Interactions between Institutional Rules and 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:
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.
Literature review. Our focus is on the relationship between
institutions, information, and resource-user behavior. A number of
existing definitions of institutions highlight these interactions.1 These
definitions suggest that institutions shape outcomes by structuring the
formal rules and informal norms that affect behavior. Faced with the
choice of adhering to rules or norms, individuals opt among various
combinations of the two by selecting the combination that maximizes
their utility given desires for higher incomes, lower risks, and/or
sustainable performance. Thus, effective formal institutions achieve
desirable outcomes by recognizing the existing preferences and
networks, and influencing incentives for a sufficient number of
individuals and households, such that desirable aggregate behavior is
achieved and/or norms are shifted towards behaviors that yield
desirable outcomes.
Where resource governance is concerned, formal institutions produce
their effects on outcomes through the information signals they provide
to their constituents in relation to 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.
Monitor Inf ormation
agent-based modeling | common pool resource | fuelwood extraction
| institutional governance | norms
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 social
networks that create norms of behavior in communities. This
interaction and various combinations of institutional effectiveness,
frequency of signaling, 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
informal networks that shape user behaviors through norms?
We distinguish between rules and norms by focusing on information
flow and signaling among actors in a natural resource governance
situation. (develop this point further – about one way vs two way, refer
to some recent work on informal vs formal…). We distinguish the oneway flow of information about resource conditions and acceptable
behaviors issued by formal institutions to households from two-way
flows of information about resource-extraction behavior disseminated
among households in a social network. We used an agent-based model
to investigate how household preferences for resource extraction and
complying by rules, together with the structure of the network over
which information is conveyed among households, affect resource-user
harvesting levels and subsequent forest-related outcomes.
Institutions
Rules
Agents
- Interactions
- Form Expectations
Social
Interactions
Take
Actions
Outcomes
(Forest Resource)
Norms
Fig. 1. Conceptual model of the flow of information between
institutions, individual agents, and social networks.
The flow of information back to institutions occurs through different
pathways for formal institutions and informal networks. Formal
institutions monitor the state of the resource and results of previous
aggregate behaviors and make subsequent judgments and policy
decisions 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 operational differences
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, informal networks
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
are generally more localized, interact at a higher frequency, and interact
by observation of behaviors and actions carried out by nearby (socially
and spatially) constituents <citation>.
Recognizing that variations in institutional forms and arrangements
matter to resource-related outcomes, literature on common-property
resources 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 how the
differences between formally designed and introduced institutions and
the different structures of informal social interactions influence
resource government outcomes. This paper focuses directly on this
question using an agent-based model built to represent the interactions
of villagers with forests based on the information they derive from rules
imposed by a formal institution to limit fuelwood extraction and norms
that are derived through social interaction with their neighbors.
Methods
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).
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).
Model Description. We developed an ABM to test the implications of
assumptions about how different combinations of rules from formal
institutions and norms derived from social interactions affect the
sustainability 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. Our
model is composed of three types of agents that represent the two key
actors (i.e. households and formal institutions) and the resource being
utilized. We model agent behaviors and interactions over time using
discrete montly time steps. At each step, household agents are
confronted with a decision problem – 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 decision making is represented using an approach
framed as bounded rationality (Simon <citation>). A utility function is
used to represent agents’ preferences over "consumption" bundles. A
household’s utility is a function of its desire for three goods
(consumption, leisure, and adherence to rules and norms) over 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 optimum extraction level for
each time step, household agents randomly select 10 different discrete
levels2 of resource extraction from a continuous range, and chose the
selected level that maximizes the following utility function:
c l
 nr
u  C
L ( n  r)
1
where u is the utility for a household, C is the level of consumption, L is
the amount of leisure, n is a function of the average extraction level of
neighbors in the previous step and an associated preference weight for
abiding by norms, r is a function of the extraction rule signaled by the
formal institution and an associated preference weight (see appendix),
and αc, αl, and αnr are the weights applied to consumption, leisure and
institutional adherence3, respectively.
