This thesis develops a new structure that explicitly combines two CGE models, a national and a regional, in an integrated structure that gives the thesis model the name IRES, in short for the Integrated Regional Equation System. The typical features of the integrated structure are the adding-up conditions and the two-way linkages between the national and the regional modules facilitated by the interface shifters. The adding-up conditions ensure the two modules produce consistent results and updated databases. The inclusion of the interface shifters on the one hand plays a role in ensuring compatibility of results of the two modules, i.e. no distortion occurs because technical or taste changes are transferred across modules. On the other hand, the interface shifters assist the operation of IRES in different modes: the model can be used as a top-down model, a bottom-up model or an integrated model where national and regional shocks can be introduced at the same time. Hence, IRES has more flexibility in its application than a regional model or a national model alone, as IRES can make use of availability of data at any levels in the economy. IRES has a new labour market in which regional migration is no longer the only factor that settles the labour market as in the original setting of the MMRF model. Regional unemployment and regional participation rates are modelled to response to changes in regional employment growth using elasticities estimated econometrically in this thesis. IRES implements historical patterns of regional migration so that results of regional migration are consistent with observed patterns. Altogether, regional migration, regional unemployment and participation rates determine the equilibrium of the labour market. IRES adopts new approaches to modelling margin demands and indirect taxes. These new approaches are very effective in reducing the size of IRES but they do not compromise the use of the model. These approaches are readily applicable to any other regional CGE models.

Cette thèse s'inscrit dans le cadre de systèmes complexes appliqués aux systèmes économiques. Dans cette thèse, un modèle multi-agent a été proposé, qui modélise le cycle de production. Il est consitué d'entreprises, ouvirers/foyers, et une banque, et repecte la conservation de la monnaie. Son hypothèse centrale est que les entreprises se basent sur une marge espérée pour déterminer leur production. Un scénario simple de ce modèle, ou les marges espérées sont homogènes, a été analysé dans le cadre de models de croissance stochastique. Les résultats sont la distribution de tailles d'entreprises rassemblant des lois de puissance, et leur distribution du taux de croissance de forme 'tente', ainsi qu'une dépendence de taille de la variance de la croissance. Ces résultats sont proches aux faits stylisés issus d'études empiriques. Dans un scénario plus complet, le modèle contient des caractéristiques supplémentaires: des marges espérées hétérogèges, ainsi que des paiements d'intérêts, la possibilité de faire faillite. Cela ramène le modèle aux modèles macro-économiques multi-agents. Les extensions sont décrites de façon théorique par des équations de replicateur. Les résultats nouveaux sont la distribution d'age d'entreprises actives, la distribution de leur taux de profit, la distribution de dette, des statistiques sur les faillites, et des cycles de vie caractéristiques. Tout ces résultats sont qualitativement en accord avec des résultats d'études empiriques de plusieurs pays.Le modèle proposé génère des résultats prometteurs, en respectant le principe que des résultats qui apparaissent simultanément peuvent soit etre générés par un même processus, soit par plusieurs aui qui sont compatibles. / The thesis is in the field of complex systems, applied to an economic system. In this thesis, an agent-based model has been proposed to model the production cycle. It comprises firms, workers, and a bank, and respects stock-flow consistency. Its central assumption is that firms plan their production based on an expected profit margin. A simple scenario of the model, where the expected profit margin is the same for all firms, has been analyzed in the context of simple stochastic growth models. Results are a firms' size distribution close to a power law, and tent-shaped growth rate distribution, and a growth rate variance scaling with firm size. These results are close to empirically found stylized facts. In a more comprehensive version, the model contains additional features: heterogeneous profits margins, as well as interest payments and the possibility of bankruptcy. This relates the model to agent-based macroeconomic models. The extensions are described theoretically theoretically with replicator dynamics. New results are the age distribution of active firms, their profit rate distribution, debt distribution, bankruptcy statistics, as well as typical life cycles of firms, which are all qualitatively in agreement with studies of firms databases of various countries.The proposed model yields promising results by respecting the principle that jointly found results may be generated by the same process, or by several ones which are compatible.

