Divisão de Observação da Terra e Geoinformática

This paper compares four freely available programming libraries for support of social scientific agent based computer simulation: RePast, Swarm, Quicksilver, and VSEit. Our aim is evaluation to determine the simulation framework that is the best suited for theory and data based modeling of social interventions, such as information campaigns. Our first step consisted in an Internet search for programming libraries and the selection of suitable candidates for detailed evaluation on the basis of 'knock out' criteria. Next, we developed a rating system and assessed the selected simulation environments on the basis of the rating criteria. The evaluation was based on official program documentation, statements by developers and users, and the experiences and impressions of the evaluators. The evaluation results showed the RePast environment to be the clear winner. In a further step, the evaluation results were weighted according to effort/time/energy saved by social scientists by using the particular ready-made programming library as compared to doing their own programming. Once again, the weighted results show RePast to win out over the other Java based programming libraries examined.

Very subjective choices of the criteria used, without references in the literature. For example, one of the topics is “easy to use,” that in terms of Human-Machine Interaction it would require a whole paper.

1. No procedure for automatically networking agents implemented
2. Elementary networks supported (such as all agents networked with all other agents, random networks)
3. Automatic generation of networks based on non-social scientific control information (such as networking of all agents within a certain distance, in the sense of spatial interaction)
4. Automatic generation of networks of agents based on social scientific control information (such as network density, centralization, etc.)
5. Automatic generation of networks based on characteristics (such as networking agents having similar attitudes) and control information
6. Automatic generation of networks based on combinations of characteristics and control information

1. No procedures for managing spatial arrangements implemented
2. Simple spatial functionality (agents possess a spatial position, simple movements supported)
3. Simple positioning algorithms (such as decreasing density with increasing distance from a certain point)
4. Simple areas of influence (for example, all agents at a particular distance from active agents can be determined)
5. Complex areas of influence (such as possibility for visual obstacles)
6. Complex positioning algorithms (such as optimization of position based on various bits of inexact position information)

At the end of the paper, there is a long list of other tools, and the reasons why they were excluded from the analysis.

In this paper we propose a preliminary reference model for the requirements specification of agent-based simulation platforms. We give the following contributions: (i) aid the identification of general principles to develop platforms; (ii) advance the analysis and prospection of technical-operational and high-level requirements; (iii) promote the identification of shared requirements, addressing them to the development of an integrated work. We present our reference model and make a comparative analysis between three well-known platforms, resulting in an unambiguous and schematic characterisation of computational systems for agent-based simulation. In effect, when evaluating the importance of requirements analysis in this field it is quite odd to find very few references in the literature about this topic. This observation becomes even more surprising since one can find a considerable number of platforms (though very heterogeneous) available to the research community.

a requirement is a feature of a system or a description of something the system is capable of doing in order to reach its objectives. […] it aims to detail the structure of a system, establishing its principles of behaviour.

• Manage Scheduling Techniques: support controlled simulations and allow repeatability. libraries with the most usual scheduling techniques, like discrete-time simulation and event-based simulation.
• Launch Agents: agent templates related with different manners to launch agents like threads, applets and objects.
• Manage Intentional Failures: two classes of failures that can be manipulated. operational failures works with disturbances on the technical-operational infrastructure (corrupted messages, server failures, etc.). logical failures manipulate patterns of behaviour that can be viewed as dysfunctional exceptions in the simulated system. The platform should offer: (i) libraries to manipulate basic operational failures; (ii) mechanisms to store and search templates of logical failures created by users.
• Integrate Controlled and Non-Controlled Environments: there are cases where the agents can (or must) perform actions outside the controlled environment, in real environments. a platform should offer agent architectures that separate the agent domain-dependent behaviour from the simulator design patterns. (: didn't understand, but it cites another paper of the same author)
• Develop Agent Architectures: templates of generic agent architectures, from reactive to intentional agents.
• Manage Communication Methods: message passing mechanisms. message models with basic attributes and mechanisms to extend or add additional attributes; APIs and parsers to check the correctness of the communication (eventually according to given standards, e.g. KQML)
• Manage Organisational Abstractions: components that explicitly structure an organisation. Roles and groups are the most common ones. The platform should provide services that define roles in terms of functions, activities or responsibilities. The platform should support the creation and management of agent and organisation collections, clustered around common relations.
• Manage Multiple Societies: a society may be seen as an aggregate of agents and organisations, which coexist and interact. Although the term society is an influential organisational metaphor in MAS, its concept is rarely specified as an explicit structural and relational entity. The platform should provide primitives to instantiate multiple societies.

Requirements related to analysis should specify the means to observe behavioural events and the internal state of AEs (agentified entities) during the simulation.

• Observe Behavioural Events: Behavioural events are events that can be observed by an external observer (e.g., message passing, creation/destruction of agents, data base access). mechanisms to select specific points to observe behavioural events. (1), (2), (3) and (4).
• Observe Cognitive Events: agents' internal architectures. For example, analyse if the effect perceived by the agent who issues behavioural events is in fact objective according to the simulation designer/observer's perspective. mechanisms to control the agents' internal mechanisms, in order to trigger specific observation methods. (5), (6) and (7).
• Manage Data Analysis: huge amount of data. technical indicators and decision support (e.g., graphical and statistical packages) to work in more depth with generated data.

The exploration of different initial conditions, sequences of method invocation, mental states or assignment of variables is thus a crucial issue. These experiments can be facilitated if such conditions are allowed to change during the simulation, in-between simulation steps (the platforms studied in this paper do not have any of these functionalities).

