Probabilistic Innovation Theory

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Primary sources Probabilistic Innovation Theory is a theory developed by Chris William Callaghan, an Associate Professor in the School of Economic and Business Sciences (SEBS) at the University of the Witwatersrand, in Johannesburg, South Africa. He is Director of the Knowledge and Information Economics/Human Resources Research Agency (KIEHRA).[1]

Probabilistic Innovation Theory

The pace of knowledge and information sharing is increasing, and (social) media enables a large number of people (the crowd) to share a variety of items, reaching millions of people within hours. The question of how this quick and massive mobilisation of the crowd can be utilised to benefit scientific research and research and development (R&D) is increasingly important. The potential for real time, or near real time knowledge management is particularly important for disaster management.

Of central importance for the attainment of near real time research productivity, however, is the knowledge aggregation problem. This problem relates to the difficulties inherent in aggregating, or bringing together, knowledge from different people across contexts that are typically separated geographically, or by organisational or other boundaries. The knowledge aggregation problem limits the creation of knowledge and innovation, and too often there is failure in the integration and distribution of information. The ‘bottleneck’ conditions associated with the knowledge aggregation problem make it difficult to dramatically increase innovation. A useful example of these constraints is evident in the slow pace of pharmaceutical innovation.

The decentralised nature of knowledge and difficulties in transferring it therefore limit knowledge creation. A novel theoretical approach is thus needed. Drawing from both the knowledge management and innovation management fields, Probabilistic Innovation Theory predicts that the problem solving capacity of the crowd can exponentially increase knowledge creation. [2] Probabilistic Innovation Theory predicts that a probabilistic relationship exists between scientific breakthroughs and the resources invested in attaining them, or between any solvable research problem and the volume of inputs that go into solving it. This relationship, however, is expected to be more likely to manifest at high volumes of problem solving input. This theory predicts that near real time research productivity will ultimately be achieved through technological innovations that better manage the knowledge aggregation problem. Probabilistic Innovation Theory suggests that there are radically improved efficiencies in research and R&D that can be harnessed using methodologies like expert crowdsourced R&D and other crowd-based data collection and analysis techniques.

The mobilisation of large flows of knowledge using crowds has received increasing interest, both in the public and academic domains. New technological developments that support the mobilisation of problem solving capacity may have important implications for how crowds can be used to solve problems in real time. Due to the absence of a global real time problem solving system, Probabilistic Innovation Theory can provide useful insights into how to solve certain societally important problems. Several possible threats endanger global populations. These include potential global pandemics such as Ebola, Middle East Respiratory Syndrome (MERS), or rapidly developing antibiotic resistance. [3]

Improving the current global frameworks for real time research and problem solving is especially important in situations where the data that is needed to solve the problem or crisis, is only available after the onset of disasters. In essence, probabilistic innovation seeks to explain how, using a methodology made possible by new developments in technology (examples being crowdsourced R&D and social media applications), data collection and analysis can be exponentially increased, thus in turn increasing exposure of knowledge problems to the problem solving inputs of large numbers of ‘citizens.’ This may increase the rate at which ideas, knowledge and information can transmit to research outputs, as well as to more effective disaster management. [4]

However, to utilise the problem solving capacity of the crowd, transparency and inclusiveness needs to be increased substantially. A growing stream of literature advocates for more transparency in research, as well as for the inclusion of populations in scientific research (they are after all the stakeholders of scientific research, and are affected by slow progress in scientific research as well as by unethical research). Stakeholder engagement in this domain may also generate a more inclusive and transparent paradigm in bioethics, a step closer towards the democratisation of science. The democratisation of science relates to how inequality in access to knowledge and also to the benefits of knowledge can be reduced through increased transparency and involvement in science and its related issues. Global societal crises such as Ebola call for a re-think current approaches to disaster-related scientific problem solving that do not apply novel technologies, or new theoretical insights that have emerged in the wake of these technological developments. The contribution of these new technologies is particularly important in that they allow a substantial increase in stakeholder engagement, and allow researchers to leverage the high volumes of the crowd with respect to data collection and analysis. Citizen science therefore offers us important insights into how stakeholder inclusivity can be used to accelerate innovation. [5]

Building on Rothwell’s (1994) five generations of innovation, [6] probabilistic innovation offers what can be described as a ‘6th generation’ within innovation theory, or a new generation of theory development within knowledge and innovation management, that specifically relates to how novel developments in technology can enable near real time research productivity. Probabilistic Innovation describes in essence a process whereby the probability of solving problems increases exponentially if the number of problem solvers (i.e. the problem solving inputs) can also be increased exponentially. Similarly, the problem solving speed can be taken to be a function of the extent to which probability mechanisms can be used in support of real time problem solving. The formula for Probabilistic Innovation would be as follows: = f, whereby the probability of solving a problem ) is a function of the interaction of time and resources, but subject to the exponential influence of constraints associated with the knowledge aggregation problem (). This derivation might be taken to be overly simplistic, but simplicity can act as a useful heuristic to focus theory development, and to provide a synthesis of literatures and theory in support of a focused goal, contrary to the often complexity innovation literature that splits the focus.

Under the ‘umbrella’ of Probabilistic Innovation Theory are streams of theory development that seek to investigate how crowds work and how crowd-based distributed knowledge management systems can contribute to real-time research systems. The field of crowd-based problem-solving is developing rapidly, and essentially assumes that large numbers of people (crowds) can be utilised to solve problems when they collaborate. A large number of people that are physically distant, but connected through the Internet, can obtain economies of scale in collaboration and thereby more effectively solve real world problems. The rapid development of information technology, including the Internet, now reduces the barrier of distance. Probabilistic Innovation Theory suggests that a system of real time research capability will ultimately evolve to solve scientific problems that are societally important and urgent [7]

A methodology is required through which knowledge can be more effectively shared and created on a global basis. Such a methodology, drawing from the tenets of Probabilistic Innovation Theory, may contribute to increased scientific breakthroughs, and therefore perhaps to a new paradigm in health outcomes. Arguably, the current biomedical R&D paradigm is not well suited to the needs of those too poor to attract pharmaceutical R%D investment, or to the needs of aging populations in certain regions where healthcare budgets crowd out other social spending. Using the crowd to apply principles of probabilistic innovation may provide a useful complement to the present R&D processes in pharmaceutical innovation. [8]


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