Interactions between Institutional Rules and Network Norms in Communal
Resource Governance
Authors:
Arun Agrawal*, Daniel G. Brown*, Gautam Rao*, Rick Riolo†, Derek Robinson*
*School of Natural Resources and Environment, University of Michigan, 440 Church Street, Ann Arbor, MI 48109-1041; and
†Center for the Study of Complex Systems, University of Michigan, 321A West Hall, 1085 S. University Ave., Ann Arbor, MI 48109-1107
Edited by:
Much of the research on common property has focused on how
different kinds of institutions shape the incentives of users who rely
on a common-pool resource system for their daily needs. Writings on
the commons have greatly advanced existing knowledge about how
institutions can be designed to improve sustainable resource
governance. A significant puzzle that has occupied this scholarship is
the nature of the differences between formally designed and
introduced institutions vs. spontaneously created informal network
norms, and how such differences affect resource governance
outcomes. This paper analyzes the differences between formal
institutions and informal norms with the help of an agent-based model
of interactions of villagers with forests based on the information they
derive from social interactions with their neighbors (an informal
network with two-way flows of information) and an externally
imposed institution that strictly enforces announced limits on forest
product extraction. The paper investigates how changes in the relative
dependence of users on information from formal institutions versus
informal networks affect user behavior, harvesting levels, and forestrelated outcomes.
agent-based modeling | common pool resource | fuelwood extraction
| institutional governance | norms | social networks
Institutions of different kinds play a particularly important role in
influencing local resource use and outcomes for renewable resources
such as forests, pastures, irrigation and drinking water and coastal
fisheries (Baland and Platteau 1996, Dietz et al. 2003, Ostrom 1990). The
literature on common-pool resources – one of the more successful
research programs in the social and ecological sciences -- has focused on
understanding how institutions can be designed to improve sustainable
resource governance, the relationship of resource users to each other,
and institutional processes themselves (Berkes 1989, Chhatre and
Agrawal 2008, Wade 1989). This literature rarely examines interactions
between two major types of institutions: formal governance
arrangements that often have a material manifestation in the form of
written rules, organizational form, and hierarchical relationships among
employees, versus less formal social networks that link different
resource users through relations of information exchange and social
interactions. The importance of such social networks has been broadly
recognized in the work on development and natural resource
governance (Lal 1999, North 1990, Putnam 1993, Pretty 2003),
particularly through writings that examine the role of social capital as
social ties and networks. The influence of social capital and institutions
on resource outcomes has been studied separately; sometimes formal
institutions have been viewed as a form of social capital. Yet, it is clear
that social capital (in the form of networks) and formal organizations are
different types of institutions, typically coexisting in a given context, and
we need to understand the how they influence each other as well as
resource outcomes jointly (cf. Leach et al. 1999, Lewins 2007).
In a given resource governance context, the signals and sanctions
provided by formal institutions interact with existing social networks
and norms, and in combination with a range of socioeconomic,
demographic, and biophysical factors to yield diverse resource
outcomes (Dietz et al. 2003, Agrawal et al. 2008). Institutional
interactions, as influenced by variations in signaling, enforcement
mechanisms, network structures, and user preferences provoke an
important question for theories and practices of natural resource
governance: How are user behaviors and resource outcomes affected by
governance rules imposed by formal resource governance institutions
and network norms as generated through informal social interactions?
This paper focuses on the relationships between institutions and
social networks related to resource use and management. It does so by
analyzing the actions of individual agents who make resource-use
choices. In our analysis, their choices depend on the information they
receive from institutions and social networks, the weight they place on
self-interested consumption versus adherence to institutional rules for
promoting sustainability, and norms that contribute to improvements in
collectively owned common-pool resources.
Household agents both ascertain and create community norms by
interacting with other agents in their social network (Bettenhausen and
Murnighan 1985, Ullmann-Margalit 1977). In the context of community
use of common-pool resources -- e.g., consumption of firewood from
collectively used forests -- norms can be seen as informally recognized
expected consumption behavior. The level of such consumption can be
set by individuals attempting to match the behaviors of others with
whom they interact. In our analysis, network norms effectively shape
individual behavior. A large literature examines the different underlying
bases of such structuring: individuals’ desires to avoid informal network
sanctions, seeking of conforming behavior by network members,
positive preferences for contributing to group well-being, acquisition of
adaptive behaviors, and the like (Axelrod 1986, Henrich and Boyd 1998).
