On the explanatory depth and pragmatic value of coarse-grained, probabilistic, causal explanations

This article considers the thesis that a more proportional relationship between a cause and its effect yields a more abstract causal explanation of that effect, thereby producing a deeper explanation. This thesis has important implications for choosing the optimal granularity of explanation for a given explanandum. In this article, I argue that this thesis is not generally true of probabilistic causal relationships. In light of this finding, I propose a pragmatic measure of explanatory depth. This measure uses a decision-theoretic model of information pricing to determine the optimal granularity of explanation for a given explanandum, agent, and decision problem

The problem of granularity for scientific explanation

This dissertation aims to determine the optimal level of granularity for the variables used in probabilistic causal models. These causal models are useful for generating explanations in a number of scientific contexts. In Chapter 1, I argue that there is rarely a unique level of granularity at which a given phenomenon can be causally explained, thereby rejecting various causal exclusion arguments. In Chapter 2, I consider several recent proposals for measuring the explanatory power of causal explanations, and show that these measures fail to track the comparative depth of explanations given at different levels of granularity. In Chapter 3, I offer a pragmatic account of how to partition the measure space of a causal variable so as to optimally explain its effect. My account uses the decision-theoretic notion of value of information, and indexes the relative depth of an explanation to a particular agent faced with a particular decision problem. Chapter 4 applies this same decisiontheoretic framework to answer the epistemic question of how to discover constitutive relationships in nature. In Chapter 5, I describe the formal details of the relationship between random variables that are meant to be coarse-grained and fine-grained representations of the same type of phenomenon. I use this formal framework to rebut a popular argument for the view that special science probabilities can be objective chances. Chapter 6 discusses challenges related to the causal interpretation of Bayes nets that use imprecise rather than precise probabilities

KInNeSS: A Modular Framework for Computational Neuroscience

Making use of very detailed neurophysiological, anatomical, and behavioral data to build biological-realistic computational models of animal behavior is often a difficult task. Until recently, many software packages have tried to resolve this mismatched granularity with different approaches. This paper presents KInNeSS, the KDE Integrated NeuroSimulation Software environment, as an alternative solution to bridge the gap between data and model behavior. This open source neural simulation software package provides an expandable framework incorporating features such as ease of use, scalabiltiy, an XML based schema, and multiple levels of granularity within a modern object oriented programming design. KInNeSS is best suited to simulate networks of hundreds to thousands of branched multu-compartmental neurons with biophysical properties such as membrane potential, voltage-gated and ligand-gated channels, the presence of gap junctions of ionic diffusion, neuromodulation channel gating, the mechanism for habituative or depressive synapses, axonal delays, and synaptic plasticity. KInNeSS outputs include compartment membrane voltage, spikes, local-field potentials, and current source densities, as well as visualization of the behavior of a simulated agent. An explanation of the modeling philosophy and plug-in development is also presented. Further developement of KInNeSS is ongoing with the ultimate goal of creating a modular framework that will help researchers across different disciplines to effecitively collaborate using a modern neural simulation platform.Center for Excellence for Learning Education, Science, and Technology (SBE-0354378); Air Force Office of Scientific Research (F49620-01-1-0397); Office of Naval Research (N00014-01-1-0624

Approximate Causal Abstraction

Scientific models describe natural phenomena at different levels of abstraction. Abstract descriptions can provide the basis for interventions on the system and explanation of observed phenomena at a level of granularity that is coarser than the most fundamental account of the system. Beckers and Halpern (2019), building on work of Rubenstein et al. (2017), developed an account of abstraction for causal models that is exact. Here we extend this account to the more realistic case where an abstract causal model offers only an approximation of the underlying system. We show how the resulting account handles the discrepancy that can arise between low- and high-level causal models of the same system, and in the process provide an account of how one causal model approximates another, a topic of independent interest. Finally, we extend the account of approximate abstractions to probabilistic causal models, indicating how and where uncertainty can enter into an approximate abstraction

Computer-aided programming for multiprocessing systems

As both the number of processors and the complexity of problems to be solved increase, programming multiprocessing systems becomes more difficult and error-prone. This report discusses parallel models of computation and tools for computer-aided programming (CAP). Program development tools are necessary since programmers are not able to develop complex parallel programs efficiently. In particular, a CAP tool, named Hypertool, is described here. It performs scheduling and handles the communication primitive insertion automatically so that many errors are eliminated. It also generates the performance estimates and other program quality measures to help programmers in improving their algorithms and programs. Experiments have shown that up to a 300% performance improvement can be achieved by computer-aided programming

Propagation of Pericentral Necrosis During Acetaminophen-Induced Liver Injury: Evidence for Early Interhepatocyte Communication and Information Exchange.

Acetaminophen (APAP)-induced liver injury is clinically significant, and APAP overdose in mice often serves as a model for drug-induced liver injury in humans. By specifying that APAP metabolism, reactive metabolite formation, glutathione depletion, and mitigation of mitochondrial damage within individual hepatocytes are functions of intralobular location, an earlier virtual model mechanism provided the first concrete multiattribute explanation for how and why early necrosis occurs close to the central vein (CV). However, two characteristic features could not be simulated consistently: necrosis occurring first adjacent to the CV, and subsequent necrosis occurring primarily adjacent to hepatocytes that have already initiated necrosis. We sought parsimonious model mechanism enhancements that would manage spatiotemporal heterogeneity sufficiently to enable meeting two new target attributes and conducted virtual experiments to explore different ideas for model mechanism improvement at intrahepatocyte and multihepatocyte levels. For the latter, evidence supports intercellular communication via exosomes, gap junctions, and connexin hemichannels playing essential roles in the toxic effects of chemicals, including facilitating or counteracting cell death processes. Logic requiring hepatocytes to obtain current information about whether downstream and lateral neighbors have triggered necrosis enabled virtual hepatocytes to achieve both new target attributes. A virtual hepatocyte that is glutathione-depleted uses that information to determine if it will initiate necrosis. When a less-stressed hepatocyte is flanked by at least two neighbors that have triggered necrosis, it too will initiate necrosis. We hypothesize that the resulting intercellular communication-enabled model mechanism is analogous to the actual explanation for APAP-induced hepatotoxicity at comparable levels of granularity

Central peak position in magnetization loops of high-TcT_c superconductors

Exact analytical results are obtained for the magnetization of a superconducting thin strip with a general behavior J_c(B) of the critical current density. We show that within the critical-state model the magnetization as function of applied field, B_a, has an extremum located exactly at B_a=0. This result is in excellent agreement with presented experimental data for a YBCO thin film. After introducing granularity by patterning the film, the central peak becomes shifted to positive fields on the descending field branch of the loop. Our results show that a positive peak position is a definite signature of granularity in superconductors.Comment: $ pages, 6 figure

Reflective visualization and verbalization of unconscious preference

A new method is presented, that can help a person become aware of his or her unconscious preferences, and convey them to others in the form of verbal explanation. The method combines the concepts of reflection, visualization, and verbalization. The method was tested in an experiment where the unconscious preferences of the subjects for various artworks were investigated. In the experiment, two lessons were learned. The first is that it helps the subjects become aware of their unconscious preferences to verbalize weak preferences as compared with strong preferences through discussion over preference diagrams. The second is that it is effective to introduce an adjustable factor into visualization to adapt to the differences in the subjects and to foster their mutual understanding.Comment: This will be submitted to KES Journa