The goal of this project is to provide a well-sound methodological
framework for the treatment of complexity by policy makers and social
scientists, endowed with an updated theoretical body of knowledge, a set of
tools that enable scenario simulation, and a collection of case studies to
guide and demonstrate the applicability of the framework. This framework will
be based on agent-oriented modelling and simulation methods and tools.
Agent oriented modelling provides a conceptual framework for analysis
and simulation of complex social systems. This comes from the fact that agent
related concepts allow the representation of organizational and behavioural
aspects of individuals in a society and their interactions. This has motivated in the last years the development of a wide range of software languages/shells/libraries to
simulate agent-based models. However, all present two difficulties for being
widely accepted by social scientists: the end-user should have certain
programming skills, and these software frameworks have been implemented
forgetting the social specifications.
This project addresses these difficulties by adopting multi-disciplinary
viewpoints:
- The software engineering view: The UCM group will
provide the infrastructure for agent-based modelling languages that allow
the specification of complex social systems, their simulation and
analysis. These tools should be flexible to adapt to specific sociological
problem domains and reuse existing agent-based simulation platforms when
appropriate, while increasing usability taking into account the
characteristics of the research object and the end users. This involves
the methodology, from a software engineering viewpoint, and the provision
of the software tools for its application. This work relies on its
experience in INGENIAS agent-oriented methods and tools.
- The methods and applications approach: The UVA
group will gather its wide experience in the application of agent-based
simulation tools for the study of complex social systems in order to
define the scientific method for the analysis of social phenomena and
policy making. This will be accompanied by a library of mechanisms for
social interaction ready to reuse, and a collection of case studies of
interest for both social and computer scientists, which can benefit
stakeholders in public administration and EPOs. Case studies will
integrate the sociological foundations (developed by UAB) of artificial
societies, with a methodology where a modelling framework provided by UCM
will play a central role.
Also, the project intends to promote the synergies between Spanish
groups working in social simulation, as well as to reinforce their
international relevance. For instance, note the compromise for participation of
top researchers in the area such as N. Gilbert (U. Surrey), M.P. Gleizes (U.
Toulouse), C. Sierra and P. Noriega (CSIC-IIIA), and L. Antunes (U. Lisbonne).
Several versions of the framework will be produced along the project, and will
be validated by case studies of the project and by EPOs, as it has been already
done in previous projects (for instance, in the INGENIAS and SIMAGUA projects).
This third party feedback, by both academics and industry, is quite useful to
get further evaluation and to promote technology transfer.
The tools will be distributed as open software in SourceForge.net, and
the results of the project will be published in international journals and
conferences, as well as standardization bodies and interest groups in the areas
of agent technology and social simulation. Main efforts will be done to produce
a set of resources (documentation, interactive presentations, tutorials,
training workshops) to expand the knowledge and the use of the methodologies
and tools into academic and professional communities.
The project aims at providing a well-sound methodological framework for the
treatment of complexity by policy makers and social scientists, endowed with an
updated theoretical body of knowledge, a set of tools that enable scenario
simulation, and a collection of case studies to guide and demonstrate the
applicability of the framework.
The main
contributions are derived from its inter-disciplinary approach and expertise in
complementary viewpoints:
1. The software engineering view: to provide the tools that will
facilitate working with agent-based models and their simulation and analysis.
These tools should be flexible to adapt to specific sociological problem
domains and reuse existing agent-based simulation platforms. This is the
subject of the SiCoSSys-Tools subproject by the Grasia research
group (Grupo de investigación en Agentes Software, Ingeniería y Aplicaciones)
from Universidad Complutense Madrid (UCM).
2. The methods and applications approach: from the experience in the
application of agent-based simulation tools for the study of complex social
systems it will be possible to define the scientific method for the analysis of
social phenomena and policy making. This will be accompanied by a library of
mechanisms for social interaction ready to reuse, and a collection of case
studies of interest for both social and computer scientists, which can benefit
stakeholders in public administration and EPOs. This is the subject of the SiCoSSys-MAS
(Methods and ApplicationS) subproject by the INSISOC research
group (Grupo de Ingeniería de los Sistemas Sociales) from the
Universidad de Valladolid (UVA).