Figure 1 indicates that the flow of information occurs through
different pathways for institutions versus social networks. Formal
institutions monitor the state of the resource and results of prior
aggregate behavior for the entire community, and make decisions
related to levels of resource extraction based on those outcomes.
These decisions are made more infrequently than the resource-use
decisions of the agents influenced by institutional decisions. These
differences in time-scale have been shown to produce lags in the system
that create large-scale inefficiencies <citation> and increase the risk of
individuals’ collectively exhausting common pool resources (Hardin
1968). In contrast to aggregate level assessments by formal institutions,
information flow through informal social networks is generally more
localized, utilizing higher frequency individual interactions that consist
of observations of behaviors and actions carried out by socially and
spatially nearby constituents <citations>.
The relationships shown in Figure 1 suggest that as institutions shape
outcomes by structuring formal rules, the prescriptions those rules
dictate may come into conflict with informal community norms that
affect behavior. Faced with the choice of adhering to either rules or to
norms, individuals choose among various combinations of the two by
selecting a combination that yields high individual “utility,” given their
1
desires for higher incomes or consumption, leisure, lower risks of
sanctions imposed by formal institutions or informal social networks, as
well as their desire to contribute to sustainable performance of the
community’s shared resources. Thus effective formal institutions
achieve desirable outcomes by recognizing the existing preferences and
social networks in a community, and then using available policy
mechanisms (e.g., prescriptive rules) to shape incentives for a sufficient
number of individuals and households, such that desired aggregate
behavior—sustainable resource use--- is achieved and norms are shifted
towards behaviors that also contribute to those outcomes.
This paper uses an agent-based model (ABM) to model explicitly and
explore how the effects of institutional rules related to common-pool
resources are structured by individual preferences and social networks
in a community of users. The ABM represents villagers’ choices of
forest-resource consumption on the basis of information derived from
(a) rules enforced by a formal institution to limit fuel-wood extraction
and (b) norms of resource use that emerge through social interactions
among networks of neighbors.
as a means to promote sustainable use of a forest resource near a small
village. The model represents a hypothetical place, but uses several
parameter values derived from data and literature on Himachal Pradesh
in India (see supplemental text). Our model is composed of three
components: two types of agents that represent the key actors
(households and formal institutions) and the resource being utilized (the
forest). We model agent behaviors and resource changes over time
using discrete monthly time steps. Our focus is on the decisions
households make: in particular, at each step, households must decide
how much resource to extract. The resource being extracted is fuelwood, which is obtained from local forests and constitutes the primary
energy used for cooking in India (ABE 1985, Misra et al. 1988, Bhatt and
Sachan 2004).
Household Agent Decisions. Household agent decision making is
represented using an approach framed as bounded rationality (Simon
<citation>). The household agent1 decides how much wood to extract
from the forest at each time step. A household’s preferences over how
much to extract, x, are represented by this utility function
U(x) = C(x)C * L(x)L * A(x)A
Fig. 1. Conceptual model of the flow of information between
institutions, individual agents, and social networks.
Methods
To examine the interactions between institutional rules and
network norms, we built an agent-based model to represent the
use of a shared forest resource. Because forests in rural areas
are one of the most common types of common-pool resource
and provide livelihood benefits to hundreds of millions of rural
residents, we use the hypothetical example of a communally
used forest to motivate the model. However, the precise choice
of forests is not critical to our results.
Agent-based modeling. Agent-based modeling (ABM) is an approach to
representing the properties, behaviors, decision-making strategies, and
actions of interacting components in a dynamic system that is
composed of actors and their environment. ABMs can be run to
evaluate the aggregate system-level implications of individual behaviors,
and the diversity and interactions thereof. Because ABMs derive systemlevel outcomes from component interactions, the approach can
represent and model non-linear dynamics, positive and negative
feedbacks, heterogeneity, learning and evolutionary decision-making
strategies (i.e. adaptation), and a range of other analytically intractable
processes (Holland 1995, PNAS-99 2002). Furthermore, the ABM
approach can be used as a framework to integrate various sources of
data, theories, and conceptual models (Janssen and Ostrom 2006;
Robinson et al. 2007) and has replicated experimental commons
dilemma results (Deadman et al. 2000).
