Worldviews, Science and Us

CONTENTS.

No abstract received.

No abstract received.

No abstract received.

No abstract received.

No abstract received.

The term ‘emergence’ is frequently found in Complexity Science; however, it gives the impression that earlier debates on this topic are not taken fully into account. By considering this situation, the article underlines the need of a consistent discussion when addressing emergence if this term is going to have an added and meaningful value for learning about complex systems. The promising suitability of process thought and the significance of detailed rule-based modeling are derived from this reflection as further paths to explore.

In this paper, I criticize Bedau's definition of ‘diachronically emergent properties’ (DEPs), which says that a property is a DEP if it can only be predicted by a simulation (simulation requirement) and is nominally emergent. I argue at length that this definition is not complete because it fails to eliminate trivial cases. I discuss the features that an additional criterion should meet in order to complete the definition and I develop a notion, salience, which together with the simulation requirement can be used to characterize DEPs. In the second part of the paper, I sketch this notion. Basically, a property is salient when one can find an indicator, namely a descriptive function (DF), that is such that its fitting description shifts from one elementary mathematical object (EMO) to another when the property appears. Finally, I discuss restrictions that must be brought to what can count as DFs and EMOs if the definition of salience is to work and be non trivial. I conclude that salience (or a refined version of it) can complete the definition of DEPs.

No abstract received.

It is argued that the truth status of emergent properties of complex adaptive systems models should be based on an epistemology of proof by constructive verification and therefore on the ontological axioms of a non-realist logical system such as constructivism or intuitionism.

‘Emergent’ properties of complex adaptive systems (CAS) models create particular epistemological and ontological challenges. These challenges bear directly on current debates in the philosophy of mathematics and in theoretical computer science. CAS research, with its emphasis on computer simulation, is heavily reliant on models which explore the entailments of Formal Axiomatic Systems (FAS). The incompleteness results of Gödel, the incomputability results of Turing, and the Algorithmic Information Theory results of Chaitin, undermine a realist (platonic) truth model of emergent properties. These same findings support the hegemony of epistemology over ontology and point to alternative truth models such as intuitionism, constructivism and quasi-empiricism.

The paper will seek to present a genesis of complexity in European philosophical thought. Following the works of Gleick and Hobsbawn it is possible to discern the emergence of complexity's ethos in the ‘age of revolutions’. Gleick, for example, has noted the influence of natural philosophy on the first precursors of chaos theory. Natural philosophy itself was deeply anchored in German romanticism. The paper will thus seek to present, following Foucault, an ‘Archaeology of Complexity’ which considers Foucault's definition of epistemes as evolving modes of thought. In particular, the paper will seek to use Foucault's description of the modern episteme in order to detect the novelty inherent within the ethos of Complexity.

Any scientific development entails many epistemological considerations. In this paper we consider two stages of scientific development - the metaphorical and empirical levels. When applied to transposition of complexity science to other disciplines, in special economics, we conclude that complexity is still, in many aspects, at the metaphorical level. This implies that transposed concepts should be scrutinized to verify its validity towards the development of complexity science.

No abstract received.

Economists traditionally see altruistic acts as irrational. However, in the Prisoner's Dilemma, a rational player can do worse than a moral player. The rules of the game imply that one cannot defend one's best interest if one tries to. Game theory has struggled to explain how an agent could have access to the strategically best outcome without behaving irrationally, but with little success. Can a complex systems approach do better?.

Peter Danielson, using Evolutionary Game Theory, has avoided some of the assumptions of Game Theory by using a complexity approach to reframe the problem, and offers a solution of sorts. According to Danielson, the foundations of altruism are mechanisms of deterrence that rely on credible threat - we are nice for fear of retaliation. He is both right and wrong. It will be argued that utilitarian, consequentialist principles must have been at work to create the conditions for altruistic acts to be performed. It is wrong to expect, however, that the same reasons are the reasons for action.

In order for a model of genuine altruism to be possible, an extra cog must be inserted in the mechanism of causality in order to distance moral action from its strategic advantages. If emotions fulfill this role, we can tell a story in which it is rational to act on altruistic motivations and materially advantageous to hold such motivations. Moral sentiments can be seen as a tool designed by evolution to help optimize cooperation in a social environment. The proposed account integrates the Humean theory of motivation with Robert Frank's commitment model and Aristotle's views on moral education, keeping an adequate story of how it can be in our material interest to be moral without having to renounce to the existence of genuine acts of altruism.

