Narrow Results

Agent-based simulation is a powerful simulation modeling technique that has seen a number of applications in the last five years, including applications to real-world business problems. In this chapter I introduce agent-based simulation and review three applications to business problems: a theme park simulation, a stock market simulation, and a bankwide simulation.

Complex systems are of vast importance in the practical world as well as presenting many theoretical challenges. The measurement of system complexity is still imprecise. For many systems, their modular construction brings challenges in understanding how modules form and the emergent behavior which may result. In other systems, it is the development of encodings and communication protocols which allow complexity to increase dramatically. We take a broad view of these issues and then consider the nature of the system space which generates complexity. We show examples from cellular automata and applications of neural networks to data mining which suggest that complex systems often occupy simple structured sub-spaces. Finally, we look at the way modularity relates to networks and the implications for understanding human cognitive processing.

We propose a stochastic dynamic model of migration and economic aggregation in a system of employed (immobile) and unemployed (mobile) agents which respond to local wage gradients. Dependent on the local economic situation, described by a production function which includes cooperative effects, employed agents can become unemployed and vice versa. The spatio-temporal distribution of employed and unemployed agents is investigated both analytically and by means of stochastic computer simulations. We find the establishment of distinct economic centers out of a random initial distribution. The evolution of these centers occurs in two different stages: (i) small economic centers are formed based on the positive feedback of mutual stimulation/cooperation among the agents, (ii) some of the small centers grow at the expense of others, which finally leads to the concentration of the labor force in different extended economic regions. This crossover to large-scale production is accompanied by an increase in the unemployment rate. We observe a stable coexistence between these regions, although they exist in an internal quasistationary non-equilibrium state and still follow a stochastic eigendynamics.

This paper follows an earlier publication in the Advances in Complex Systems journal (Heusse et al., 1998) where we presented a new algorithm based on collaborative agents for routing in communication networks. In this document, we shall investigate its load-balancing capability. This capability is required as a first step to achieve quality of delivery and service. We also compare our new approach to the classical ones and discuss their respective benefits.