The Probabilistic Innovation Field of Scientific Enquiry

The Probabilistic Innovation Field of Scientific Enquiry

Chris William Callaghan (School of Economic and Business Sciences, University of the Witwatersrand, South Africa)
Copyright: © 2019 |Pages: 18
DOI: 10.4018/978-1-5225-8362-2.ch083


This paper outlines a developing stream of literature related to a theoretical framework, namely probabilistic innovation, relating it to certain literature critiquing developing academic fields and offering criteria against which a developing field can be held to be scientific or not. With ontological and epistemological roots in citizen science, participant-research and other emerging academic movements, probabilistic innovation theory is underpinned by a shift toward the democratisation of knowledge, increasing inclusivity and transparency in its processes, and a novel methodology drawing from methods such as expert crowdsourcing, crowdfunding, swarm solving and maximisation of efficiencies related to collaborations in research. With the enablement of real time research capability, or ability to solve serious scientific problems in hours and days instead of years and decades as its primary raison d'être, probabilistic innovation seeks to maximise probability of scientific breakthroughs. However, for this stream of literature to develop, it is important to proactively identify academic or scientific pitfalls facing developing areas of enquiry, and this paper attempts to offer this, deriving propositions potentially useful for developing fields in general. It is hoped real time research capability will one day become a reality, and to increase the likelihood of this, theoretical frameworks seeking this end need to face useful critique to ensure their robust development, and this paper proposed four core tensions such fields need to manage in order to successfully evolve.
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This paper outlines a developing stream of literature related to a theoretical framework, namely probabilistic innovation (PI), and relates it to certain literature that offers criteria against which a developing scientific field can be held to be scientific or not. According to Callaghan (2015), PI has taken form in the wake of theoretical developments related to how the knowledge aggregation problem (Hayek, 1945; von Hippel, 1994) can better be managed due to technological advances (Kaplan and Haenlein 2011; Scannell, Blanckley, Boldon, & Warrington 2012), big data (Manyika, Chui, Brown, Bughin, Dobbs, Roxburgh, & Byers, 2011), and the successes of novel methods of scientific research such as crowdsourcing and crowdsourced research and development (R&D) (Torr-Brown, 2013). PI theory can therefore offer useful insights into how real-time research problem solving can be enabled (Callaghan, 2015). The knowledge aggregation problem relates to the ‘stickiness of knowledge’ (von Hippel, 1994), or the difficulties inherent in bringing knowledge together across geographical or other boundaries. Given the extensive use of crowdsourcing in fields such as medicine, both for large scale data collection as well as for analysis, the ethical use of such methods is important (Callaghan, 2016a), and therefore knowledge of the criteria against which a field can be considered scientific can provide a useful roadmap for researchers studying and using these methods, lest they be used in way that are not ethical, or violate the ethical principles of PI. Ontological and epistemological perspectives of PI are aligned with the emergence of social movements such as citizen science and participant-led research (Bonney, Cooper, Dickinson, Kelling, Phillips, Rosenberg & Shirk, 2009; Shirk, 2009; Shirk, Ballard, Wilderman, Phillips Wiggins, Jordan, McCallie, Minarchek, Lewenstein, Krasny, & Bonney, 2012; Vayena & Tasioulas, 2013) which maximise transparency and widespread engagement of affected stakeholders in the scientific research process, as well as with an ethical framework based on these principles (Funtowicz & Ravetz, 1994). PI theory (Callaghan, 2015) makes the argument that real time research capability is ultimately possible, or that serious scientific problems (for example, Zika, Ebola, cancer, diabetes, etc.) will eventually be able to be solved in a matter of hours or days, rather than years or decades. Following Callaghan (2014), PI theory is defined as a body of theory that relates to how a probabilistic relationship relates inputs into scientific problem solving to the outcomes of the research process itself, or how probabilistic systems of knowledge creation can be used to harness economies of scale in scientific research. The objective of PI as a field of enquiry is theory development as well as drawing multidisciplinary insights to bear on the goal of developing real time research capability.

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