2008,06: Evolutionary modelling in economics : a survey of methods and building blocks

In this paper we present an overview of methods and components of formal economic models employing evolutionary approaches. This compromises two levels: (1) techniques of evolutionary modelling, including multi-agent modelling, evolutionary algorithms and evolutionary game theory; (2) building blocks or components of formal models classified into core processes and features of evolutionary systems - diversity, innovation and selection - and additional elements, such as bounded rationality, diffusion, path dependency and lock-in, co-evolutionary dynamics, multilevel and group selection, and evolutionary growth. We focus our attention on the characteristics of models and techniques and their underlying assumptions. -- bounded rationality ; evolutionary algorithms ; evolutionary game theory ; evolutionary growth ; innovation ; multilevel evolution ; neo-Schumpeterian models

Computability and Evolutionary Complexity: Markets As Complex Adaptive Systems (CAS)

The purpose of this Feature is to critically examine and to contribute to the burgeoning multi disciplinary literature on markets as complex adaptive systems (CAS). Three economists, Robert Axtell, Steven Durlauf and Arthur Robson who have distinguished themselves as pioneers in different aspects of how the thesis of evolutionary complexity pertains to market environments have contributed to this special issue. Axtell is concerned about the procedural aspects of attaining market equilibria in a decentralized setting and argues that principles on the complexity of feasible computation should rule in or out widely held models such as the Walrasian one. Robson puts forward the hypothesis called the Red Queen principle, well known from evolutionary biology, as a possible explanation for the evolution of complexity itself. Durlauf examines some of the claims that have been made in the name of complex systems theory to see whether these present testable hypothesis for economic models. My overview aims to use the wider literature on complex systems to provide a conceptual framework within which to discuss the issues raised for Economics in the above contributions and elsewhere. In particular, some assessment will be made on the extent to which modern complex systems theory and its application to markets as CAS constitutes a paradigm shift from more mainstream economic analysis

An Artificial Stock Market with Interaction Network and Mimetic Agents

Agent-based artificial stock markets attracted much attention over the last years, and many models have been proposed. However, among them, few models take into account the social interactions and mimicking behaviour of traders, while the economic literature describes investors on financial markets as influenced by decisions of their peers and explains that this mimicking behaviour has a decisive impact on price dynamics and market stability. In this paper we propose a continuous double auction model of financial market, populated by heterogeneous traders who interact through a social network of influence. Traders use different investment strategies, namely: fundamentalists who make a decisions based on the fundamental value of assets; hybrids who are initially fundamentalists, but switch to a speculative strategy when they detect an uptrend in prices; noise traders who don’t have sufficient information to take rational decisions, and finally mimetic traders who imitate the decisions of their mentors on the interactions network. An experimental design is performed to show the feasibility and utility of the proposed model

From evolutionary ecosystem simulations to computational models of human behavior

We have a wide breadth of computational tools available today that enable a more ethical approach to the study of human cognition and behavior. We argue that the use of computer models to study evolving ecosystems provides a rich source of inspiration, as they enable the study of complex systems that change over time. Often employing a combination of genetic algorithms and agent-based models, these methods span theoretical approaches from games to complexification, nature-inspired methods from studies of self-replication to the evolution of eyes, and evolutionary ecosystems of humans, from entire economies to the effects of personalities in teamwork. The review of works provided here illustrates the power of evolutionary ecosystem simulations and how they enable new insights for researchers. They also demonstrate a novel methodology of hypothesis exploration: building a computational model that encapsulates a hypothesis of human cognition enables it to be tested under different conditions, with its predictions compared to real data to enable corroboration. Such computational models of human behavior provide us with virtual test labs in which unlimited experiments can be performed. This article is categorized under: Computer Science and Robotics > Artificial Intelligence

Market-Based Approach to Mobile Surveillance Systems

The active surveillance of public and private sites is increasingly becoming a very important and critical issue. It is, therefore, imperative to develop mobile surveillance systems to protect these sites. Modern surveillance systems encompass spatially distributed mobile and static sensors in order to provide effective monitoring of persistent and transient objects and events in a given area of interest (AOI). The realization of the potential of mobile surveillance requires the solution of different challenging problems such as task allocation, mobile sensor deployment, multisensor management, cooperative object detection and tracking, decentralized data fusion, and interoperability and accessibility of system nodes. This paper proposes a market-based approach that can be used to handle different problems of mobile surveillance systems. Task allocation and cooperative target tracking are studied using the proposed approach as two challenging problems of mobile surveillance systems. These challenges are addressed individually and collectively

