Catálogo de publicaciones - libros

No matter how commonly the term has been used in economics, a concrete analytical or computational model of innovation is not yet available. This paper argues that a breakthrough can be made with , and proposes a functional-modularity approach to an agent-based computational economic model of innovation.

We use case-based decision theory to evaluate the repeated choices of individuals who are using the experience of a selected set of other individuals over a given time horizon as the basis for their own decisions. It is observed that for certain parameter constellations a large fraction of individuals make identical decisions over a long period of time, which is not implied by the information. The result of this behavior is herding, which, however, has its origin in the way individuals process information rather than a desire to be part of the herd and imitating the behavior of others.

A general theory of cultural evolution is formulated using a cognitive dimension reduction scheme. Rational but cognitively limited agents iteratively invent and redefine abstract concepts in order to best represent their natural and social environment. These concepts are used for decision making and determine the agents’ overall behavior. The collection of concepts an agent uses constitutes his/her cultural profile. As the importance of social interactions increase and/or agents become more intelligent we find a series of dynamical phase transitions by which the coherence of concepts advances in the society. Our model explains the so-called “cultural explosion” in human evolution 50,000 years ago as a spontaneous ordering phenomenon of the individual mental representations.

Current auction theory relies crucially on the assumption that all bidders bid homogeneously according to their Nash Equilibrium bidding strategies. However, it remains silent on whether and how a priori heterogeneous bidders arrive at the NE. This paper investigates computationally whether evolutionary learning in repeated auctions could justify the assumption of NE bidding. Simulations show that Best Response learning of a priori heterogeneous bidders in first-price auctions does not converge to the NE. Instead, bidders involve in permanent mutual adaptation that shows non trivial characteristics.

In this paper, we studied a matching process which arises when there is a cost to collect information about agents in the labour market. We found that its implication is consistent with data in many ways including the behaviour on the plane. The main reason that the behaviour on the plane becomes not trivial is that the it plots two variables. It is considered that wage rate under the existence of labour market friction becomes a function of the ratio. Since a shock does not reflect immediately to the ratio, wage rate responds slowly. It deteriorates the adjustment power of wage rate between imbalance of labour supply and demand.

In this paper, the statistical properties of high-frequency data are investigated by means of computational experiments performed with the Genoa Artificial Stock Market (Raberto et al. 2001, 2003, 2004). In the market model, heterogeneous agents trade one risky asset in exchange for cash. Agents have zero intelligence and issue random limit or market orders depending on their budget constraints. The price is cleared by means of a limit order book. The order generation is modelled with a renewal process where the distribution of waiting times between two consecutive orders is a Weibull distribution. This hypothesis is based on recent empirical investigation made on high-frequency financial data (Mainardi et al. 2000, Raberto et al. 2002, Scalas et al. 2003). We investigate how the statistical properties of prices and of waiting times between transactions are affected by the particular renewal process chosen for orders. Results point out that the mechanism of the limit order book is able to recover fat tails in the distribution of price returns without ad-hoc behavioral assumptions regarding agents; moreover, the kurtosis of the return distribution depends also on the renewal process chosen for orders. As regarding the renewal process underlying trades, in the case of exponentially distributed order waiting times, also trade waiting times are exponentially distributed. Conversely, if order waiting times follow a Weibull, the same does not hold for trade waiting times.

We study Complex Adaptive Systems in which information is shared locally within a heterogeneous population of adaptive nodes (‘agents’) competing for a limited global resource. The emerging collective behaviour is found to depend strongly on the level of available resource, the connectivity between agents and the accuracy of information transmission.

In this paper, we constructed three types of agents, which are different in efficiency and accuracy of learning. They were compared using acquired payoff in a game-theoretic situation that is called Minority game. As a result, different types of learning methods got the highest payoff according to the complexity of environmental change and learning speed.

In this paper we analyze a simple adaptive model of competition called the Minority Game, which is used in analyzing competitive phenomena such as the operation of the market economy. The Minority Game is played by many simple autonomous agents, which develop collective self-organization as a result of simple behavioral rules. Many algorithms that produce the desired behavior in the game have been proposed. In all work to date, however, the focus has been on the macroscopic behavior of the agents as a whole. We focus on the behavior of individual agents, paying particular attention to the original form of the Minority Game. We suggest that the core elements responsible for the development of self-organization are (i) rules that place a good constraint on the behaviors of individual agents and (ii) the existence of rules that lead to effective indirect coordination. We also show that when efficient organization is formed, a power-law can be seen among behavior of individual agents.

Probability Collectives (PC) provides the information-theoretic extension of conventional full-rationality game theory to bounded rational games. Here an explicit solution to the equations giving the bounded rationality equilibrium of a game is presented. Then PC is used to investigate games in which the players use bounded rational best-response strategies. Next it is shown that in the continuumtime limit, bounded rational best response games result in a variant of the replicator dynamics of evolutionary game theory. It is then shown that for team (shared-payoff) games, this variant of replicator dynamics is identical to Newton-Raphson iterative optimization of the shared utility function.