The first chapter analizes conditional assistance programs. They generate conflicting relationships between international financial institutions (IFIs) and member countries. The experience of IFIs with conditionality in the 1990s led them to allow countries more latitude in the design of their reform programs. A reformist government does not need conditionality and it is useless if it does not want to reform. A government that faces opposition may use conditionality and the help of pro-reform lobbies as a lever to counteract anti-reform groups and succeed in implementing reforms. The second chapter analizes economies saddled with taxes and regulations. I consider an economy in which many taxes, subsidies, and other distortionary restrictions are in place simultaneously. If I start from an inefficient laissez-faire equilibrium because of some domestic distortion, a small trade tax or subsidy can yield a first-order welfare improvement, even if the instrument itself creates distortions of its own. This may result in "welfare paradoxes". The purpose of the chapter is to quantify the welfare effects of changes in tax rates in a small open economy. I conduct the simulation in the context of an intertemporal utility maximization framework. I apply numerical methods to the model developed by Karayalcin. I introduce changes in the tax rates and quantify both the impact on welfare, consumption and foreign assets, and the path to the new steady-state values. The third chapter studies the role of stock markets and adjustment costs in the international transmission of supply shocks. The analysis of the transmission of a positive supply shock that originates in one of the countries shows that on impact the shock leads to an inmediate stock market boom enjoying the technological advance, while the other country suffers from depress stock market prices as demand for its equity declines. A period of adjustment begins culminating in a steady state capital and output level that is identical to the one before the shock. The the capital stock of one country undergoes a non-monotonic adjustment. The model is tested with plausible values of the variables and the numeric results confirm the predictions of the theory.

This study examines the claim that individuals misinterpret the mean of a dataset (displayed as a scatterplot) more when the convex hull of the dataset is less representative of the data. In addition, this study also tests whether outliers in the data can predict the magnitude of error that individuals make in interpreting the mean of the dataset. Lastly, the study investigates whether individuals’ interpretations are predicted better by the mean of the convex hull than by the full dataset’s mean. The method used to conduct these investigations is through a survey, followed by several linear regression analyses. Applications of this study include improving the communication of data in economic policy and business contexts, along with broader applications in extending models that heavily rely on agents’ interpretations of information: especially bounded rationality and social norm-based models. The results show that convex hull unrepresentativeness correlates positively with error in mean interpretation; however, that the convex hull mean is not predictive of the interpretations’ direction. Overall, the study contributes to the field of visual information interpretation by investigating the effect of data structure on its interpretation – an unexplored area of research. This is done while initiating the concretization of bounded rationality in economics, by exploring the idea that individuals perceive a general shape of the information presented to them rather than a detailed, full picture. This can lead to misinterpretations whenever the general shape (convex hull) is not representative of the dataset.

