results of the test.
Hypothesis A is only rejected for treatment M. For the
four other treatments p-values are typically very low.
Across treatments, all parameter values are close to being
within two standard deviations of each other. The test thus
detects no significant difference in behaviour between the
remaining four treatments. The R2-values are low, as they
typically are in logistic regressions. Synthetic data from a
simulation of the investment rule for treatment U (Figure
2) results in a R2 value in the same low range.
Figure 3 shows observed behaviour for treatment U.
Comparing Figures 2 and 3, we see that the simulations
seem to produce behaviour with the same characteristics
as the observations. Possible exceptions are the two cases
with the highest initial investments, where only the simulation model predicts investments after the initial year.
The simulations show that the investment rule leads to
overinvestments for a stock resource such as cod.
The investment rule above does not consider feedback
about the resource. The lower part of Table 5 tests for the
importance of this feedback. Hypothesis B holds that investments are influenced by both improvements in profits
and measurements of fish stocks, i.e., we have two explanatory variables xIt and xSt. For treatments with resource information, U, C, and R, the effect of resource information turns out to be significant and to have the
Table 5
Logistic Model Tests of Behaviour (standard deviations in parentheses)
* denotes significance at 5% level, ** denotes significance at 0.1% level. R 2s are based on log likelihood ratios.
Treatment:
U
C
M
R
I
Vessels ordered Oi:
1, 2, and 3
1, 2, and 4
1 and 2
1, 2, and 5
1, 2, 3, 4, 8
Observations
342
327
269
252
282
0.070
0.000016
0.029
0.0054
0.002
0.505
0.092
0.000008
0.030
0.0013
2.01** (0.18)
3.90** (0.41)
5.01** (0.70)
2.10** (0.19)
3.68** (0.36)
5.79** (1.00)
1.89** (0.18)
4.48** (0.58)
2.15** (0.23)
3.93** (0.44)
5.78** (1.02)
0.73** (0.21)
0.90* (0.42)
00.28 (0.42)
1.38** (0.35)
1.43** (0.16)
3.16** (0.31)
4.50** (0.58)
4.91** (0.71)
5.61** (1.01)
0.81* (0.35)
0.108
0.000001
0.056
0.00057
0.012
0.269
0.167
0.000000
0.031
0.0053
3.82** (0.64)
5.75** (0.75)
6.89** (0.95)
4.92** (1.17)
6.53** (1.22)
8.64** (1.54)
3.22** (0.93)
5.81** (1.09)
4.39** (0.66)
6.35** (0.82)
8.35** (1.30)
0.59* (0.24)
0.17* (0.05)
0.82 (0.48)
0.26* (0.11)
00.33 (0.42)
1.40 (0.94)
1.37** (0.38)
0.17** (0.04)
1.25** (0.44)
2.97** (0.52)
4.31** (0.71)
4.72** (0.82)
5.42** (1.09)
0.84* (0.36)
00.02 (0.04)
Hypothesis A
R2
p-level model
Intercept, a1
Intercept, a2
Intercept, a3
Intercept, a4
Intercept, a5
Improvement, bI $
Hypothesis B
R2
p-level model
Intercept, a1
Intercept, a2
Intercept, a3
Intercept, a4
Intercept, a5
Improvement, bI $
Resource info., bS #$
$ Estimated parameters are multiplied by 1000 to save space.
# bS denotes MSY information in treatment M; otherwise it denotes resource information.
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Figure 2
Simulation of Logistic Model for Treatment U. Pairs of Runs
Have Initial Investments of 10, 5, 2, 1, and 0 Vessels. Investments Are Random with Probabilities Given by the Investment
Rule
extended investment rule, not shown, shows that the
same general pattern of behaviour remains.
With treatment I, resource information is not available to the subjects (but it is available for analysis), so
that no effect should be expected. The test shows no
spurious correlations. This indicates that none of the
positive findings for treatments U, C, and R represent
spurious correlations. For treatment M, resource information is substituted for by MSY information. The effect
is in the expected direction; however, the effect is not
significant at the 5% level.
6. Discussion
Figure 3
Observed Number of Vessels for Design U, Groups N, W, B,
and F
expected sign. The bS values vary between treatments by
less than one standard deviation. The a-parameters increase in value and the bI parameters drop slightly compared to the case without resource feedback.
How important is the effect of resource information?