Agent-Based Simulation
Simulation is a third way of doing science [3], and an important
type of simulation in Social Sciences is agent-based modelling. This
type of simulation is characterized by the existence of many agents that
interact with each other with little or no central direction [29]. The emergent
properties of an agent-based model are then the result of a bottom-up
processes, rather than top-down direction [56].
A multi-agent model consists of a number of software entities, the agents,
interacting within a virtual environment [19]. The agents are
programmed to have a degree of autonomy, to react to and to act on their
environment and on other agents, and to have goals that they aim to satisfy. In
such models, the agents can have a one-to-one correspondence with the
individuals, organisations, or other actors that exist in the real social world
that is being modelled, while the interactions between the agents can likewise
correspond to the interactions between the real world actors [45].
Agents are generally programmed in an object-oriented programming
language and using some special-purpose simulation library or modelling
environment, and are constructed using collections of condition-action rules to
be able to perceive and react to their situation, to pursue the
goals they are given, and to interact with other agents, for example by
exchanging messages [46]. Many hundreds of
multi-agent social simulation models have now been designed and built to
examine a very wide range of social phenomena [28][49][64][74].
Like deduction, agent-based social simulation starts with a set of
explicit assumptions. But unlike deduction, it does not prove theorems.
Instead, a simulation generates data that can be inductively analyzed. Unlike
typical induction, however, the simulated data comes from a rigorously
specified set of rules rather than direct measurement of the real world. While
induction can be used to find patterns in data, and deduction can be used to
find consequences of assumptions, simulation modelling can be used as an aid
intuition.
Complex Social Systems
Some recent developments in the social sciences [40] claim for the
usefulness of both experimentation (such the Experimental Economics research
stream) and computer simulation as generative methods to analyse the
complexity of social systems [87].
A complex social system consists of a collection of individuals that
directly interact among them or through their social and technological
environment. These individuals own a set of attributes that autonomously
evolve, are motivated by their own beliefs and personal goals, and act under
the specific circumstances of their social environment. This environment also
contributes to shape their beliefs (values and knowledge about the world), can
evolve in time, and has a complex structure so it is not easy to predict its
net effects because of the different influence of each kind of interaction
context [21]. It has also to be
taken into account that social phenomena are contingent, so they are
unpredictable and changing.
Universal laws that have been used in explanations for a general
or average individual often prove to be inappropriate when
modelling complex social systems. The problem is that no one behaves like this average
person [46]. All these facts
contribute to make social systems highly dynamic and complex. For this reason,
abstracting them to functional mathematical models (by using, for instance,
structural equation modelling, multivariate statistical analysis or statistical
processing of temporal series) should be complemented by other techniques that
consider how global and emergent behaviour can be derived from the real
subjects’ behaviours, which are fundamental in any social system [44].
Yet another problem linked with the formalization of complex social
systems is the selection of the basic elements to include; that is
because formalization is a kind of reduction or simplification. Recent
cognitive and decisional models of interacting social agents claim for the
inclusion of a wider range of attributes than those used by early computational
economics in the pioneer socio-economic simulations. So there is a strong
challenge in the social science domain dealing with the theoretical
justification of the minimum set of attributes that can be considered
constitutive of a social agent.
The representative agent is not a realistic assumption to start with [46]. We have to deal
with bounded rational agents, with finite processing capacity and without
explicit utility functions. They adapt and settle for satisfaction under rules
of thumb. They have emotions. And they are rather heterogeneous. Even if the
resulting model with a representative full rational agent has high predictive
capacity, it is still important to replicate the observed patterns from models
with heterogeneous and bounded rational agents.
Multi Agent Systems
On the other hand, from a software engineering point of view, a
multi-agent system (MAS) consists of a set of autonomous software entities (the
agents) that interact among them and with their environment. Autonomy means
that agents are active entities that can take their own decisions. In this
sense, the agent paradigm assimilates quite well the individual in a social
system. In fact, there are numerous works in agent theory on organisational
issues of MAS. Besides, theories from the field of Psychology have been
incorporated to design agent behaviour, being the most extended the Believes-Desires-Intentions
(BDI) model, on the work of Bratman [11].
