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.

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.

Progress in Artificial Intelligence, 2009

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.

DAAAM International Scientific Book, 2021

Simulation is experimenting with an imitation, or model, of the observed system, and observing its behaviour over time, with the purpose of better understanding and/or improving the system. If the system is inaccessible, dangerous or unacceptable for inclusion in the research, if it is designed but not yet built, abstract or just does not exist, system simulation is often the best option for researching such systems. Elements of these models can be implemented as software entities that perceive their environment and respond autonomously to stimulation. We call these entities intelligent agents. Software tools that enable the exploration of complex natural, social, and technical phenomena and systems are called Agent-Based Modelling and Simulation tools. This paper provides an overview of the wide range of possible applications of these modern computer tools.

Austral Ecology, 2003

Lagoon is a computer simulation aimed at allowing users to investigate the principles controlling equilibria in complex systems by carrying out virtual experiments. This conceptual tool was developed to provide students with an interactive learning experience that demonstrates the importance of complexity in ecosystems. The simulation represents a 'coastal embayment or estuary' governed by biological dynamic equilibria. Users can manipulate the model parameters to explore eutrophication, competition for limiting nutrients, predator-prey relationships, and food chains. Well, so much for the synopsis. A PDF manual on the CD-ROM describes how Lagoon can be used. The software is easy to install, and has modest system requirements (minimum: Windows 95, Pentium 100 MHz, 16 MB RAM, CD-ROM drive 4 ϫ). The program interface is colourful, pleasing to the eye, and user-friendly. The software is largely mouse driven, with controls consisting of buttons and scroll bars, and pop-up menus giving descriptions of the controls. For someone with a childhood love and professional interest in the tidal parts of estuaries, but currently dryfooted research-wise, the opportunity to review this program provided a most pleasant diversion. In this regard though, the name Lagoon is deceptive. The program consists of a relatively simple closed system that more closely resembles a coastal embayment than an estuary, with parameterization of riverine inflow (and equivalent oceanic outflow) rate, but no parameterization of tidal fluxes or salinity. Two types of physical variables can be altered: (i) the rate of oxygen transfer from the atmosphere; and (ii) the rate and concentration of nitrate inflow. The presence and growth of each of a range of organisms is governed by Michaelis-Menten kinetics and can be manipulated by changing: (i) their maximum resource uptake (or growth) rate and half saturation constant; (ii) the efficiency of conversion of resources consumed into biomass; and (iii) the background biomass loss (or death) rate. The organisms, forming a linear food chain, consist of primary producers (green algae), grazers (zooplankton eats green algae), carnivores (shark eats fish eats krill eats zooplankton), and decomposers (bacteria regenerate nitrogen). Competition between green algae and blue-green algae can also be simulated.

Agent-based modeling in biology allows, firstly, to simulate a multi-component environment with a number of entities (plants, microbes, fungi, insects, birds, mammals etc.) and, secondly, to perform numerical experiments for different research purposes. The article presents modeling software for natural and rural ecosystems based on the food chain concept. This software encompasses C++ core and Jupyter/Python data analysis environment; it supports both an interactive and a batch run modes; it allows exporting data for further processing and visualizes the data analysis results. Agent features and motion strategies can be defined with the program interface. The software allows modeling ecosystem dynamics with respect to inter-and cross-species relations through the simulation of agent analytical models in ecosystem world. We applied a three-species test model "plant-herbivore-carnivore" (PHC model) to perform software code validation. As a testing scenario, foraging impact to the overall PHC model balance was evaluated during the series of software try-outs.

Simulating ecological models is always a difficult task, not only because of its complexity but also due to the slowness associated with each simulation run as more variables and processes are incorporated into the complex ecosystem model. The computational overhead becomes a very important limitation for model calibration and scenario analysis, due to the large number of model runs generally required. This paper presents a framework for ecological simulations that intends to increase system performance through the ability to do parallel simulations, allowing the joint analysis of different scenarios. This framework evolved from the usage of one simulator and several agents, that configure the simulator to run specific scenarios, related to possible ecosystem management options, one at a time, to the use of several simulators, each one simulating a different scenario concurrently, speeding up the process and reducing the time for decision between the alternative scenarios proposed by the agents. This approach was tested with a farmer agent that seeks optimal combinations of bivalve seeding areas in a large mariculture region, maximizing the production without exceeding the total allowed seeding area. Results obtained showed that the time needed to acquire a "near" optimal solution decreases proportionally with the number of simulators in the network, improving the performance of the agent's optimization process, without compromising its rationality. This work is a step forward towards an agent based decision support system to optimize complex environmental problems.

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...