Probabilities for ordering vessels are approximately unchanged from the simple improvement rule when the
resource stock is 11500 tonnes, or 85% of the initial biomass. On average, for all 83 subjects, the resource stock
is reduced to 85% after five years. Before the 85% point
is reached, the probabilities of investing are higher than
the simple improvement rule predicts, but after this
point they become lower. At a 50% stock level the probabilities are approximately halved. A simulation of the
6.1. Initial Investments
Sixteen percent of the subjects overinvest during the
first year. This indicates that they are unable to make a
proper initial analysis of the situation. When 84% of the
subjects do not overinvest during the first year, this
might be a reflection of good initial analysis. However,
it might also reflect lack of analysis. For instance, of all
subjects that receive resource information (all treatments except I), 24% do not order at all during the first
year. However, 45% of this subgroup ends up with four
or more vessels, and an average maximum fleet of 5.5.
An appropriate initial analysis should have indicated
an upper limit below this average.
A significantly higher variance in first-year investments for pooled treatments UCR than for treatment I
indicates that resource information facilitates and inspires initial analysis. A lower variance for treatment M
than for UCR indicates that MSY information leads to
more homogeneous first-year investments than the
stock assessment.
Large variations in first-year investments indicate
that different errors are being made. Availability heuristics (see Kahneman and Tversky 1974) imply that
subjects make only the calculations they remember to
do or are able to do. The subject who was excluded from
the data set as an outlier probably divided the initial
resource estimate by the initial catch per vessel. A few
others used a rule of thumb to the effect that one can
catch one third of the resource each year. One of these
said that he had applied this rule to the measurement
of the virgin resource and not to the expected equilibrium level. Both these comments and the cases of high
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initial investments indicate that CPUE and the economics of the simulated fishery were not considered by
many subjects.
6.2. Adjustments over Time
The major impression obtained from the comparison of
treatments and groups is that there are only insignificant or minor differences between them. This is the case
for maximum fleet sizes, utilization, and time intervals
between investments. With the exception of treatment
M, the time-series analyses show that the same investment rule gives a good explanation of behaviour for all
treatments. Hence, the major cause of overinvestments
seems to be largely independent of groups and treatments, i.e., independent of whether the subjects are fishermen, bureaucrats or researchers, independent of the
presence or absence of measurement errors, variability
in fishing luck or stochasticity in recruitment, and to
some extent independent of the availability of stock estimates.
The investment rule, which says that ordering should
go on as long as profits are perceived to increase from
investment to investment, is intendedly rational and
would work well for a flow resource with only minor
information delays. It is much too aggressive to be applied to a stock resource because additional investments
are made long before the full effects of previous investments have been realized or identified. This is nicely
illustrated by the results for one of the subjects; see Figure 4. Building expectations about long-term profits on
the observations of profits after one, two, or three years
of fishing would lead to overly optimistic estimates
since the CPUE and profits would continue to drop with
the stock level. It thus seems clear that the profitgradient investment rule does not take knowledge of
the stock nature of the cod resource into account.
The extended investment rule, which allows for feedback about the stock level, provides a good explanation
of behaviour for the treatments where this information
is available, i.e., U, C, and R. The effect is fairly strong,
approximately halving the probability of investments
when the stock level is halved. However, it is still
weaker than seems optimal. According to the frequently
used Schaefer model (Schaefer 1957), the regrowth of
the resource reaches its maximum when the stock is at
half its carrying capacity. Investing at half speed at this
point is excessive.
Figure 4
Results for Subject f2C. Treatment C (No Uncertainty), Median
Investment, and No Use of Lay-ups
The results correspond to previous findings of misperception of feedback in dynamic decision making
(e.g., Sterman 1989a). While Sterman finds decision
rules that are intendedly rational with respect to a goal
of balancing supply and demand, we find rules that are
intendedly rational with respect to profit maximization.
In both cases, the rules fail due to misperceptions of
feedback. According to Sterman, ‘‘. . . there is a strong
tendency to ignore the time lag between the initiation
of a control action and its full effect . . . .’’
In this connection Sterman reports a tendency to undervalue what is in supply lines. We find that investments on average take place after only three years of
experience, much too little to see the full consequences
of earlier investments. Sterman also concludes, ‘‘. . .
there is a strong tendency for subjects to be overly aggressive in their attempts to correct discrepancies . . . .’’
While Sterman reports overly high weights on the discrepancy between desired and actual capital stock, we
observe this effect directly in terms of subjects ordering
more than one vessel at the time.
Our analysis indicates that most subjects base their
decisions on outcome feedback, hoping to find the optimal fleet size by trial and error. At best, only a few
subjects made an appropriate use of prior information.
However, understanding the stock nature of the fish resource and of the fleet could have influenced subjects to
choose less aggressive investment strategies. One fisherman commented that each new vessel would exploit
the fish stock for many years to come. Consequently he
chose a very careful investment strategy and earned one
of the highest fortunes. To what extent others were
aware of and behaved according to their knowledge of
stocks and flows cannot be deduced from the experi-
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ment. On discrepancies between system knowledge and
behaviour see Brehmer (1989), Morris and Rouse
(1985), and Broadbent et al. (1986).