With this interlinked perspective, agent-based simulation tools have
been developed in the last years to explore the complexity of dynamics of
complex social systems. An agent-based simulation executes several agents,
which can be of different types, in an observable environment where agents’
behaviour can be monitored. Observations on agents can assist in the analysis
of the evolution of their mental state (that is, individual values and reasons
to act), the collective behaviour and the general trends of system
evolution. This provides a platform for empirical studies of social systems
evolution. As simulation is performed in a controlled environment, on one or
several processors, this kind of tools allows the implementation of experiments
and studies that would not be feasible otherwise [30][54].
There are, however, some limitations when trying to simulate real social
systems. The main issue is that individuals, with regard to a software agent,
are by nature complex systems, whose behaviour is unpredictable and less
determined than for a software agent, whose behaviour and perception capabilities
can be designed with relative simplicity. Moreover,
it is not possible in practice to consider the simulation of countless nuances
that can be found in a real social system with respect to agent interaction,
characterization of the environment, etc. For this reason, it is impractical to
intend the simulation of a social system in all dimensions. On the other hand,
we should and can limit to simulate concrete social processes in a systemic and
interactive context. Therefore, the simulation of social systems should be
considered in terms of focus on a concrete process under research attention.
In spite of these limitations, the agent paradigm offers many advantages
to express the nature and peculiarities of social phenomena, and to overcome
limitations of statistical modelling [45]. However, social
scientists that want to use this new methodology must confront a difficulty of practical order that should not be minimized. The use of
existing agent based simulation tools is not simple because models have to be
specified as programs, usually with an object-oriented programming language.
This makes the definition of models a complex task for sociologists and other
social scientists and professionals, as usually they have not developed the
skills for computer programming.
That is the main reason why some tools start to offer
some graphical modelling capabilities. For instance, SeSam (www.simsesam.de)
allows the graphical specification of state machines and provides a library of
basic behaviours. In addition, Repast Py (repast.sourceforge.net/repastpy)
facilitates the visual construction of simple simulations out of some component
pieces, although at the end the user needs to write Python scripts. The problem
with these solutions is that they also require some programming skills and the
type of systems that can be modelled are quite simple (they are mainly rapid
prototyping tools).
Agent-oriented software engineering, however, offers
powerful modelling languages, at a more abstract level. Concepts in these
languages are closer to those that a social scientist would use, and this makes
them more appropriate to solve this usability issue [88].
The approach
With this working hypothesis, the goal of the project
is to develop an agent-based modelling and simulation framework by extending a
concrete agent-oriented methodology, INGENIAS [69][70]. This
framework will allow the specification of social systems with a graphical
modelling language, the simulation of the models of these systems by exploiting
the capabilities of existing agent-based simulation tools/platforms, and the
identification and analysis of social patterns (at a macroscopic, or aggregate,
level) in terms of the atomic elements of the social system specification (at a
microscopic, or individual/interaction, level). The advantages go further than
usability. As it has been discussed in [82] this
solution facilitates the replication of an experiment on different simulation
engines, in order to contrast results. The availability of a graphical view of
the system facilitates its understanding too and improves the identification of
patterns in the system [67].
There are two main reasons for the choice of INGENIAS
as starting point for this work. First, its modelling language [70] supports
well the specification of organisation structure and dynamics, as well as agent
intentional behaviour, characteristics that are present in social systems. This
language is supported by the INGENIAS Development Kit (IDK) with a graphical
editor, which can be extended to introduce new modelling concepts. Second,
INGENIAS promotes a model-driven engineering approach [71] that
facilitates the independence of the modelling language with respect to the
implementation platform. This is especially important here in order to abstract
away programming details and concentrate on modelling and analysis of social
patterns. With this purpose the IDK supports the definition of transformations
between models and code for a range of implementation platforms.
Reasons and initial hypothesis
This proposal has emerged from the interaction between the applicant
research groups at the First Int. Workshop on Social Simulation and Analysis
of Artificial Societies (SSASA) organized by SSASA-UAB in Barcelona in may
2007. The debate and discussions resulted exciting and we found that we could
exploit important synergies and mutually benefit coordinating our research
programmes to gain important issues in the field of Agent Based Social
Modelling and Simulation.
The main issue that arose at the SSASA workshop was that, although there
is a wide range of software languages/shells/libraries to simulate agent-based
models, all present two difficulties to be widely accepted by social scientists:
the end-user should have certain programming skills, and these
software frameworks have been implemented forgetting the social specifications.
