A Multi-Agent based Approach for Simulating the Impact of Human Behaviours on Air Pollution

International audienceThis paper presents a Multi-Agent System (MAS) approach for designing an air pollution simulator. The aim is to simulate the concentration of air pollutants emitted from sources (e.g. factories) and to investigate the emergence of cooperation between the emission source managers and the impact this has on air quality. The emission sources are controlled by agents. The agents try to achieve their goals (i.e. increase production, which has the side effect of raising air pollution) and also cooperate with others agents by altering their emission rate according to the air quality. The agents play an adapted version of the evolutionary N-Person Prisoners' Dilemma game in a non-deterministic environment; they have two decisions: decrease or increase the emission. The rewards/penalties are influenced by the pollutant concentration which is, in turn, determined using climatic parameters. In order to give predictions about the Plume Dispersion) model and an ANN (Artificial Neural Network) prediction model. The prediction is calculated using the dispersal information and real data about climatic parameters (wind speed, humidity, temperature and rainfall). Every agent cooperates with its neighbours that emit the same pollutant, and it learns how to adapt its strategy to gain more reward. When the pollution level exceeds the maximum allowed level, agents are penalised according to their participation. The system has been tested using real data from the region of Annaba (North-East Algeria). It helped to investigate how the regulations enhance the cooperation and may help controlling the air quality. The designed system helps the environmental agencies to assess their air pollution controlling policies

A multi-agent simulation approach to sustainability in tourism development

In the last decades the increasing facility in moving and the simultaneous fall of the transportation costs have strongly increased the tourist flows. As a consequence, different destinations, especially those which are rich of natural resources, unable or unready to sustain huge tourism flows, present serious problems of sustainability and Tourism Carrying Capacity (TCC). At the present, it is universally recognized that every tourist destination should plan effective and pro-reactive protection policies of its cultural, environmental and social resources. In order to facilitate policies definition it may be useful to measure the Tourist Carrying Capacity, but the literature has highlighted that this is not an easy task for different reasons: among the others, the complexity and the dynamicity of the concept, the absence of a universally accepted definition and the impossibility of assigning an objective scientific value and to apply a rigorous analysis. Thereby, more recently an alternative, or even complementary, interpretation of TCC has developed; it is called LAC, Limit of Acceptable Changes where the focus shifts from: “How much use an area can tolerate?†to “How much change is acceptable?â€, aiming at evaluating the costs and benefits from alternative management tourism actions. The aim of the paper is to present an innovative framework, based on the LAC approach - MABSiT, Mobile Agent Behavior Simulation in Tourism - developed by the authors, which is composed by five modules: elaboration data, DBMS, ad-hoc maps, agents and ontology. Its modular structure allows to easily study the interactions among the components in order to observe the behavior of the single agents. In an aggregate form, it is possible to define group dynamics, where one possible effect is the influence on the variation of agents’ satisfaction perception in comparison to the surroundings environment. The paper will be structured as follows: an introduction will be followed by a literature review; than the methodology and the framework will be presented and applied to a case study: Vieste, a known maritime destination of South of Italy, which is characterized by high problems of seasonality in the summer. Finally, some conclusions and policy recommendations will be drawn.

Modelling PM10 Crisis Peaks Using Multi-Agent based Simulation: Application to Annaba City, North-East Algeria

International audienceThe paper describes a MAS (multi-agent system) simulation approach for controlling PM10 (Particulate Matter) crisis peaks. A dispersion model is used with an Artificial Neural Network (ANN) to predict the PM10 concentration level. The dispersion and ANN models are integrated into a MAS system. PM10 source controllers are modelled as software agents. The MAS is composed of agents that cooperate with each other for reducing their emissions and control the air pollution peaks. Different control strategies are simulated and compared using data from Annaba (North-East Algeria). The simulator helps to compare and assess the efficiency of policies to control peaks in PM10

Implementing Air Pollution and Health Damage Costs in Urban Multi-Energy Systems Modelling

