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Review
From actors to agents in socio-ecological
systems models
M. D. A. Rounsevell*, D. T. Robinson and D. Murray-Rust
School of Geosciences, University of Edinburgh, Drummond Street, Edinburgh EH8 9XP, UK
The ecosystem service concept has emphasized the role of people within socio-ecological systems
(SESs). In this paper, we review and discuss alternative ways of representing people, their behaviour
and decision-making processes in SES models using an agent-based modelling (ABM) approach.
We also explore how ABM can be empirically grounded using information from social survey.
The capacity for ABM to be generalized beyond case studies represents a crucial next step in mod-
elling SESs, although this comes with considerable intellectual challenges. We propose the notion of
human functional types, as an analogy of plant functional types, to support the expansion (scaling)
of ABM to larger areas. The expansion of scope also implies the need to represent institutional
agents in SES models in order to account for alternative governance structures and policy feed-
backs. Further development in the coupling of human-environment systems would contribute
considerably to better application and use of the ecosystem service concept.
Keywords: socio-ecological systems; agent-based modelling; social survey; spatial scaling;
human behaviour; decision-making processes
1. INTRODUCTION
There is increasing recognition of the need to represent
human behaviour and decision-making processes
in models of complex socio-ecological systems (SESs)
[1]. This includes behaviour at the individual and
household levels, as well as the emergent (adaptive)
properties of SESs that are reflected in institutional be-
haviour, policy formation and the broader attitudes of
society. Frameworks for SES models increasingly seek
to address the characteristics of people and their
dynamic interactions with the environment [2]. The
anthropocentric nature of the ecosystem service concept,
for example, has gone some way to re-focusing attention
in ecosystem analysis from the ecology of ‘nature’ to the
important influence of people [3]. Notions such as eco-
system service beneficiaries are used to reflect that the
attributes and roles of the people within SESs are at
least as important as the attributes and roles of other
organisms [4].
As new ways of thinking about SESs evolve, there will
be an increasing need to empirically ground SES models
with data not only about the biophysical components of
the system, but also about the human dimensions. The
term ‘empirically grounded’ model is used here to refer
to the systematic identification of process represen-
tations through inductive or deductive methods as well
as calibration and validation of models that encapsulate
these process representations. Social-survey data are
fundamental in achieving this goal, especially in set-
ting model boundary conditions, identifying plausible
human behaviour and establishing decisional outcomes.
Empiricism cannot, however, achieve everything, which
is why SES models are important. Models can be used
to integrate different data sources, different repre-
sentations of human decision-making and can then be
leveraged to interrogate the system in areas where data
are limited. Thus, within this paper we:
— explore how SES models can be empirically
grounded using information from social surveys;
— outline alternative ways of representing human
behaviour and decision-making in SES models; and
— discuss how human behavioural models might be
generalized beyond their ‘empirically grounded
space’ for application over large geographical areas.
Further development in the coupling of human and
ecosystem models within an SES paradigm would con-
tribute considerably to on-going global change debates
that are played out at national, continental and global
scales. Top-down, equation-based approaches to model-
ling SESs are unlikely to be sufficiently representative of
human decisional processes, and model development is
likely to be incremental rather than stepwise [5]. Like-
wise, traditional economic models of human decision-
making based on optimizing behaviour have little further
to offer in terms of understanding the role of people in
modifying landscapes. An iterative and integrative
modelling, data collection and analysis approach are
required to address the limitations of existing SES
models, and a discussion of these new approaches is
the purpose of this paper.
Here, we discuss the application of agent-based
modelling (ABM) as the focus for discussion, findings
and understanding of SESs. Specifically, ABM provides
*Author for correspondence (mark.rounsevell@ed.ac.uk).
One contribution of 16 to a Discussion Meeting Issue ‘Predictive
ecology: systems approaches’.
Phil. Trans. R. Soc. B (2012) 367, 259–269
doi:10.1098/rstb.2011.0187
259 This journal is q2011 The Royal Society
a novel approach to representing feedbacks between
human and environment systems that can provide
new insights and improve our understanding of SESs.
The ABM approach is computational and involves the
creation of virtual objects with autonomous behaviour
(i.e. agents), to represent real-world actors and their
interactions amongst each other and with their environ-
ment. While flexible in representing complex systems
that include human and ecological processes [6], the
ABM approach may also provide a one-to-one mapping
between virtual and real-world entities that makes the
approach appealing for calibration, validation, predic-
tion, and the exploratory and explanative modelling of
complex systems. The approach has been used for
theoretical developments and to guide empirical
research and evaluate plausible scenarios, e.g. [7]. As
such, ABM has a major contribution to make in the
future of SES models.
