Knowledge Generation in Knowledge Economies

Knowledge has always been a difficult thing to define. It is, nevertheless, necessary to say what is meant by it and what assumptions we make about it when constructing and evaluating knowledge policy such as science and innovation policy. Not to do so would render policy conceptually weak, lacking in substance, likely to miss-specify the generative mechanism of knowledge, and evaluations of policy initiatives unreliable. Such a situation would be less than citizens have the right to demand. Knowledge is a constellation of phenomena comprised of ideas, assumptions, beliefs, intuitions, memories, cognitions, etc., that are taken to have socially justifiable truth values, and that are emergent properties of relations. In this sense knowledge is taken to have truth values that are (re)constructed in social relations and through communication. Knowledge policy and its evaluation, therefore, should include the stewardship, steering and facilitating of these relationally-generated phenomena in pursuit of the community’s goals.

What this means in practical policymaking terms can be seen at three levels. First, knowledge has to be seen as a broad concept that is more than fact, data and information. Knowledge has to also be seen as ideas, insight, wisdom, creativity and so on. Furthermore, sociologists frequently point out that knowledge is essentially a cultural artefact, particularly given its profound links to values, shared meanings, language and the expressive nature of humans. On top of this, it must be recognised that the links between effective knowledge creation, diffusion and use, and social capital and social networks is now well known. An important aspect of knowledge-related policy should, therefore, be that it deals with the way components of knowledge systems are interconnected; it is about relationships. These aspects of knowledge dynamics cannot be over emphasised in the context of an increasingly networked economy. We suggest that a serious misunderstanding of these dimensions of how knowledge works will in all likelihood lead not to the development of a knowledge-based economy but merely to a technocratic economy with limited meaningful returns to tax payers.

Second, translated into science and innovation policy terms, theory says that public investment in science and innovation requires an enabling investment in the social, cultural and communicative foundations of science and innovation work. More broadly, for science and innovation to be working best, it must be built on a large social and cultural foundation. Another side of it is that while culture and society actually generate knowledge, science and innovation should nourish those foundations by responding appropriately to their needs. This is a matter of democratic and open science. Responses to cultural issues, social objectives and community values are critical elements of all knowledge work. In this light, technology for the sake of technology or simply for the sake of commercial imperatives is likely to be short-sighted, socially unproductive, and unhelpful to taxpayers. In other words, the products of public investment in science and technology must be shown not only to respond to social and cultural imperatives, it must also contribute to the social and cultural foundations of its society.

Third, theory suggests strongly that policymakers should take a stewardship or steering role and that much of that effort should be focussed on connections or relationships between components of knowledge systems. These relationships can be between individuals and groups, between people and knowledge (ideas, data, insights, information, etc.), between different kinds of knowledges, and even between people and the physical environment (both natural and built environments) that include and are shaped by the products of science and innovation systems. Part of that stewardship role is to be informed by data collected in light of a valid and reliable evaluative framework. While evaluating the quality of relations and the effectiveness of stewardship activities is challenging, we cannot claim to effectively be developing knowledge about the effectiveness of knowledge-related policy and policymakers unless we find ways. Modern social scientific and economic research methods make doing these kinds of assessments more possible now than in the past.

We are reminded at this point to ask what the purpose of public policy is in a representative democracy. While a complete answer to this question is outside the scope of this document, it is pertinent to say that public policy in a democracy must serve a broad range of interests and needs, and that a goal of policymaking should be to strike a balance across competing interests and needs. This is no easy task. With this challenge in mind, recent research suggests that any policy, including science and innovation policy, should account not simply for knowledge but for wisdom.


My Home Page