The growing global urbanization rate implies that the sustainability challenges are increasingly concentrated in cities. At today, around 75% of global energy is consumed in urban areas, so efforts must be addressed to transform existing urban energy systems into more sustainable systems. In this perspective, a key aspect to evolve toward a cleaner and affordable energy system is the development of Multi-Energy Systems (MES) modelling, whereby heat, electricity, fuels, transport, and other energy carriers closely interact with each other at various scales. MES can optimize technical, economic and environmental performance with respect to “traditional” independent energy systems, at both the operational and the planning stage. This paper presents a development of the existing MESsi modelling platform, consisting in the implementation of a model estimating the impacts on air quality and human health. MESsi is a novel distributed infrastructure for modelling and co-simulating Multi-Energy-Systems. It exploits modern software design patterns (i.e. microservices) to guarantee scalability, extendibility and easy maintenance of the system. Thus, MESsi is flexible in modelling and co-simulating different energy flows in a single solution made of different interoperable modules that can be deployed in a plug-and-play fashion. The module to be implemented in MESsi infrastructure is the DIATI integrated dispersion and externalities model (DIDEM). The DIDEM model is based on the impact pathway approach, linking the simulation of pollutants dispersion to the concentration-exposure-response functions provided by latest WHO recommendations. An overview of the potential integration steps in the modelling infrastructure is described in this paper. A discussion on possible application scenarios that have different spatio-temporal resolutions is also reported. The integration of DIDEM model in MESsi platform allows the inter-connection of a detailed impact assessment to a high-level energy system simulation

Multi-level agent-based modeling - A literature survey

During last decade, multi-level agent-based modeling has received significant and dramatically increasing interest. In this article we present a comprehensive and structured review of literature on the subject. We present the main theoretical contributions and application domains of this concept, with an emphasis on social, flow, biological and biomedical models.Comment: v2. Ref 102 added. v3-4 Many refs and text added v5-6 bibliographic statistics updated. v7 Change of the name of the paper to reflect what it became, many refs and text added, bibliographic statistics update

Li-BIM, an agent-based approach to simulate occupant-building interaction from the Building-Information Modelling

International audienceBuilding design involves many challenges and requires to take into account the interaction between the building and the users. Different occupant behaviour models implemented with building simulation tools (thermal, air quality, lighting) have been proposed. Among these, models based on the agent approach seem to be the most promising. However, existing models poorly describe human cognition and the social dimension. Moreover, they are often oriented towards a specific use (thermal simulation, waste management) without being transposable to another field, and they require a significant instantiation effort for each new case, making their use difficult. This article proposes an agent-based model called Li-BIM that simulates the behaviour of the occupants in a building and their indoor comfort. Li-BIM model is structured around the numerical modelling of the building-BIM-(with standard exchange format IFC), a high-resolution cognitive model, and the coupling with various physical models. Li-BIM simulates the reactive, deliberative and social behaviour of occupants in residential dwellings based on the Belief-Desire-Intention architecture. This model, thanks its ease of use and flexibility, is an operational and relevant tool to support building design process with a human-centred approach. An application of the model is presented, focusing on energy consumption and the inhabitants' comfort. In-situ data obtained from the instrumented house that served as case study have been compared with simulation results from Li-BIM and a standard energy simulation software, demonstrating the reliability of the proposed model

Quantitative modelling of the human–Earth System a new kind of science?

The five grand challenges set out for Earth System Science by the International Council for Science in 2010 require a true fusion of social science, economics and natural science—a fusion that has not yet been achieved. In this paper we propose that constructing quantitative models of the dynamics of the human–Earth system can serve as a catalyst for this fusion. We confront well-known objections to modelling societal dynamics by drawing lessons from the development of natural science over the last four centuries and applying them to social and economic science. First, we pose three questions that require real integration of the three fields of science. They concern the coupling of physical planetary boundaries via social processes; the extension of the concept of planetary boundaries to the human–Earth System; and the possibly self-defeating nature of the United Nation’s Millennium Development Goals. Second, we ask whether there are regularities or ‘attractors’ in the human–Earth System analogous to those that prompted the search for laws of nature. We nominate some candidates and discuss why we should observe them given that human actors with foresight and intentionality play a fundamental role in the human–Earth System. We conclude that, at sufficiently large time and space scales, social processes are predictable in some sense. Third, we canvass some essential mathematical techniques that this research fusion must incorporate, and we ask what kind of data would be needed to validate or falsify our models. Finally, we briefly review the state of the art in quantitative modelling of the human–Earth System today and highlight a gap between so-called integrated assessment models applied at regional and global scale, which could be filled by a new scale of model