The emergence of specialization in heterogeneous artificial agent populations

In this dissertation, I present the Weight-Allocated Social Pressure System (WASPS). WASPS is a computational framework that when applied, can allow for the increase in agent specialization within a multi-agent population. Research has shown that specialization can lead to an overall increase in the productivity levels within a population [55]. WASPS aims to provide a mix of features from existing frameworks such as the genetic threshold and social inhibition models. It also subsumes these models, and allows hybrids of them to be created. It provides individual level behaviour as found in the genetic threshold model. As in some variations of the genetic threshold model [49], WASPS also allows for individual level learning. As found in the social inhibition models, WASPS allows for social influence, or population level learning. Unlike some models, WASPS allows agents to self-organize based on available tasks. In addition, it makes allowances for agents to allocate a resource among multiple tasks during a work period, wherein most models allow the selection of only one task. WASPS allows the assumption that agents are heterogeneous in their task performance aptitudes. It thus aims to create skill-based agent specialization within the population. This will allow more skilled agents to allocate more resources to tasks for which they have comparative advantages over their competition. Because WASPS is self-organizing, it can handle the addition and removal of agents from social networks, as well as changes in the connections between agents. WASPS does not limit the definition of many or its parameters, which allows it to deal with changing definitions for those parameters. For example, WASPS can easily adjust to deal with changing definitions of agent skill and influence. In fact, the individual level learning can be implemented in such a way that an agent can self-optimize even when it has no competitors to influence it

Extended Inclusive Fitness Theory bridges Economics and Biology through a common understanding of Social Synergy

Inclusive Fitness Theory (IFT) was proposed half a century ago by W.D. Hamilton to explain the emergence and maintenance of cooperation between individuals that allows the existence of society. Contemporary evolutionary ecology identified several factors that increase inclusive fitness, in addition to kin-selection, such as assortation or homophily, and social synergies triggered by cooperation. Here we propose an Extend Inclusive Fitness Theory (EIFT) that includes in the fitness calculation all direct and indirect benefits an agent obtains by its own actions, and through interactions with kin and with genetically unrelated individuals. This formulation focuses on the sustainable cost/benefit threshold ratio of cooperation and on the probability of agents sharing mutually compatible memes or genes. This broader description of the nature of social dynamics allows to compare the evolution of cooperation among kin and non-kin, intra- and inter-specific cooperation, co-evolution, the emergence of symbioses, of social synergies, and the emergence of division of labor. EIFT promotes interdisciplinary cross fertilization of ideas by allowing to describe the role for division of labor in the emergence of social synergies, providing an integrated framework for the study of both, biological evolution of social behavior and economic market dynamics.Comment: Bioeconomics, Synergy, Complexit

Empirical models, rules, and optimization

This paper considers supply decisions by firms in a dynamic setting with adjustment costs and compares the behavior of an optimal control model to that of a rule-based system which relaxes the assumption that agents are explicit optimizers. In our approach, the economic agent uses believably simple rules in coping with complex situations. We estimate rules using an artificially generated sample obtained by running repeated simulations of a dynamic optimal control model of a firm's hiring/firing decisions. We show that (i) agents using heuristics can behave as if they were seeking rationally to maximize their dynamic returns; (ii) the approach requires fewer behavioral assumptions relative to dynamic optimization and the assumptions made are based on economically intuitive theoretical results linking rule adoption to uncertainty; (iii) the approach delineates the domain of applicability of maximization hypotheses and describes the behavior of agents in situations of economic disequilibrium. The approach adopted uses concepts from fuzzy control theory. An agent, instead of optimizing, follows Fuzzy Associative Memory (FAM) rules which, given input and output data, can be estimated and used to approximate any non-linear dynamic process. Empirical results indicate that the fuzzy rule-based system performs extremely well in approximating optimal dynamic behavior in situations with limited noise.Decision-making. ,econometric models ,TMD ,

Emergent Behavior Development and Control in Multi-Agent Systems

Emergence in natural systems is the development of complex behaviors that result from the aggregation of simple agent-to-agent and agent-to-environment interactions. Emergence research intersects with many disciplines such as physics, biology, and ecology and provides a theoretical framework for investigating how order appears to spontaneously arise in complex adaptive systems. In biological systems, emergent behaviors allow simple agents to collectively accomplish multiple tasks in highly dynamic environments; ensuring system survival. These systems all display similar properties: self-organized hierarchies, robustness, adaptability, and decentralized task execution. However, current algorithmic approaches merely present theoretical models without showing how these models actually create hierarchical, emergent systems. To fill this research gap, this dissertation presents an algorithm based on entropy and speciation - defined as morphological or physiological differences in a population - that results in hierarchical emergent phenomena in multi-agent systems. Results show that speciation creates system hierarchies composed of goal-aligned entities, i.e. niches. As niche actions aggregate into more complex behaviors, more levels emerge within the system hierarchy, eventually resulting in a system that can meet multiple tasks and is robust to environmental changes. Speciation provides a powerful tool for creating goal-aligned, decentralized systems that are inherently robust and adaptable, meeting the scalability demands of current, multi-agent system design. Results in base defense, k-n assignment, division of labor and resource competition experiments, show that speciated populations create hierarchical self-organized systems, meet multiple tasks and are more robust to environmental change than non-speciated populations

ANTIDS: Self-Organized Ant-based Clustering Model for Intrusion Detection System

Security of computers and the networks that connect them is increasingly becoming of great significance. Computer security is defined as the protection of computing systems against threats to confidentiality, integrity, and availability. There are two types of intruders: the external intruders who are unauthorized users of the machines they attack, and internal intruders, who have permission to access the system with some restrictions. Due to the fact that it is more and more improbable to a system administrator to recognize and manually intervene to stop an attack, there is an increasing recognition that ID systems should have a lot to earn on following its basic principles on the behavior of complex natural systems, namely in what refers to self-organization, allowing for a real distributed and collective perception of this phenomena. With that aim in mind, the present work presents a self-organized ant colony based intrusion detection system (ANTIDS) to detect intrusions in a network infrastructure. The performance is compared among conventional soft computing paradigms like Decision Trees, Support Vector Machines and Linear Genetic Programming to model fast, online and efficient intrusion detection systems.Comment: 13 pages, 3 figures, Swarm Intelligence and Patterns (SIP)- special track at WSTST 2005, Muroran, JAPA

A Multi-Agent Systems Approach to Microeconomic Foundations of Macro

This paper is part of a broader project that attempts to gener- ate microfoundations for macroeconomics as an emergent property of complex systems. The multi-agents systems approach is seen to produce realistic macro properties from a primitive set of agents that search for satisfactory activities, "jobs", in an informationally constrained, computationally noisy environment. There is frictional and structural unemployment, inflation, excess capacity, fi- nancial instability along with the possibility of relatively smooth expansion. There is no Phillips curve but an inegalitarian distribution of income emerges as fundamental property of the system.Multi-agent system, agent-based models, microeconomic foundations, macroeconomics.