• Intervene on Behavioural Events: select specific points to intervene on behavioural events. The intervention should permit the suppression, modification or creation of behavioural events.
• Intervene on Cognitive Events: mechanisms to intervene on the agents' internal mechanisms, for instance on the agents' beliefs or order of method invocation. This may alter the order of invocation and nature of behavioural events.
• Manage Social Opacity: conditions under which the control of cognitive information transfer between agents in different societies is possible (organisational borders). instantiate different topologies of opaque social spaces in a dynamic way. while the observed agents and societies must be visible to the observer agent, the observer agent and societies must be opaque to the observed agents.
• Provide Models of Cognitive Reflectivity: identification of cognitive structures and internal procedures of agents at run time.

This paper attempts to articulate the general role of infrastructure for multi-agent systems (MAS), and why infrastructure is a particularly critical issue if we are to increase the visibility and impact of multi-agent systems as a universal technology and solution. Second, it presents my current thinking on the socio-technical content of the needed infrastructure in four different comers of the multi-agent systems world: science, education, application, and use.

RETSINA is an implemented Multi-Agent System infrastructure that has been developed for several years and applied in many domains ranging from financial portfolio management to logistic planning. In this paper, we distill from our experience in developing MASs to clearly define a generic MAS infrastructure as the domain independent and reusable substratum that supports the agents' social interactions. In addition, we show that the MAS infrastructure imposes requirements on an individual agent if the agent is to be a member of a MAS and take advantage of various components of the MAS infrastructure. Although agents are expected to enter a MAS and seamlessly and effortlessly interac.t with the agents in the MAS infrastructure, the current state of the art demands agents to be programmed with the knowledge of what infrastructure they will utilize, and what are various fall-back and recovery mechanisms that the infrastructure provides. By providing an abstract MAS infrastructure model and a concrete implemented instance of the model, RETSINA, we contribute towards the development of principles and practice to make the MAS infrastructure “invisible” and ubiquitous to the interacting agents.

(some interesting papers cite this one): “Agents are not part of the problem, agents can solve the problem”, “Environments in multiagent systems”, “Environment as a first class abstraction in multiagent systems”, “Environments for Situated Multi-Agent Systems: Beyond Infrastructure”

It is generally accepted that the environment is an essential compound of multiagent systems (MASs). Yet the environment is typically assigned limited responsibilities, or even neglected entirely, overlooking a rich potential for the paradigm of MASs. Opportunities that environments offer, have mostly been researched in the domain of situated MASs. However, the complex principles behind the concepts and responsibilities of the environment and the interplay between agents and environment are not yet fully clarified. In this paper, we first give an overview of the state-of-the-art on environments in MASs. The survey discusses relevant research tracks on environments that have been explored so far. Each track is illustrated with a number of representative contributions by the research community. Based on this study and the results of our own research, we identify a set of core concerns for environments that can be divided in two classes: concerns related to the structure of the environment, and concerns related to the activity in the environment. To conclude, we list a number of research challenges that, in our opinion, are important for further research on environments for MAS.

The range of tools designed to help build agent-based models is briefly reviewed. It is suggested that although progress has been made, there is much further design and development work to be done. Modelers have an important part to play, because the creation of tools and models using those tools proceed in a dialectical relationship.

The authors compare the standardization that occurred in statistical packages to the development of ABM, and the advantages over “rolling your own”, and the limitations of having to know the programming language. They talk a bit about the following tools: repast, swarm, ascape, starlogo, agentsheets, sdml, cormas, desire. The facilities for other phases of a model’s life cycle, model evaluation, model maintenance, and many types of model use are rather limited at this time. The primary supports for model use are visualizations of model state (especially the ubiquitous displays of two-dimensional grids of agent positions) and modest facilities for collecting statistics in a single run. Issues: comparing multiple model runs, loading or calibrating models from data, automatically generating large numbers of cases from experimental designs, collecting and statistically analyzing the results of large numbers of experiments.

A clear consensus among the papers in this Colloquium is that agent-based modeling is a revolutionary development for social science. However, the reasons to expect this revolution lie more in the potential seen in this tool than through realized results. In order for the anticipated revolution to occur, a series of challenges must be met. This paper surveys the challenges suggested by the papers of this session.

to evaluate this proposed revolution, what matters is not the computer science advances that make ABM possible, but rather the social science challenges that make it necessary.

Three generic reasons that make ABM important to social sciences:

1. the unsuitability of competing modeling formalisms: systems of differential equations and statistical modeling are viewed as imposing restrictive or unrealistic assumptions that limit their use for many problems: linearity, homogeneity, normality, and stationarity. The need to pose problems in a form tractable for mathematical analysis or proof often requires assumptions that can be relaxed with computer simulation. In the social sciences, simulation may allow more aggressive exploration of the implications of, for example, imperfect rationality, the effects of learning and information, and social and institutional structure.
2. agents as a natural ontology for many social problem: provides a place to express the enormous amount of data and knowledge about the behavior, motivations, and relationships of social agents, be they human individuals or institutions. ABM must evolve not only in representation, but also in case loading, uncertainty analysis, calibration of models to data, and methodologies for using models to answer specific questions or to solve problems.
3. emergence: as long as demonstrations of emergence are confined to the use of computer graphics for attractive demonstrations, the scientific importance of emergence and of ABM demonstration of emergent phenomena will remain small. Formal definition of what is meant by emergence is the exception rather than the rule, and quantitative tests that a given model achieves the sort of emergence advertised are rare.