Efeito Combinado das Estratégias e do Limit Order Book num Mercado Artificial

Mestrado em Gestão de Sistemas de InformaçãoO desenvolvimento de modelos de sociedades artificiais tem tido um papel importante no estudo do comportamento dos mercados financeiros. Ainda que o recurso aos dados empíricos seja a prática mais comum das abordagens computacionais aplicadas àqueles mercados, tem-se assistido uma cada vez mais frequente utilização de ambientes artificiais, quer em complemento, quer em alternativa às abordagens empíricas. Ao longo dos últimos anos, a generalidade dos ambientes artificiais tem recorrido ao desenvolvimento de Modelos Baseado em Agentes (Agent-based models - ABM), o qual consiste num sistema computacional onde é possível reproduzir o comportamento das entidades intervenientes no fenómeno a estudar, e as interacções dessas entidades entre si e com o ambiente em que se encontram. A reprodução dos referidos comportamentos tem em vista a confirmação de hipóteses teóricas e experimentais que contribuam para explicar o fenómeno estudado. Em linhas gerais, o tema proposto para este Trabalho de Fim de Mestrado é a criação de um ABM com o objectivo de avaliar o efeito da participação conjunta de dois diferentes tipos de comportamento. O primeiro consiste na existência de um conjunto de estratégias dos agentes individuais. O segundo restringe-se a existência de um Limit Order Book, ou seja, de um mecanismo de registo de pedidos de compra e de venda de acções disponíveis no mercado. Sabe-se que em Economia, a validação dos resultados conseguidos em ambientes artificiais é com frequência efectuada através da comparação dos referidos resultados com um conjunto de factos estilizados. Assim sendo, esta dissertação tem início com a apresentação dos principais factos estilizados dos mercados financeiros. De seguida passar-se-á ao enquadramento da simulação computacional de sociedades artificiais e à identificação dos aspectos fundamentais e específicos dos Modelos Baseados em Agentes. O terceiro capítulo apresenta as ideias fundamentais do Limit Order Book e dos comportamentos baseados em estratégias. A apresentação e a análise dos resultados incide prioritariamente sobre a avaliação de três diferentes cenários, onde: 1) Considera-se a existência de um mecanismos onde actuam estratégias individuais responsáveis pelas opções de compra e de venda de acções constituintes do mercado; 2) Considera-se apenas a existência de um mecanismo designado Limit Order Book (LOB) e 3)É considerado o efeito da participação conjunta dos dois diferentes mecanismos (estratégia e LOB). Em termos gerais, conclui-se que num mercado artificial com agentes inteligentes, a existência de um Limit Order Book tem um papel preponderante sobre a existência de um conjunto de estratégias, ou seja, sobre a inteligência dos agentes do mercado. Por fim, são indicadas as várias possíveis melhorias e ampliações do modelo apresentado, no sentido de o tornar mais completo, tanto sobre o ponto de vista das funcionalidades contempladas como do ponto de vista da sua versatilidade.Agent-based models are increasingly being used to model artificial societies of financial markets. Though the usage of empirical data is the most common practice of computational approaches applied to those markets, there has been an increasingly frequent use of artificial environments, either in addition or as an alternative to empirical approaches. Over the past years, the generality of artificial environments is being supported by Agent-Based Models - ABM, which are able to reproduce the behavior of the entities involved in a given phenomenon. Interactions take place among the model entities and between the entities and the environment. In so doing, ABM models allows to confirm some research questions and help explaining the phenomenon at hand. The topic of this work is the creation of an ABM in order to evaluate the effect of joint participation of two different types of behavior. The first is the existence of a set of strategies of individual agents. The second is restricted to the presence of a Limit Order Book - LOB. It is known that in economics, the validation of the results obtained in artificial environments is often carried out by comparing these results with a set of stylized facts. Therefore, this dissertation begins with the presentation of the main stylized facts of financial markets, followed by the framing of computer simulation of artificial societies and the identification of fundamental and specific aspects of ABM. The third chapter presents the basic ideas of the Limit Order Book and some behavior-based strategies. The presentation and analysis of results focuses primarily on the evaluation of three different scenarios: 1) We consider the existence of a mechanism where agent's individual strategies are used in deciding to buy, to sell or just to do nothing; 2) We consider only the existence of a simplified Limit Order Book, where the investor decisions are taken at random and the price dynamics is only carried out by the Limit Order Book, and 3) We consider the combined effect of the above (strategies and LOB), were the price dynamics continues being handled by the LOB but the buying/selling decisions are now defined by the agent strategies. Overall, we conclude that in an artificial market with intelligent agents, the presence of a Limit Order Book plays the leading role over the existence of a set of strategies, i.e., on the intelligence of the players. Finally, some possible enhancements and extensions to the model in order to make it more complete are presented