(1)
Where C(x) is utility from consumption of the extracted fuel-wood, L(x)
is the utility from “leisure” (i.e., from time not spent gathering fuelwood) and A(x) is utility from adhering to institutional rules or
community norms regarding fuel-wood extraction (which affect the
sustainability of the common forest shared by the community). The ’s
(each in [0, 1] and summing to 1) represent the household’s relative
preferences for the three components, and thus can vary across
households. Details on the form of C, L, A, and the parameter values we
used are given in the supplemental text. In short: utility from consuming
firewood, C(x), increases with x, but with diminishing returns as x
approaches a value that depends on household size2. L(x) varies
inversely with x, since more time gathering amount x means less time
for other things; L(x) also depends on the state of the forest – less wood
in the forest means more time required to collect fuel-wood (see
supplementary material). A(x) is a weighted function of two factors that
represent the relative importance the agent places on adhering to
institutional rules or community norms:
A(x) = r * R(x) + (1-r)* N(x), 0 <= r <= 1
(2)
R(x) increases to the extent the household’s extraction level x is less
than the amount prescribed for that household by the formal
institution’s rules (designed to maintain a sustainable forest), and N(x)
increases to the extent the household’s extraction level x matches the
level of extraction suggested by community norms, as indicated by the
average extraction level of the household’s neighbors (spatial or social—
see below) during the previous time step. The weighting factor r, which
can vary across households, determines the degree to which a particular
household places more importance on adhering to institutional rules
(larger r) or on community norms (smaller r).
To determine how much to extract in a given time step, a household
agent selects 10 candidate levels of extraction and chooses the level x*
that maximizes the household’s utility, calculated as described above.
The candidate levels are drawn from a normal distribution centered on
1
We assume the household acts as a single entity.
The utility a household derives from consumption is a decreasing returns
function of the amount extracted and on the size of the household, which
determines the level required for subsistence, as described in the supplemental
materials. Note that markets for fuel-wood are essentially non-existent in the
locations we studied (Heltberg et al. 2002).
2
Model Description. We developed an ABM to explore how different
individual preferences, social networks and the community norms that
result alter the effectiveness of rules implemented by formal institutions
2
the extraction level the household chose the prior time step 3. The
household then extracts the amount x* from the forest, reducing the
amount of resource remaining.
Norms, Household Interactions, and Social Networks. Household
agents assess (and create) community norms by interacting with agents
in their social networks. In rural India, a household’s social network
primarily reflects interactions with spatially proximate households. In
our model households are embedded in a 2D grid, one household per
cell, such that a household’s social network includes its Moore-neighbor
households4. Households also may interact with more distant
households, as a result of various social relationships (e.g., family ties,
friendships, etc.). In order to study the effects of different social
network structures resulting from varying combinations of adjacent and
distant “neighbors,” our model includes a parameter, p, which controls
the fraction of non-adjacent neighbors in each household’s social
interaction network. In particular, after a household h is placed in the
2D grid and its social network is set to be the list of its Moore neighbors,
each neighbor i on that list has probability p of being replaced on the list
with a household j randomly selected from the community at large
(other than the agent itself), so that h and i are no longer in each other’s
social network, but h and j are<footnote>. Once created, the social
networks remain fixed for the model run.
In our model each household agent uses its social network to assess
the community norm regarding extraction levels. In particular, each step
the agent computes the average extraction level (from the previous
step) of the other agents in its network. That average is taken as the
extraction level the agent prefers to match when calculating utility N(x)
as described for equation 2, above. Thus the dissemination of
information among household agents provides an indication to each
agent of how much resource other agents are using, information that is
then factored into their own decision making (in Equations 1 and 2).
Note that when p is 0 the interaction network of each agent is its
adjacent neighbors, so that clustering is high (many of h’s neighbors are
neighbors of each other) and the average path length (following the
agents’ links to their neighbors) through the community is long. At the
other extreme, when p is 1, each agent has a social network based on
neighbors drawn randomly from the community, so that there are very
few or no clusters but path lengths between any two agents are also
short. And at moderately low p values, the social network has “small
world” properties (Watts and Strogatz, 1998), i.e., there are clusters but
the average path lengths are short. Thus varying p alters the overall
interaction patterns and the flow of information about resource use in a
community, which in turn can affect the dynamics of norm formation
and stability. By varying p across experiments, we can explore how
these social network structures and the dynamics they induce in the
spread and stability of norms can alter the effectiveness of institutional
rules designed to alter agent behavior.