One of the areas where ABM will play a major role in
SES research is in the representation of institutional and
governance structures. Since many ecosystem services
are intangible, changes in their provision may not be
obvious until a specific threshold is crossed (e.g.
groundwater depletion). When this occurs (owing to
scarcity, over consumption or changes in external
factors), a governing body is typically required to coor-
dinate the restoration of the service. Similarly, it is
typically a governing body that monitors the changes
in the provision of an ecosystem service so as to mitigate
any disruption to the service. In some cases, the market
plays a role in this process, but we know that markets
work differently in relation to public goods (e.g. tragedy
of the commons [8]). This suggests that the represen-
tation of institutional and governance structures
in SES models is crucial to understanding the ways in
which organizations and policy provide feedbacks to
agent behaviour. At present, there are few agent-based
models (ABMs) that explicitly incorporate macro-level
formal institutions or governance bodies (e.g. govern-
ment) as agents such that there is a hierarchical
interaction across scales between individuals and insti-
tutions. As a necessary first step to incorporating
governing agents in SESs, the governing organization
or authority typically imposes specific actions on the
agents, which acts more like an exogenous parameter
setting than an interacting agent (e.g. [9]).
This paper uses a review of the relevant literature
with specific examples to discuss the use of ABM in
simulating SESs. We explore the ways in which ABM
represents human behaviour and how social-survey
data can be used to provide the empirical basis for
model design and application. We also discuss how
ABM might be applied at different spatial scale
levels, with the aim of moving away from traditional
landscape-based case studies.
2. REPRESENTING HUMAN BEHAVIOUR AND
DECISION-MAKING IN SOCIO-ECOLOGICAL
SYSTEM MODELS
ABM has emerged as an appropriate tool for represent-
ing human decision processes, especially with respect to
the land system as an exemplar of human–environment
interactions [10–14]. ABM came out of thinking in the
1970s within the artificial intelligence (AI) research
community [15] and has become increasingly popular in
the social sciences and land system science [16–19].
In early agent-based models (e.g. [20,21]) the desired
agent behaviour was derived from the simplest possible
set of rules. More recently, however, ABM has become
increasingly complex and this evolution has been asso-
ciated with attempts to empirically ground the
representation of human behavioural processes (e.g.
[22–24]) which has led to models with increasing
specificity with respect to individual case studies [25].
An agent is a computational representation of a
real-world actor, which could be used to represent,
amongst other decision-making or behavioural entities,
a person, a household, a firm, an organization, an
animal or a plant. The structure of an agent is typically
formed by a combination of descriptive characteristics: a
set of behaviours (e.g. decision-making structures,
actions/responses, goals and mental maps), and a set
of constraints and labels identifying types and roles.
An agent-based model, within the context of SES
change, is then composed of a population of agents
and a landscape within which they act and interact.
A description of these components is provided here
along with examples illustrating how social-survey data
have been used to empirically ground agent and ABM
components within the context of SES modelling.
ABM can be used to formalize computationally
a priori knowledge of the system by creating a suite
of conceptual- and theory-driven models (sometimes
termed toy models, figure 1). These models are used
to evaluate key research questions and to determine
the conditions under which complex spatial and
temporal patterns in management behaviours and sub-
sequent ecosystem properties can emerge from a
limited set of behaviour– response functions. For
example, the environment behaviour theory (EBT)
defines behavioural intention based on situational,
psychological and social-environmental factors [26].
Results from conceptual models provide new know-
ledge about how SESs may change in simple contexts
(e.g. situation). The modelling process is iterative, with
each cycle—through figure 1—providing an opportunity
for refinements such as: incorporating new data and find-
ings from social surveys and ecological fieldwork;
adjusting the conceptual framework and hence the
agent-based model; adding geographical information sys-
tems and social-survey data to the agent-based model;
integrating with ecological models; experimenting with
different decision-making, learning and adaptation strat-
egies for different actors in the system. The combined
understanding of the decision-making structure and rate
of knowledge adoption provides insights that the model
can leverage to better simulate agent behaviour or prac-
tices. Each iteration through the process (of figure 1)
allows new ‘what if’ questions to be posed and tested.
ABM is especially well suited to interaction with
social-survey data since both the models and the
data are inherently qualitative. ABM may include
quantitative, equation-based approaches, but the
rules that characterize this approach are qualitative.
The key components of agents to be modelled are:
the rate of agent creation, decision-making strat-
egies, the drivers of behaviour, agent types and their
260 M. D. A. Rounsevell et al. Review. Actors to agents in SES models
Phil. Trans. R. Soc. B (2012)
characteristics. All of these model components can be
informed empirically using social-survey methods.
There are many social-survey methods based on
interviews and questionnaires that range in scope.
These include semi-structured interviews and struc-
tured approaches to collect categorical data. Some
form of respondent mapping is also usually applied to
establish basic socio-economic attributes. In terms of
empirical ABM development, the crucial methodologi-
cal step is often associated with translating qualitative
social-survey outputs either into qualitative agent rule
bases defining, for example, preferences, values or
strategies, or into parameter-based algorithms (e.g.
calculating agent utility functions). Quantitative
methods tend to seek to assign parameter weights to
values of judgements. Qualitative methods based on
coding approaches are, however, at least as important
in defining qualitative agent rules, especially with respect
to non-economic behavioural factors.
3. EMPIRICALLY GROUNDING AGENT-BASED
MODELLING, USING SOCIAL SURVEYS
(a)Agent attributes
The characteristics of an actor, such as age, income, edu-
cation and marital status, influence its decision-making
and subsequently the SES within which it is situated.
