Consumers' Behavior and the Bertrand Paradox: An ACE approach

We analyze the classical Bertrand model when consumers exhibit some strategic behavior in deciding from which seller they will buy. We use two related but different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the two of them consumers' behavior in uences the competition between the sellers. The results obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers as it works in their best interest. First, we consider a learning procedure described by a deterministic dynamic system and, using strong simplifying assumptions, we can produce a description of the process behavior. Second, we use nite automata to represent the strategies played by the agents and an adaptive process based on genetic algorithms to simulate the stochastic process of learning. By doing so we can relax some of the strong assumptions used in the rst approach and still obtain the same basic results. It is suggested that the limitations of the rst approach (analytical) provide a good motivation for the second approach (Agent-Based). Indeed, although both approaches address the same problem, the use of Agent-Based computational techniques allows us to relax hypothesis and overcome the limitations of the analytical approach.Agent-Based Computational Economics, Evolutionary Game Theory, Imperfect Competition

Complementarities between Mathematical and Computational Modeling: The Case of the Repeated Prisoners' Dilemma

We study the properties of the well known Replicator Dynamics when applied to a finitely repeated version of the Prisoners' Dilemma game. We characterize the behavior of such dynamics under strongly simplifying assumptions (i.e. only 3 strategies are available) and show that the basin of attraction of defection shrinks as the number of repetitions increases. After discussing the difficulties involved in trying to relax the 'strongly simplifying assumptions' above, we approach the same model by means of simulations based on genetic algorithms. The resulting simulations describe a behavior of the system very close to the one predicted by the replicator dynamics without imposing any of the assumptions of the mathematical model. Our main conclusion is that mathematical and computational models are good complements for research in social sciences. Indeed, while computational models are extremely useful to extend the scope of the analysis to complex scenarios hard to analyze mathematically, formal models can be useful to verify and to explain the outcomes of computational models.Computational Economics, Model-To-Model Analysis, Genetic Algorithms, Evolutionary Game Theory, Prisoners' Dilemma

A Model-to-Model Analysis of The Repeated Prisoners' Dilemma: Genetic Algorithms vs. Evolutionary Dynamics

We study the properties of the well known Replicator Dynamics when applied to a finitely repeated version of the Prisoners' Dilemma game. We characterize the behavior of such dynamics under strongly simplifying assumptions (i.e. only 3 strategies are available) and show that the basin of attraction of defection shrinks as the number of repetitions increases. After discussing the difficulties involved in trying to relax the 'strongly simplifying assumptions' above, we approach the same model by means of simulations based on genetic algorithms. The resulting simulations describe a behavior of the system very close to the one predicted by the replicator dynamics without imposing any of the assumptions of the analytical model. Our main conclusion is that analytical and computational models are good complements for research in social sciences. Indeed, while on the one hand computational models are extremely useful to extend the scope of the analysis to complex scenarAgent-Based Computational Economics, Evolutionary Game Theory, Replicator Dynamics, Model-to-Model Analysis, Repeated Prisoners' Dilemma

Theoretical view on the origin and implications of structural distortions in polyoxometalates

Structural features of polyoxometalates (POMs) —versatile inorganic clusters of academic and technological interest— are discussed in the present article. POMs are, in general, very regular structures presenting a high symmetry in most cases. Distortions are, however, important for some electronic and magnetic properties. We herein discuss some particular geometric features that are crucial for the theoretical treatment and comprehension of well-known experimental phenomena. For instance, we have been able to understand and rationalize the geometrical distortions present in molybdenum POMs. Moreover, we can affirm that these geometrical distortions are caused by a pseudo Jahn Teller effect. In what concerns NMR chemical shifts, we present a discussion on the importance of geometry for the correct description of the signals and the key role played by the interatomic distances. Finally, a study on the adsorption of Keggin clusters on silver surfaces shows how the POM structure looses its regular shape to adapt to that new situation

A model-to-model analysis of Bertrand competition

Financial support from the Spanish Ministry of Education and Science through grant SEJ2005-01481/ECON and FEDER, SGR2005-0712 of the Direcció General de Recerca (Generalitat of Catalonia), CONSOLIDER-INGENIO 2010 (CSD2006-00016), and CREA (Barcelona Economics) are gratefully acknowledged.This paper studies a version of the classical Bertrand model in which consumers exhibit some strategic behavior when deciding from what seller they will buy. We use two related but different tools. Both consider a probabilistic learning (or evolutionary) mechanism, and in the two of them consumers' behavior influences the competition between the sellers. The results obtained show that, in general, developing some sort of loyalty is a good strategy for the buyers as it works in their best interest. First, we consider a learning procedure described by a deterministic dynamic system and, using strong simplifying assumptions, we can produce a description of the behavior of the process. Second, we use finite automata to represent the strategies played by the agents and an adaptive process based on genetic algorithms to simulate the stochastic process of learning. By doing so we can relax some of the strong assumptions used in the first approach and still obtain the same basic results. It is suggested that the limitations of the first approach (analytical) provide a good motivation for the second approach (Agent-Based). Indeed, although both approaches address the same problem, the use of Agent-Based computational techniques allows us to relax hypothesis and overcome the limitations of the analytical approach while obtaining the same basic results

Self-management: utopia and disillusionment

Self-management: utopia and disillusionmen

L´autogestió: utopia i desengany

L´autogestió: utopia i desengan