The Formal Institution Agent. The formal institution agent represents a
branch of the Indian government that manages the forest resources and
aims to maintain both the ecological quality of the forest and its ability
to function as a common-pool resource for fuel-wood harvest. The
formal institution agent determines the sustainable per capita harvest
for a given time-period by dividing the net growth of the forest by the
population size and weighting it for each household based on its size
and estimated fuel-wood requirements. It then informs each household
of its allowable (sustainable) extraction level for that time step, which
the household uses to calculate utilities for the extraction levels it is
considering, as described above.
Thus household agents also interact with the formal institutional
agent, but unlike the bi-directional inter-household interactions, this
interaction is unidirectional in that the institutional agent sends each
household a signal indicating the prescribed level of resource extraction
that is deemed sustainable, based on the institution’s assessment of the
state of the forest. In contrast, the inter-household interactions are bidirectional, and the signals sent reflect the level of resource extracted
by the interacting households.
Forest Resource. The forest resource is assumed to be a closed-canopy
maturing mixed pine and oak forest. The model represents the resource
aspatially, as a total amount of biomass for the entire forest, which
grows at a specified rate per year. We introduce some variability
around the mean growth rate of 2.7% per year (0.01% per month) to
incorporate stochastic climatic conditions. Household agents use forest
fodder and lop off branches for fuel-wood. In a given time step, they
can extract fuel-wood until a specified minimal amount of forest
biomass remains. Observations of above-ground biomass and carbon
allocated to individual tree components (e.g. stem, bark, branches, and
foliage) vary widely. Jenkins et al. (2003) demonstrated that a range of
7-30% of biomass for softwood species and 15-95% for hardwood
species is allocated to branches, which is the biomass of use to villagers
for fuel-wood. For our model, we divided the above-ground biomass
and carbon values in half to estimate the amount found in branches.
The fraction of the initial forest resource remaining after some period
of resource extraction is the primary outcome of interest in our
analyses. We use this value as an indicator of the sustainability of
extraction levels generated by various household preferences and
network structures, and of the influences of rules, norms, and their
interactions on resource sustainability.
Computational Experiments
We conducted four sets of computational experiments. The
experiments allow comparisons among model runs with alternative
parameter settings. The experiments were designed to explore how (a)
the relative weight agents place on adhering to rules and norms, (b) the
proportion of the population with a high preference for consumption,
(c) social network structure and (d) the proportion of the population
with a high preference for adherence to rules, all affected resource
outcomes. The model was run thirty times for each combination of
parameter values, and the average and variance of resource remaining
were computed. This metric best captures our concern with the
sustainability of resource extraction.5
Each model run was composed of 625 household agents and was run
for 600 time steps (i.e. 50 years). Initialization of a model run involved
creating and placing household agents on a grid (25 x 25), with one
household agent per cell. Households varied in size, with an average
size of 4.75 based on rural household survey data (Misra et al. 1988).
After all households were created, they established a social network
that remained stable for the entire model run.
In each time step, resource grows and the formal institution tells each
household a prescribed sustainable extraction level. Each household
uses the social capital ties in its network to determine the current norm
for extraction, combining that information with the prescribed level
from the institution to calculate utilities for candidate levels of
3
In other experiments, the candidate extraction levels were chosen from a
uniform distribution over the allowed range for x; results were qualitatively
similar and therefore not reported.
4
Since the agents are in a bounded 2D grid, agents have 8, 5 or 3 adjacent
neighbors.
5
Other metrics such as the distribution of agent consumption levels, and
XXXX were also examined, and were found to correlate strongly with
the amount of forest remaining at the end of the simulation.
3
extraction as described earlier. Each household selects the candidate
extraction level with maximum utility and extracts that amount from the
forest (or as much as is remaining above the minimum forest resource
level). The forest remaining and other measures are recorded after all
households have had a chance to extract resource. The formal
institution re-calculates sustainable extraction levels every 12 steps
(once a year). Agents are activated asynchronously, in a different
random order each step.
