Human Life as a State of Mediocrity in John Locke

This chapter investigates the genesis and evolution of Locke’s idea of human life as a “state of mediocrity”. While this idea had ancient roots going back to the early Church fathers, it remained current in the seventeenth century where mediocrity was generally equated with a condition of partial ignorance and imperfection. Locke’s account of it is original; while life is a time of mediocrity, death opens the way to the extremes of eternal misery or eternal happiness. Initially, inspired by the Church fathers, Locke conceived of human life as a condition of intellectual mediocrity. Subsequently, and arguably prompted by his reading of the pessimistic outlooks of Nicole and Pascal, he redefined the state of mediocrity in more optimistic terms: humans are naturally suited to their mediocre state. A further development of his conception of mediocrity, again involving a partial rethinking of the human condition, can be found in the Essay, where Locke represents mediocrity as an imperfect state of insatiable desire. It is redeemed, however, by the ability of living human beings to attain perfect knowledge of morality.

Modeling and Validation of Hydro Cascade Operation Considering Price Uncertainty

Most studies involving the use of hydropower in an electric power system tend to consider the point of view of the system operator even though under liberalized markets in Europe, the operation of hydro units is set by the owner to maximize their profits. Such studies also often neglect uncertainties related to hydropower operation and instead assume perfect knowledge of the system conditions over the simulation horizon. This paper presents a methodology to overcome the aforementioned limitations. We optimize the operational choices of a hydropower cascade owner with multiple linked hydro assets and the ability to participate in several energy and reserve markets while also accounting for the impact of market price uncertainties on the owner’s operating decisions. The versatile optimization model created includes a detailed representation of any selected hydro cascade’s topology, constraints to reflect the machinery characteristics, and a rolling horizon approach to account for the price uncertainties in the daily operating schedule. The model is first validated using historical data for a hydro cascade in Switzerland and a perfect-knowledge approach. Next, price uncertainty is added to improve the historical simulation results and find a trade-off between accuracy and computational time.

Stopping criteria for ending autonomous, single detector radiological source searches

While the localization of radiological sources has traditionally been handled with statistical algorithms, such a task can be augmented with advanced machine learning methodologies. The combination of deep and reinforcement learning has provided learning-based navigation to autonomous, single-detector, mobile systems. However, these approaches lacked the capacity to terminate a surveying/search task without outside influence of an operator or perfect knowledge of source location (defeating the purpose of such a system). Two stopping criteria are investigated in this work for a machine learning navigated system: one based upon Bayesian and maximum likelihood estimation (MLE) strategies commonly used in source localization, and a second providing the navigational machine learning network with a “stop search” action. A convolutional neural network was trained via reinforcement learning in a 10 m × 10 m simulated environment to navigate a randomly placed detector-agent to a randomly placed source of varied strength (stopping with perfect knowledge during training). The network agent could move in one of four directions (up, down, left, right) after taking a 1 s count measurement at the current location. During testing, the stopping criteria for this navigational algorithm was based upon a Bayesian likelihood estimation technique of source presence, updating this likelihood after each step, and terminating once the confidence of the source being in a single location exceeded 0.9. A second network was trained and tested with similar architecture as the previous but which contained a fifth action: for self-stopping. The accuracy and speed of localization with set detector and source initializations were compared over 50 trials of MLE-Bayesian approach and 1000 trials of the CNN with self-stopping. The statistical stopping condition yielded a median localization error of ~1.41 m and median localization speed of 12 steps. The machine learning stopping condition yielded a median localization error of 0 m and median localization speed of 17 steps. This work demonstrated two stopping criteria available to a machine learning guided, source localization system.

Does Science Need God? A Theistic Argument from Science

In our paper, we put forward an argument for the existence of God that starts with a description of the goal of science. The fact that science approximates perfect knowledge opens the problem of its status. We proceed to three resolutions of the problem: perfect knowledge is only a kind of fictional idealization; it will be reached by humanity in the future; it is God’s knowledge. We point out the weaknesses of the first two options. Next, we go on to draw the conclusion that it is hardly possible to describe the goal of science without some theistic or near-theistic concepts.

Instrumental diagnostic methods applied for orchiectomy in cockerels

Any common surgical procedure, including orchiectomy in cockerels, requires perfect knowledge in topography and syntopy of the organ the surgery is performed on. This also implies acquiring the necessary information via the application of a number of instrumental methods such as diagnostic roentgenography and diagnostic sonography. Both methods have positive (intravital, painless and quick procedure) as well as negative (possible flaws in diagnostics due to age peculiarities) aspects.

Inquiry Systems as Coping Mechanisms

First and foremost, Inquiry Systems or ISs are major models for the production and authentication of credible knowledge in which, along with Ethics, we put our basic trust to guide our lives. However, at the same time, ISs also serve as fundamental coping mechanisms to alleviate the intense anxiety that accompanies the immense uncertainty associated with less than perfect knowledge, especially in today’s problematic and highly uncertain world.

Control power as a special case of protean power: thoughts on Peter Katzenstein and Lucia Seybert's Protean Power: Exploring the Uncertain and Unexpected in World Politics

Human experience of control is an illusion; all forms of power are a special, transient, and unstable case of protean power. Taking risks is governed by critical uncertainty less because of our lack of perfect knowledge than because the world is physically and socially indeterminate. Power, thus, lies not only in agents' potential to dominate each other, but also in acting in concert to turn propensities into reality. Radical uncertainty is, therefore, not necessarily bad news. Whether protean power endangers or protects humanity depends less on calculating risks than on agents practicing common humanity values. I revise Katzenstein's and Seybert's concepts accordingly and illustrate by discussing Artificial Intelligence's challenges to humanity.

From Knowability to Conjecturability

Arguments from knowability have largely been concerned with cases for and against realism, or truth as an epistemic vs. non-epistemic concept. This article proposes bringing Peirce’s pragmaticism, called here ‘action-first’ epistemology, to bear on the issue. It is shown that a notion weaker than knowability, namely conjecturability, is epistemologically a better-suited notion to describe an essential component of scientific inquiry. Moreover, unlike knowability, conjecturability does not suffer from paradoxes. Given fundamental uncertainty that permeates inquiry, knowability and what Peirce took to be ‘perfect knowledge’ lose their appeal in epistemology of science. From the points of view of the logic for pragmatics and the modal translations given in this article, conjecturability and pragmaticism provide an enriched epistemology for scientific practices that can accommodate both epistemic and non-epistemic values.