Interactions between Institutional Rules and Community Norms in Natural
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:
<ABSTRACT NEEDS TO BE REVISED>
Much of the literature on common property has focused on how
different kinds of institutions shape the incentives of users who rely
on harvesting subsistence related products from a common-pool
resource system for their daily need. With the recognition that
variations in institutional forms and arrangements matter to resourcerelated outcomes, the explosion of writings on the commons has
advanced existing knowledge about how institutions can be designed
to improve sustainable resource governance, the relationship of users
to each other in relation to resources, and institutional processes
themselves. A significant puzzle that has occupied this scholarship is
the nature of the differences between formally designed and
introduced institutions, and more spontaneously created informal
networks, and how such differences have a bearing on resource
governance outcomes. This paper focuses directly on this question
with the help of an agent-based model built around the interactions of
villagers with forests based on the information they derive from their
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 forest-related
outcomes.
agent-based modeling | common pool resource | fuelwood extraction
| institutional governance | norms | social networks
Institutions 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. However,
the signals and sanctions issued by institutions interact with the existing
social networks that create norms of behavior in communities. This
interaction and various combinations of institutional signaling and
enforcement mechanisms, frequency of signaling, social network
structure and resource-user’s preferences give rise to an important
question for governance theory and the practice of natural resource
governance: How are user behaviors and resource-related outcomes
affected by rules imposed by formal resource governance institutions in
the context of established preferences and informal networks that
shape user behaviors through norms?
Recognizing that variations in institutional forms and social
arrangements matter to resource-related outcomes, the literature on
common-property resources has focused on understanding how
institutions can be designed to improve sustainable resource
governance, the relationship of users to each other in relation to
resources, and institutional processes themselves. <Citations – and
maybe say a bi t more?>
Our focus is on the relationships between institutions, social networks
and individuals who are making resource-use choices based on
information they receive and their preferences for self-interested
consumption versus adhering to rules and norms that contribute to
sustaining the community’s common-pool resources.
A number of existing definitions of institutions highlight these
interactions.1 Where resource governance is concerned, formal
institutions produce their effects on outcomes through the information
signals they provide to their constituents regarding the use,
management, and governance of resources (Fig. 1). When reliably
communicated, such information, together with knowledge about the
nature of rule enforcement, sanctions, and adjudication, shapes user
incentives and behavior, affecting resource outcomes.
Household agents both ascertain and create community norms by
interacting with agents in their social networks. In the context of
community use of common pool resources, e.g., consuming fuel-wood
from public forests, norms are informally recognized expected
consumption levels established through individuals attempting to match
the behaviors of others in their spatial and social networks . Norms can
effectively shape behavior through individuals’ desires to avoid
informal sanctions or because individuals have positive preferences for
contributing to group well-being. <add citations for this paragraph>
Note that the flow of information occurs through different pathways
for formal institutions and informal social networks. Formal institutions
typically monitor the state of the resource and results of previous
aggregate behaviors and make subsequent judgments, policy decisions
and prescriptions based on those outcomes. Usually these decisions are
made more infrequently than the resource-use decisions of the
constituents influenced by those policies <citation>. 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
1
“institutions are the formal and informal rules of the game in a society (North
1990);” they are “complexes of norms and behavior that persist over time by
serving socially valued purposes (Uphoff and Buck 2006);” and they are “humanly
crafted mechanisms that structure, mediate, and attenuate social outcomes
(Ostrom 1990).”
1
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
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 explores how the effectiveness of institutional rules
regarding consumption of common pool resources are affected by the
individual preferences and social network structure in a community,
using an agent-based model (ABM) built to represent villagers’ choices
of forest-resource consumption levels, based on the information they
derive from (a) rules imposed by a formal institution to limit fuel-wood
extraction and (b) norms that emerge through social interactions with
their neighbors.
Monitor Inf ormation
Institutions
Rules
Agents
- Interactions
- Form Expectations
Social
Interactions
Take
Actions
While the legally defined forests cover 66.5% of the 55,673 km2 area
of Himachal Pradesh, only 8976 km2 or 24% of the lands legally defined
as forest have crown density above 40% (FDHP 2001). In this context of
high population density and competing uses, a number of different
institutional mechanisms are in evidence to secure the formal
participation of local residents in forest management in Himachal
Pradesh. In the context of this trade-off that households face between
abiding by formal institutional rules related to forest management and
forest sustainability versus meeting subsistence requirements and
adhering to norms emerging from social interactions, we used an agentbased model to study the effectiveness of formal institutional rules
under varying conditions of household preferences and network
interactions.