The existence of sunspots or sunspot equilibria has been debated for several decades on its influence in the field of Economics. While models of sunspots or sunspot equilibria have fitted well for some subsets of empirical features, it comes at a cost of moving further away from economic believability and robustness. Studies on the theoretical plausibility of sunspot equilibria have been addressed extensively in several different economic models, but exist almost entirely within the framework of the homogeneous rational expectations equilibrium devised of representative agents. This framework shapes later arising learning approaches to sunspot equilibria. These models have proposed various ways of learning, but they deal mainly with the learning of representative agents. Models of adaptive learning with heterogeneous agents, however, enable us to explicitly tackle coordination issues, such as the coordination mechanism of expectations. This is certainly desirable since sunspots are often used as a coordination device of expectations. In this dissertation, we continue this line of research, investigating the plausibility of sunspot equilibria in stock markets within the framework of heterogeneous agents and the dynamic relationship between sunspot variables and stock returns. We adopt an Agent-based Computational Approach, now known as Agent-based Computational Economics or ACE, to study the plausibility of sunspot equilibria. More specifically, we deal with this issue in the context of an Agent-based Artificial Stock Market (AASM). We contemplate AASMs to be highly suitable to the issue we examine here. Currently, none of the theoretical, empirical, experimental, or simulation models of sunspot equilibria directly capture sunspots within a stock market composed of heterogeneous agents. We conducted three series of experiments to examine this issue. From the results of these three series of simulations, we observed that sunspot variables generally do not have influence on market dynamics. This indicates that sunspot variables remain largely exogenous to the system. Furthermore, we traced the evolution of agents' beliefs and examined their consistency with the observed aggregate market behavior. Additionally, this dissertation takes the advantage of and investigates the micro-macro relationship within the market. We argue that a full understanding of the dynamic linkage between sunspot variables and stock returns cannot be accomplished unless the feedback relationship between individual behaviors, at the micro view, and aggregate phenomena, at the macro view, is well understood / The existence of sunspots or sunspot equilibria has been debated for several decades on its influence in the field of Economics. While models of sunspots or sunspot equilibria have fitted well for some subsets of empirical features, it comes at a cost of moving further away from economic believability and robustness. Studies on the theoretical plausibility of sunspot equilibria have been addressed extensively in several different economic models, but exist almost entirely within the framework of the homogeneous rational expectations equilibrium devised of representative agents. This framework shapes later arising learning approaches to sunspot equilibria. These models have proposed various ways of learning, but they deal mainly with the learning of representative agents. Models of adaptive learning with heterogeneous agents, however, enable us to explicitly tackle coordination issues, such as the coordination mechanism of expectations. This is certainly desirable since sunspots are often used as a coordination device of expectations. In this dissertation, we continue this line of research, investigating the plausibility of sunspot equilibria in stock markets within the framework of heterogeneous agents and the dynamic relationship between sunspot variables and stock returns. We adopt an Agent-based Computational Approach, now known as Agent-based Computational Economics or ACE, to study the plausibility of sunspot equilibria. More specifically, we deal with this issue in the context of an Agent-based Artificial Stock Market (AASM). We contemplate AASMs to be highly suitable to the issue we examine here. Currently, none of the theoretical, empirical, experimental, or simulation models of sunspot equilibria directly capture sunspots within a stock market composed of heterogeneous agents. We conducted three series of experiments to examine this issue. From the results of these three series of simulations, we observed that sunspot variables generally do not have influence on market dynamics. This indicates that sunspot variables remain largely exogenous to the system. Furthermore, we traced the evolution of agents' beliefs and examined their consistency with the observed aggregate market behavior. Additionally, this dissertation takes the advantage of and investigates the micro-macro relationship within the market. We argue that a full understanding of the dynamic linkage between sunspot variables and stock returns cannot be accomplished unless the feedback relationship between individual behaviors, at the micro view, and aggregate phenomena, at the macro view, is well understood.

I have developed a multiagent system platform that provides a valuable complement to the alternative research methods. The platform facilitates the development of heterogeneous agents in complex environments. The first application of the multiagent system is to the study of sealed-bid auctions with two-dimensional value signals from pure private to pure common value. I find that several auction outcomes are significantly nonlinear across the two-dimensional value signals. As the common value percent increases, profit, revenue, and efficiency all decrease monotonically, but they decrease in different ways. Finally, I find that forcing revelation by the auction winner of the true common value may have beneficial revenue effects when the common-value percent is high and there is a high degree of uncertainty about the common value. The second application of the multiagent system is to the study of English auctions with two-dimensional value signals using agents that learn a signal-averaging factor. I find that signal averaging increases nonlinearly as the common value percent increases, decreases with the number of bidders, and decreases at high common value percents when the common value signal is more uncertain. Using signal averaging, agents increase their profit when the value is more uncertain. The most obvious effect of signal averaging is on reducing the percentage of auctions won by bidders with the highest common value signal. The third application of the multiagent system is to the study of the optimal payment rule in Treasury auctions using Canadian rules. The model encompasses the when-issued, auction, and secondary markets, as well as constraints for primary dealers. I find that the Spanish payment rule is revenue inferior to the Discriminatory payment rule across all market price spreads, but the Average rule is revenue superior. For most market-price spreads, Uniform payment results in less revenue than Discriminatory, but there are many cases in which Vickrey payment produces more revenue.