Our intuition tells us that there is a general trend in the evolution of nature, a trend towards greater complexity. However, there are several definitions of complexity and hence it is difficult to argue for or against the validity of this intuition. Christoph Adami has recently introduced a novel measure called physical complexity that assigns low complexity to both ordered and random systems and high complexity to those in between. Physical complexity measures the amount of information that an organism stores in its genome about the environment in which it evolves. The theory of physical complexity predicts that evolution increases the amount of ‘knowledge’ an organism accumulates about its niche. It might be fruitful to generalize Adami's concept of complexity to the entire evolution (including the evolution of man). Physical complexity fits nicely into the philosophical framework of cognitive biology which considers biological evolution as a progressing process of accumulation of knowledge (as a gradual increase of epistemic complexity). According to this paradigm, evolution is a cognitive ‘ratchet’ that pushes the organisms unidirectionally towards higher complexity. Dynamic environment continually creates problems to be solved. To survive in the environment means to solve the problem, and the solution is an embodied knowledge. Cognitive biology (as well as the theory of physical complexity) uses the concepts of information and entropy and views the evolution from both the information-theoretical and thermodynamical perspective. Concerning humans as conscious beings, it seems necessary to postulate an emergence of a new kind of knowledge - a self-aware and self-referential knowledge. Appearence of selfreflection in evolution indicates that the human brain reached a new qualitative level in the epistemic complexity.

The main purpose of this paper is the conceptual exploration into the pre-biotic world, a world that we consider to be made up of a relatively continuous sequence of systems directed toward the origin of the first life forms. In a tentative way, we propose the ‘Informational Dynamic System’. This type of system would constitute the ancestral system that definitively opened the door to the pre-biotic world. Also, it explains, in a naturalistic way, the physical emergence of functions and information.

In this paper we intend to address the conceptual challenge posed by complexity by focusing on living systems. Biological complexity has traits that seem to be more amenable to scientific scrutiny and description, without having to turn to higher order capacities as sociality or human intelligence. Notwithstanding, biological complexity keeps the conceptual depth and suggestive power that appear lacking in more formal or physical expressions of the idea. The paper presents a conceptual overview of the different perspectives that along the 20^th century were developed with the intention to grasp biological complexity (i.e., Organicism, Theory of Levels of Integration, etc.). Concepts that nowadays are so common to the different approaches on complexity such as emergence, hierarchy, downward causation, wholeness, etc.- arose for the first time in these theoretical perspectives during the early 20^th century and in the 60's and 70's. The goal of this paper is to point out that these accounts still have deep insights and potentialities that are worthy to recast in order to advance in the understanding and exploration of biological complexity and complexity in a wider sense, specially the ideas of autonomous hierarchies and the notion of constraints as enabling not only as restrictive material structures.

The formal and empirical-generative perspectives of computation are demonstrated to be inadequate to secure the goals of simulation in the social sciences. Simulation does not resemble formal demonstrations or generative mechanisms that deductively explain how certain models are sufficient to generate emergent macrostructures of interest. The description of scientific practice implies additional epistemic conceptions of scientific knowledge. Three kinds of knowledge that account for a comprehensive description of the discipline were identified: formal, empirical and intentional knowledge. The use of formal conceptions of computation for describing simulation is refuted; the roles of programming languages according to intentional accounts of computation are identified; and the roles of iconographic programming languages and aesthetic machines in simulation are characterized. The roles that simulation and intentional decision making may be able to play in a participative information society are also discussed.

This paper investigates the seeming incompatibility of reductionism and non-reductionism in the context of complexity sciences. I review algorithmic information theory for this purpose. I offer two physical metaphors to form a better understanding of algorithmic complexity, and I briefly discuss its advantages, shortcomings and applications. Then, I revisit the non-reductionist approaches in philosophy of mind which are often arguments from ignorance to counter physicalism. A new approach called mild non-reductionism is proposed which reconciliates the necessities of acknowledging irreducibility found in complex systems, and maintaining physicalism.

No abstract received.

The aim of this paper is to address the question: Can an artificial neural network (ANN) model be used as a possible characterization of the power of the human mind? We will discuss what might be the relationship between such a model and its natural counterpart. A possible characterization of the different power capabilities of the mind is suggested in terms of the information contained (in its computational complexity) or achievable by it. Such characterization takes advantage of recent results based on natural neural networks (NNN) and the computational power of arbitrary artificial neural networks (ANN). The possible acceptance of neural networks as the model of the human mind's operation makes the aforementioned quite relevant.

Algebra has emergent properties that are neither found in the cultural context in which mathematicians work, nor in the evolved cognitive abilities for mathematical thought that enable it. In this paper, I argue that an externalization of mathematical operations in a consistent symbolic notation system is a prerequisite for these emergent properties. In particular, externalism allows mathematicians to perform operations that would be impossible in the mind alone. By comparing the development of algebra in three distinct historical cultural settings - China, the medieval Islamic world and early modern Europe - I demonstrate that such an active externalism requires specific cultural conditions, including a metaphysical view of the world compatible with science, a notation system that enables the symbolic notation of operations, and the ontological viewpoint that mathematics is a human endeavour. I discuss how extending mathematical operations from the brain into the world gives algebra a degree of autonomy that is impossible to achieve were it performed in the mind alone.