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).
Outcomes
(Forest Resource)
Norms
Fig. 1. Conceptual model of the flow of information between
institutions, individual agents, and social networks.
Methods
For a concrete example of the interaction of institutional rules
and community norms, we built an agent-based model (ABM) to
represent the use of shared forest land as a source of fuel-wood
in rural india. <add more words to explain why this particular
target system?>
Forest Use and Management in Himachal Pradesh, India.
Over the last century, forests in Himachal Pradesh, India, have been
distributed to the landless, have been the sites of extensive road
construction and infrastructure development, and have served as
important commercial resources by providing resin for turpentine (Pinus
roxburghii), raw materials for paper and pulp – including bamboo
(Bambusa bambos) and bhabhar grass (Euloliopsis binata) – and wood
packing cases for Himachal Pradesh's important fruit industry.
In 2001, over 90 percent of the state’s 6 million inhabitants lived in
16,000 rural villages (DOP 1997), most of which have relied on forest
resources to provide fuelwood for cooking and heating. The
overwhelming demand for forest resources is further exacerbated by
the low-energy density of fuelwood and the inefficient cooking devices
with which it is used (Prasad and Verhaart, 1982).
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
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 agent 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 l
 nr
u  C
L ( n  r)
U(x) = C(x)^alphaC * L(x)^alphaL * A(x)^alpha
(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
alpha’s (each in [0,1] and summing to 1) represent the household’s
relative preferences for the three components, and thus can vary across
2
households. Details on the form of C, L and 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 size<footnote>.
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 fuelwood<footnote>. 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 institution
al 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
the extraction level the household chose the prior time step2. 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 households<footnote>. Households also may interact
with some 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
norm that 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
2
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.
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 resouce
aspatially, as a total amount of biomass for the entire forest, which
grows at some specified growth 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. The villagers use
forest fodder and lop off branches for fuel-wood. In a given time step,
the agents 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 was the primary outcome of interest in our
analyses. We used 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 and norms on
sustainability.
3
Computational Experiments
We conducted four sets of computational experiments. Each
experiment was conducted by comparing 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. 3
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 its prescribed maximum extraction level. Each household
uses its social network to determine the current norm for extraction,
combining that information with the prescribed level from the instution
to calculate utilities for candidate levels of 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 mimimum 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.
(c=0.5;l=nr=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 the Experiment 1. The landscape was divided into two
sections with one sub-population on each side of a single shared
boundary.
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
the r, 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=nb=0.33.
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=Gr=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 higher
preference for consumption altered the affects of varying the weight, r,
on adhering to institutional rules versus social norms on the resource
outcome. The households were divided into two groups: (1) households
with equal preferences for consumption, leisure and adherence (equal
alpha values) as in Experiment 1, and (2) a group of “high consumer”
<footnote> agents which had weight on consumption that was twice
that placed on leisure and adherence to rules or norms
3
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.
4
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 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.
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 (<citation>Watts and Strogatz, 1998).
200%
200%
a. WR1=WR2=0.2
150%
b. WR1=WR2=0.3
150%
p = 0.0
p = 0.5
p = 1.0
100%
% Forest Remaining
Results
100%
50%
50%
0%
0%
0%
25%
50%
75%
200%
25%
50%
75%
100%
200%
d. WR1=WR2=0.5
c. WR1=WR2=0.4
150%
150%
100%
100%
50%
50%
0%
% Consumerists
0%
100%
0%
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
% Consumerists
Fig. 3. Results from Experiment 3 illustrate the fffect on forest remaining of
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 subpopulations 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 (d) r1 = r2 = 0.5.
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
information on agents’ behaviors through different social networks
altered the affects 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-linearreductions 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
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 network consists only of adject households (Figure 4, p=0).
As we altered the network parameter p from a highly clustered network
(p = 0) to a randpmly connected network (p = 1), the amount of forest
remaining increased, for all proportions of rule adherents less than one.
At moderate levels of social mixing (p = 0.25), strong non-linearity 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 propogate rapidly across a population.
5
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, Cardenaas etal).
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 by which they can affect the outcomes of
collective resource use and encourage more sustainable levels of use.