Identification of the actor characteristics that are relevant
to SES processes is one of the goals of using social
surveys to empirically ground ABM. Mapping actor
characteristics to their agent counterparts, for which we
use the term agent attributes, defines the level of agent
heterogeneity in an agent-based model, and often
the agents’ attributes are used to classify them within
a typology or to drive specific actions and decision-
making behaviour. The ability to represent heterogeneity
in agent attributes within an agent population and the
individual outcomes and interactions that result from
that heterogeneity is a key quality that sets the ABM
approach apart from equation-based models. In
equation-based modelling (EBM), it is possible to
incorporate stochastic elements and age- or life-stage
transitions in dynamic population models, but individual
characteristics and interactions are lost as EBM typically
represents an average or ‘typical’ agent (see [27] for a full
comparison of EBM and ABM).
The acquisition of data describing the characteristics
of human populations via social-survey approaches (e.g.
census data) typically tell us little about the actions,
reactions and decision-making strategies that actors
employ within a population. Efforts to use these data
to predict preference weights that inform location-
based decisions are often unsuccessful [28,29]. While
identifying the types of preferences that can be
explained via respondent characteristics is a research
question that remains to be addressed, agent attributes
do play a critical role in SESs in a variety of ways.
First, agent attributes may act to enable or constrain
behaviour. In many regions, the age of an actor defines
its ability to drive legally, to consume alcohol and to
retire. Whether the reference point is age, household
demographic stages, education or records of past
experiences, actor characteristics provide capabilities
or constraints on the agents’ behavioural rules.
Second, changes in agent attributes may alter the
decisions that are made. This approach has been used
to estimate the impact of population growth on panda
habitat in Wolong Nature Reserve, China [30]by
formalize
perspective
of the system
external findings,
theory, literature,
social survey
communicate
results
project team
stakeholders
education
science community
model as medium
for discussion
guide project design,
and proposal, research
questions, model design
conceptual model
structure
guide data collection
and social survey
develop/implement
model versions
proofs of existence
simple human system
ABM empirically
informed complex
coupled natural-human
systems ABM
computational
laboratory
hypothesis testing
model evaluation
results analysis
Figure 1. The agent-based modelling process as an approach to scientific enquiry.
Review. Actors to agents in SES models M. D. A. Rounsevell et al. 261
Phil. Trans. R. Soc. B (2012)
triggering changes in agent attributes that arise from
changes in the household demographic profile through
time. As agents age and change their marital and
family status, they also change their resource consump-
tion behaviour and the subsequent impact on forest
cover and panda habitat. The distribution of household
sizes, timing of behavioural changes, resource extraction
levels and contextual factors driving decisions were
empirically grounded using survey data from several
sources, including 220 local households.
Third, knowledge of agent attributes by other agents
may provide a signal or an identification ‘flag’ [31]that
acts to enable or prevent interactions from occurring. In
some cases, flags may identify specific types of agents
(e.g. male, female, household, developer, etc.) and, in
others, they can promote or inhibit interactions between
agents sharing the same classification type (e.g. marital
status, education level).
(b)Decision-making strategies
A number of theoretical representations of decision-
making strategies have been used to provide agents
with cognitive abilities. Such strategies include: heuris-
tics [32], bounded rationality [33,34] and utility
maximization [17] and evolutionary processes [35].
(i) Heuristics (decision trees)
One of the simplest approaches to represent decision-
making in an agent-based model is through the use of
heuristics or decision trees. Heuristics involve Boolean
evaluations of a variable or measurement that returns a
true or false result and a subsequent action. The
approach is implemented using simple ‘if... then...
else...’ statements that closely correspond to concep-
tual decision-making models. Unlike the ‘black box’
approach to decision-making used in genetic algor-
ithms or neural network solutions ([36], see §3b(iii)),
heuristic strategies provide a transparent view of the
decision-making process and therefore aid in the
understanding of the human – environment relation-
ships being modelled.
The simplicity of the approach also lends itself to
empirical-grounding with a range of qualitative or
quantitative data. In the Land Use Change In The
Amazon project (LUCITA), a spatially explicit
agent-based model was developed to investigate the
degree to which farm-household demographics
explained the rate and extent of deforestation along
the Trans-Amazon highway [37]. The model com-
prises farm-household agents that make cropping
decisions based upon their capital, labour and soil
quality endowments, which change as a function of
time spent farming (i.e. capital accumulation), house-
hold demographics (labour pool and subsistence
requirements), labour market and crop selection.
Social scientists conducting long-term survey
research in the Altamira region of Para
´identified
three overarching decision questions that guide
farmer behaviour: are subsistence requirements met,
is the soil suitable for arable crops and does the house-
hold have sufficient capital and labour resources to
undertake farming? As part of these overarching
decisions, a range of household characteristics were
recorded that included amongst others the timing of
arrival, demographics, level of farming knowledge
and capital resources when arriving in the area from
other regions of Brazil.
A simple decision tree reflecting the qualitative
nature of the three overarching decisions was derived
from a number of surveys and this is used by the
farm-household agents within the LUCITA model to
determine what type of crop to farm or whether to
leave a field fallow (figure 2). The heuristic approach
is interesting in that it mixes qualitative survey findings
with quantitative metrics to implement behaviours.
