This review presents a review of prevalent results within research pertaining to emergent cooperation in biologically inspired artificial social systems. Results reviewed maintain particular reference to biologically inspired design principles, given that current mathematical and empirical tools have provided only a partial insight into elucidating mechanisms responsible for emergent cooperation, and then only in systems of an abstract nature. This review aims to provide an overview of important and disparate research contributions that investigate utilization of biologically inspired concepts such as emergence, evolution, and self-organization as a means of attaining cooperation in artificial social systems. An introduction and overview of emergent cooperation in artificial life is presented, followed by a survey of emergent cooperation in swarm-based systems, the pursuit-evasion domain, and RoboCup soccer. The final section draws conclusions regarding future directions of emergent cooperation as a problem-solving methodology that is potentially applicable in a wide range of problem domains. Within each of these sections and their respective themes of research, the mechanisms deemed to be responsible for emergent cooperation are elucidated and their key limitations highlighted. The review concludes that current studies in emergent cooperative behavior are limited by a lack of situated and embodied approaches, and by the research infancy of current biologically inspired design approaches. Despite these limiting factors, emergent cooperation maintains considerable future potential in a wide variety of application domains where systems composed of many interacting components must cooperatively perform unanticipated global tasks.

This article aims to show that linguistics, in particular the study of the lexico-syntactic aspects of language, provides fertile ground for artificial life modeling. A survey of the models that have been developed over the last decade and a half is presented to demonstrate that ALife techniques have a lot to offer an explanatory theory of language. It is argued that this is because much of the structure of language is determined by the interaction of three complex adaptive systems: learning, culture, and biological evolution. Computational simulation, informed by theoretical linguistics, is an appropriate response to the challenge of explaining real linguistic data in terms of the processes that underpin human language.

Agent-based computational economics (ACE) is the computational study of economies modeled as evolving systems of autonomous interacting agents. Thus, ACE is a specialization of economics of the basic complex adaptive systems paradigm. This study outlines the main objectives and defining characteristics of the ACE methodology and discusses similarities and distinctions between ACE and artificial life research. Eight ACE research areas are identified, and a number of publications in each area are highlighted for concrete illustration. Open questions and directions for future ACE research are also considered. The study concludes with a discussion of the potential benefits associated with ACE modeling, as well as some potential difficulties.