Why Social Sciences Do Need Nature Inspired Computing

For nearly four billions years, life has invaded Earth. Throughout eras geological upheavals have deeply transformed the environment. Here, deserts left place to a tropical environment; there ices were replaced by conifers forests… Despite these transformations, life is so vigorous that one can find it on the top of the highest mountains, in the depths of oceans, as well as in clouds, or deep underground.
Life has proved its capacity to cope with deep transformations; its ability to overcome mass extinctions and to rebirth after disasters. These adapting and surviving capabilities have inspired computer scientists who try to conceive algorithms imitating nature, hoping to confer them some of the aptitudes of living beings.

Darwinian natural adaptation has been transferred into Evolutionary Algorithms; Artificial Neural Networks are a metaphor of nervous systems; ants foraging behaviors gave rise to Ant Colony Optimization; birds flocks or fish schools inspired Particle Swarm Optimization; Artificial immune systems mimic the biological one; Insect or animal autonomy and abilities inspired Distributed Artificial Intelligence, Multi-Agent Systems and Artificial Societies…

In the fields of social sciences, economics and management, two types of contributions must be emphasized:

• Social insects, buffalo herds or human societies show that social life dominates the macro-fauna. This fascinating characteristic of living systems and more particularly the study of social insect behavior, inspired the rebirth of artificiality. Starting with artificial life and artificial intelligence, modern artificiality now reaches social sciences with the development of artificial societies which contributes to renewed approaches of social and economic phenomena.
• Living systems are supple and able to adapt to huge transformations in their environment. Transposition of these properties into algorithms provide with ground-breaking tools able to deal with complex problems.

After a six chapters introduction to nature inspired computing for modeling and optimization, the first volume of the handbook is oriented toward social sciences (sociology and economics) modeling and experiments; the second volume mainly handles modeling, exploration and optimization for management.

Nature Inspired Computing and Social Sciences
The core hypothesis of artificial societies is that human societies are Complex Adaptive Systems (CAS) whose properties emerge from non-linear interactions between their members. Since the famous Jurassic Park by Steven Spielberg, everyone has been aware of the existence of "chaos" and "complexity". Despite the highly disputable treatment of chaos in that movie it questioned wide audience to a core problem of modern science that Nobel Prize recipient Illya Prigogine nicely termed “The End of Certainty” (Prigogine, 1997).
At least since the French philosopher René Descartes in the 17th century, science has mainly been based on analysis. Analysis tries to understand reality by breaking down observed phenomena, thanks to the fundamental hypothesis that a global process is the result of the mere (linear) aggregation of its components.
At the end of the 19th century French mathematician Henri Poincaré proved that analysis is unable to describe most systems based on non-linear interactions. Those systems are now said to be more than the sum of their parts (Waldrop, 1992). The resulting complex dynamic is unpredictable. The only way to know their state at a given step is to compute each step. The usual analytical method is of few help; the necessary mathematics still are to be invented. Non-linearity thus challenges the traditional approach which tries to understand a system by analyzing its components: "The key feature of non-linear systems is that their primary behaviors of interest are properties of the interactions between parts, rather than being properties of the parts themselves, and these interactions-based properties necessarily disappear when the parts are studied independently." (Langton, 1989, p.41, original italics). How to study processes which are "more than the sum of their parts"? How to analyze properties that cannot be forecasted? The solution proposed by computer scientists is termed bottom-up modeling.
Since core properties disappear when the components are studied independently, bottom-up modeling is based on the gathering of interacting components. Such constructions and the study of the dynamic resulting from non-linear interactions of the simple components constitute the "bottom-up method". Instead of modeling the global dynamic of the studied system ("top-down method" usually based on differential equations) one merely models the components to study the potentially emerging regularities.
Bottom-up modeling is based on Multi-Agent Systems (MAS). Agents are a kind of “living organism”, whose behavior, which can be summarized as communicating and acting is aimed at satisfying its needs and attaining its objectives (Ferber, 1999, p.10). MAS are gathering of interacting agents. In social sciences (mainly sociology and economics) they are the base of artificial societies.

The Social Modeling section includes seven chapters providing a global view of these researches. Chapters by Robert Axelrod and Harko Verhagen demonstrate the huge potential contribution of artificial societies to social sciences. Corruption, trust and academic science are then studied in the light of MAS, showing the cross-fertilization of social sciences and multi-agent systems.

The Economics section includes thirteen chapters providing with a global coverage of the use of nature inspired computing for economics. After an introduction to Agent-based Computational Economics (ACE), original researches using multi-agent systems, evolutionary algorithms or neural networks to deal with fundamental economic forces are presented. Clusters, innovation and technology are then particularly emphasized to enlighten the complex cross dynamics of space and technology.

Nature Inspired Computing and Management

Management is confronted to growing difficulty challenges:

• The complexity of the environment, the unprecedented speed of its evolutions and the unmanageable resulting mass of information require specific powerful tools to cope with.
• The never ending search for productivity has made optimization a core concern for engineers. Quick process, low energy consumption, short and economical supply chains are now key success factors.

The six sections, thirty two chapters second volume provides with a comprehensive coverage of the contributions of nature inspired computing to management. It shows its ability to solve problems beyond the capabilities of more traditional methods.

The first section, Design and Manufacturing, presents pioneering researches particularly using evolutionary algorithms. Applied to design, project management as well as to manufacturing, these researches clearly demonstrate the capacity of nature inspired algorithms to stimulate design creativity and to manage complex associated issues.

The second section, Operations and Supply Chain Management, contains twelve chapters. After an introduction to evolutionary optimization and ant colony optimization for operations management, main nature inspired tools are used to solve very diverse operations and supply chain management problems (scheduling, organization of production, distribution…). The section includes the presentation of a powerful Java framework designed to use evolutionary computation to solve operations and supply chain problems.

The section three, Information Systems, presents the novel agent oriented paradigm of information systems and provides with innovative researches, demonstrating the power and suppleness of nature inspired computing when applied to information management, e-learning and peer to peer systems.

The section four, Commerce and Negotiation, includes a synthesis of agents for multi-issue negotiation and presents original researches on automatic negotiations and auctions using agent-based modeling and evolutionary computation. These researches outstandingly lead the way toward future virtual organizations.

The section five, Marketing, uses evolutionary computation and agent-based modeling to analyze price wars and word-of-mouth and to contribute to the understanding of complex socio-economic systems to provide with a decision support tool for commercial organizations.

The section six¸ Finance, uses genetic programming, evolutionary computation, neural networks and agent-based modeling to deal with complex financial problems. They are applied to housing prices, financial decision aid, insurance-linked derivatives and stock-market simulations.

The fifty eight chapters of this two volumes handbook provide with a unique cross section of researches using nature inspired computing for economics and management. Social scientists, economists and people dealing with management science will find both an introduction and a valuable presentation of state of the art researches in these fields, giving them a unique reference tool for their researches. Students in computer sciences, social sciences and management will find all the necessary material to master the field and to help them in their training. Managers, engineers and practitioners will find a great deal of efficient and powerful tools to help them solve daily difficult problems and to anticipate the use of tools which will undoubtedly be part of to-morrow key success factors.

Jean-Philippe Rennard
Annecy, France
March, 15, 2006

References

Ferber, J. (1999). Multi-agent systems. London: Addison-Wesley.

Langton, C. G. (1989). Artificial life. In C. G. Langton (Ed.), Artificial life 1. Redwood city:Addison-Wesley.

Prigogine, I. (1997). The End of Certainty: Free Press.

Waldrop, M. (1992). Complexity: The emerging science at the edge of chaos. New York: Simon and Schuster.