Qualitative and quantitative mixing is often necessary
when continuous data are used in combination with
decision outcomes based on threshold values. For
example, farm-household subsistence requirements
provide quantitative constraints on farm-household
agent behaviour that are met through the area of
annual crops and capital accumulated from other crop
sales or off-farm labour activities. In contrast, the
authors of LUCITA imposed a quantitative threshold
used as a proxy for qualitative behaviour that was not
directly recorded by survey and too difficult to
implement within the model. The authors took expert
are subsistence requirements met?
soil pH < 5.5?
yesno
no yes no yes
no yes
fallow plant
p
erennials
no yes
fallow plant
p
asture
enough capital
for labour and
perennials?
enough capital
and labour for
pasture?
fallow plant annuals
enough capital and
labour for annuals?
Figure 2. General heuristic decision tree of household decision-making in LUCITA (Deadman et al.[37]). Reprinted with
permission from Pion Ltd., London.
262 M. D. A. Rounsevell et al. Review. Actors to agents in SES models
Phil. Trans. R. Soc. B (2012)
knowledge that farmers in the region determine soil
quality based on existing vegetation and soil colour
and texture [38,39], and incorporated soil quality evalu-
ation into the agent decision-making by using the pH
values that were produced from crop models and soil
information. The use by agents of pH values to distin-
guish between good and poor soil quality incorporates
a feedback between crop choice and nutrient limitations
that couple the human-environment systems within the
context of land-use and land management (figure 3).
(ii) Utility maximization and bounded rationality
Traditional economic theory, based on the ideas of
homo-economicus and rational decision-making, assumes
that individuals make rational choices to maximize
their utility under conditions of perfect information
[40]. While the idea of utility maximization can be
disputed, there are strong arguments against the assump-
tion of the availability of perfect information. Not only is
the availability of perfect information unlikely, but
humans are also unable to process the large number of
possible combinations that are required in complex
decisions as assumed in perfect rationality [41]. Due in
part to these arguments, most ABMs of SESs represent
agent decision-making with utility-based approaches
assuming bounded rationality. Bounded rationality in
its simplest form involves creating a subset of all possible
outcomes that represent a reasonable selection of options
over which an agent makes a rational decision [42].
A bounded rational approach to decision-making has
been implemented by a suite of ABMs that represent
residential settlement [43] and exurban development
in southeastern Michigan [44,45]. The residential
settlement model (named SOME) uses a utility-based
approach that is empirically grounded with a social
survey conducted in the Detroit region [28]. Through
factor analysis, survey questions were reduced to iden-
tify three factors driving residential location decisions
at an appropriate scale of representation, which were:
nearness to schools and work, the aesthetic quality of
the landscape and neighbourhood similarity. The
factor scores for each of these factors were rescaled
and used as preference weights in the following utility
function:
urðx;yÞ¼Y
m
i¼1
ð1j
b
i
g
iðx;yÞjÞ
a
ir ;
where u
r(x,y)
is the utility agent racquires from the grid
location (x,y); mis the number of factors evaluated by
the agent, which is three in this case;
b
i
is the preferred
value for factor i, which is assumed to be 1 for each
factor and represents a desire to be close to schools
and work, high aesthetic quality, and similarity amongst
neighbours;
g
i(x,y)
is the location value for factor i;
a
ir
is
the preference weight agent rhas for factor i, which is a
scalar product of the corresponding factor score.
To represent bounded rationality within the model,
new residential household agents evaluate their
expected utility from only a subset of all available
settlement locations. From this subset, an agent settles
at a location that maximizes its utility, as described
above. The utility approach is useful as it allows
agents to combine different types of drivers that
cannot be translated easily to a common metric for
comparison (e.g. money).
(iii) Learning and adaptation
AI and machine learning algorithms provide the ability
for agents to retain knowledge, change their behaviour
and thus learn and adapt over the course of a model
run. For example, Reschke [35] examines the use of
genetic algorithms, genetic programming and neural
networks, amongst other methods, to represent agent
cognition. As well as allowing agents to learn and
adapt over the course of a simulation, a further
advantage of these models is that they can be
trained on existing data to provide highly predictive
outcomes.
An example of the implementation of an evolution-
ary process to represent agent decision-making is
provided by Manson [46] in a model of land-use and
land-cover change in the southern Yucatan peninsular
region of Mexico. In this model, the agents apply a
genetic programming, decision-making algorithm to
determine the allocation of various land uses. Results
conform to various facets of land-use theory that pre-
dict the impacts of population on the spatial
configuration of agricultural and forest lands and simi-
larly the role of biophysical characteristics and
landform on land-use choices. The difficulty in using
AI algorithms to represent agent decision-making
occurs when mapping the formulated algorithms or
rules back to real-world behaviour or processes.
Many of these processes can be opaque—‘black
boxes’—which do not give insight into the behaviour
that is being modelled.
(c)Creating agent typologies
Typologies are useful in simplifying SESs where there
are very many actors. The creation of agent typologies
generally follows one of two strategies: inductive ana-
lysis (clustering, participatory approaches) or deductive
reasoning (based on theory or expert opinion). In
inductive analysis, the use of national social surveys
and statistical databases of broad socio-economic
characteristics of the population is potentially useful
in defining typologies of individuals or households
and their decisional rules using factor analysis and
clustering (e.g. [47]). However, collecting primary
data through a targeted social survey is also an
households make farming decisions
based on needs, endowments and
soil nutrient values
decisions of land-use alter
the land cover
land-use/cover changes
alter soil nutrient values
through deposition or
nutrient uptake
crop yields influence
household capital
endowments
soil nutrient values
affect crop yields and
future land uses
Figure 3. Cyclical feedback process of a coupled human-
environment system within the context of land-use change
and farming.