These include communication of rules and their rationales and the
nature of rule enforcement, sanctions, and adjudication. Because these
activities are carried out in the context of systems of social interaction
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 can make no claims
on quantitative magnitude of these effects in specific cases, our model
reveals, qualitatively, the effects that household preferences, social
interactions and norm formation can have on the effectiveness of an
institution’s activities.
The results of Experiment 1 suggest that, when households in a
community have a homogenous level of preference for adhering to the
rules pronounced by a formal institution, the goal of which is to
maintain sustainable harvesting levels, the level of resource use
decreases and remaining resource amount increases, non-linearly with
increases in that preference. This non-linearity is governed by the
process of norm formation in the community, which we modeled
through households seeking to match the level of extraction of their
neighbors. Low levels of preference for adhering to rules in our model
might be interpreted as representing low levels of (a) trust in the
institution, (b) communication from the institution, (c) enforcement of
rules, or (d) sanctions for violating rules. In such cases, decision making
of households is more strongly influenced by the behavior of their
neighbors and by the utility households derive by balancing
consumption and leisure. Small increases in preference for rule
adherence achieve little to decrease consumption, until a tipping point
in that preference is reached, such that the norms of the community are
influenced by the rules issued by the institution. Resource-management
institutions governing common-pool resources, therefore, 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 a level of resource use that is consistent with the goals of the
institution. A possible confounding factor is the degree to which agents
preferences change in response to actions of formal institutions. For
example, the imposition of institutional rules and associated sanctions
to promote behavior in the group interest (ie., reduce extraction levels
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, one
group with balanced preferences for consumption, leisure, and
adherence to rules or norms and a second with greater preference for
consumption, and found that as the number of agents preferring
consumption increased, the effectiveness of institutional rules
decreased essentially linearly. This decreased effectiveness was
evidenced by a decline in the amount of resources remaining, regardless
of the level of perference for following rules versus norms.
Additionally, the preference for following rules (r) vs norms required to
achieve a tipping point, where norms shifted towards more sustainable
behavior, was higher (Fig. 2). These results suggest that some
understanding of the diversity and types of preferences in a community
is, therefore, necessary for institutions to identify a level effort 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 agents’ social networks include more long-range ties, a
smaller number of rule adherents is needed to reduce extraction levels
and so 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 might be able to use the process of norm formation to
enhance the effectiveness of their rules by enhancing the commitment
of a fraction of the community to adhering to rules. 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 can be useful for
designing effective policies.
6
Conclusions
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7
Supporting documentation
Fuelwood collection. Fuelwood and community forests are perceived as a free common pool resource for use by local
households. Therefore, markets for fuelwood are non-existent in the study region and instead the cost of fuelwood is a
function of collection or gathering time, which is typical for fuelwood and minor forest products in rural areas in India
(Heltberg, Arndt et al. 2000). However, as households extract fuelwood resources they degrade the forest from the edge
inward and are forced to spend more effort and time in subsequent fuelwood collections (Kumar and Hotchkiss 1988;
Baland, Bardhan et al. 2004). We use a simplified approach to model time spent collecting fuelwood and assume that
when fuelwood is abundant, a minimum of 2 hrs is required to make a single fuelwood gathering trip. However,
gathering time increases exponentially as the resource is depleted (Fig. S1). The rate of increase in gathering time is a
function of the initial and remaining size of the forest, proportion of the forest in branches, the population of the village,
and average biomass per square meter. The average head-load carried by an adult individual in a single trip
approximates 30 kg (Bembridge and Tarlton 1990, Irfanullah 2002, Adhikari et al. 2004). Since households must make
several trips to satisfy their subsistence cooking requirements, we calculate the overall time allocated to fuelwood
collection per month using the following equation:


Gm  Min xcmax 
d
have


g l
where Gm is the gathering time per month, x is the extraction level of the household, cmax is the maximum consumption
level of the household, have is the average head load per trip, d is the average density of oak and pine (600 kg·m-3), g is
the gathering time for a single trip, and l is the total labor endowment that each household has.
Fig. S1: Time spent for a single trip gathering fuelwood based on the available resource level.