I have developed a multiagent system platform that provides a valuable complement to the alternative research methods. The platform facilitates the development of heterogeneous agents in complex environments. The first application of the multiagent system is to the study of sealed-bid auctions with two-dimensional value signals from pure private to pure common value. I find that several auction outcomes are significantly nonlinear across the two-dimensional value signals. As the common value percent increases, profit, revenue, and efficiency all decrease monotonically, but they decrease in different ways. Finally, I find that forcing revelation by the auction winner of the true common value may have beneficial revenue effects when the common-value percent is high and there is a high degree of uncertainty about the common value. The second application of the multiagent system is to the study of English auctions with two-dimensional value signals using agents that learn a signal-averaging factor. I find that signal averaging increases nonlinearly as the common value percent increases, decreases with the number of bidders, and decreases at high common value percents when the common value signal is more uncertain. Using signal averaging, agents increase their profit when the value is more uncertain. The most obvious effect of signal averaging is on reducing the percentage of auctions won by bidders with the highest common value signal. The third application of the multiagent system is to the study of the optimal payment rule in Treasury auctions using Canadian rules. The model encompasses the when-issued, auction, and secondary markets, as well as constraints for primary dealers. I find that the Spanish payment rule is revenue inferior to the Discriminatory payment rule across all market price spreads, but the Average rule is revenue superior. For most market-price spreads, Uniform payment results in less revenue than Discriminatory, but there are many cases in which Vickrey payment produces more revenue.

I have developed a multiagent system platform that provides a valuable complement to the alternative research methods. The platform facilitates the development of heterogeneous agents in complex environments. The first application of the multiagent system is to the study of sealed-bid auctions with two-dimensional value signals from pure private to pure common value. I find that several auction outcomes are significantly nonlinear across the two-dimensional value signals. As the common value percent increases, profit, revenue, and efficiency all decrease monotonically, but they decrease in different ways. Finally, I find that forcing revelation by the auction winner of the true common value may have beneficial revenue effects when the common-value percent is high and there is a high degree of uncertainty about the common value. The second application of the multiagent system is to the study of English auctions with two-dimensional value signals using agents that learn a signal-averaging factor. I find that signal averaging increases nonlinearly as the common value percent increases, decreases with the number of bidders, and decreases at high common value percents when the common value signal is more uncertain. Using signal averaging, agents increase their profit when the value is more uncertain. The most obvious effect of signal averaging is on reducing the percentage of auctions won by bidders with the highest common value signal. The third application of the multiagent system is to the study of the optimal payment rule in Treasury auctions using Canadian rules. The model encompasses the when-issued, auction, and secondary markets, as well as constraints for primary dealers. I find that the Spanish payment rule is revenue inferior to the Discriminatory payment rule across all market price spreads, but the Average rule is revenue superior. For most market-price spreads, Uniform payment results in less revenue than Discriminatory, but there are many cases in which Vickrey payment produces more revenue.