Review. Actors to agents in SES models M. D. A. Rounsevell et al. 263
Phil. Trans. R. Soc. B (2012)
important, inductive approach. Typologies based on a
deductive approach using cultural theory and plural
rationality have led to agent categories such as fatalist,
hierarchical, individual, egalitarian and hermit [48].
The dimensions along which agent typologies can
be defined are many, but typically for SES models
three dimensions are used. The first dimension
describes the functional role of the agents, i.e. the
real-world ‘actors’ to which they correspond. Typic-
ally, these agent types are used to represent
residential households, farmers, developers, municipal
actors (government institutions) or park managers.
The roles can correspond to a single individual, a
household, or an organization, institution, or other
collection of individuals. The second and third dimen-
sions are typically nested within the first so that within
a functional role (e.g. farming household), there is a
subset of agent types.
The second dimension describes the agent type based
on a series of preference weights that are used to define
the desires of the agents and guide their decision-
making. In most cases, all agents within this functional
role have the same decision-making structure, but sub-
types are defined by groups of agents with similar
preferences (e.g. farmers who value economic outcomes
more than environmental impact) that yield similar
behaviours and actions within the modelled SES.
An example of defining agent types based on prefer-
ence similarity is provided by the SOME model
described in §3b(ii), whereby residential location pre-
ferences were empirically grounded using a social
survey [43]. A cluster analysis on the factor scores
derived from survey responses yielded seven clusters
of respondents who shared similar factor scores for dis-
tance to schools and work, neighbourhood aesthetics,
neighbourhood similarity and household aesthetics,
than amongst clusters [28]. These data were used to
populate the proportion of residential agent types
within the SOME model as well as to define their pref-
erences for the three factors driving residential
settlement decisions.
Statistical techniques for clustering and grouping
survey respondent preferences are sometimes unable
to identify structure, and respondent characteristics
cannot be used to predict preferences [29]. In these
cases, an alternative to the preference-based typology
is required to represent each agent as a unique entity
within the space of possible agent preferences. Such
an approach could explore the consequence of differ-
ent agent preference distributions by leveraging the
model and systematically exploring the impacts of
different preference structures amongst the population
or groups of agents within the population. In data-
rich situations, where the survey sample is representa-
tive of the population of actors, this approach fully
embraces the one-to-one mapping capabilities of
agents-to-actors and harnesses a high degree of agent
heterogeneity.
The third dimension of agent typologies is based on
the behavioural mechanisms that the agents use to
fulfil their goals and desires (i.e. preferences). Typic-
ally, this involves classifying agents into types based
on their decision-making process or cognitive strategy.
For example, a type of agent may be defined based on
the form of a utility function that is used by the agents
to make decisions (e.g. risk averse or risk-taking [49]).
Similarly, decisions may be made in relation to other
agents’ behaviours (e.g. repetition, deliberation, imita-
tion and social comparison [50]). The benefit of
working inductively within previously defined behav-
ioural theories is that relatively general rules governing
behaviour can be created.
The typological assignment from social-survey inter-
view transcripts is itself a useful output allowing a
detailed picture to be built up of the reasoning behind
decision processes. Where this has occurred in SES
research, it is used in combination with behavioural
economics experiments (e.g. [51]). In the context of
model creation, this third dimension of agent typologies
provides empirical support that the theoretical models
of behaviour used by the agents and clustered to
create the typology are relevant to modelling human be-
haviour. However, in order to orient the model towards
case study use, it should be possible to represent the
needs and desires of individuals in a more continuous
space, to create a multi-dimensional preference space
and to allow a complementary, deductive approach to
understanding the behaviour of decision-makers.
Many ABMs tend to focus on either the second or
third aspect of this analysis; for example, Brown &
Robinson [43] and Fontaine & Rounsevell [47] use
the same decision-making mechanism for all agents
while varying the preferences—they use a constant
decision-making strategy in a multi-dimensional pref-
erence space. On the other hand, Jaeger et al.[50]
use a range of different decision-making strategies
with a uni-dimensional preference space.
The use of typologies in the evolution of dynamic
vegetation models (DVMs) represents an interesting
precedent for the development of ABM. Many DVMs
were derived from landscape scale forest gap models
[52], but have since been developed for application at
global scale levels (e.g. Lund–Potsdam–Jenna-General
EcoSystem Simulator (LPJ-GUESS) [53], Lund– Pots-
dam– Jenna-managed land (LPJ-mL) [54]). The
primary principle that has guided this model generaliz-
ation is the use of plant functional types (PFTs) to
represent the attributes of generic plant organisms.
