Catálogo de publicaciones - libros

The paper analyzes the dynamics in a model with heterogeneous agents trading in simple markets under different trading protocols. Starting with the analytically tractable model of [4], we build a simulation platform with the aim to investigate the impact of the trading rules on the agents’ ecology and aggregate time series properties. The key feature of the model is the presence of a finite set of simple beliefs which agents choose each time step according to a fitness measure. The price is determined endogenously and our focus is on the role of the assumption about the market architecture. Analyzing dynamics under such different trading protocols as the Walrasian auction, the batch auction and the ‘order-book’ mechanism, we find that the resulting time series are similar to those originating from the noisy version of the model [4]. We distinguish the randomness caused by a finite number of agents and the randomness induced by an order-based mechanisms and analyze their impact on the model dynamics.

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.

We analyze with a bottom-up approach the competition between artificial intelligent agents in Continuous Double Auction markets in terms of allocative efficiency, price convergence and emergence or not of Nash equilibriums. In previous works agents have a fixed bidding strategy during the auction, usually under symmetric environments. In our simulations we allow the soft-agents to learn not only about how much they should bid or offer, but also about possible switching between the alternative strategies. We examine the behaviour of strategic traders under general supply and demand schedules (asymmetric environments) thus extending previous results.

The results clarify the limitations and the scope of Gode and Sunder conjecture and related recent works, and show the emergence of Hayeks and A. Smith endogenous order. Institutions matter and so does intelligence as far as the rate of convergence and agents surplus is concerned. These results are of importance in the design and performance of auctions in the real world and in the applications of auction theory to many problems in management and production, far beyond market design (market oriented programming).

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.

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.

Agent-based simulations show that some kinds of speculators are able to stabilize the price in a market and to make this market more efficient. Instead of a single market, we consider a supply chain comprising a sequence of three markets in order to check that such speculators can also stabilize a supply chain. Specifically, we verify if these speculators reduce the price fluctuations caused by a phenomenon called the bullwhip effect, which is the amplification of order variability in supply chains. Our simulations show that speculation reduces such price fluctuations, even if price bubbles may appear. Another point is that the speculators we consider lose money in reducing these fluctuations while all the other agents would get richer and richer when the equilibrium is achieved in every market of the supply chain.

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.

This article summarizes the main results we obtained in an agent-based extension of an industrial organization analytical model for studying emerging coalition structures in electronic markets.

In this paper the methodology of Agent-Based Computational Economics (ACE) is used to explore under what conditions trust is viable in markets. The emergence and breakdown of trust is modeled in a context of multiple buyers and suppliers. Agents develop trust in a partner as a function of observed loyalty. They select partners on the basis of their trust in the partner and potential profit. On the basis of realized profits, they adapt the weight they attach to trust relative to profitability, and their own trustworthiness, modeled as a threshold of defection. Trust turns out to be viable under fairly general conditions.

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.