Agent-based Modelling of Socio-Ecological Systems: Models, Projects and Ontologies

Socio-Ecological Systems (SESs) are the systems in which our everyday lives are embedded, so understanding them is important. The complex properties of such systems make modelling an indispensable tool for their description and analysis. Human actors play a pivotal role in SESs, but their interactions with each other and their environment are often underrepresented in SES modelling. We argue that more attention should be given to social aspects in models of SESs, but this entails additional kinds of complexity. Modelling choices need to be as transparent as possible, and to be based on analysis of the purposes and limitations of modelling. We recommend thinking in terms of modelling projects rather than single models. Such a project may involve multiple models adopting different modelling methods. We argue that agent-based models (ABMs) are an essential tool in an SES modelling project, but their expressivity, which is their major advantage, also produces problems with model transparency and validation. We propose the use of formal ontologies to make the structure and meaning of models as explicit as possible, facilitating model design, implementation, assessment, comparison and extension

Effect of resource spatial correlation and Hunter-Fisher-Gatherer mobility on social cooperation in Tierra del Fuego

This article presents an agent-based model designed to explore the development of cooperation in hunter-fisher-gatherer societies that face a dilemma of sharing an unpredictable resource that is randomly distributed in space. The model is a stylised abstraction of the Yamana society, which inhabited the channels and islands of the southernmost part of Tierra del Fuego (Argentina-Chile). According to ethnographic sources, the Yamana developed cooperative behaviour supported by an indirect reciprocity mechanism: whenever someone found an extraordinary confluence of resources, such as a beached whale, they would use smoke signals to announce their find, bringing people together to share food and exchange different types of social capital. The model provides insight on how the spatial concentration of beachings and agents’ movements in the space can influence cooperation. We conclude that the emergence of informal and dynamic communities that operate as a vigilance network preserves cooperation and makes defection very costly.MICINN http://www.idi.mineco.gob.es/ CSD2010-00034 (SimulPast CONSOLIDER-INGENIO 2010) and HAR2009-06996; the government of Castilla y Leónhttp://www.jcyl.es/ GREX251-2009; the Argentine CONICET http://www.conicet.gov.ar/PIP-0706; and the Wenner-Gren Foundation for Anthropological Researchhttp://www.wennergren.org/ "Social Aggregation: A Yamana Society's Short Term Episode to Analyse Social Interaction, Tierra del Fuego, Argentina". The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscrip

Defining and simulating open-ended novelty: requirements, guidelines, and challenges

The open-endedness of a system is often defined as a continual production of novelty. Here we pin down this concept more fully by defining several types of novelty that a system may exhibit, classified as variation, innovation, and emergence. We then provide a meta-model for including levels of structure in a system’s model. From there, we define an architecture suitable for building simulations of open-ended novelty-generating systems and discuss how previously proposed systems fit into this framework. We discuss the design principles applicable to those systems and close with some challenges for the community

Agent-based land-use models: a review of applications

Agent-based modelling is an approach that has been receiving attention by the land use modelling community in recent years, mainly because it offers a way of incorporating the influence of human decision-making on land use in a mechanistic, formal, and spatially explicit way, taking into account social interaction, adaptation, and decision-making at different levels. Specific advantages of agent-based models include their ability to model individual decision-making entities and their interactions, to incorporate social processes and non-monetary influences on decision-making, and to dynamically link social and environmental processes. A number of such models are now beginning to appear-it is timely, therefore, to review the uses to which agent-based land use models have been put so far, and to discuss some of the relevant lessons learnt, also drawing on those from other areas of simulation modelling, in relation to future applications. In this paper, we review applications of agent-based land use models under the headings of (a) policy analysis and planning, (b) participatory modelling, (c) explaining spatial patterns of land use or settlement, (d) testing social science concepts and (e) explaining land use functions. The greatest use of such models so far has been by the research community as tools for organising knowledge from empirical studies, and for exploring theoretical aspects of particular systems. However, there is a need to demonstrate that such models are able to solve problems in the real world better than traditional modelling approaches. It is concluded that in terms of decision support, agent-based land-use models are probably more useful as research tools to develop an underlying knowledge base which can then be developed together with end-users into simple rules-of-thumb, rather than as operational decision support tools

Agent-based land-use models: a review of applications

Agent-based modelling is an approach that has been receiving attention by the land use modelling community in recent years, mainly because it offers a way of incorporating the influence of human decision-making on land use in a mechanistic, formal, and spatially explicit way, taking into account social interaction, adaptation, and decision-making at different levels. Specific advantages of agent-based models include their ability to model individual decision-making entities and their interactions, to incorporate social processes and non-monetary influences on decision-making, and to dynamically link social and environmental processes. A number of such models are now beginning to appear-it is timely, therefore, to review the uses to which agent-based land use models have been put so far, and to discuss some of the relevant lessons learnt, also drawing on those from other areas of simulation modelling, in relation to future applications. In this paper, we review applications of agent-based land use models under the headings of (a) policy analysis and planning, (b) participatory modelling, (c) explaining spatial patterns of land use or settlement, (d) testing social science concepts and (e) explaining land use functions. The greatest use of such models so far has been by the research community as tools for organising knowledge from empirical studies, and for exploring theoretical aspects of particular systems. However, there is a need to demonstrate that such models are able to solve problems in the real world better than traditional modelling approaches. It is concluded that in terms of decision support, agent-based land-use models are probably more useful as research tools to develop an underlying knowledge base which can then be developed together with end-users into simple rules-of-thumb, rather than as operational decision support tools