PFTs are primarily delineated by their carbon fix-
ation functions, which are common amongst certain
types of plants and differ between others (e.g. C3 and
C4 grasses). However, leaf type (i.e. broad or needle),
habit (i.e. tree, herbaceous or grass) and other growth
processes and characteristics (i.e. phenological pro-
cesses, rooting depth, and temperature and light
tolerances) are also used to define the PFT. Similar to
the earlier mentioned typology discussion, it is difficult
to generalize a plant along a single typological dimen-
sion. The ‘function’ in PFTs is a combination of
dimensions describing how the plant is able to extract
resources, out-compete other organisms and carry on
its livelihood (photosynthesis). In the LPJ-GUESS
DVM, a collection of PFTs comprise leaf type (i.e.
broad leaved or needle leaved), phenology (i.e. ever-
green or summer green), plant habit (tree, herbaceous,
grass) and climate zone (i.e. tropical, temperate,
boreal). Other DVMs represent a similar combination
of plant characteristics and processes to derive a set of
264 M. D. A. Rounsevell et al. Review. Actors to agents in SES models
Phil. Trans. R. Soc. B (2012)
generalized plant, biome or ecosystem types (e.g.
BIOME-BGC [55]).
It is plausible that the approach to PFTs could
guide, through analogy, the definition of generic
human functional types (HFTs) that achieve the
same outcomes for ABM as PFTs did for DVMs.
HFTs should account for: (i) the functional role of
an agent, e.g. a farmer, forester, resident and its func-
tional traits (attributes), (ii) the preferences that
represent the agent desires, (iii) the decision-making
strategy and behavioural actions and responses and,
possibly, (iv) the geographical niches (locations) that
they occupy. This perspective is directly analogous to
the resource-constrained niches of PFTs in which cer-
tain functional types are best adapted to the
characteristics of a location and come to dominate at
a given moment in time.
In the case of PFTs, dominance is determined by
the availability of resources (water, light, nutrients)
and the PFT traits. For HFTs, physical resources are
important (e.g. the productive potential of land due
to soils and water), but resource availability should
also refer to access to markets, labour and capital,
reflecting the comparative advantage of some locations
over others. Such access may be moderated by the pol-
itical and policy situation (e.g. trade barriers and
preferences) or physical infrastructure (e.g. transport
networks). In addition to functional roles, however,
HFTs need to account for the diversity in human be-
haviour, and the parallels here with the PFT approach
are less convincing. Whether landscape scale level
classifications of behavioural types are applicable at
global scale levels is a moot point.
The behavioural traits of HFTs might be created
from latitudinal case study results that compare simi-
larities and differences in agent types across different
landscapes, or they could be created from analyses of
national scale statistical databases (e.g. [47]). It is
likely also that initial HFT formulations would follow
more deductive, expert-opinion-based approaches.
However, the availability of unified databases of
human behavioural traits across the globe would be
invaluable in allowing the variation in behavioural
types and attributes to be determined inductively.
Similar exercises exist for the establishment of global
PFT trait databases [56].
(d)Agent population
The population of agents can change over time or
remain static. In most representations of SES, the
agent population changes over time. In the case of mod-
elling human populations or households, most SES
research uses census or housing survey data to derive his-
torical trends and future trajectories of population and
households. These data are then interpreted to define
the rate of agent creation or removal from models. In
some instances, these survey data are used to create
initial population conditions that are extended through
demographic processes to create endogenous growth or
decline via birth, death, immigration and emigration
(e.g. [47]). In both cases, the system is not considered
to be closed since exogenous factors (e.g. immigration)
contribute to the rate of population growth.
When ABMs of SES are considered to be closed
systems, the agent population may be fixed. Land-
use models that include land markets often use a
fixed agent approach to address research questions
associated with market clearing and the resulting
spatial patterns of land rent (i.e. house prices, land
price gradients, etc.), e.g. [57]. In these cases, census
data are coupled with housing survey data to provide
better estimates of the number of households and
housing units, e.g. [58]. However, these data sources
are inadequate when empirically grounding the
characteristics of the agents and the market such as
the asking, bid and sale price or the willingness to
pay or accept prices.
4. SCALING
Because of the need to collect primary empirical data
about agent attributes and behavioural rules, previous
ABM applications have typically been carried out at
the case study or landscape scale. The transition to
ABM applications over larger geographical regions will
require advancements in knowledge, methodology and
technology. There are, however, pressing reasons to sup-
port the application of ABMs over larger geographical
regions, not least to facilitate the integration of agent-
based models with ecosystem and vegetation models at
different scale levels. The application of ABMs across
large geographical regions would also provide the
means of generating model outputs at a scale level that
is relevant to a range of policy processes and political jur-
isdictions. Here, we discuss the ways in which ABM
could be generalized for applications over larger geo-
graphical extents, in order to propose suitable ways
forward for SES modelling based on agent-based
approaches. There are three basic methods to change
the scale of model applications (figure 4):
— scaling out: applying the same model across a
larger spatial extent by increasing the extent of
the input data;
— scaling up: aggregating model behaviour to a
higher representational level, such that former
entities are represented as groups; and
— nesting (multi-model approach): representing
higher level processes as aggregate models that
individuals
towns
case study region
scaled
out and
nested
scaled up
scaled out
representational scale
spatial extent
original
model
e.g.
e.g.
Figure 4. Graphical representation of scaling out, scaling up
and nesting.
Review. Actors to agents in SES models M. D. A. Rounsevell et al. 265
Phil. Trans. R. Soc. B (2012)
influence agent behaviour at lower scale levels,
e.g. institutions that monitor agent practice or eco-
system function can change the constraints,
incentives or rules that influence agent behaviour.
