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

Organization, learning and cooperation.

This paper models the organization of the firm as a type of artificial neural network in a duopoly setting. The firm plays a repeated Prisoner’s Dilemma type game, and must also learn to map environmental signals to demand parameters and to its rival’s willingness to cooperate. We study the prospects for cooperation given the need for the firm to learn the environment and its rival’s output. We show how profit and cooperation rates are affected by the sizes of both firms, their willingness to cooperate, and by environmental complexity. In addition, we investigate equilibrium firm size and cooperation rates.Artificial neural networks;Prisoner’s Dilemma;Cooperation;Firm learning;

Are agent-based simulations robust? The wholesale electricity trading case

Agent-based computational economics is becoming widely used in practice. This paper explores the consistency of some of its standard techniques. We focus in particular on prevailing wholesale electricity trading simulation methods. We include different supply and demand representations and propose the Experience-Weighted Attractions method to include several behavioural algorithms. We compare the results across assumptions and to economic theory predictions. The match is good under best-response and reinforcement learning but not under fictitious play. The simulations perform well under flat and upward-slopping supply bidding, and also for plausible demand elasticity assumptions. Learning is influenced by the number of bids per plant and the initial conditions. The overall conclusion is that agent-based simulation assumptions are far from innocuous. We link their performance to underlying features, and identify those that are better suited to model wholesale electricity markets.Agent-based computational economics, electricity, market design, experience-weighted attraction (EWA), learning, supply functions, demand aggregation, initial beliefs.

The Spatial Agent-based Competition Model (SpAbCoM)

The paper presents a detailed documentation of the underlying concepts and methods of the Spatial Agent-based Competition Model (SpAbCoM). For instance, SpAbCoM is used to study firms' choices of spatial pricing policy (GRAUBNER et al., 2011a) or pricing and location under a framework of multi-firm spatial competition and two-dimensional markets (GRAUBNER et al., 2011b). While the simulation model is briefly introduced by means of relevant examples within the corresponding papers, the present paper serves two objectives. First, it presents a detailed discussion of the computational concepts that are used, particularly with respect to genetic algorithms (GAs). Second, it documents SpAbCoM and provides an overview of the structure of the simulation model and its dynamics. -- Das vorliegende Papier dokumentiert die zugrundeliegenden Konzepte und Methoden des Räumlichen Agenten-basierten Wettbewerbsmodells (Spatial Agent-based Competition Model) SpAbCoM. Anwendungsbeispiele dieses Simulationsmodells untersuchen die Entscheidung bezüglich der räumlichen Preisstrategie von Unternehmen (GRAUBNER et al., 2011a) oder Preissetzung und Standortwahl im Rahmen eines räumlichen Wettbewerbsmodells, welches mehr als einen Wettbewerber und zweidimensionalen Marktgebiete berücksichtigt. Während das Simulationsmodell in den jeweiligen Arbeiten kurz anhand eines Beispiels eingeführt wird, dient das vorliegende Papier zwei Zielen. Zum Einen sollen die verwendeten computergestützten Konzepte, hier speziell Genetische Algorithmen (GA), detailliert vorgestellt werden. Zum Anderen besteht die Absicht dieser Dokumentation darin, einen Überblick über die Struktur von SpAbCoM und die während einer Simulation ablaufenden Prozesse zu gegeben.Agent-based modelling,genetic algorithms,spatial pricing,location model.,Agent-basierte Modellierung,Genetische Algorithmen,räumliche Preissetzung,Standortmodell.

Organization, Learning and Cooperation

We model the organization of the firm as a type of artificial neural network in a duopoly framework. The firm plays a repeated Prisoner's Dilemma type game, but also must learn to map environmental signals to demand parameters. We study the prospects for cooperation given the need for the firm to learn the environment and its rival's output. We show how a firm's profit and cooperation rates are affected by its size, its rival's size and willingness to cooperate and environmental complexity.Artificial Neural Networks, Cooperation, Firm Learning

Herbert Scarf: a Distinguished American Economist

Herbert Eli Scarf (born on July 25, 1930 in Philadelphia, PA) is a distinguished American economist and Sterling Professor (Emeritus as of 2010) of Economics at Yale University. He is a member of the American Academy of Arts and Sciences, the National Academy of Sciences and the American Philosophical Society. He served as the president of the Econometric Society in 1983. He received both the Frederick Lanchester Award in 1973 and the John von Neumann Medal in 1983 from the Operations Research Society of America and was elected as a Distinguished Fellow of the American Economic Association in 1991.

Spatial Pricing and the Location of Processors in Agricultural Markets

Spatially dispersed production and processing, endemic for most agricultural or renewable resource markets, causes oligopsonistic competition. The possibility and use of spatial price discrimination in these markets is well documented. It is also well known that the location of processors relative to competitors crucially affects the intensity of competition. However, insights regarding the relation between spatial price discrimination and the spatial differentiation of firms are barely present because the simultaneous investigation of these issues is often intractable analytically. We use computational economics to study these problems under a general theoretical framework. For instance, we show whether and under which conditions firms choose to differentiate their locations and/or price strategies. Results are consistent with observations in agricultural markets.spatial price competition, spatial differentiation, price discrimination, computational economics, Agribusiness,

Network models of innovation and knowledge diffusion

Much of modern micro-economics is built from the starting point of the perfectly competitive market. In this model there are an infinite number of agents — buyers and sellers, none of whom has the power to influence the price by his actions. The good is well-defined, indeed it is perfectly standardized. And any interactions agents have is mediated by the market. That is, all transactions are anonymous, in the sense that the identities of buyer and seller are unimportant. Effectively, the seller sells “to the market” and the buyer buys “from the market”. This follows from the standardization of the good, and the fact that the market imposes a very strong discipline on prices. Implicit here is one (or both) of two assumptions. Either all agents are identical in every relevant respect, apart, possibly, from the prices they ask or offer; or every agent knows every relevant detail about every other agent. If the former, then obviously my only concern as a buyer is the prices asked by the population of sellers since in every other way they are identical. If the latter, then each seller has a unique good, and again what I am concerned with is the price of it. In either case, we see that prices capture all relevant information and are enough for every agent to make all the decisions he needs to make....economics of technology ;