The impact of increasing returns on knowledge and big data: from Adam Smith and Allyn Young to the age of machine learning and digital platforms

Author:

Hu Yao-Su

Abstract

Allyn Young's concept of increasing returns (not to be confused with static, equilibrium constructs of economies of scale and increasing returns to scale) is applied to analyse how and why increasing returns arise in the production (generation) and use (application) of knowledge and big data, thereby driving economic growth and progress. Knowledge is chosen as our focus because it is said to be our most powerful engine of production, and big data are included to make the analysis more complete and recent. We analyse four mechanisms or sources of increasing returns in the production of knowledge, and four in the use of knowledge. Turning to big data, we analyse increasing returns in the functioning of digital platforms and increasing returns in machine learning from gigantic amounts of training data. Concluding remarks concern some key differences between big data and knowledge, some policy implications, and some of the social negative impacts from the ways in which big data are being used.

Publisher

Pluto Journals

Subject

Earth-Surface Processes,Geography, Planning and Development

Reference103 articles.

1. ‘Whether it be the older literature on research and development or the modern New Growth Theory, the mainstream account runs along the following lines. Knowledge is produced privately using a sausage-machine called research and development that takes in inputs and gives off technological knowledge, which then immediately augments the production function for other goods’ (Langlois, 1999, p.249). This characterization applies to Paul Romer's path-breaking paper (1990) in which knowledge and new designs are generated in the R&D department of firms, and the spillover of such knowledge to all other firms is reflected by including the total stock of knowledge in the economy in the production function of the representive firm (see also Kurz, 2012, pp.95–7). The growing literature on ‘non-R&D’ sources of knowledge production and/or innovation (Barge-Gil, Nieto and Santamaria., 2011; Lee and Walsh, 2016), together with the difficulty of drawing the line, within firms, between R&D, design, engineering, prototyping and scaling up from pilot plants (Freeman and Soete, 2009), should put question marks over such an approach.

2. Economic growth is quantitative, progress qualitative.

3. According to Loasby (1999, p.135), 'The division of labour is the primary means of increasing the division of knowledge, and thereby of promoting the growth of knowledge. Knowledge grows by division: each of us can increase our knowledge only by accepting limits on what we can know.' According to Metcalfe (2014, p.17), 'The division of labour is a division of knowing and, moreover, the division of labour applies to the development of knowledge as well as to its application.'

4. Joan Robinson (1979, p.58) repeatedly pointed out that ‘a confusion between comparisons of imagined equilibrium positions and a process of accumulation going on through history’ was ‘an error in methodology’ on the part of neoclassical economists.

5. The knowledge management literature identifies at least four kinds of knowledge processes at the organizational level – knowledge creation, knowledge application, knowledge integration and knowledge retention (Kraaijenbrink, 2012). The sociology of knowledge literature distinguishes between processes of knowledge production – knowledge organization, dissemination-distribution, and storage-retrieval – and knowledge application (Holzner and Marx, 1979). The history of knowledge identifies at least 32 processes that can be grouped under the four main stages of knowledge gathering, analysing, disseminating and employing (Burke, 2016).

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

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