Theories about innovation and diffusion assign a relevant role to the
user-friendliness of a new methodology or technology.
Our approach considers an alternative hypothesis,
based on the main relevance of the specific reception community: in a social
scientists community context, whose identity lays on some disciplinary domain
fundamental assumptions, the diffusion of a new methodology or technology will
be more influenced by the community-foundations-friendliness than by the plain
user-friendliness. That is to say, innovations will have a high reception
level, among other considerations, if they are embedded of some of the core
assumptions of the disciplinary domain.
In this sense, it is necessary the collaboration of
social scientists to identify the foundations on which the social simulation tools
should be built. From the software engineering viewpoint, the use of
meta-modelling techniques provides flexibility to define appropriate modelling
languages, compliant with requirements from social scientists, and implement
tools that facilitate models analysis and simulation. The feasibility of this
assumption has been already validated with initial prototypes with INGENIAS,
which have been assessed at international conferences and journals.
The proposal has received the interests of stakeholders: EPOs
considering that it could be an opportunity for commercial exploitation in
their sectors (providing consultancy services to public administration and
private companies); international experts that endorse the scientific goals of
the project; PhD students actually working in the field who increase their
stock of knowledge; the applicant universities that increase their
international visibility; the national groups doing research in complexity and
social simulation that receive technological transfer; the Spanish Ministry of
Science that gains a high return due the international impact of applied funds.
Antecedents and motivation
“Imagine how hard physics would be if
electrons could think” Murray Gell-Mann (Physics Nobel Prize)
There is an increasing interest in Social Simulation
as the new paradigm to study Complex Systems. While the physical world is
considered constituted of systems that are linear or approximately linear, it
is evident that human societies, institutions and organisations are complex
systems, using ‘complex’ in the technical sense to mean that the behaviour of
the system as a whole cannot be determined by partitioning it and understanding
the behaviour of each of the parts separately (a classic strategy of physical
sciences) [29].
Social Simulation requires the construction of
computer programs that simulate aspects of social behaviour. Table 3.1 presents
a list that includes those preferred by the scientific community. There is a
preference for Special Purpose Languages and Environments although they require
some degree of programming skills. As it has been previously indicated, most of
them present, however, two difficulties to be widely accepted by social scientists:
the end-user should have certain programming skills, and these software
frameworks have been implementing forgetting the social specifications.
|
Generic
agent platforms |
AOSE
tools |
|
|
AScape |
ACT-RBOT |
AgentBuilder |
|
EcoLab |
AgentSheets |
agentTool |
|
Glider |
Jack |
INGENIAS |
|
JAS |
JADE |
Islander |
|
LSD (Laboratory for Simulation
Development) |
Zeus |
Madkit |
|
Magsy |
|
Passi |
|
MASON |
|
Prometheus |
|
Mimose |
|
Tropos |
|
Multi-Agent Simulation Suite (MASS) |
|
|
|
NetLogo |
|
|
|
POP-11 |
|
|
|
Powersim |
|
|
|
RePast |
|
|
|
SDML |
|
|
|
SeSAm |
|
|
|
SIM_AGENT |
|
|
|
Simile |
|
|
|
StarLogo |
|
|
|
Stella |
|
|
|
Swarm |
|
|
|
Vensim |
|
|
Table 3.1. Computer software for social simulation.
UCM background
From the software engineering viewpoint, UCM has shown
the flexibility of the model driven engineering (MDE) approach to provide
abstract modelling tools that adapt well to different application domains. In
concrete, MDE has been used in combination with the agent paradigm to propose
the INGENIAS methodology for the development of MAS.
The main assumptions have been to consider models as
the main artefacts of the software development process and agents and agent
organizations as the foundation elements to build and work with models. By the
application of transformations on models it is possible to generate
implementation code on heterogeneous platforms and abstract from platform
specific issues. It is also possible to transform MAS models in other forms
that are more convenient to apply verification and validation tools. All this
is supported by a tool framework, the INGENIAS Development Kit (IDK), which has
been developed in the context of a current project [9].
