Is the Human Distinguishable from the Animal by being a Rational Animal? \ud In Principles of Nature and Grace by G. W. Leibniz

The present paper aims to proceed to a succinct analysis of Leibniz"s Principles of Nature and Grace (section 5), by exploiting the cardinal arguments acquainted in it – namely, is he human distinguishable from the animal thanks to his capacity of being endowed with rationality? Henceforth, for answering this question, the herewith paper obeys to a linear analysis and presents a corpus structured in three main parts. The first two parts aims to highlight the text, through furnishing explanations on the main concepts and concerns, while the third part of the corpus offers at prima facie a criticism towards the Leibnizian principle – according to which humans are rational – in order to finally strengthen this latter principle, by emphazing that there are no solid disparagements (confutatio) towards it

A Neglected Additament: Peirce on Logic, Cosmology, and the Reality of God

Two different versions of the ending of the first additament to C. S. Peirce's 1908 article, "A Neglected Argument for the Reality of God," appear in the Collected Papers but were omitted from The Essential Peirce. In one, he linked the hypothesis of God's Reality to his entire theory of logic as semeiotic, claiming that proving the latter would also prove the former. In the other, he offered a final outline of his cosmology, in which the Reality of God as Ens necessarium is indispensable to both the origin and order of our existing universe of Signs. Exploring these passages, as well as the unpublished manuscript drafts of the article, provides important insights into the key concepts of instinct and continuity within Peirce's comprehensive system of thought

Diagrammatic Reasoning and Modelling in the Imagination: The Secret Weapons of the Scientific Revolution

Just before the Scientific Revolution, there was a "Mathematical Revolution", heavily based on geometrical and machine diagrams. The "faculty of imagination" (now called scientific visualization) was developed to allow 3D understanding of planetary motion, human anatomy and the workings of machines. 1543 saw the publication of the heavily geometrical work of Copernicus and Vesalius, as well as the first Italian translation of Euclid

Stated Choice Experiments with Complex Ecosystem Changes: The Effect of Information Formats on Estimated Variances and Choice Parameters

Stated choice experiments about ecosystem changes involve complex information. This study examines whether the format in which ecosystem information is presented to respondents affects stated choice outcomes. Our analysis develops a utility-maximizing model to describe respondent behavior. The model shows how alternative questionnaire formats alter respondents’ use of filtering heuristics and result in differences in preference estimates. Empirical results from a large-scale stated choice experiment confirm that different format presentations of the same information lead to different preference parameter estimates and error variances. A tabular format results in choice parameter estimates with statistically smaller variances than parameters estimated from data obtained with a text-based format. A text-based format also appears to induce greater use of decision heuristics than does a tabular format.choice experiments, heuristics, stated preference, valuation, web surveys, wetland mitigation, Crop Production/Industries, Demand and Price Analysis,

Mechanistic Models and the Explanatory Limits of Machine Learning

We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with (i.e. intelligibility) severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex (i.e. it includes an increasing number of components), the less explanatory it will be. Since machine learning increases its performances when more components are added, then it generates models which are not intelligible, and hence not explanatory

Computer-assisted argument mapping: A Rationale Approach

Computer-Assisted Argument Mapping (CAAM) is a new way of understanding arguments. While still embryonic in its development and application, CAAM is being used increasingly as a training and development tool in the professions and government. Inroads are also being made in its application within education. CAAM claims to be helpful in an educational context, as a tool for students in responding to assessment tasks. However, to date there is little evidence from students that this is the case. This paper outlines the use of CAAM as an educational tool within an Economics and Commerce Faculty in a major Australian research university. Evaluation results are provided from students from a CAAM pilot within an upper-level Economics subject. Results indicate promising support for the use of CAAM and its potential for transferability within the disciplines. If shown to be valuable with further studies, CAAM could be included in capstone subjects, allowing computer technology to be utilised in the service of generic skill development

Mechanistic Models and the Explanatory Limits of Machine Learning

We argue that mechanistic models elaborated by machine learning cannot be explanatory by discussing the relation between mechanistic models, explanation and the notion of intelligibility of models. We show that the ability of biologists to understand the model that they work with (i.e. intelligibility) severely constrains their capacity of turning the model into an explanatory model. The more a mechanistic model is complex (i.e. it includes an increasing number of components), the less explanatory it will be. Since machine learning increases its performances when more components are added, then it generates models which are not intelligible, and hence not explanatory

Scientific Argumentation as a Foundation for the Design of Inquiry-Based Science Instruction

Despite the attention that inquiry has received in science education research and policy, a coherent means for implementing inquiry in the classroom has been missing [1]. In recent research, scientific argumentation has received increasing attention for its role in science and in science education [2]. In this article, we propose that organizing a unit of instruction around building a scientific argument can bring inquiry practices together in the classroom in a coherent way. We outline a framework for argumentation, focusing on arguments that are central to science—arguments for the best explanation. We then use this framework as the basis for a set of design principles for developing a sequence of inquiry-based learning activities that support students in the construction of a scientific argument. We show that careful analysis of the argument that students are expected to build provides designers with a foundation for selecting resources and designing supports for scientific inquiry. Furthermore, we show that creating multiple opportunities for students to critique and refine their explanations through evidence-based argumentation fosters opportunities for critical thinking, while building science knowledge and knowledge of the nature of science