الصفحة 1
الصفحة 1
Nonlinear Dynamics and Heterogeneous Interacting Agents
Economic application of nonlinear dynamics, microscopic agent-based modelling, and the use of artificial intelligence techniques as learning devices of boundedly rational actors are among the most exciting interdisciplinary ventures of economic theory over the past decade. This volume provides us with a most fascinating series of examples on "complexity in action" exemplifying the scope and explanatory power of these innovative approaches.
New Tools of Economic Dynamics
New Tools of Economic Dynamics gives an introduction and overview of recently developed methods and tools, most of them developed outside economics, to deal with the qualitative analysis of economic dynamics. It reports the results of a three-year research project by a European and Latin American network on the intersection of economics with mathematical, statistical, and computational methods and techniques. Focusing upon the evolution and manifold structure of complex dynamic phenomena, the book reviews and shows applications of a variety of tools, such as symbolic and coded dynamics, interacting agents models, microsimulation in econometrics, large-scale system analysis, and dynamical systems theory. It shows the potential of a comprehensive analysis of growth, fluctuations, and structural change along the lines indicated by pioneers like Harrod, Haavelmo, Hicks, Goodwin, Morishima, and it highlights the explanatory power of the qualitative approach they initiated.
Information, Interaction, and Agency
Agents perform actions based on the available information and in the presence of other interacting agents. From this perspective Information, Interaction, and Agency neatly ties together classical themes like rationality, decision-making and belief revision with games, strategies and learning in a multi-agent setting.Unified by the central notions Information, Interaction, and Agency, the essays in this volume provide refreshing methodological perspectives on belief revision, dynamic epistemic logic, von Neumann games, and evolutionary game theory; all of which in turn are central approaches to understanding our own rationality and that of other agents.
Learning and Adaption in Multi-Agent Systems ; 1st International Workshop, LAMAS 2005, Utrecht, The Netherlands, July 25, 2005, Revised Selected Papers
Contains selected and revised papers of the International Workshop on Lea- ing and Adaptation in Multi-Agent Systems (LAMAS 2005), held at the AAMAS 2005 Conference in Utrecht, The Netherlands, July 26. An important aspect in multi-agent systems (MASs) is that the environment evolves over time, not only due to external environmental changes but also due to agent int- actions. For this reason it is important that an agent can learn, based on experience, and adapt its knowledge to make rational decisions and act in this changing environment autonomously. Machine learning techniques for single-agent frameworks are well established. Agents operate in uncertain environments and must be able to learn and act - tonomously. This task is, however, more complex when the agent interacts with other agents that have potentially different capabilities and goals. The single-agent case is structurally different from the multi-agent case due to the added dimension of dynamic interactions between the adaptive agents. Multi-agent learning, i.e., the ability of the agents to learn how to cooperate and compete, becomes crucial in many domains. Autonomous agents and multi-agent systems (AAMAS) is an emerging multi-disciplinary area encompassing computer science, software engineering, biology, as well as cognitive and social sciences. A t- oretical framework, in which rationality of learning and interacting agents can be - derstood, is still under development in MASs, although there have been promising ?