This has been validated in different settings, by
cooperation with EPOs in specific technology transfer projects (such as Boeing
R&T, Telefónica I+D, MindFields) as reported in [68]. The MDE
approach is currently used in European project MOMOCS for modernization of
complex systems. Other research groups are using the IDK as mentioned in
section 6.
In this sense it is interesting to mention that IDK
can be adapted to concrete application domains. For instance, at Univ.
Politécnica Valencia, A. Giret has adapted INGENIAS meta-models to create a
modelling language for holonic manufacturing systems [31], which
shows that IDK can be customized by third parties. With respect to the current
proposal, the feasibility of the approach for the simulation of social systems
has been proved by the PhD thesis of C. Sansores at UCM, with some case studies
that have been presented in main conferences in the area [79][80][82][83]. This is
being continued by a case study in cooperation with the Fac. of Sociology at
UCM on the evolution of values in Spanish society, which has been published in
a relevant journal [67] and
specific aspects in some conferences [38][39][43].
These works have shown that the agent paradigm can be
appropriate for modelling social systems. However, it is still necessary to
expand the number of case studies and the collaboration with experts in
modelling societal problems in order to find abstractions that are closer to
the application domain. In fact, it can be expected that there should be more
than a single modelling language: A customizable framework for each social
problem domain should be easier to apply by experts in such domain. This can be
achieved with the flexibility that the MDE approach has shown with INGENIAS for
the agent domain [31][72][71].
UVA background
In the last decade, UVA group has applied agent based
modelling techniques to different domains: freshwater management, urban
dynamics, design of market institutions, behavioural finance, yield management,
industrial dynamics and evolutionary game theory, are the most relevant. All
these applications have been developed in different projects and have benefited
of international partners expertise that contributed in different ways to
develop successful models and simulations.
The group has a deep expertise in different software
programming shells and environments: Swarm, SDML, Vensim, Stella, Powersim,
Mason, Repast, Netlogo, AgentBuilder, Mathematica, Jade, AgentSheets and
INGENIAS. This has given to the group a wide view of facilities and limitations
of available software for social simulation.
The INSISOC research on Agent-Based Modelling and
Social Simulation has received financial support from: Spanish Ministry of
Science and Education, Spanish Ministry of Science and Technology, Regional
Government of Castilla y Leon, European Commission, and the Stockholm
Environmental Institute Oxford Office, to fund the following works:
§ ISIA: Socio-Economics Research and Artificial
Intelligence: Contributions in honour Herbert Simon.
§ FIRMA: Freshwater Integrated Resource Management with
Agents.
§ SocSimNet: Competence Network for Introduction of
Modern ICTE Technologies in Vocational Learning in Social Systems Simulation
and Research.
§ GIAVA: Integrative Water Management in the
Metropolitan Area of Valladolid.
§ SIMAGUA: Agent Based Simulator of Water Management Policies
in Metropolitan Areas.
Results of these research activities have been
published in (see more details in section 6. Background of the group) principal
journals in social simulation (Journal of
Artificial Societies and Social Simulation, Simulation; Cybernetics,
and Advances in Complex Systems),
relevant journals in sociology and economics (Journal of Socioeconomics; the Journal
of Business Research and Games and
Economic Behaviour) and international conferences in the field (European
Social Simulation Conference, World Conference on Social Simulation and
Artificial Economics).
Goals
The goal of this project is to provide a well-sound methodological
framework for the treatment of complexity by policy makers and social
scientists, endowed with an updated theoretical body of knowledge, a set of
tools that enable scenario simulation, and a collection of case studies to
guide and demonstrate the applicability of the framework.
This can be refined in a set of concrete objectives:
1. To develop a modelling framework based on the application
of multi-agent based modelling and simulation to enable social scientists and
decision makers to model, play simulations, test and analyse social (complex)
systems.
2. To develop sound theoretical and architectural foundations
for this framework, based on existing agent-oriented methodologies and the
INGENIAS model-driven approach.
3. To apply the framework to a number of challenging case
studies in order to refine the framework, and to get policy assessment
in the selected case studies.
4. To demonstrate to social scientists and policy makers that the framework
can be used to conduct validation of social hypothesis as well as realistic
policy design, deployment, and assessment, so fostering increasingly
trusted outcomes.
5. To design and test a suite of training materials for
social scientists, policy-makers, advisers, and postgraduate students.
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