Financial fragility in a basic agent-based model

A simple agent-based model of business units lending money to one another is sufficient to understand on what conditions avalanches of bankruptcies may arise. The model highlights the consequences of specialisation into money lending as well as the impact of preferential lending relations

Credit Rationing in a Basic Agent-Based Model

A simple agent-based model of business units lending money to one another is sufficient to understand on what conditions avalanches of bankruptcies may arise. The model highlights the consequences of specialisation into money lending as well as the impact of preferential lending relations.Financial Fragility, Avalanches of Bankruptcies, Agent-Based Models

Agent Based Traffic Signals Regulating Flow On a Basic Grid

A simulation study on traffic light optimisation with agent-based behaviour of the traffic signals

Projective simulation with generalization

The ability to generalize is an important feature of any intelligent agent. Not only because it may allow the agent to cope with large amounts of data, but also because in some environments, an agent with no generalization capabilities cannot learn. In this work we outline several criteria for generalization, and present a dynamic and autonomous machinery that enables projective simulation agents to meaningfully generalize. Projective simulation, a novel, physical approach to artificial intelligence, was recently shown to perform well in standard reinforcement learning problems, with applications in advanced robotics as well as quantum experiments. Both the basic projective simulation model and the presented generalization machinery are based on very simple principles. This allows us to provide a full analytical analysis of the agent's performance and to illustrate the benefit the agent gains by generalizing. Specifically, we show that already in basic (but extreme) environments, learning without generalization may be impossible, and demonstrate how the presented generalization machinery enables the projective simulation agent to learn.Comment: 14 pages, 9 figure

Design patterns for multi-agent simulations

The advent of mobile agent technology has brought along a few difficulties in designing a stable, efficient and scalable system for a certain problem. Agent-based simulations prove to be powerful tools for economic analyses. In this paper we aim at describing a set of design patterns which were specifically built for agents and multi-agent systems. The details of each design pattern discussed are presented and the possible applications and known issues are noted. In order to aid the software designers, we provide some examples of the basic implementation of these patterns using the JADE multi-agent framework.intelligent agent, multi-agent design, multi-agent simulation.

Agent-based modelling - A methodology for the analysis of qualitative development processes

The tremendous development of an easy access to computational power within the last 30 years has led to the widespread use of numerical approaches in almost all scientific disciplines. The first generation of simulation models was rather focused on stylized empirical phenomena. With agent-based modelling, however, the trade-off between simplicity in modelling and taking into account the complexity of the socio-economic reality has been enhanced to a large extent. This paper serves as a basic instruction on how to model qualitative change using an agent-based modelling procedure. The necessity to focus on qualitative change is discussed, agent-based modelling is explained and finally an example is given to show the basic simplicity in modelling.agent-based modelling, methodology, evolutionary economics, qualitative change