(a)Scaling out
In scaling out, the approach is to use fundamentally the
same model, but to increase the extent of the data base-
line from a small to large geographical area [59]. This
has the advantage that the basic model functionality
does not change, so there is no need to build new algor-
ithms into the model since these do not change when
scaling out an existing agent-based model. In applying
this approach, however, the quantity of input data
increases greatly, which may be limited by data avail-
ability. With more data, computer processing time also
increases greatly and, therefore, scaling out approaches
are likely to require high-performance or massively par-
allel computing [60]. The approach also assumes that
the processes being represented are as relevant to the
larger area as to the original area within which
the model was developed, which in practice may be
beyond the calibration range of the model.
To date, only a handful of ABMs could be considered
to have been scaled-out. The transportation analysis
and simulation system (TRANSIMS) agent-based
model was developed over 9 years at Los Alamos
National Laboratory. TRANSIMS models the activity
patterns of individuals within a city and models city-
wide traffic flows and behaviour that result from the
individual agent decisions [61]. The model was later
extended to address epidemiology research questions
by modelling 1.6 million people in Portland, Oregon
(epidemiological simulation system; EPISIMS). Both
models relied on census data to create an agent popu-
lation with similar demographics. Additionally, a
survey was conducted with thousands of households
to obtain information about daily activity patterns,
which were used to construct activity patterns for the
agent population [61].
The ability to scale out ABMs by researchers without
access to super computers is increasing with the develop-
ment and increased use of general purpose graphic
processing units (GPGPUs). Lysenko & D’Souza [62]
implement the well-known Sugarscape agent-based
model by Axtell and Epstein [63]inaGPGPUframe-
work that modelled over 2 million agents and ran at a
speed significantly quicker than traditional ABM tools
(e.g. Repast Simphony) that are run on central processing
units and do not execute agent behaviours in parallel.
Although the use of GPGPUs, parallel programming
and high-performance computers provides a way of scal-
ing out ABMs of SESs, these approaches typically require
modification to, or different, conceptual models that con-
flict with object-oriented programming concepts that are
the foundation of ABM (see [62] for more details).
(b)Scaling up
We define the scaling up of an agent-based model as
changing the entities represented so that individuals
become aggregated, i.e. reducing the representational
granularity to allow a larger spatial extent to be covered.
As a hypothetical example, an agent-based model may
be used to evaluate how policy changes the dynamics
of land competition in an SES among different actors
(e.g. farmers, developers, residents, businesses). When
scaled up, the agents would be municipalities, counties
or states, and the model would be used to explore how
land demand, supply and quantities of land-use and
land-cover change over time across a larger spatial
extent. A key issue is how far it is possible to apply the
mechanisms used at one scale to a larger scale—for
example, can competition between municipalities be
modelled in the same manner as between individ-
uals—and how much do new processes need to be
modelled, for example drawing on political science.
Political scientists have been applying ABM at
national and state levels to investigate national boundary
formation [64], regime change [65], inter-state conflict
[66] and voting behaviour. The approaches to using
survey data by political scientists and the types of inter-
actions they are interested in may aid our understanding
of how to scale up models of SESs. Typically, we are
interested in scaling up instead of scaling out because
we do not have data at the fine resolution of individual
actors across the larger geographical extent of desired
application: even when data exist, acquisition may be
too costly or too difficult. Similarly, the researcher
may be time constrained or not interested in specific
micro-level interactions and outcomes (e.g. precise
location of land-use change). Under these circum-
stances, scaling up an agent-based model would be
preferred to scaling out, as coarse-grained data are
easier to obtain and work with.
(c)Nesting
A nesting approach to ABM involves using feedbacks
and interactions between agents or processes that act
at different spatial or temporal scales. Typically, case-
study-based applications of ABMs treat policy and
the institutions responsible for these policies as
exogenous. Policy evaluation is typically undertaken
by imposing ex ante scenarios of alternative policy
options. However, as one scales-up and -out from
detailed (and small) case study areas to wider geo-
graphical extents, system boundaries shift to such an
extent that the governance structures reflected in insti-
tutions become a component part of the system itself.
Consequently, there are feedbacks associated with
strategic policy implementation. As policy institutions
observe or perceive a problem or lack of performance
within an SES, they intervene through regulations
or incentives that seek to mitigate the problem by
influencing actor behaviour. Through such feedbacks,
institutions become endogenized within an SES. This
raises interesting questions about the need to incorpor-
ate the representation of governance processes and
even institutional emergence in SES models.
ABM has the potential to play a much larger role in
integrating science and policy and, as a result, have a
larger impact by reaching a broader audience (of both
basic science research and decision-makers). Insti-
tutions as organizations can be represented as agents
with the heterogeneous agent attributes, a unique
goal-orientation, and rule-driven behaviours and inter-
actions with other agents and their environment.
266 M. D. A. Rounsevell et al. Review. Actors to agents in SES models
Phil. Trans. R. Soc. B (2012)
Again, typologies would be useful in understanding
the types of roles, preferences and behaviours of insti-
tutions and organizations, and how they affect, and are
affected by, SESs. A large number of individual agents
at lower scale levels could be nested within an SES
with a limited number of institutional agents operating
at a higher scale level. The institutional agents observe
the landscape that changes in response to the actions
of individual agents and respond accordingly through
policy implementation.
