Epistemology and Ethics of Simulation Sample Clauses

Epistemology and Ethics of Simulation. 3.2.1.1 Epistemological challenges of simulation Simulation is a powerful method in science and engineering. However, simulation is an umbrella term, and its meaning and goals differ among disciplines. Rapid advances in neuroscience and computing draw increasing attention to large-scale brain simulations. Therefore, we asked: what is the meaning of simulation, and what should the method be expected to achieve? In their first article, Xxxxx XXXXX and Xxxxxxxx XXXXX discussed the concept of simulation from an integrated scientific and philosophical vantage point, and pinpointed selected issues specific to brain simulation [1]. They proposed a comprehensive practical taxonomy of simulation, and placed the goals of computer brain simulation within it. They also identified and described a set of challenges and boundary conditions for current attempts at brain simulation. For example: • The scarcity of neurobiological data: contemporary knowledge of the brain is limited, and the collection of data for large-scale brain simulation is not trivial. When federating data from different labs, even small differences in methodology and conditions can have an impact in terms of neuronal state and activity. The invariants identified may mask important features. • Epistemic opacity: is the Vico maxim, that we can only understand what we can build, applicable to computer simulations of complex systems? Do we understand what we are able to imitate, model, or reconstruct – and in what sense? • Representational parsimony: much of our scientific progress and understanding stems from our cognitive ability to extract and generalise laws of nature. Representational parsimony, describing the universe in a minimal number of equations, is regarded as the ultimate understanding. However, should we expect a small set of laws to adequately describe a complex adaptive system like the brain? Engaging in large-scale brain simulation is a question of knowledge-dependent timing, proper integration of multiple bottom-up and top-down approaches, and realistic expectations. This was done while interacting with a wide community of experimental and computational neuroscientists, engineers and modellers at multiple levels of analyses worldwide, inside and outside the HBP. In their second article [2], DUDAI and XXXXX discuss in greater detail the role of context in brain simulation. The results will be included in a forthcoming HBP report (Milestone 220 of Task 12.2.1) on the role of context in bra...
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