Orientador: Fernando Antônio Campos Gomide / Tese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de Computação / Made available in DSpace on 2018-08-16T03:39:08Z (GMT). No. of bitstreams: 1 Walter_IgorAlexandre_D.pdf: 1762436 bytes, checksum: 257485271a6580f86b0b466799ceff14 (MD5) Previous issue date: 2010 / Resumo: Sugerimos uma abordagem evolutiva para o projeto de estratégias de interação em sistemas multiagentes, especialmente estratégias de oferta modeladas como sistemas baseados em regras nebulosas. O objetivo é a aprendizagem das estratégias de oferta em leilões em modelos em que a base de conhecimento sofre evolução para melhorar o desempenho dos agentes atuando em um ambiente competitivo. Dados para aprendizagem e otimização das estratégias são raros em ambientes competitivos como os leilões. Introduzimos um modelo de sistema genético fuzzy (GFS) cujos operadores genéticos utilizam uma representação de tamanho variável do cromossomo e uma relação hierárquica estabelecida através do fitness dos indivíduos, em um esquema que explora e explota o espaço de busca ao longo das gerações. A evolução de estratégias de interação permite a descoberta de comportamentos dos agentes previamente desconhecidos e inesperados, permitindouma análise mais rica dos mecanismos de negociação e seu papel como protocolo de coordenação. A aplicação da abordagem proposta no mercado de energia elétrica permite a simulação destes mercados através da evolução de estratégias de oferta (bidding) em leilões de energia. A reestruturação destes mercados nas economias contemporâneas apresenta novos desafios e oportunidades, uma vez que não há consenso sobre qual seria sua melhor organização. A evolução da estrutura organizacional destes mercados salienta a falta de discernimento sobre as principais questões a serem analisadas e levadas em consideração. Argumenta-se que a abordagem econômica neoclássica se mostra limitada na análise dos efeitos da reestruturação e no estudo do comportamento dos agentes econômicos competindo nos mercados de energia elétrica reestruturados. Apresentamos uma arquitetura computacional inspirada na Economia Computacional baseada em Agentes que permite a modelagem, estudo e simulação destes mercados. Aplicamos ferramentas de inteligência computacional adequadas à concepção dos agentes participantes nos mercados de energia e que podem ser estendidas a outros mecanismos de mercado e negociação. Os mercados de energia elétrica são sistemas complexos habitados por agentes econômicos com interesse próprio que interagem entre si. Concluímos que é natural modelar e simular estes mercados como sistemas multiagentes. A evolução de estratégias de oferta permite a descoberta de comportamentos que auxiliam na tomada de decisão de um participante e na avaliação do mecanismo de negociação por parte de seus projetistas / Abstract: We suggest an evolutionary approach to design interaction strategies for multiagent systems, focusing on strategies modeled as fuzzy rule-based systems. The aim is to learn models represented by evolving knowledge bases to achieve agents' performance improvement when playing in a competitive environment. In competitive situations data for learning and tuning are rare and rule bases must jointly evolve with the databases. We introduce an evolutionary algorithm whose operators use variable length chromosome, a hierarchical relationship among individuals through fitness, and a scheme that successively explores and exploits the search space along generations. Evolution of interaction strategies uncovers unknown and unexpected agent behaviors and allows a richer analysis of negotiation mechanisms and their role as a coordination protocol. An application concerning an electricity market illustrates the effectiveness of the approach and allows to simulate the market through evolutionary bidding strategies. The restructuring process of power markets raises new challenges and opportunities, since there is no consensual market architecture. The evolution of the power industry organization shows a lack of insight about the issues to be addressed and taken into account. Several authors have considered the available tools based on the neoclassical economics theory a limited approach to analyze the effects of the industry restructuring and to study economical agents behavior participating in a restructured electricity market. We present Artificial Economy Multiagent System (AEMAS), a computational architecture inspired on Agent-based Computational Economics (ACE) that allows to model, study and simulate a power market. We apply Computational Intelligence tools to conceive the market agents that we expect could be extended to other negotiation environments. A power market is a complex system populated by self interested economical agents that interact. We conclude that it is feasible to model and simulate these markets on a multiagent system based approach. The evolution of bidding strategies allows to uncover new and unexpected behaviors that help to address the negotiation mechanism analysis by its designers and to support a market player decision process / Doutorado / Engenharia de Computação / Doutor em Engenharia Elétrica

我們把經濟體視為一個複雜適應系統(complex adaptive system), 強調系統中異質性(heterogeneous)agent的學習適應行為與agent之間的互動性交互作用, 此時主流經濟學裡的分析架構, 如:代表性個人模型(represesentive agent model)、理性預期(rational expectation)、固定點均衡分析(fixed-point equilibrium analysis)等將不再適用, 取而代之的是演化經濟學(evolutionary economics)的研究典範, 這樣的研究架構下, 並沒有適當的數學分析工具可資運用, 因此我們改以agent-based建模(agent-based modelng)的社會模擬(social simulation)來建構一個人工的經濟體(artificial economy), 以此為主要研究方法, 這就是agent-based計算經濟學(agent-based computational economics)或稱人工經濟生命(artificial economic life)。 本文中以股票市場為主要的研究課題, 我們以遺傳規劃(genetic programming)的人工智慧(artificial intelligence)方法來模擬股市中有限理性(bounded rational)異質交易者的交易策略學習行為, 建構出一個人工股票市場(artificial stock market), 在這樣的架構下, 我們成功地產生出類似真實股票市場的股價時間序列特性, 我們同時也檢定了人工股票市場中價量的因果關係, 說明了在沒有外生因素之下, 人工股票市場的複雜系統可自發地產生出雙向的價量因果關係, 進一步地, 我們研究下層agent(交易者)行為與上層股價時間序列行為的關聯性, 我們也發現個體的行為並不能直接加總或推論出複雜適應系統的總體行為, 這就是突現性質(emergent property)的發生, 最後, 本文描述了agent-based計算經濟學研究架構的優勢與缺點, 再附帶介紹一個用以進行agent-based建模相關研究的軟體程式庫-SWARM。