Experiment 1: Weight on rules versus norms. In this experiment we
tested the effects of altering the weight agents place on adherence to
rules over norms. We performed this experiment to test whether the
amount of forest remaining would increase as the population placed
greater emphasis on adhering to rules set by the formal institution.
Increasing the importance of rules in our model can be interpreted as an
increasingly good reputation of the formal institution among the
population of agents, or increasingly strict enforcement or sanctions
that make rule non-compliance more costly to the agents. We set up a
series of cases where the weight the on rules (r in Equation 2) of all
agents was varied from 0.0 (no attention to rules, complete attention to
norms) to 1.0 (complete attention to rules, no attention to norms). For
this experiment, agents were each given preference weights of C = L
= A = 0.33 (Equation 1) and social networks composed of their spatially
adjacent neighbors (p=0).
Experiment 2: Agents with high preference for consumption. In the
second experiment, we evaluated how households with a higher
preference for consumption altered the affects of varying the weight, r,
on adhering to institutional rules versus social norms on resource
outcomes. Households were divided into two groups: (1) households
with equal preferences for consumption, leisure and adherence (equal 
values) as in Experiment 1, and (2) a group of “high consumer”6 agents
that had a weight on consumption that was twice that placed on leisure
and adherence to rules or norms (C = 0.5; L = A = 0.25). We varied
the proportion of the population composed of high consumers from 0%
to 100%, for each of the values of r used in Experiment 1. The landscape
was divided into two sections with one sub-population on each side of a
single shared boundary.
the weight placed on adhering to institutional rules (versus the weight
on norms, 1-r). To evaluate the degree to which a small number of
agents with a high r might be able to influence norms sufficiently to
yield desirable resource outcomes, we created two groups with
different levels of r: (1) ‘rule adherents’ weight rules much higher than
norms (r=0.9); and (2) ‘norm adherents’ have the opposite weights
(r=0.1). In this experiment, we varied the relative proportion of rule
adherents (0% to 100%) and the network structure parameter (p, from 0
to 1). Like Experiment 1, household agents’ preference weights were C
= L = A = 0.33.
Results
Experiment 1: Weight on rules versus norms. Altering the level of rule
adherence (r) among agents in the different model runs resulted in a
non-linear response of the amount of forest remaining (the 0% line in
Fig. 2). At very low values of r much of the forest was consumed.
However, at low to medium levels (i.e. 0.2 to 0.4) households
dramatically altered their extraction behavior, which led to much higher
levels of forest resources remaining.
Experiment 2: Agents with high preferences for consumption. When
we repeated Experiment 1 but with a population of agents that had a
greater preference for consumption, we found that, not only did it take
a much higher weight on rule adherence over norms (r) to achieve
nearly the same level of remaining forest (the 100% line in Fig. 2), but
the effect was damped both in the rate at which it altered agent
behavior and the amount of forest remaining. Including a mix of agents
from the two subpopulations (i.e. agents with high versus moderate
preference for consumption) produced moderate responses of forest
remaining to varying levels of r (Fig. 2). For all proportions of agents
preferring higher levels of consumption, low values of r resulted in the
lowest levels of forest remaining. Increasing the proportion of high
consumer agents decreased the amount of forest remaining and
flattened the non-linear response to r. As the proportion of agents with
higher weight on consumption increased, agents needed to place
increasing weight on rules (higher r) before improvements in forest
resources were realized.
Experiment 3: Network Structure. In the Experiments 1 and 2,
interaction among agents was constrained to the spatially adjacent
neighbors of each household (p=0). For this experiment, we replaced
social interaction network connections to adjacent households with
connections that extended beyond immediate neighbors as described
earlier. The objective was to examine how different social network
structures affected the dissemination of information and the formation
of norms that, along with institutional rules, influence aggregate
fuelwood extraction behavior. We implemented this experiment by
varying the parameter p from 0 (only adjacent neighbors) to 1 (only
randomly selected neighbors).
Experiment 4: Agents with high weight on rules. In Experiments 1,2 and
3 all households within a model run were given the same value for r,
6
Note that agents with a high preference for consumption, i.e.,
agents with a high C relative to the other ’s, will not
necessarily be high consumers---what they actually consume
depends on other factors such as resource availability and current
norms. However, other things being equal, agents with higher C
values will consume more than agents with lower C values, so
for brevity we will refer to them as “high consumer” agents.
