Reviewing agent-based modelling of socio-ecosystems: a methodology for the analysis of climate change adaptation and sustainability

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. A vast body of literature on agent-based modelling (ABM) shows its potential to couple social and environmental models, to 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 publications which concretely apply this methodology to the study of climate change related issues. The analysis of the state of the art reported in this paper supports the idea that today ABM is an appropriate methodology for the bottom-up exploration of climate policies, especially because it can take into account adaptive behaviour and heterogeneity of the system's components.Review, Agent-Based Modelling, Socio-Ecosystems, Climate Change, Adaptation, Complexity.

Applied Computational Intelligence for finance and economics

This article introduces some relevant research works on computational intelligence applied to finance and economics. The objective is to offer an appropriate context and a starting point for those who are new to computational intelligence in finance and economics and to give an overview of the most recent works. A classification with five different main areas is presented. Those areas are related with different applications of the most modern computational intelligence techniques showing a new perspective for approaching finance and economics problems. Each research area is described with several works and applications. Finally, a review of the research works selected for this special issue is given.Publicad

Modelling Socially Intelligent Agents

The perspective of modelling agents rather than using them for a specificed purpose entails a difference in approach. In particular an emphasis on veracity as opposed to efficiency. An approach using evolving populations of mental models is described that goes some way to meet these concerns. It is then argued that social intelligence is not merely intelligence plus interaction but should allow for individual relationships to develop between agents. This means that, at least, agents must be able to distinguish, identify, model and address other agents, either individually or in groups. In other words that purely homogeneous interaction is insufficient. Two example models are described that illustrate these concerns, the second in detail where agents act and communicate socially, where this is determined by the evolution of their mental models. Finally some problems that arise in the interpretation of such simulations is discussed