Organizational learning | EPFL Graph Search

Organizational learning is the process of creating, retaining, and transferring knowledge within an organization. An organization improves over time as it gains experience. From this experience, it is able to create knowledge. This knowledge is broad, covering any topic that could better an organization. Examples may include ways to increase production efficiency or to develop beneficial investor relations. Knowledge is created at four different units: individual, group, organizational, and inter organizational. The most common way to measure organizational learning is a learning curve. Learning curves are a relationship showing how as an organization produces more of a product or service, it increases its productivity, efficiency, reliability and/or quality of production with diminishing returns. Learning curves vary due to organizational learning rates. Organizational learning rates are affected by individual proficiency, improvements in an organization's technology, and improvements in the structures, routines and methods of coordination. Organizational learning happens as a function of experience within an organization and allows the organization to stay competitive in an ever-changing environment. Organizational learning is a process improvement that can increase efficiency, accuracy, and profits. A real-world example of organizational learning is how a new pizza store will reduce the cost per pizza as the cumulative production of pizzas increases. As the staff creates more pizza; they begin to make pizzas faster, the staff learns how to work together, and the equipment is placed in the most efficient location leading to cheaper costs of creation. An example of a more formal way to track and support organizational learning is a learning agenda. Organizational learning is an aspect of organizations and a subfield of organizational studies. As an aspect of an organization, organizational learning is the process of creating, retaining, and transferring knowledge.

It’s All Relative: Learning Interpretable Models for Scoring Subjective Bias in Documents from Pairwise Comparisons

Matthias Grossglauser, Aswin Suresh, Chi Hsuan Wu

We propose an interpretable model to score the subjective bias present in documents, based only on their textual content. Our model is trained on pairs of revisions of the same Wikipedia article, where one version is more biased than the other. Although pr ...

2024