Complexity Economics: A Different Framework for Economic Thought
By M. Moersch. 2013
213 citations recorded by the source corpus.
Subfields: Economics and Econometrics
View Paper: https://www.semanticscholar.org/paper/a108a33ae76a150d678ae571dd3554b012ea33bc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Josh Ryan‐Collins
- M. Sanders
- J. Content
- E. Stam
- Andrew H. Van de Ven
- Mariana Mazzucato
- Rainer Kattel
- N. Bosma
Citations in the corpus
- Challenge-Driven Innovation Policy: Towards a New Policy Toolkit (2019, background): “For our purposes, this definition should be rejected, since a) it is in fact allocative efficiency by another name; and b) under conditions of uncertainty where there is no known finite range of possibilities, the concept of ‘optimality’ is meaningless (Arthur 2014).”
- Institutions, entrepreneurship, and economic growth in Europe (2018, background): “A promising direction to do so understands the economy as a complex system (Beinhocker 2006; Arthur 2013), in which entrepreneurs are key agents who drive the creation of new organizational arrangements, technologies, and institutions, to better solve problems in society.”
- Entrepreneurial ecosystem elements (2019, background): “The aim of our entrepreneurial ecosystem model is not to predict, but to better understand how (entrepreneurial) economies function (Thurik et al. 2013) and in particular how they ‘produce’ entrepreneurship as an emerging property of the system (Arthur 2013).”
- Measuring Entrepreneurial Ecosystems (2016, background): “Complexity economics (Arthur 2013) provides the conceptual basis for analysing the relation of entrepreneurial ecosystems with outputs (entrepreneurship) and outcomes (value creation and structural change).”
- Digital technologies, innovation, and skills: emerging trajectories and challenges (2021, background): “The first challenge that we face is understanding and predicting the trajectories of digital technologies, due to the complex and nonlinear relation and interdependence among digital technologies, organizations, and skills (Archibugi, 2017; Arthur, 2009; 2013).”