Fig. 2. Results from Experiments 1 and 2 show the effect on forest remaining of
changing the weight placed on rules and the relative number of agents with a high
preference for consumption. Error bars represent 95% C.I.
Experiment 3: Network Structure. By altering the social network
structure between agents, we explored how the dissemination of
4
information on agents’ behaviors through different social networks
altered the effects of weight on rules (r) and the number of agents with
higher preference for consumption (Fig. 3). In particular, for r values of
0.3 to 0.4, increasing p produced non-linear reductions in the amount of
forest remaining with respect to the proportion of agents with a high
preference for consumption. Strong declines in forest remaining when
25% of agent populations had high preference for consumption suggests
that as social mixing increases, a smaller number of agents preferring
consumption are required to create a high-consumption norm.
Additionally, when values of r were less than or equal to 0.2 or greater
than or equal to 0.5, the network structure had little effect on forest
remaining. In these cases the strong influence of r overwhelmed the
effects of having some agents with high preference for consumption.
There is no further effect of increasing p beyond p = 0.5 to produce a
completely mixed network (p = 1.0), which means that a small number
of long-range interactions in the network can have a large effect on
propagation of information – consistent with findings in network theory
(Watts and Strogatz, 1998).
Experiment 4: Agents with high weight on rules. When we increased
the proportion of rule adherents (high r in the population) the model
produced a near-linear increase in forest remaining outcomes when
agents social networks consisted only of adjacent households (Figure 4,
p = 0). As we altered the network parameter p from a highly clustered
network (p = 0) to a randomly connected network (p = 1), the amount of
forest remaining increased, for all proportions of rule adherents less
than 100%. At moderate levels of social mixing (p = 0.25), strong nonlinearity in relative outcomes of forest remaining were evident. In
particular, small numbers of rule adherents made large differences
when household networks had a small number of distant non-adjacent
connections. The effect of additional rule adherents tapered off
strikingly beyond 20%. These results were achieved despite rule
adherents not having a special place in the network (such as a higher
degree of connections). This is consistent with the explanation for the
effect of social mixing advanced above, i.e., a small number of long
range connections enables information to propagate rapidly across a
population.
Fig. 3. The effect on forest remaining by network structure (p), weight placed on
rules (r1 = r2) and the relative number of agents with high preference for
consumption. Error bars represent 95% confidence intervals. The weight on rule
adherence is the same for both sub-populations with different preferences for
consumption (r1 = r2) for each r value: (a) r1 = r2 = 0.2, (b) r1 = r2 = 0.3, (c) r1 = r2
= 0.4, and (d) r1 = r2 = 0.5.
5
Fig. 4. Results from Experiment 4 illustrate the effect on forest remaining of
relative number of rule adherents and network structure. Error bars represent 95%
C.I.
Discussion
Formal resource-management institutions have a number of
mechanisms at their disposal through which they affect collective
resource use patterns, resource outcomes, and thereby, sustainability.
These include communication of rules and their rationales and rule
enforcement, sanctions, and adjudication. Because these management
activities are carried out in the context of social interactions through
information networks that generate norms for behavior, understanding
the effects on resource outcomes of the choices a formal institution
makes can be challenging. Although we cannot make claims about the
magnitude of these effects in specific cases, our model reveals,
qualitatively, the effects of household preferences, social interactions,
and network norms on the effectiveness of an institution’s activities.
The results of Experiment 1 suggest that when households in a
community have homogenous preferences for adhering to the rules of a
formal institution that aims to maintain sustainable harvesting levels,
the level of resource use decreases and remaining resource amount
increases non-linearly with greater weight on the preference to adhere
to institutional rules. This non-linearity is governed by the process of
norm formation in the community. We modeled norm formation
through households seeking to match the level of extraction of their
neighbors. Low levels of preference for adhering to rules in our model
can be interpreted as representing low levels of (a) trust in the
institution, (b) confidence in communications from the institution, (c)
enforcement of institutional rules, or (d) sanctions for rule violations. In
such cases, household decision making is influenced more by the
behavior of neighbors and the utility households derive by balancing
consumption and leisure.
