Model organisms are a living form of scientific models. Despite the widespread use of model organisms in scientific research, the actual representational relationship between model organisms and their target species is often poorly characterized in the context of cross-species research. Many model organisms do not represent the target species adequately, let alone accurately. This is partly due to the complex and emergent life phenomena in the organism, and partly due to the fact that a model organism is always taken to (...) represent a broad range of diverse organisms. More often than not, model organisms are taken as a reference point for an extrapolation to be made to the unknown characteristics of other species. I propose to view model organisms as analogue simulators which represent the emergent phenomenon in the context of cross-species research. A model organism represents a wide range of species by simulating their molecular microstates which underlie various emergent phenomena. I show that although model organisms represent the target species inadequately at many levels of complexity, they have epistemic values as a simulator in virtue of which the emergent phenomenon can be modeled dynamically, a virtue that is hardly attainable by non-dynamic models. (shrink)

We consider processes of emergence within the conceptual framework of the Information Loss principle and the concepts of systems conserving information; systems compressing information; and systems amplifying information. We deal with the supposed incompatibility between emergence and computability tout-court. We distinguish between computational emergence, when computation acquires properties, and emergent computation, when computation emerges as a property. The focus is on emergence processes occurring within computational processes. Violations of Turing-computability such as non-explicitness and incompleteness are intended to represent partially the (...) properties of phenomenological emergence, such as logical openness, given by the observer’s cognitive role; structural dynamics where change regards rules rather than only values; and multi-modelling where multiple non-equivalent models are required to model such structural dynamics. In this way, we validate, from an epistemological viewpoint, models and simulations of phenomenological emergence where the sequence of events constitutes the natural, analogical non-Turing computation which a cognitive complex system can reproduce through learning. Reproducibility through learning is different from Turing-like computational iteration. This paper aims to open a new, non-reductionist understanding of the conceptual relationship between emergence and computability. (shrink)

The robust behavior of the patent citation network is a complex target of recent bottom-up models in science. This paper investigates the purpose and testing of three especially simple bottom-up models of the citation count distribution observed in the patent citation network. The complex causal webs in the models generate weakly emergent patterns of behavior, and this explains both the need for empirical observation of computer simulations of the models and the epistemic harmlessness of the resulting epistemic opacity.

Despite the common use of the concept of emergence, no uncontroversial theoretical framework has been yet formulated in this regard. In this paper, I examine what this circumstance suggests about the significance and usefulness of this concept. I first trace a brief history of the notion of emergence from its first formulation among the British Emergentists to its contemporary uses. Then, I outline its most common features and examine three examples of emergent phenomena, namely particle decay, free will, and division (...) of labour in ant colonies. These three cases of emergence exhibit different features and imply criteria which only partially overlap. I then suggest that the multiplicity of features and criteria recognised as defining emergence, rather than being a threat to the tenability of the concept, should encourage the assumption of a pluralist attitude that is consistent with both the employment of this idea in different sciences and the recognition of emergent phenomena across different levels of organisation. Finally, I propose that emergence can be approached in a similar way to how Richard Boyd approached the problem of natural kinds, namely by identifying an open cluster of properties, rather than a set of necessary and sufficient conditions. (shrink)

Top-down synthetic biology makes partly synthetic cells by redesigning simple natural forms of life, and bottom-up synthetic biology aims to make fully synthetic cells using only entirely nonliving components. Within synthetic biology the notions of complexity and emergence are quite controversial, but the imprecision of key notions makes the discussion inconclusive. I employ a precise notion of weak emergent property, which is a robust characteristic of the behavior of complex bottom-up causal webs, where a complex causal web is one that (...) is incompressible and its behavior cannot be derived except by crawling through all of the gory details of the interactions in the web. The central thesis of this article is that synthetic biology centrally is the activity of engineering the desired weak emergent properties of synthetic cells. Synthetic biology has many different ways to engineer desired weak emergent properties of synthetic cells, including Edisonian trial and error, standardized parts, refactoring, and reprogramming synthetic genomes. The article ends by noting two philosophical consequences of engineering weak emergence. One is epistemological: synthesis is crucial for discovering weak emergent properties. The other is metaphysical: simple life forms are nothing but complex chemical mechanisms. (shrink)

Synthetic biology is a series of scientific and technological practices involved in the application of engineering principles to the design and production of predictable and robust biological systems. While policy discussions abound in this area, emerging technologies like synthetic biology present considerable challenges in the articulation of concrete policy options given that their introduction into society may still be in the distant future. This paper reports on a series of governance workshops that focused on synthetic biology’s ethical, legal, and social (...) implications as they pertain to human health, and discusses particular limitations of the ELSI approach that we encountered in our work. In an attempt to avoid policymaking for potential implications of uncertain future applications we instead conclude by proposing tangible forms of anticipatory governance that may be more adequate in addressing the more immediate concerns raised by synthetic biology. (shrink)