Open Source Process Insights From ‘Microbial Learning': Toward Real-Time Scientific Research Capability

Open Source Process Insights From ‘Microbial Learning': Toward Real-Time Scientific Research Capability

Chris William Callaghan (School of Economic and Business Sciences, University of the Witwatersrand, Johannesburg, South Africa & University of the Witwatersrand, Johannesburg, South Africa)
Copyright: © 2019 |Pages: 15
DOI: 10.4018/IJSKD.2019040101


Microbes learn through exponential processes, whereby they reproduce in exponential form (one can produce two, which can produce four, and so on). A single mutation against an antibiotic, for example, can form the basis for a new strain, via this process of exponential replication, which is conceptualised here as microbial ‘learning.' This article plots the hypothesised trajectory of microbial learning against that of human research and development (R&D) efforts, or R&D learning that is tasked with replacing the categories of antibiotic drugs that are failing. The research problem addressed in this research is the failure of scientific research, conceptualised as human learning, to keep pace with problems such as the growth in antibiotic resistance, or microbial learning. Open processes of learning described in terms of networked science theory are identified as an important theoretical framework within which to locate this knowledge problem. In light of potentially catastrophic threats like total antibiotic failure, it is argued that the formalisation of a scientific methodology in the form of crowdsourced R&D derived from networked science principles may offer useful insights into how to improve R&D efficiency and effectiveness, across fields and contexts.
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Kuhn’s (1972) and Popper’s (1963) arguments are useful in their illustration of problems with the progression of science. According to Popper (1963, p. 6), Einstein’s theory that light is influenced by gravitational forces is falsifiable, in contrast to examples of other postulated theory that are “compatible with the most divergent human behaviour, so that it was practically impossible to describe any human behaviour that might not be claimed to be a verification of these theories”. Whereas Popper’s (1963) criterion of falsification promises a useful basis for scientific advancement, Kuhn’s (1972) theory predicts that the paradigms created by scientific shared values and norms fundamentally hold back scientific discovery, as such criteria are subordinate to the paradigms of researchers. According to Kuhn’s (1972) theory, scientific advancement may be fundamentally constrained by the ‘social science’ aspects of human research endeavour.

Certain evidence can be taken to support Kuhn’s theory. Certain of the highest cited papers in history, as well as certain of its most influential books were first rejected by journal reviewers and editors. This includes work eventually awarded the Nobel Prize in Physics, Chemistry, Physiology and Medicine (Campanario, 2009). It is not only social forces that seem to constrain scientific advancement, however.

Certain theoretical frameworks suggest that innovation, or returns to investments in research, are declining over time. An example is Kortum’s (1997) theory, from the field of economics, which explains that patents per researcher decrease because technological breakthroughs become harder to achieve over time. In contrast Romer’s (1990) endogenous growth theory provides a rationale for positive externalities and spillover effects that result in a production function that does not exhibit decreasing returns to scale. The implication of Romer’s theory is that there should be increasing returns to scale in research. Nevertheless, evidence of a decline in technological progress persists (Cowen, 2011; Gordon, 2016), lending support to Kortum’s theory, instead.

The consequences of declining returns to investment in research have serious implications for our ability to deal with societal dangers that require timeous research solutions. Key to Romer’s (1990) theory is the notion that ideas are non-rivalrous. In other words, ideation, or the creation of ideas cause technological progress as they can be used by others. However, also within the endogenous growth framework, Segerstrom (1997) uses the example of microprocessor industry, where challenges increase exponentially with complexity to offer a model that demonstrates how R&D becomes progressively more difficult over time, as endogenous growth can occur without scale effects.

If the attainment of research breakthroughs is becoming progressively more difficult over time, a declining technological rate of progress does not bode well for our rate of global R&D learning. When compared with microbial learning, or the exponential rate at which microorganisms learn, an extrapolation of the rate of R&D learning against that of microorganisms offers cautionary insights. Indeed, certain societal consequences may result from declining technological rates of human research progress, and the lack of a theoretical rationale to explain how scale effects in R&D can be effectively enabled.

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