Candelaria E. Sansores

Agent-based modelling and simulation for the analysis of social patterns

Agent-based modelling facilitates the implementation of tools for the analysis of social patterns. 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. An agent can characterize an individual with capabilities to perceive and react to events in the environment, taking into account its mental state (beliefs, goals), and to interact with other agents in its social environment. There are already tools to perform agent-based social simulation but these are usually hard to use by social scientists, as they require a good expertise in computer programming. In order to cope with such difficulty, we propose the use of agent-based graphical modelling languages, which can help to specify social systems as multi-agent systems in a more convenient way. This is complemented with transformation tools to be able to analyse and derive emergent social behavioural patterns by using the capabilities of existing simulation platforms. In this way, this framework can facilitate the specification and analysis of complex behavioural patterns that may emerge in social systems.

Agent-based simulation replication: a model driven architecture approach

In Multi-agent based simulation (MABS) systems, computational models are built as multi-agent systems (MAS). Replication of these models can contribute to improve the reliability of the results and understanding of the system. One of the main problems for facilitating replication is the lack of a simulation integrated environment that supports the whole research process from conceptual modeling to simulation implementation and analysis. We address this issue providing a high-level conceptual modeling abstraction for simulation development, including transformation tools that facilitate the implementation of simulations on different simulation platforms. In this way, agent-based simulation development process is driven by modeling, because users focus on conceptual modeling, while implementation code is generated automatically.

Modelling and simulation of social systems with INGENIAS

Most agent-based simulation toolkits are based on the Java programming language. This makes their use difficult for social scientists, who are usually not skilled in computer programming. However, agent modelling concepts are not unlike those which could be used for the modelling of social systems. This assumption is considered in proposing the use of a graphical agent-oriented language for the specification of social simulation models, and for transforming (automatically) these models to code for an agent-based simulation toolkit. In this manner, a social scientist could prepare social models in a more convenient way, and execute simulations on existing simulation toolkits, getting results back in terms of the model. This framework is built with a set of agent development tools, specifically, the INGENIAS Development Kit (IDK), which provides a customisable model editor and modules for automatic code generation.

Visual modeling for complex agent-based simulation systems

Currently there is a diversity of tools for agent-based simulation, which can be applied to the understanding of social phenomena. Describing this kind of phenomena with a visual language can facilitate the use of these tools by users who are not necessarily experts in computer programming, but in social sciences. With this purpose, we propose to define such visual language, which is based on well established concepts of agent-oriented software engineering, and more concretely on the INGENIAS methodology. The proposed language is independent of any particular simulation platform and, by using INGENIAS code generation support, it is possible to generate implementations for the desired target platforms. Also, we consider that modeling should be application domain oriented and that a generic language itself does not suffice. Thus, we discuss at the end how specific domain simulation environments could be achieved.

Agent based simulation for social systems: from modeling to implementation

This paper presents experimental results of our model-driven approach to agent based simulation. According to this, the development process for agent based simulation should focus on modeling rather than implementation (i.e., programming on some concrete agent simulation platform). This requires the use of transformation tools from simulation models to implementation code. Describing social phenomena with a visual, high-level modeling language and implementing the simulation in this way should facilitate the use of toolkits by experts in social sciences without a deep knowledge of programming concerns. This simulation approach is supported here by the INGENIAS Development Kit (IDK), which provides a model editor for multi-agent systems, and code generation support. To validate the transformation mechanism, we have modeled a concrete social system with the INGENIAS agent-oriented modeling language, and generated two independent implementations for two different platforms (Repast and Mason simulation toolkits). This experimentation shows the feasibility of the model driven implementation approach and has enabled the study of facilities provided by simulation toolkits that can have impact on the transformation process, in particular, the scheduling techniques. Also, comparing the simulation results of the case study with the original work and between implementations has led us to discover biases introduced by simulation mechanisms that can be found in most simulation platforms.

Agent-Based modeling of social complex systems

This thesis proposal aims to provide a new approach to the study of complex adaptive systems in social sciences through a methodological framework for modeling and simulating these systems like artificial societies. Agent based modeling (ABM) is well fitted for the study of social systems as it focuses on how local interactions among agents generate emergent larger and global social structures and patterns of behavior. The issues addressed by our framework are presented as well as its most important components.

On the Improvements of Computational Individualism of an IBM

Individual based modeling is an alternative research method for ecologist that overcomes certain limitations of traditional techniques. This method simplifies hypothesis testing and analysis that may explain the dynamics and evolution of populations and/or ecological patterns. Ecological systems are so vast and complex that a unique tool for creation and use of these models would not be viable. Due to its nature, available tools are not easy to use by ecologist without expertise in computer programming. However, the main drawback of these tools is their lack of mechanisms to build true individual models. In order to cope with individualism we state six characteristics that we consider an IBM must comply with (when implemented under the multi-agent system paradigm). Finally, we propose a framework easy to use by ecologists for the modeling and simulation of ecosystems that complies with the characteristics mentioned above.

A Motivation-Based Self-organization Approach

In this paper we propose a self-organizing mechanism to model adaptive multi-agent systems. The mechanism is inspired in a psychological approach of motivation. Under this approach motivation is an internal state or condition that serves to activate or energize behavior and give it direction. Thus, we propose an intrinsic motivation or reason for engaging in a particular behavior called goal-achievement, in this way agents will have a drive to reach a clearly defined state. To increase the strength of this particular behavior we propose a reinforcement mechanism based on agents past experience as a feedback. The intention of this mechanism is to mimic the adaptation exhibited by entities of complex systems composed of multiple autonomous entities that make local decisions leading to a global organized behavior for the system. Such organization is reached autonomously in different ways, thus adapting to task and environment.