The following differences between treatments are interesting. First, in treatment M the proposed investment
rule is clearly rejected by the time-series analysis. A significant difference between lay-ups for M and the
pooled treatment UCR strengthens the impression that
information about MSY had an effect. While the maximum number of vessels in treatment M is not significantly lower than that of UCR at the 13% level, the observed sample difference is in the expected direction.
The sample average in treatment M equals the fleet
needed to catch the MSY, i.e., 4.2 vessels. However, it
still seems as if the average maximum fleet is larger than
the benchmark, at the 8% level. This indicates that MSY
information is helpful in avoiding fishing in excess of
MSY. However, and equally important, it indicates a
lack of understanding of the difference between MSY
and MEY. The relationship between the resource stock
and the CPUE, and hence the economics of the problem,
seems to be ignored.
Second, the time-series analysis gives strong indications that resource information influences investment
behaviour for all three treatments with such information available. A comparison of the maximum number
of vessels for the 84% of the subjects that did not overinvest in the first year showed a significantly higher
fleet size for treatment I than the pooled treatments with
resource information, UCR. Correspondingly, overutilization seems to have been higher for treatment I than
for UCR. Altogether this indicates that information
about stocks is used both to bring investments to a halt
and to reduce utilization. Equally interesting is the observation that the lack of resource estimates in treatment
I does not lead to rapid investments to accelerate learning. On the contrary, for subjects without initial overinvestments, fleets reach their highest levels after on average 13.2 years with treatment I, while it takes only 9.8
years with treatment U. In the early years risk aversion
seems to dominate a possible desire to accelerate learning. In later years, investments go on in spite of mounting evidence that the marginal profits from additional
vessels are negative.
Third, observed reinvestments in cases with maximum fleets of four or more vessels indicate either that
learning is slow or that some subjects end up maximizing catch rather than profits.
7. Concluding Remarks
7.1. Main Findings
An experiment has been performed where 83 subjects
were asked to manage the same virgin fish stock, one
subject at a time. Exclusive property rights were granted
such that the subjects faced no competition for the resource. Each year decisions had to be made about irreversible investments in vessels and lay-ups of the fleet.
The experiment lasted for 20 ‘‘virtual’’ years. The goal
was to maximize infinite horizon profits. The underlying bioeconomic model builds on principles acceptable
to both biologists and economists. The participants were
Norwegian fishermen, fishery administrators, fishery
researchers, and a diverse group of subjects mostly unfamiliar with the management problem.
The main finding is one of overinvestment leading to
an average maximum fleet overcapacity of 60% in all
groups. The median fleet size gave an overcapacity of
56%. On average the subjects reached minimum resource levels 15% below the minimum level for a closeto-optimal strategy. On average capacity utilization
(lay-ups) was not found to differ from appropriate levels, given installed capacities. However, subjects with
large overcapacity over-utilize their fleet and overfish
while subjects with small fleets underutilize their fleet.
In terms of infinite horizon net present value, the subjects reached 72% of the benchmark value on average,
even when the worst cases were excluded.
It is interesting to note that the main findings of this
study are representative of the situation in Norwegian
fisheries. For the last 15 years capacity has been estimated to be about twice the optimal size, while quotas
probably have been closer to optimal levels. It is also
worth noting that the experiment reported here was
conducted in the aftermath of severe quota reductions
in Norwegian fisheries. Many subjects expressed concern about this situation when they received the instructions. This makes the results even more dramatic.
Sixteen percent of the subjects overinvest in the first
year, indicating their initial analysis is erroneous. The
quality of the initial analysis seems negatively influenced by the availability of resource estimates and
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positively influenced by information about MSY. Over
time, most subjects seem to be using an investment rule
which is intendedly rational for a flow resource: add
vessels until profits stagnate to end up in a situation
with maximum profits. However, they misperceive the
stock nature of the cod resource and fail to incorporate
these dynamics into their decision strategy. By relying
on current measures of profitability, which are not representative of the long run, they continue buying vessels
even after the optimal fleet size has been reached. These
findings are in line with literature on misperceptions of
feedback. In addition it is worth noting that subjects
seem to fail to take into account the fact that catch per
unit effort (CPUE) declines with the stock level; they
tend to maximize maximum sustainable (MSY) yield
rather than maximum economic yield (MEY).
7.2. Implications for the Understanding of
Renewable Resource Management
The experiment produces behaviour—overexploitation
of a renewable resource—which is normally explained
by the commons problem. It is interesting to ponder
what the combined effects of misperceptions and competition could be. Ostrom (1990) expects matters become more complex: ‘‘When this difficult long-term
problem [management] is combined with the freeriding incentives of multiple appropriators, we see that
organizing to maintain a system is a challenging task.’’
