3,991 research outputs found
Institutional Cognition
We generalize a recent mathematical analysis of Bernard Baars' model of human consciousness to explore analogous, but far more complicated, phenomena of institutional cognition. Individual consciousness is limited to a single, tunable, giant component of interacting cogntivie modules, instantiating a Global Workspace. Human institutions, by contrast, seem able to multitask, supporting several such giant components simultaneously, although their behavior remains constrained to a topology generated by cultural context and by the path-dependence inherent to organizational history. Surprisingly, such multitasking, while clearly limiting the phenomenon of inattentional blindness, does not eliminate it. This suggests that organizations (or machines) explicitly designed along these principles, while highly efficient at certain sets of tasks, would still be subject to analogs of the subtle failure patterns explored in Wallace (2005b, 2006). We compare and contrast our results with recent work on collective efficacy and collective consciousness
Institutional paraconsciousness and its pathologies
This analysis extends a recent mathematical treatment of the Baars consciousness model to analogous, but far more complicated, phenomena of institutional cognition. Individual consciousness is limited to a single, tunable, giant component of interacting cognitive modules, instantiating a Global Workspace. Human institutions, by contrast, support several, sometimes many, such giant components simultaneously, although their behavior remains constrained to a topology generated by cultural context and by the path-dependence inherent to organizational history. Such highly parallel multitasking - institutional paraconsciousness - while clearly limiting inattentional blindness and the consequences of failures within individual workspaces, does not eliminate them, and introduces new characteristic dysfunctions involving the distortion of information sent between global workspaces. Consequently, organizations (or machines designed along these principles), while highly efficient at certain kinds of tasks, remain subject to canonical and idiosyncratic failure patterns similar to, but more complicated than, those afflicting individuals. Remediation is complicated by the manner in which pathogenic externalities can write images of themselves on both institutional function and therapeutic intervention, in the context of relentless market selection pressures. The approach is broadly consonant with recent work on collective efficacy, collective consciousness, and distributed cognition
Distributionally Robust Optimization: A Review
The concepts of risk-aversion, chance-constrained optimization, and robust
optimization have developed significantly over the last decade. Statistical
learning community has also witnessed a rapid theoretical and applied growth by
relying on these concepts. A modeling framework, called distributionally robust
optimization (DRO), has recently received significant attention in both the
operations research and statistical learning communities. This paper surveys
main concepts and contributions to DRO, and its relationships with robust
optimization, risk-aversion, chance-constrained optimization, and function
regularization
Geometry of Radial Basis Neural Networks for Safety Biased Approximation of Unsafe Regions
Barrier function-based inequality constraints are a means to enforce safety
specifications for control systems. When used in conjunction with a convex
optimization program, they provide a computationally efficient method to
enforce safety for the general class of control-affine systems. One of the main
assumptions when taking this approach is the a priori knowledge of the barrier
function itself, i.e., knowledge of the safe set. In the context of navigation
through unknown environments where the locally safe set evolves with time, such
knowledge does not exist. This manuscript focuses on the synthesis of a zeroing
barrier function characterizing the safe set based on safe and unsafe sample
measurements, e.g., from perception data in navigation applications. Prior work
formulated a supervised machine learning algorithm whose solution guaranteed
the construction of a zeroing barrier function with specific level-set
properties. However, it did not explore the geometry of the neural network
design used for the synthesis process. This manuscript describes the specific
geometry of the neural network used for zeroing barrier function synthesis, and
shows how the network provides the necessary representation for splitting the
state space into safe and unsafe regions.Comment: Accepted into American Control Conference (ACC) 202
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