The utility a household derives from consumption is a decreasingreturn function of subsistence requirements, resource extraction levels,
and the weight households place on consumption versus leisure and
institutional adherence. Because markets for fuelwood are essentially
non-existent in the locations we studied (Heltberg et al. 2002), agents
face a trade-off between time spent extracting fuelwood and time spent
in leisure. However, extraction time increases exponentially as the
resource is depleted (Kumar and Hotchkiss 1988, Baland et al. 2004).
The rate of increase in extraction time is a function of the initial and
remaining size of the forest, proportion of forest branches, village
population, average biomass per m2, and average head-load carried by
an adult.
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 fuelwood harvest. The formal-institution
agent determines the sustainable per capita harvest for a given timeperiod by dividing the net growth of the forest by the population size
and weighting it for each household based on its size and estimated
fuelwood requirements. It then informs each household of its allowable
(sustainable) extraction level.
2
A second extraction level selection method was used and is described in the
Appendix. Results were qualitatively similar and therefore not reported.
3 Institutional adherence refers to the degree to which household behavior
matches the signals about expected extraction levels coming from both the
institutional rules and the social norms.
2
Forest Resource. The forest resource is represented by an agent as a
closed-canopy maturing mixed pine and oak forest. 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 fuelwood. Observations of aboveground 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 fuelwood. 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 various household preferences and network
structures, and of the influences of rules and norms on sustainability.
Agent Interaction.
At the beginning of a model run, an agent forms an interaction network
with its eight neighbors. Based on a probability parameter p, the agent
may swap each member in its current network with one randomly
selected from within the community at large. The agent computes the
average extraction level of the other agents in its network from the
previous time step. 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 (n in equation 1). When p is 0 the interaction network
of an agent is its eight adjacent neighbors, and when p is 1 the agent has
a social network based on eight neighbors drawn randomly from the
community. 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. When p is low, neighbors who are
adjacent share information in both directions; increasing p results in
agents who are not adjacent sharing information only in one direction.
Household agents also interact with the formal-institutional agent.
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 (r in equation 1). When r is nonzero, the household agent derives increasing utility from matching the
sustainable harvest level. The household agent experiences a direct
trade-off between matching formal rules and informal norms defining
expected resource extraction levels
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 defined to
explore how (a) the relative weights on rules and norms, (b) various
levels of preference for consumption, (c) network structure and (d) the
proportion of the population with a high level of attention 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.4
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 (i.e. month), the resource grows and the formal
institution calculates the sustainable extraction for the following year.
Households asynchronously, and in a different order each time step,
choose how much resource to extract.
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 sanction that
make rule non-compliance more costly to the agents. We set up a series
of cases where the weight the on rules (wr) 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=nr=0.33 (Equation 1) and social networks composed of their
eight immediate neighbors (p=0).
Experiment 2: Varying preferences for consumption. In the second
experiment, we evaluated how important the weight on consumption
was compared to the institutional rules and social norms on the
resource outcome. A second group of ‘high consumer’ agents was
added to the group included in the Experiment 1, where their weight on
consumption was twice that placed on leisure and institutional
adherence (c=0.5;l=nr=0.25). We varied the proportion of the
population composed of high consumers from 25% to 100%, and
included the variations on wr used in the Experiment 1. The landscape
was divided into two sections with one 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 immediate neighbors
of each household. For this experiment, we added network connections
that extended beyond immediate neighbors. 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).
Experiment 4: Heterogeneity in weight on rules. In Experiments 1 and 2
all households within a model run were given the same value for the wr.
To evaluate the degree to which a small number of agents with a high
wr might be able to influence norms sufficiently to yield desirable
resource outcomes, we created two groups with different levels of wr.
‘Rule adherents’ weight rules much higher than norms (wr=0.9), while
‘Norm adherents’ have the opposite weights (wr=0.1). In this
experiment, we varied the relative proportion of rule adherents (0 to 1)
and the network structure parameter (p, from 0 to 1). Like Experiment
1, household agents’ preference weights were c=l=nb=0.33.