Obviously it is not sufficient to find a solution to only
one of the problems to succeed; both require careful attention.
Over time, as the exploitation of a resource approaches the levels of MSY or MEY, misperceptions
would imply that fishermen and regulators do not perceive the need to limit efforts. The commons problem is
erroneously thought to be of no concern. As overcapacity and overutilization become apparent, often as a crisis, differences of interest and competence among competing individuals and regulators complicate the learning process. While the crisis is likely to lead to policy
innovation, the insufficient learning is likely to lead to
inappropriate policies. This is similar to how Wilson
and Lent (1993) describe policy evolution in the fishery
sector (also see Kingdon 1984). Furthermore, the time
needed to solve the commons problem may well be underestimated or ignored. This delay—created by the
slow workings of the political process—comes in addition to the delay in rebuilding the cod stock. The
longer delay would further exacerbate the tendency towards overinvestment and overfishing. The experiment
did not test for this effect since it allowed for immediate
action by the subjects.
Numerous renewable resources can be categorized as
stock resources. Misperceptions of feedback are of particular importance for the resources that are exploited
on national, regional, or global scales with no substitutes readily available. For these resources it is imperative to make the correct response the first time. The
greenhouse problem is a prime example. The world’s
emissions of CO2 and other greenhouse gases is currently larger than the absorptive capacity of the atmosphere. For this reason the stock of greenhouse gases in
the atmosphere is steadily increasing, and will continue
to do so even after fairly large reductions in current
emission rates. Thus, the world’s population may already have exceeded the ‘‘optimal’’ level for emissions
without knowing it from current experience.
7.3. Policy Implications
A simple answer to the question ‘‘how can performance
be improved?’’ is to tell decision makers the ‘‘optimal
strategy.’’ While this is possible in an experiment, we
think that the answer is too simplistic in a real setting.
A simple rebuttal is to say that mismanagement persists
in many areas in spite of individuals and organizations
revealing ‘‘optimal strategies.’’ In reality ‘‘optimal strategies’’ are uncertain, imprecise, and not generally
known and agreed upon by all groups of experts and
interest groups. Hence, decision makers always face the
problem of having to filter available information. Therefore the challenge is to design institutions and pedagogical devices that will ensure that decision makers follow
the best strategies.
Since the researchers and advisers who participated in
the experiment did no better than the others, it seems
important that they improve their intuitive understanding of the stock nature of fish resources. Another message
is that marine researchers should acknowledge the importance of the CPUE relationship and hence of the economics of the management problem. The potential for
misperceptions to influence model building, stock estimates, judgements of results, etc., implies that a mini-
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mum of dialogue among biologists, marine researchers,
statisticians, and economists should be institutionalized.
Decision makers should also learn the above lessons
from the experiment, be they bureaucrats or fishermen.
However, the potential for misperceptions among decision makers strengthens the need for advisers to be
independent of the political system; see Finlayson
(1994) on the influence of political forces.
Contrary to what economic theory predicts, the experiment indicates that privatization is no guarantee of successful management of renewable resources, at least in the
short run. However, these facts should not be used to rule
out privatization in terms of individual transferable quotas, ITQs. With this system, fishermen are given fairly
strong incentives to adjust their total capacity to the total
allowable catch, TAC. Still the success of ITQs depends
on appropriate choices of TACs. However those who hold
ITQs now have clear incentives to search for and accept
the TACs that maximize profits and asset values. Hence
the ITQ system could create a stronger demand for information about the stock nature of fish resources and the
importance of the CPUE relationship.
7.4. Further Research
While the current experiment suggests that misperceptions of feedback are likely to exist in fisheries management, there remain many questions about the underlying reasons for misperceptions and about policy design.
How much of the behaviour can be explained by efforts
to learn? Does behaviour converge towards that predicted by optimal strategies? How will subjects choose
between conflicting advice; will, for instance, the MSYadvice sustain its effect in such a setting? What happens
if groups rather than individuals make decisions? (Will
careful individuals agree to more aggressive investments over time due to the path dependence implicit in
the proposed decision rule?) What happens if the management problem is coupled with the commons problem? How well will subjects do under a regime of ITQs?
Finally, what pedagogical devices and what institutional changes are successful in correcting misperceptions of feedback which can lead to overexploitation of
renewable resources? 3
3
Thanks are due to two anonymous referees, an associate editor, Heidi
Bjőrstad, Hans Olav Husum and Magnus Hatlebakk at SNF, Sigurd
Tjelmeland at the Institute of Marine Research in Bergen, Per Sandberg
at the Norwegian Fisheries Directorate, and to all the participants in
the experiment. The research received financial support by the Norwegian Ministries of the Environment and of Fisheries.
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