Reason

Summary

Reason is the capacity of applying logic consciously by drawing conclusions from new or existing information, with the aim of seeking the truth. It is closely associated with such characteristically human activities as philosophy, science, language, mathematics, and art, and is normally considered to be a distinguishing ability possessed by humans. Reason is sometimes referred to as rationality. Reasoning is associated with the acts of thinking and cognition, and involves the use of one's intellect. The field of logic studies the ways in which humans can use formal reasoning to produce logically valid arguments. Reasoning may be subdivided into forms of logical reasoning, such as deductive reasoning, inductive reasoning, and abductive reasoning. Aristotle drew a distinction between logical discursive reasoning (reason proper), and intuitive reasoning, in which the reasoning process through intuition—however valid—may tend toward the personal and the subjectively opaque. In some social

Official source

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

About this result

This page is automatically generated and may contain information that is not correct, complete, up-to-date, or relevant to your search query. The same applies to every other page on this website. Please make sure to verify the information with EPFL's official sources.

Related publications (100)

Related publications

Related people (16)

Related people

Related units (10)

Related units

Related concepts (155)

Related concepts

Related courses (106)

Related courses

Related lectures (161)

Related lectures

A Digital Platform connecting Academia and Small Businesses: An opportunity for Knowledge Management Experiments

Quentin Adrien Cavillier

Digital transformation is one of the biggest phenomena of this decade. One enabler of this revolution was cloud computing. Today, software engineers can deliver software as a service (SaaS), which is accessible to users through the Internet. Slack, Shopify, and Dropbox are examples of successful SaaS. We are experiencing an emergence of numerous small web applications with easy-to-use features for specific domains such as project management, accounting, resources management, customer relationships, and so forth. Thus, SaaS has been recognised as particularly well suited for the digital journey of small enterprises. However, surveys conducted by governmental agencies reveal a low adoption rate of SaaS by small enterprises in traditional sectors such as construction, services, and dealership. Previous studies have highlighted a lack of top management support, a weak relative advantage perceived, and a lack of knowledge as the key reasons for this low level of adoption. Whilst academic research defines these critical factors, it has not found potential solutions to mitigate them. Thus, this thesis presents two exploratory studies to enhance the adoption of digital practices by small traditional enterprises (STEs). A first study explored the value of leading collaborative projects between students and STEs about the assessment of SaaS solutions. A secondary objective of this initiative was the design of a platform acting as an open innovation platform. A three-year study allowed the completion of thirty-nine projects and the testing of two different platform designs. The feedback from students demonstrated the value of a well-structured platform composed of different management models for each project phase. Furthermore, 67 per cent of the STEs involved on the platform acknowledged the value of such collaborative projects. However, the collaborations did not improve top management support, and projects tended to stagnate because of various organisational factors. A second study, using a mixed-methods research design, was performed to explore the relations amongst the organisational factors acting as enablers and barriers to the digitalisation of STEs. The objective was to improve understanding of TSEs' situation and devise a proposition to enhance top management support. An academic contribution of this study was the design and application of a conceptual framework combining organisational factors from the literature on dynamic capabilities, organisational inertia, and intellectual capital. The combination of the qualitative observations and quantitative results highlighted the importance of the sensing and leadership capabilities for STE directors. They also revealed the mediator effect of the relational capability on the development of these latter. Therefore, this thesis makes important contributions to the literature on open innovation by defining a new field for leading knowledge management experiments. It also contributes to develop a comprehensive overview of the organisational factors affecting STEs' adoption. A further major contribution is the potential value of the digital platform to innovate the services of practitioners, such as governmental agencies and consultants, that support STEs on their digital transformation journey. In conclusion, this research is valuable in opening the discussion about potential innovations that might reshape the relationship academia-business.

EPFL2019

Transformations silencieuses : étude sur l'architecture alpine

Patrick Giromini

This thesis addresses the question of abandonment in architecture, not in its negative sense of desertion, but as a possibility offered to buildings which have lost their original purpose, which is to say those buildings from social and economic contexts which no longer exist. In particular, this study focusses on rural Alpine architecture, which has been considered in light of the findings of a case study on the Hérens valley in Valais. Although scholarship exists on abandonment, it has not yet been the subject of a specific investigation and is often used as a simple heuristic tool to read the phenomena which characterise mountainous regions. The objective is to understand rural architecture of the Alpine region and to guide future interventions on this area of research. This is accomplished by putting aside rhetorical readings, especially those which apply a notion of the vernacular without analysing the real scope of this term and its relevance when it is used in the description of the context which is treated here. A bipartite methodology corresponds to the two volumes of this thesis, which were produced in parallel throughout the research process. On one side, a series of key words that are habitually associated with rural architecture are analysed. Existing discourses are deconstructed, with the aim of reconstructing a new discourse that is capable of taking specificity into account on many levels, notably in relation to architectural plans. On the other side, there is fieldwork and the survey of the chosen locale, the Hérens valley, in order to test the formulated hypotheses, and to bring them to life through drafting tools. This has made it possible to invent an interpretative grid and a novel operating tool, which, relying on the analysis and knowledge of the original reasons for rural architecture in the Alpine region, will guide future interventions. As such, abandonment becomes a place of materialisation in the construction of the Alpine territory, giving it a horizon of meaning, which is to say that it is a limit case which directs and retains.

EPFL2021

"I choose this restaurant because they have vegan sandwiches" could be a typical explanation we would expect from a human. However, current Reinforcement Learning (RL) techniques are not able to provide such explanations, when trained on raw pixels. RL algorithms for state-of-the-art benchmark environments are based on neural networks, which lack interpretability, because of the very factor that makes them so versatile – they have many parameters and intermediate representations. Enforcing safety guarantees is important when deploying RL agents in the real world, and guarantees require interpretability of the agent. Humans use short explanations that capture only the essential parts and often contain few causes to explain an effect. In our thesis, we address the problem of making RL agents understandable by humans. In addition to the safety concerns, the quest to mimic human-like reasoning is of general scientific interest, as it sheds light on the easy problem of consciousness. The problem of providing interpretable and simple causal explanations of agent's behavior is connected to the problem of learning good state representations. If we lack such a representation, any reasoning algorithm's outputs would be useless for interpretability, since even the "referents" of the "thoughts" of such a system would be obscure to us. One way to define simplicity of causal explanations via the sparsity of the Causal Model that describes the environment: the causal graph has the fewest edges connecting causes to their effects. For example, a model for choosing the restaurant that only depends on the cause "vegan" is simpler and more interpretable than a model that looks at each pixel of a photo of the menu of a restaurant, and possibly relies as well on spurious correlations, such the style of the menu. In this thesis, we propose a framework "CauseOccam" for model-based Reinforcement Learning where the model is regularized for simplicity in terms of sparsity of the causal graph it corresponds to. The framework contains a learned mapping from observations to latent features, and a model predicting latent features at the next time-steps given ones from the current time-step. The latent features are regularized with the sparsity of the model, compared to a more traditional regularization on the features themselves, or via a hand-crafted interpretability loss. To achieve sparsity, we use discrete Bernoulli variables with gradient estimation, and to find the best parameters, we use the primal-dual constrained formulation to achieve a target model quality. The novelty of this work is in learning jointly a sparse causal graph and the representation taking pixels as the input on RL environments. We test this framework on benchmark environments with non-trivial high-dimensional dynamics and show that it can uncover the causal graph with the fewest edges in the latent space. We describe the implications of our work for developing priors enforcing interpretability.

2021