4
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
Experiment 1: Weight on rules versus norms. Altering the level of rule
adherence (wr) among agents in the different model runs resulted in a
non-linear response on the amount of forest remaining (0% line in Fig.
2). At very low values of wr 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: Varying preferences for consumption. When we
recreated 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 (wr) to achieve
nearly the same level of remaining forest, but the effect was damped
both in the rate at which it altered agent behavior and the amount of
forest remaining (100% line in Fig. 2). 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 wr (Fig. 2). For all proportions of agents preferring
higher levels of consumption, low values of wr resulted in the lowest
levels of forest remaining. Increasing the proportion decreased the
amount of forest remaining and altered the non-linear response to wr.
As the proportion of agents with higher weight on consumption
increased, agents needed to place increasing weight on rules (higher wr)
before improvements in forest resources were realized.
% Consumerists
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 acquisitive agents. Error bars represent 95% C.I.
Experiment 3: Network Structure. By altering the way information on
agents’ behaviors is propagated through social networks, we measured
how changes in network structure affected the interaction between
weight on rules (wr) and the number of agents with higher preference
for consumption (Fig. 3). In particular, for wr values of 0.2 to 0.4,
increasing p non-linearly reduced 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 wr were less than or equal to 0.1 or greater
than or equal to 0.5, the network structure had little effect on forest
remaining. In these cases the strong influence of wr overwhelmed the
effects of preference heterogeneity. There is no further effect of
increasing p 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 network theory (<citation>).
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%
0%
100%
200%
25%
50%
75%
100%
200%
d. WR1=WR2=0.5
c. WR1=WR2=0.4
150%
150%
100%
100%
50%
50%
0%
0%
0%
25%
50%
75%
100%
0%
25%
50%
75%
100%
% Consumerists
Fig. 3. Results from Experiment 3 illustrate the Effect on forest remaining of
network structure (p), weight placed on rules (wR1=wR2) and the relative
number of consumerists. Error bars represent 95% confidence intervals. (a)
wR1 = wR2 = 0.2 (b) wR1 = wR2 = 0.3 (c) wR1 = wR2 = 0.4 (d) wR1 = wR2
= 0.5.
Experiment 4: Heterogeneity in weight on rules. When we increased
the proportion of rule adherents in the population the model produced
a near-linear increase in forest remaining outcomes. As we altered the
network parameter p from a highly clustered network (p = 0) to a
perfectly mixed 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 (Fig. 4). 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.
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.
4
Discussion
Formal resource-management institutions have a number 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 social interactions and norm
formation can have on the effectiveness of an institution’s activities.
The results of our 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 neighbor. 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 commonpool 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. 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.
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 and norms) and the relative importance of
adherence to norms versus rules. The second assumption was that there
were no long-range ties within the social network. In Experiment 2, we
relaxed the first assumption to form two groups, one group with
balanced preferences for consumption, leisure, and adherence to rules
and 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 interest in following
rules versus norms. Additionally, the level of interest in following rules
(wr) required to achieve a tipping point, where norms shifted towards
more sustainable behavior, was higher (Fig. 2). 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 structures the formation of social
norms, indicate that, at moderate levels of rule adherence (0.2<wr<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 weight on rules, a reasonably
small number of households with greater preference for consumption
and produce a relatively large decline in the forest outcome, as these
preferences have a greater influence on norms throughout the
community because the social network has greater connectedness. 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 influence on outcome to greater
influence.
The results of Experiment 3 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. It might also be possible that 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 to adhere to the 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 that social network
includes more long-range ties, a smaller number of rule adherents is
needed to 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 can 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.
Conclusions
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findings in a general dynamic model of cooperative behavior in resource
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Ostrom (1990)
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Uphoff and Buck, (2006)
6
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
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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 
8
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).
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