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Explicating Top-Down Causation Using Networks and Dynamics | Philosophy of Science: Vol 84, No 2
In many fields in the life sciences investigators refer to downward or top-down causal effects. Craver and I defended the view that such cases should be understood in terms of a constitution relation between levels in a mechanism and intralevel causal relations (occurring at any level). We did not, however, specify when entities constitute a higher-level mechanism. In this article I appeal to graph-theoretic representations of networks, now widely employed in systems biology and neuroscience, and associate mechanisms with modules that exhibit high clustering. As a result of interconnections within clusters, mechanisms often exhibit complex dynamic behaviors that constrain how individual components respond to external inputs, a central feature of top-down causation.
philosophy_of_science  networks  social_networks  dynamics  explanation  causality 
5 hours ago by rvenkat
Explainable Artificial Intelligence
New machine-learning systems will have the ability to explain their rationale, characterize their strengths and weaknesses, and convey an understanding of how they will behave in the future. The strategy for achieving that goal is to develop new or modified machine-learning techniques that will produce more explainable models. These models will be combined with state-of-the-art human-computer interface techniques capable of translating models into understandable and useful explanation dialogues for the end user (Figure 2). Our strategy is to pursue a variety of techniques in order to generate a portfolio of methods that will provide future developers with a range of design options covering the performance-versus-explainability trade space.
A-I  explanation  Futurism  engineering  machine_learning 
3 days ago by suitable
En hypotetisk riksdag
Höginkomsttagare röstar blått, och LO-medlemmar rött. Eller? Med data från SCB* kan du själv testa hur riksdagen skulle kunna se ut om en viss grupp fick bestämma.
19 days ago by johanl
What is a likelihood anyway?
In order to fit Bayesian models we need to construct a function that tells us when certain values of model unknowns are good or bad. For example, in image Figure 2, we plot a histogram of values of some random variable X. We want to fit a density function to this histogram so as to be able to make probabilistic statements about the likely distribution of yet-unseen observations. To gauge which proposed density is a good one, we would like a function that gives a higher value for the proposed model unknowns that lead to the distribution in the bottom panel and lower values for the proposed model unknowns that lead to distribution in the top panel.
stats  math  explanation  jupyter  bayes  read-later 
21 days ago by kmt

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