The modeling of complex systems such as ecological or socio-economic systems can be very challenging. Although various modeling approaches exist, they are generally not compatible and mutually consistent, and empirical data often do not allow one to decide what model is the right one, the best one, or most appropriate one. Moreover, as the recent financial and economic crisis shows, relying on a single, idealized model can be very costly. This contribution tries to shed new light on problems that arise when complex systems are modeled. While the arguments can be transferred to many different systems, the related scientific challenges are illustrated for social, economic, and traffic systems. The contribution discusses issues that are sometimes overlooked and tries to overcome some frequent misunderstandings and controversies of the past. At the same time, it is highlighted how some long-standing scientific puzzles may be solved by considering non-linear models of heterogeneous agents with spatio-temporal interactions. As a result of the analysis, it is concluded that a paradigm shift towards a pluralistic or possibilistic modeling approach, which integrates multiple world views, is overdue. In this connection, it is argued that it can be useful to combine many different approaches to obtain a good picture of reality, even though they may be inconsistent. Finally, it is identified what would be profitable areas of collaboration between the socio-economic, natural, and engineering sciences. 1

In this chapter, we reflect on the concept of Meaning-Based Natural Intelligence- a fundamental trait of Life shared by all organisms, from bacteria to humans, associated with: semantic and pragmatic communication, assignment and generation of meaning, formation of self-identity and of associated identity (i.e., of the group the individual belongs to), identification of natural intelligence, intentional behavior, decision-making and intentionally designed self-alterations. These features place the Meaning-Based natural Intelligence beyond the realm of Information-based Artificial Intelligence. Hence, organisms are beyond man-made pre-designed machinery and are distinguishable from non-living systems. Our chain of reasoning begins with the simple distinction between intrinsic and extrinsic contextual causations for acquiring intelligence. The first, associated with natural intelligence, is required for the survival of the organism (the biotic system) that generates it. In contrast, artificial intelligence is implemented externally to fulfill a purpose for the benefit of the organism that engineered the “Intelligent Machinery”. We explicitly propose that the ability to assign contextual meaning to externally gathered information is an essential

If today’s software developers use models at all, they use them mostly as simple sketches of design ideas, often discarding them once they’ve written the code. This is sufficient for traditional code-centric development. With a model-driven approach, however, the models themselves become the primary artifacts in the development of software. In this case, a clear, common understanding of the semantics of our modeling languages is at least as important as a clear, common understanding of the semantics of our programming languages. There has been, and continues to be, a great deal of discussion within the software community on modeling and metamodeling and the relationships between modeling languages and metamodeling languages. Such relationships’ circular nature makes them particularly

In this paper I discuss whether Gödel's incompleteness theorems have any implications for studies in Artificial Life (AL). Since Gödel's incompleteness theorems have been used to argue against certain mechanistic theories of the mind, it seems natural to attempt to apply the theorems to certain strong mechanistic arguments postulated by some AL theorists. We find that an argument using the incompleteness theorems can not be constructed that will block the hard AL claim, specifically in the field of robotics. However, we will see that the beginnings of an argument casting doubt on our ability to create living systems entirely resident in a computer environment might be suggested by looking at the incompleteness theorems from the point of view of Gödel's belief in mathematical realism.

The work of the mathematician Kurt Gödel changed the face of mathematics forever. His famous incompleteness theorem proved that any formalized system of mathematics would always contain statements that were undecidable, showing that there are certain inherent limitations to the way many mathematicians studies mathematics. This paper provides a history of the mathematical developments that laid the foundation for Gödel's work, describes the unique method used by Gödel to prove his famous incompleteness theorem, and discusses the farreaching mathematical implications thereof. 2 I.

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Ten “common grace ” social capital investments are developed as normative propositions for sustaining ethical conduct with a variety of new economy and emerging economy challenges. Drawing primarily upon Clay Christensen and Geoff Moore’s findings regarding adoption risks with “disruptive technologies ” authenticity/integrity and competence issues are explored within the larger perspectives of true excellence and goodness derived from Morris ’ “If Aristotle Ran G.M.”. The cognitive psychological limits to moral development such as the illusion of ethical superiority and inoculation effects attending legalistic judgmentalism constitute some of the more serious impediments that suggest the need for a combination of trust and gratitude resolutions. When practical applications of trust and gratitude-based mentoring are synthesized with the “social capital ” guidelines from Prusak & Cohen we begin to see the desirability of a “common grace ” social capital construct worked-out in terms of five trust and five gratitude guidelines for sustainable ethical conduct robust enough to handle extraordinary pressures from “new economy pathologies ” and emerging economies.