Mainstream economics

Résumé

Mainstream economics is the body of knowledge, theories, and models of economics, as taught by universities worldwide, that are generally accepted by economists as a basis for discussion. Also known as orthodox economics, it can be contrasted to heterodox economics, which encompasses various schools or approaches that are only accepted by a minority of economists. The economics profession has traditionally been associated with neoclassical economics. This association has however been challenged by prominent historians of economic thought like David Collander. They argue the current economic mainstream theories, such as game theory, behavioral economics, industrial organization, information economics, and the like, share very little common ground with the initial axioms of neoclassical economics. History Economics has always featured multiple schools of economic thought, with different schools having different prominence across countries and over time. The current use of

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https://en.wikipedia.org/wiki/Mainstream_economics

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'Peak Oil' As Classical Economic Process

François Meynard

Forecasts about 'peak oil', the limits of oil production, are the subject of controversies. 'Peak oil' forecasts are based on extrapolations of a priori chosen mathematical models fitted to field data. Scientifically that is not very sound. To clarify this point, in this paper 'peak oil' is modelled in a generic reductionist, approach, in which the conflicting dynamic between economic forces and physical laws are well identified. This model finds a natural interpretation just supress in a classical economic analysis frame. The 'peak oil' controversy is thus brought into the field of economic thought as a debate between classical and mainstream economics, in particular between an objective and a faith based appraisal about this historical event. The goal is not to improve forecasts about 'peak oil', which is very likely not a predictable event, but to propose an objective framework for the analysis of its systemic effects on economic production.

Multi-Science Publ Co Ltd2014

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The building cultures of regions where plant resources abound show the adaptation of these local bio-based materials to the roofing of buildings: thatch, reed, bark or shingles. Despite a low environmental impact, these techniques require skilled manual work and remain unused compared to mainstream industrialised solutions based on terracotta, metal sheets or bituminous products. The emergence of digital tools capable of appreciating and manipulating objects with variable characteristics creates an opportunity for developing partially automated construction techniques by capitalising on pre-existing know-how. In particular, a type of wood shingle known as tavillon in Romandy, Switzerland is the primary object of this study. The objective is to describe and validate an entire construction scenario based on automation tools. The research plan paving the way for scenario implementation is another deliverable. We first carry out a state of the art of traditional tavillon technique before translating those rules into an algorithm. This instruction set is implemented in a parametric geometric model to design such roofs. Towards automation, the geometrical variability of the shingles makes it necessary to specify the model to these individual variations. A field study then demonstrates benefits from the automated installation are secured when framed into an industrial context, leading to the investigation of a panel-based prefabrication scenario.

2021

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OPPORTUNITY: Towards opportunistic activity and context recognition systems

Ricardo Andres Chavarriaga Lozano, Yu Fang, Daniel Roggen

Opportunistic sensing allows to efficiently collect information about the physical world and the persons behaving in it. This may mainstream human context and activity recognition in wearable and pervasive computing by removing requirements for a specific deployed infrastructure. In this paper we introduce the newly started European research project OPPORTUNITY within which we develop mobile opportunistic activity and context recognition systems. We outline the project’s objective, the approach we follow along opportunistic sensing, data processing and interpretation, and autonomous adaptation and evolution to environmental and user changes, and we outline preliminary results.

2009

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