11,889 research outputs found
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
A modular modelling framework for hypotheses testing in the simulation of urbanisation
In this paper, we present a modelling experiment developed to study systems
of cities and processes of urbanisation in large territories over long time
spans. Building on geographical theories of urban evolution, we rely on
agent-based models to 1/ formalise complementary and alternative hypotheses of
urbanisation and 2/ explore their ability to simulate observed patterns in a
virtual laboratory. The paper is therefore divided into two sections : an
overview of the mechanisms implemented to represent competing hypotheses used
to simulate urban evolution; and an evaluation of the resulting model
structures in their ability to simulate - efficiently and parsimoniously - a
system of cities (the Former Soviet Union) over several periods of time (before
and after the crash of the USSR). We do so using a modular framework of
model-building and evolutionary algorithms for the calibration of several model
structures. This project aims at tackling equifinality in systems dynamics by
confronting different mechanisms with similar evaluation criteria. It enables
the identification of the best-performing models with respect to the chosen
criteria by scanning automatically the parameter along with the space of model
structures (as combinations of modelled dynamics).Comment: 21 pages, 3 figures, working pape
Integrated urban evolutionary modeling
Cellular automata models have proved rather popular as frameworks for simulating the physical growth of cities. Yet their brief history has been marked by a lack of application to real policy contexts, notwithstanding their obvious relevance to topical problems such as urban sprawl. Traditional urban models which emphasize transportation and demography continue to prevail despite their limitations in simulating realistic urban dynamics. To make progress, it is necessary to link CA models to these more traditional forms, focusing on the explicit simulation of the socio-economic attributes of land use activities as well as spatial interaction. There are several ways of tackling this but all are based on integration using various forms of strong and loose coupling which enable generically different models to be connected. Such integration covers many different features of urban simulation from data and software integration to internet operation, from interposing demand with the supply of urban land to enabling growth, location, and distributive mechanisms within such models to be reconciled. Here we will focus on developin
Can geocomputation save urban simulation? Throw some agents into the mixture, simmer and wait ...
There are indications that the current generation of simulation models in practical,
operational uses has reached the limits of its usefulness under existing specifications.
The relative stasis in operational urban modeling contrasts with simulation efforts in
other disciplines, where techniques, theories, and ideas drawn from computation and
complexity studies are revitalizing the ways in which we conceptualize, understand,
and model real-world phenomena. Many of these concepts and methodologies are
applicable to operational urban systems simulation. Indeed, in many cases, ideas from
computation and complexity studies—often clustered under the collective term of
geocomputation, as they apply to geography—are ideally suited to the simulation of
urban dynamics. However, there exist several obstructions to their successful use in
operational urban geographic simulation, particularly as regards the capacity of these
methodologies to handle top-down dynamics in urban systems.
This paper presents a framework for developing a hybrid model for urban geographic
simulation and discusses some of the imposing barriers against innovation in this
field. The framework infuses approaches derived from geocomputation and
complexity with standard techniques that have been tried and tested in operational
land-use and transport simulation. Macro-scale dynamics that operate from the topdown
are handled by traditional land-use and transport models, while micro-scale
dynamics that work from the bottom-up are delegated to agent-based models and
cellular automata. The two methodologies are fused in a modular fashion using a
system of feedback mechanisms. As a proof-of-concept exercise, a micro-model of
residential location has been developed with a view to hybridization. The model
mixes cellular automata and multi-agent approaches and is formulated so as to
interface with meso-models at a higher scale
Gossip, Sexual Recombination and the El Farol Bar: modelling the emergence of heterogeneity
Brian Arthur's `El Farol Bar' model is extended so that the agents also learn and communicate. The learning and communication is implemented using an evolutionary process acting upon a population of mental models inside each agent. The evolutionary process is based on a Genetic Programming algorithm. Each gene is composed of two tree-structures: one to control its action and one to determine its communication. A detailed case-study from the simulations show how the agents have differentiated so that by the end of the run they had taken on very different roles. Thus the introduction of a flexible learning process and an expressive internal representation has allowed the emergence of heterogeneity
Diffusion processes in demographic transitions: a prospect on using multi agent simulation to explore the role of cognitive strategies and social interactions
Multi agent simulation (MAS) is a tool that can be used to explore the dynamics of different systems. Considering that many demographic phenomena have roots in individual choice behaviour and social interactions it is important that this behaviour is being translated in agent rules. Several behaviour theories are relevant in this context, and hence there is a necessity of using a meta-theory of behaviour as a framework for the development of agent rules. The consumat approach provides a basis for such a framework, as is demonstrated with a discussion of modelling the diffusion of contraceptives. These diffusion processes are strongly influenced by social processes and cognitive strategies. Different possible research lines are discussed which might be addressed with a multi-agent approach like the consumats.
Modelling and trading the Greek stock market with gene expression and genetic programing algorithms
This paper presents an application of the gene expression programming (GEP) and integrated genetic programming (GP) algorithms to the modelling of ASE 20 Greek index. GEP and GP are robust evolutionary algorithms that evolve computer programs in the form of mathematical expressions, decision trees or logical expressions. The results indicate that GEP and GP produce significant trading performance when applied to ASE 20 and outperform the well-known existing methods. The trading performance of the derived models is further enhanced by applying a leverage filter
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
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