This article reviews the seven “visions” of evolution proposed by Depew and Weber , concluding that each posited relationship between natural selection and self-organization has suited different aims and approaches. In the second section of the article, we show that these seven viewpoints may be collapsed into three fundamentally different ones: natural selection drives evolution; self-organization drives evolution; and natural selection and self-organization are complementary aspects of the evolutionary process. We then argue that these three approaches are not mutually exclusive, (...) since each may apply to different stages of development of different systems. What emerges from our discussion is a more encompassing view: that self-organization proposes what natural selection disposes. (shrink)

In this paper, we argue for the centrality of prediction in the use of computational models in science. We focus on the consequences of the irreversibility of computational models and on the conditional or ceteris paribus, nature of the kinds of their predictions. By irreversibility, we mean the fact that computational models can generally arrive at the same state via many possible sequences of previous states. Thus, while in the natural world, it is generally assumed that physical states have a (...) unique history, representations of those states in a computational model will usually be compatible with more than one possible history in the model. We describe some of the challenges involved in prediction and retrodiction in computational models while arguing that prediction is an essential feature of non-arbitrary decision making. Furthermore, we contend that the non-predictive virtues of computational models are dependent to a significant degree on the predictive success of the models in question. (shrink)

I argue that much of current concern with the role of causality and strong emergence in natural processes is based upon an unreasonable expectation placed on our ability to formalize scientific knowledge. In most disciplines our formalization ability is an expectation rather than a scientific result. This calls for an empirical approach to the study of causation and emergence. Finally, I suggest that for advances in complexity research to occur, attention needs to be paid to understanding what role computation plays (...) in this experimental approach. (shrink)

I argue that much of current concern with the role of causality and strong emergence in natural processes is based upon an unreasonable expectation placed on our ability to formalize scientific knowledge. In most disciplines our formalization ability is an expectation rather than a scientific result. This calls for an empirical approach to the study of causation and emergence. Finally, I suggest that for advances in complexity research to occur, attention needs to be paid to understanding what role computation plays (...) in this experimental approach. (shrink)