Average head-load carried by an adult - (i.e. 30 kg, Bembridge and Tarlton 1990, Irfanullah 2002, Adhikari et al. 2004)
Fuelwood demands are particularly high in India due to the low energy density of fuelwood and the inefficient cooking devices with which it is
used (Prasad and Verhaart, 1982). Fuelwood energy requirements for cooking range from 6-32 MJ per capita per day (Ravindranath and
Ramakrishna 1997, Nayak et al. 1993). While the specific density, moisture, and calorific value of forest species vary, a general conversion factor (1
kg = 19.89 MJ – ABE 1985) can be used to calculate the corresponding range of per capita fuelwood requirements, 0.3-1.61 kg (6-32 MJ). The
National Council of Applied Economic Research and the state calculated consumption values of 1.31 and 1.97 kg/capita/day, respectively (Pandey
2002). Similar measurements have been found at other areas of India (Nayak et al. 1993, Bhatt et al., Reddy 1981, Bhatt and Sachan 2004).
Household Utility. In our model, household utility is a function of its desire for three goods (consumption, leisure, and
adherence to institutional pressures) and a bounded set of resource extraction levels. The extraction level that
maximizes a household’s utility is the level of resource depletion carried out by the household. To determine the
8
optimum extraction level for each time step, household agents randomly select 10 different levels of resource extraction
and chose the level that maximizes the following utility function:
ui  C
c
l
L ( n  b)
nb
where ui is the utility for household i, c is the level of consumption, l is the amount of leisure, n is the influence of
informal institutions, b is the influence of formal institutions on sustainability of the resource, and αc, αl, and αnb are
the weights applied to consumption, leisure and institutional influence, respectively. The three overarching preference
weights sum to one and ensure diminishing returns on consumption, leisure, and matching formal (i.e. sustainable) and
informal (i.e. social norms) extraction level beliefs.
The consumption component of a household’s utility is a decreasing function of their subsistence cooking
requirements, a stochastic set of possible extraction levels, and the weight the household places on consumption versus
leisure and institutional influence.
Fig. S2: Consumption curves for different consumption preference weights (i.e. alpha values) and extraction levels.
The subsistence cooking requirements for each household are calculated as a function of household size and per capita
energy requirements:
h e
S 
i
i
c d
where Si is the subsistence wood requirement (m3) for household i, hi is the size of household i, e is the per capita energy
requirement for cooking per month (240 MJ), c is the energy content of wood (16 MJ, World Bank 2004), and d is the
average density of oak and pine (600 kg·m-3).
Since markets for fuelwood are largely non-existent in the locations we studied, the chief expense incurred by agents
in fuelwood extraction is time - invariably, the cost of the leisure time that agents forsake to extract the resource. The
leisure component is calculated based on the gathering time for fuelwood collection, the available amount of time
devoted to labor, and the weight the household places on leisure versus consumption and institutional influence. The
function takes the following form:


L   1 
Gm 

l 
9
where L is the result of the leisure component ranged 0-1. A value of 0 for leisure means that the household spends no
time in leisure, a value of 1 means the household spends all of its time in leisure. Gm is the time spent gathering
fuelwood per month and l is the total labor endowment, which we set to 5 hrs per day per household.
The last factor in the household agent’s utility function represents the type and level of adherence to institutional
influence. Each household faces a trade-off regarding whether to match its extraction level to the network of
households with which it shares information (i.e. social norms) or to match institution rules that define a sustainable
level of extraction. We represent the outcome of matching social norms using the following equation:
N 
nf
2
( 1  x  nnet )
where N is the value placed on social norms, nf is a ratio defining the households adherence to social norms versus
institutional rules, x is the extraction level, and nnet is the mean extraction level of the eight household social network
that the household gathers information from. Similarly, the outcome of matching institutional rules is calculated as
follows:
 ( 1  nf )  [ 1  ( x  sus)]
 2 
F  
where F is the value placed on institutional rules and sus is the sustainable extraction level recommended to the
household by the institution. Both the social norm and institutional rule values are added together and weighted by the
household’s preference for abiding by institutional influences versus its preference for consumption and leisure.
(i.e. the preference weights placed on sustainability and social norms sum to one, following Mosler and Brucks 2003).
ABM
We created the ABM using the RePast simulation libraries (Collier 2000).
Increasing levels of p mean that more of the interaction network is drawn at random, as opposed to being selected from the spatial neighborhood
of the agent. The parameter is operationalized by having an agent draw a random number between 0-1 for each of the eight neighbors, and if that
random number is smaller than p then the connection to that neighbor is swapped with a randomly selected household in the landscape.
Forest
The forest grows on average at a rate of 2.7% per year (0.14 kg · m-2· yr-1, Birdsey 1992).
10