Catálogo de publicaciones - libros

The fact that selfish behavior may not achieve full efficiency at the aggregate level has been well known in the literature. Therefore we need to cope with the socio-economic system by attempting to stack the deck in such a way that individuals with selfish incentives have to do what is the desirable thing. Of particular interests is the question how social interactions among individuals can be restructured so that they are free to choose their actions while avoiding outcomes that none would have chosen. In this paper, we study the collective construction process of social norms and the emergence of collective intelligence of networked evolving agents. The wisdom of collective agents is interpreted as emergence of behavioral rules that constitute constraints on social interactions so that self-interested agents can achieve efficient and equitable outcomes.

In this paper, we use an agent based artificial stock market to explore the relations between the heterogeneity of investors behaviour and the aggregated behaviour of financial markets. In particular, we want to recover the main statistical features of the Spanish Stock Market, as the high levels of kurtosis, excess volatility, non normality of prices and returns, unit roots and volatility clustering.

We realise that we cannot catch up most of this features in a market populated only with fundamental investors, so we need to include more heterogeneity in agents behaviour. We include psychological investors who change their risk aversion following the ideas by Kahneman and Tversky (1979) and technical traders who buy or sell depending on crosses of moving averages. The main conclusion is that, in this particular artificial stock market, psychological investors are related to volatility clustering whereas technical trading has more to do with unit roots.

Financial markets are subject to long periods of polarized behavior, such as bull-market or bear-market phases, in which the vast majority of market participants seem to almost exclusively choose one action (between buying or selling) over the other. From the point of view of conventional economic theory, such events are thought to reflect the arrival of “external news” that justifies the observed behavior. However, empirical observations of the events leading up to such market phases, as well events occurring during the lifetime of such a phase, have often failed to find significant correlation between news from outside the market and the behavior of the agents comprising the market. In this paper, we explore the alternative hypothesis that the occurrence of such market polarizations are due to interactions amongst the agents in the market, and not due to any influence external to it. In particular, we present a model where the market (i.e., the aggregate behavior of all the agents) is observed to become polarized even though individual agents regularly change their actions (buy or sell) on a time-scale much shorter than that of the market polarization phase.

This paper illustrates the effects of global or local social influences upon binary choice. Analytical results are summarized and an ACE (Agent based Computational Economics) approach is used to investigate the corresponding mechanisms of interdependence in the case of a coordination problem and finite size effects.

This paper illustrates the effects of global or local social influences upon binary choice. Analytical results are summarized and an ACE (Agent based Computational Economics) approach is used to investigate the corresponding mechanisms of interdependence in the case of a coordination problem and finite size effects.

In this work we analyze the time evolution of the wealth of a group of agents in a public-investment-game scenario. These are part of a small-world network, where connections depend on a probability p and investment depends on a binary variable (motivation). This variable tries to emulate one’s perception of other players’ actions. We study the effect of the connectivity on the wealth of the group as well as the dynamics when idyosincratic types are introduced in the game.

The fact that selfish behavior may not achieve full efficiency at the aggregate level has been well known in the literature. Therefore we need to cope with the socio-economic system by attempting to stack the deck in such a way that individuals with selfish incentives have to do what is the desirable thing. Of particular interests is the question how social interactions among individuals can be restructured so that they are free to choose their actions while avoiding outcomes that none would have chosen. In this paper, we study the collective construction process of social norms and the emergence of collective intelligence of networked evolving agents. The wisdom of collective agents is interpreted as emergence of behavioral rules that constitute constraints on social interactions so that self-interested agents can achieve efficient and equitable outcomes.

This paper addresses the problem of finding the appropriate method for conducting empirical validation in AB models. We identify a first set of issues that are common to both AB and neoclassical modellers and a second set of issues which are specific to AB modellers. Then, we critically appraise the extent to which alternative approaches deal with these issues. In particular, we examine three important approaches to validation that have been developed in AB economics: indirect calibration, the Werker-Brenner approach, and the history-friendly approach. Finally, we discuss a set of open questions within empirical validation.

We provide explanations for the results of the Levy, Levy and Solomon model, a recent simulation model of financial markets. These explanations are based upon mathematical analysis of a dynamic model of a market with an arbitrary number of heterogeneous investors allocating their wealth between two assets. The investors’ choices are endogenously modeled in a general way and, in particular, consistent with the maximization of an expected utility. We characterize the equilibria of the model and their stability and discuss implications for the market return and agents’ survival. These implications are in agreement with the results of previous simulations. Thus, our analytic approach allows to explore the robustness of the previous analysis and to expand its spectrum.

In this paper, we use an agent based artificial stock market to explore the relations between the heterogeneity of investors behaviour and the aggregated behaviour of financial markets. In particular, we want to recover the main statistical features of the Spanish Stock Market, as the high levels of kurtosis, excess volatility, non normality of prices and returns, unit roots and volatility clustering.

We realise that we cannot catch up most of this features in a market populated only with fundamental investors, so we need to include more heterogeneity in agents behaviour. We include psychological investors who change their risk aversion following the ideas by Kahneman and Tversky (1979) and technical traders who buy or sell depending on crosses of moving averages. The main conclusion is that, in this particular artificial stock market, psychological investors are related to volatility clustering whereas technical trading has more to do with unit roots.