Natural immune systems protect animals from dangerous foreign pathogens, including bacteria, viruses, parasites, and toxins. Their role in the body is analogous to that of computer security systems in computing. Although there are many differences between living organisms and computer systems, this article argues that the similarities are compelling and could point the way to improved computer security. Improvements can be achieved by designing computer immune systems that have some of the important properties illustrated by natural immune systems. These include multi-layered protection, highly distributed detection and memory systems, diversity of detection ability across individuals, inexact matching strategies, and sensitivity to most new foreign patterns. We first give an overview of how the immune system relates to computer security. We then illustrate these ideas with two examples.

Abstract. In this paper we try to replicate the simulation results reported by Axelrod [1] in an influential paper on the evolution of social norms. Our study shows that Axelrod’s results are not as reliable as one would desire. We can obtain the opposite results by running the model for longer, by slightly modifying some of the parameters, or by changing some arbitrary assumptions in the model. This re-implementation exercise illustrates the importance of running stochastic simulations several times, of exploring the parameter space adequately, of complementing simulation with analytical work, and of being aware of the scope of our simulation models.

Practitioners of agent-based modeling are often tasked to model or design self-organizing systems while the theoretical foundation of self-organization in science remains to be set. This paper explores self-organization in the context of an agent-based model of ant colony food foraging. We gather specific measures of order-creation and constraint construction particular to leading theories of nonequilibrium thermodynamics that purport to govern self-organizing dynamics. These measures are used to explore three claims: (a) Constraints are constructed from entropy-producing processes in the bootstrapping phase of self-organizing systems; (b) positive feedback loops are critical in the structure formation phase; and (c) constraints tend to decay. The continued presence of far-from-equilibrium boundary conditions are required to reinforce constraints in the maintenance phase.

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Modeling Internet growth is important both for understanding the current network and to predict and improve its future. To date, Internet models have typically attempted to explain a subset of the following characteristics: network structure, traffic flow, geography, and economy. In this paper we present a discrete, agent-based model, that integrates all of them. We show that the model generates networks with topologies, dynamics, and more speculatively spatial distributions that are similar to the Internet. Categories and Subject Descriptors

Abstract. A swarm algorithm framework (SWAF), realized by agent-based modeling, is presented to solve numerical optimization problems. Each agent is a bare bones cognitive architecture, which learns knowledge by appropriately deploying a set of simple rules in fast and frugal heuristics. Two essential categories of rules, the generate-and-test and the problem-formulation rules, are implemented, and both of the macro rules by simple combination and subsymbolic deploying of multiple rules among them are also studied. Experimental results on benchmark problems are presented, and performance comparison between SWAF and other existing algorithms indicates that it is efficiently. 1

Abstract. Social cognitive optimization (SCO) is a simple behavioral model based on human social cognition. By formalizing the fundamental social cognitive agent, the single-agent and multiagent models of SCO are studied. After realizing the goodness evaluation, the experiments results of SCO are compared with existing results on five engineering design problems, which show that SCO can get high-quality solutions efficiently, even by the single-agent model.

Agent-based simulation is increasingly used to analyze the performance of complex systems. There are two main ways agentbased models are built — from equation-based models and directly from data. We are building models in both ways, investigating approaches for creating them and for validating them. In this paper we describe results of our work on one specific agent-based model, showing how it can be validated against the equation-based model from which it was derived, and the extent to which it can be used to derive additional results over and above those that the equationbased model can provide.

Teknillisen fysiikan ja matematiikan osasto

Abstract- The adaptive constraints relaxing rule for swarm algorithms to handle with the problems with eqaulity constraints is presented. The feasible space of such problems may be similiar to ridge function class, which is hard for applying swarm algorithms. To enter the solution space more easily, the relaxed quasi feasible space is introduced and shrinked adaptively. The experimental results on benchmark functions are compared with the performance of other algorithms, which show its efficiency. I.