Luis R Izquierdo

La economía artificial es uno de los métodos o enfoques de investigación para el estudio de sistemas socioeconómicos complejos con mayor crecimiento durante los últimos años. Este artículo presenta una visión crítica sobre sus características, su potencial y los riesgos relativos al uso de esta metodología. Para ello, encontramos útil relacionar y comparar a la economía artificial con la economía teórica más tradicional. Desde nuestro análisis, la economía teórica y la economía artificial comparten los mismos objetivos, presentan menos diferencias metodológicas de las que a primera vista pudiera parecer, y sus aproximaciones son sin duda complementarias.

Research from the Beagle Channel (Tierra del Fuego) offers a rich ethnographic and historical record produced by the late inclusion of Tierra del Fuego in the industrial world (the Beagle Channel was discovered by R. Fitz-Roy in 1830). This is an interesting frame for using new techniques (social simulation by Agent Based Modelling (ABM)) to generate new hypotheses in archaeology. In this case, the hypothesis is focused on the role of social cooperation in Yámana hunter-fisher-gatherer society.

This paper describes EVO-2x2, a computer simulation modelling framework designed to formally investigate the evolution of strategies in 2-player 2-strategy (2x2) symmetric games under various competing assumptions. EVO- 2x2 enables the user to explore the implications of alternative assumptions (all of which are fully consistent with the essence of the theory of evolution) in a coherent and systematic way. It thus provides a single framework within which results obtained using different assumptions can be contrasted and compared with analytical approaches. We illustrate the usefulness of EVO-2x2 by studying the evolution of cooperation in the Prisoner's Dilemma under different settings. 1. Introduction This paper describes EVO-2x2, a computer simulation modelling framework designed to formally investigate the evolution of strategies in 2-player 2-strategy (2x2) symmetric games under various competing assumptions. EVO- 2x2 enables the user to set up and run many computer simulations (effectively many different models) aimed at investigating the same question from different angles. The specific question to be addressed is usually of the form: "In a population of individuals who interact with each other by repeatedly playing a certain 2x2 symmetric game, what strategies are likely to emerge and be sustained under evolutionary pressures?". Naturally, the answer to such a question may depend on a number of assumptions regarding population size, magnitude of payoffs, population structure, the implementation of the natural selection algorithm, and the mutation mechanism. EVO-2x2 enables the researcher to assess the impact of many such assumptions by providing a single coherent framework within which results obtained from different models can be contrasted and compared with analytical approaches. EVO-2x2 implements a wide range of competing plausible assumptions, all of which are fully consistent with the most basic principles of the theory of evolution. Thus, in broader terms, EVO-2x2 can be used to assess the extent to which the assumptions made in mainstream evolutionary game theory (Weibull, 1995) for the sake of tractability (e.g. infinite populations, random pairings, finite set of deterministic strategies, continuous time, and proportional fitness rule) are affecting its conclusions. This paper is divided into two parts: a description of EVO-2x2 (section 2) and an illustration of how EVO-2x2 can be used in practice (section 3). This latter section is focused on a study of the evolutionary emergence of cooperation among individuals playing the Prisoner's Dilemma.

In this article, we seek to shed new light on the sources of industrial leadership and catch-up in science-based industries. We propose an evolutionary model that incorporates scientists' training and migration, endogenous R&D decisions, and the possibility of funding capital accumulation through debt. The analysis of the model allows us to characterize a robust pattern of industrial catch-up. Likewise, the sensitivity analysis shows which parameters act as pro-catch-up factors or slow down the process. The identification of stationary-state conditions of the model helps us to interpret the simulations, and highlights crucial interactions between technology-supporting institutions and market demand at the basis of industrial catch-up. Finally, the robustness analysis reveals further interdependencies among innovation, scientist mobility, and demand.

* Abstract The objectives of this paper are to define and classify different types of errors and artefacts that can appear in the process of developing an agent-based model, and to propose activities aimed at avoiding them during the model construction and testing phases. To do this in a structured way, we review the main concepts of the process of developing such a model–establishing a general framework that summarises the process of designing, implementing, and using agent-based models.

The classical argument used to explain why markets can fail when there is product quality variability (eg the used car market) relies heavily on the presence of asymmetric information—ie there must exist some reliable quality indicators that can be observed by sellers, but not by buyers. Using computer simulation, this paper illustrates how such market failures can occur even in the absence of asymmetric information.

Operations Research, Game Theory, Physics, Fluid Dynamics, Modelling and Control of Queuing and Production Systems, Stochastic Processes, and Network Theory) make extensive use of the so-called mean-field analysis. In these subjects, the mean-field analysis often appears as an approximation of a discrete time stochastic process where a) the inherent stochasticity of the original process is replaced with determinism, and b) the time discreteness of the original process is replaced with time continuity.