Small increases in preference for rule adherence reduces consumption
only by small amounts until a tipping point is reached such that the
norms of the network are influenced by the rules used by the
institution. One preliminary implication of this result is that commonpool resource-management institutions should seek levels of
investment in communication, enforcement, sanctions, and/or
adjudication that are sufficient to influence the norms of a community
and tip the behavior toward those desired by the institution. The good
news is that once there is sufficient interest in rule adherence to affect
the process of norm formation, little additional effort may be required
to achieve levels of resource use consistent with institutional goals. A
possible confounding factor is the degree to which agents preferences
change in response to actions by formal institutions. For example, the
imposition of institutional rules and associated sanctions to promote
behavior in the group interest (i.e., reduce extraction levels to a
sustainable level) may have the side-effect of “re-framing” how the
agents see their preferences, such that they increase the weight they
put on self-interest (Bpwles 2008, Cardenas et al. 2000).
Our initial experiment was predicated on two key assumptions. The
first was a homogeneous population, in terms of the importance
households place on various contributors to their utility (i.e.,
consumption, leisure, and adherence to rules or norms) and the relative
importance of adherence to norms versus rules. The second assumption
was that there were no long-range ties within the agents’ social
networks. In Experiment 2, we relaxed the first assumption to form two
groups. The first group had equally balanced preferences for
consumption, leisure, and adherence to sustainability rules and the
second group had a greater preference for consumption. We find that
as the number of agents with high consumption preferences increases,
the effectiveness of institutional rules decreased in a linear fashion (i.e.,
relatively even spacing between lines in Figure 2). The decreased
effectiveness was evidenced by a decline in the amount of resources
remaining, regardless of the level of preference for following rules
versus norms. Additionally, increasing preference for following rules (r)
versus norms produced a tipping point, where norms shifted towards
more sustainable behavior, and more forest remaining (Fig. 2). These
results suggest that an understanding of the diversity and types of
preferences in a community is necessary for institutions to identify an
effort level that is likely to be successful.
The results of Experiment 3, which evaluated the importance of longrange ties in the social network that influences the formation of social
norms, indicate that, at moderate levels of rule adherence (0.2 < r <
0.5), the presence of long-range ties results in a non-linear relationship
between the number of households preferring consumption and forest
condition. At these moderate levels of preferences for adhering to
rules, a reasonably small number of households with greater preference
for consumption can produce a relatively large decline in the forest
outcome, as these preferences have a greater influence on norms
throughout the community because the social networks result in short
path lengths between all agents. This effect of network structure
enhances the sensitivity of the response of resource outcomes to rule
adherence at just the levels of rule adherence of most interest to the
institution, i.e., in the range of effort levels near the tipping point from
little to great influence on outcome.
The results of Experiment 2 indicate that a small group of households
with a preference for consumption can reduce the effectiveness of
efforts by an institution to reduce resource consumption, through the
influence on norms. The results of experiment 4 show how a small
group of households with a strong interest in adhering to the rules of
the institution, over norms, can have a strong positive influence on the
community’s tendency to behave in ways consistent with those rules.
The results of Experiment 4 indicate that (a) as the number of such rule
adherents increases, the amount of resource remaining also increases
and (b) as the social network includes more long-range ties, a smaller
number of rule adherents are needed to reduce extraction levels and
achieve a high-level of resource remaining. Although a formal
institution may have little influence on the structure of the social
network itself, these results suggest that resource-management
institutions may be able to use the process of norm formation to
enhance the effectiveness of rules. The most obvious way to do so
would be to focus on rule adherence by a fraction of the community
rather than all community members. Such commitments might be
secured through greater participation in the institution within the
community. In any case, these results suggest that knowing the
structure of the social networks in a community is crucial to design
6
effective resource governance institutions, and in consequence, for
sustainable resource management.
In contrast to many studies of institutions and resource management
that focus on specific institutional forms, this paper initiates an analysis
of interactions between two major institutional forms that affect
communal resource governance -- social networks and formal
institutions. The paper finds considerable evidence for important
interaction effects between informal social networks and formal
institutions. The nature of these effects depends on the strength of
agent preferences to sustain resources, network structure, and the
length over which agents can form ties with each other. As the length of
social ties among agents increases, there is stronger evidence for nonlinear effects and tipping points with small changes in the proportion of
agents who have strong preferences for high levels of consumption or
conversely agents who have a strong preference for following
institutional rules.
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