Agent-Based Simulation of Ecological Models

Advances in computational intelligence and robotics book series, 2017

The objective of this paper is to reflect on our experience in a serious game research project, named SimParc, about multi-agent support for participatory management of protected areas for biodiversity conservation and social inclusion. Our project has a clear filiation with the MAS-RPG methodology developed by the ComMod action-research community, where multi-agent simulation (MAS) computes the dynamics of the resources and role-playing game (RPG) represents the actions and dialogue between stakeholders about the resources. We have explored some specific directions, such as: dialogue support for negotiation; argumentation-based decision making and its explanation; technical assistance to the players based on viability modeling. In our project, multi-agent based simulation focuses on the negotiation process itself, performed by human players and some artificial participants/agents, rather than on the simulation of the resources dynamics. Meanwhile, we have also reintroduced the modeling of the socioecosystem dynamics, but as a local technical assistance/analysis tool for the players.

There is a growing demand for " end-to-end " models, which are modeling tools used to analyze and understand the fundamental complexities of marine ecosystems and processes emerging from the interaction of individuals from different trophic groups with respect to the physical environment and, even, human activity. These models are valuable quantitative tools for ecosystem-based management. To explore potential answers to complex questions regarding ecosystems using these models, it is necessary to incorporate classical ontogenic changes through the life cycle of target individuals, in addition to inherited behavioral strategies, as an additional differentiating aspect, particularly when the behavior has a direct impact on the ecosystem phenomena under study. However, it is difficult to combine different fine scale time and spatial granularities to infer animal behavior and ontogenic development. This complexity has kept these two levels of analysis separated, because most current tools do not have the required computational resources and advanced software architecture. To address this issue, we propose an individual-based modeling framework that is capable of handling and unifying the two experimental categories with a comprehensive biological and behavioral model that strictly adheres to the physiological functions of ingestion, growth, and metabolism of organisms. In addition, this model incorporates the exchange and transfer of mass and energy through local interactions at all trophic levels (lower to higher), the physical environment, and anthropogenic activity. For the framework to model short time events, such as classical predator–prey interactions , while also generating long-term ecosystem emergent properties, a special interleaving scheduling engine and physical space computer model was devised, which optimizes memory and processing resources. The framework was tested through several experiments with a three-population ecosystem containing up to 40 thousand organisms evolving inside a 200,000 m 2 simulation environment during 12,000 model-hours; yet, requiring only a few hours of program execution on a regular personal computer. The model included various environmental physical elements, such as several hundred shelters, the number of which can be easily modified in each experiment to simulate substrate degradation and its impact on populations. With the aid of the quantitative and qualitative tools provided by the model, it was possible to observe a coupling between prey and predator population dynamics. In conclusion, we confirmed that the end-to-end model developed here could successfully generate detailed specific hypotheses about fish behavior and quantify impacts on population dynamics.

Mathematics and computers in simulation, 2004

This work describes a software architecture that couples intelligent agent technology with Geographic Information System (GIS). The agents simulate individual behaviour, while GIS-based simulation models represent the landscape that is observed and acted upon by the agents. Our motivation in developing the software is to assist in understanding and resolving complex, semi- structured, environmental management problems, where multiple actors participate in decision-making and management. Like real stakeholders, agents are individuals and have different knowledge and interests. The agents' design is based upon the Belief, Desire, and Intention (BDI) theory of intelligent agents and implemented in Visual Basic. Agents are initialised with a set of goals they want to achieve and a library of plans that stores knowledge about landscape management actions. Each plan is like a 'if-then' rule where the 'if' is a set of conditions that need to be matched to agents be...

2016

The potential of Agent-Based Modeling is now well-established to address natural resource management and policy issues in social-ecological systems where a number of biophysical and anthropic processes interact. The selection of anthropic processes to be considered in a model according to the research question is not easy, due to modelers' difficulty to objectify and identify these processes. The paper proposes some criteria to identify anthropic processes, decide which ones to discard or retain and how to implement the latter. It is illustrated by the analysis of the governance sub-model of the MAELIA platform dedicated to the integrated assessment and simulation of water use and water management policy, in a context of land use / land cover and climate changes.

1998

Customizing ecosystem simulation applications is a complex operation and the design of multi-agent systems remains expensive and error-prone. Object-Oriented frameworks are a promising technology for the reuse of proven designs and implementation (i.e. software components and design patterns). This paper focuses on the design of agentoriented, visual-interactive ecosystem simulations. We propose a customizable application framework, the novel aspects being the use of new advanced software engineering techniques for the reuse of simulation models. The proposed framework is presented with the Unified Modeling Language applied to complex ecosystems with behavioral models encapsulated into active autonomous objects with social interactions (i.e. agents). The framework is also designed to support 2D or 3D visual-interactive feedback to end-users at run-time in addition to its ability to interface with Geographical Information Systems and the Web.

19th European Conference …, 2005

Journal of Environmental Protection

Computer programs have been categorized as a useful tool to evaluate the complexity of systems. In fact, agent-based modeling (ABM) is considered a new method to model complex systems characterized by the role of independent and interrelating agents. Simulations contribute in estimating and comprehending emerging behaviors that require the development of new regulations for local agents that would make improvements to the system. This paper offers an example of a methodology and a process utilized to develop a simulation model named Befergyonet, an ABM used to conduct computer simulations within a spatio-intertemporal environment. The methodology discussed in this paper is intended solely to stimulate the use of innovative computer programs to simulate complex systems as an approach to represent real world events and may be a methodological guide for readers interested in developing their own ABM.

International Journal of Agent Technologies and Systems, 2010

The integrated—environmental, economic and social—analysis of climate change calls for a paradigm shift as it is fundamentally a problem of complex, bottom-up and multi-agent human behaviour. There is a growing awareness that global environmental change dynamics and the related socio-economic implications involve a degree of complexity that requires an innovative modelling of combined social and ecological systems. Climate change policy can no longer be addressed separately from a broader context of adaptation and sustainability strategies. Past research on artificial intelligence and social simulation has developed a promising methodology. Literature on agent-based modelling (ABM) shows it’s potential to couple social and environmental models and incorporate the influence of micro-level decision making in the system dynamics and to study the emergence of collective responses to policies. However, there are few studies that concretely apply this methodology to the study of climate c...

Understanding Complex Systems, 2013

Why Read This Chapter? To understand the recent shift of paradigms prevailing in both environmental modelling and renewable resources management that led to the emerging rise in the application of ABMS. Also, to learn about a practical way to characterize applications of ABMS to environmental management and to see this framework applied to review a selection of recent applications of