This type of modelling strategy could lead to a
number of simulation experiments that explore the
role of different governance and institutional structures
and processes in determining SES responses to envir-
onmental change. One might assume that policy
intervention would mitigate many of the environmental
change problems faced by SESs, but this could be tested
experimentally with such models. It would also provide
an opportunity to explore how individual agents fare
under different governance regimes, and what would
be the consequences for their behaviour. This could
assist in the design of more effective governance
structures and policy options.
5. DISCUSSION AND CONCLUSIONS
The use of ABM to address SES research questions
provides a unique opportunity for scientists and prac-
titioners to represent a range of interacting human and
environment processes that act out at different scales.
To fully use ABM as an approach to scientific enquiry
requires the identification of the actors who drive SES
changesintherealworldandmapthemontoagents
in models. This mapping process is not straightforward
since it involves data collection beyond the typical house-
hold characteristics found in census data. While these
data are useful, targeted social surveys are necessary to
record actor preferences, behaviours and how they
make decisions. This often requires an iterative model-
ling process that collects data and information over
time, while systematically incorporating those develop-
ments into an agent-based model. Using this type of
iterative modelling process changes the agent-based
model from an application tool to an approach for scien-
tific enquiry that: (i) acts as a medium for discussion
amongst interdisciplinary research teams and stake-
holders, (ii) formalizes assumptions about the way
SESs behave, (iii) acts as a repository for data, findings
and information, and (iv) provides a computational lab-
oratory to experiment with policies and actions that aim
to change the SES in a particular way.
Most new data collection will involve socio-economic
variables since these data are most commonly lacking. A
number of methods exist for the collection and analysis
of new socio-economic data based on the principles of
social survey. This might include the use of interviews,
questionnaires or other semi-quantitative elicitation
methods such as conjoint analysis [67]. The principle
that underpins the use of these methods in ABM devel-
opment is to understand the rules that determine agent
behaviour and preferences and how this underpins
decision-making as well as to create typologies that
seek to simplify agent representation where a system
comprises many actors.
While the focus of this paper has been on mapping
actors to agents using data from social surveys, scaling
ABMs is probably the best approach to map back from
agents to actors. By scaling out and scaling up, ABMs
become more useful in addressing changes in the pro-
vision of ecosystem services across SESs. This involves
linking ABMs to ecosystem process models such as
LPJ-GUESS and BIOME-BGC to provide estimates
of ecosystem function [68].
There are many intellectual problems that need to be
resolved in scaling out and scaling up ABM applications
from landscapes to nations and beyond, but the use of
typologies to simplify the modelled system is likely to
be crucial. We argue that typologies play a crucial role
in scaling out, scaling up or nesting ABMs because:
(i) it is necessary to generalize and simplify complex
SESs to gain understanding, (ii) the data requirements
for high-fidelity simulations of detailed SESs are enor-
mous, and (iii) modelled SESs are not deterministic
and incorporating stochastic elements requires a
number of model runs to create an envelope around
potential SES outcomes. As a result, there is a need to
derive generalized representations of the actors in
SESs across multiple dimensions of role, preference
and behaviour, and this is the basis of HFTs.
The concept of PFTs and HFTs may also be
applied to the notion of higher representational units
such as institutions in order to derive a set of insti-
tutional functional types (IFTs). Institutional agents
would need, however, to be coupled with individual
agents, such as land managers or ecosystem benefici-
aries, and this is the role for a nested-scaling approach.
We conclude this paper with a number of key points
about further development in ABM techniques as a
means of improving the assessment of change in SESs:
— a number of approaches exist to represent human
behavioural and decisional processes in SES
models, although little is known about how these
different approaches compare across different
contexts;
— the capacity for ABM to generalize the knowledge of
SES processes and mechanisms that act at the local
level and to apply this to large geographical regions
represents a crucial next step in modelling SESs,
although this comes with considerable intellectual
challenges;
— the development of thinking around the notion of
HFTs, as an analogy of PFTs, would allow ABMs
to be scaled to larger geographical extents; and
— the representation of institutional agents in SES
models would allow a number of important research
questions to be tackled that relate to the role of
governance structures and policy formulation in
determining SES change.
The authors acknowledge inputs to this paper derived from a
number of funded research projects: the MISTRA-SWECIA
project (SWEdish research programme on Climate, Impacts and
Adaptation), the Knowledge for Climate CARE project
(Climate Adaptation for Rural arEas) and the European
Commission funded projects, Ecochange (Challenges in
assessing and forecasting biodiversity and ecosystem changes in
Europe; FP6-036866), Plurel (Peri-urban land-use
Review. Actors to agents in SES models M. D. A. Rounsevell et al. 267
Phil. Trans. R. Soc. B (2012)
relationships—strategies and sustainability assessment tools for
urban-rural linkages; FP6-036921) and Volante (Visions Of
LANd use Transitions in Europe; FP7-265104). The authors
also thank Ben Smith, Martin Sykes and Almut Arneth of
Lund University and Martha Bakker of Wageningen
University for their insightful comments during the
preparation of this paper.
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