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Risk Calculation as Experience and Action—Assessing and Managing the Risks and Opportunities of Nanomaterials


Discussions about the appropriate way of assessing and managing new or emerging technologies—like nanomaterials—expose the problematic relationship between scientific knowledge production and regulatory decision-making. On one hand, there is a strong demand for scientific expertise to support decisions, especially by analyzing risks and hazards when uncertainties are prevalent and society’s stakes are high. On the other hand, science is criticized for its authoritative claim to objectivity and for keeping the inherent uncertainty, ambiguity, and selectivity of scientific observation latent. Requests for more transparency in science can lead to revealing, to risk managers and the public, the indeterminacy in knowledge production processes. This has consequences for the prevalence of scientific knowledge in decision-making, because it increases uncertainty on both sides of the breach between science and decisions: scientists lose confidence regarding the scientifically tested knowledge which they pass on, and risk managers lose confidence regarding their decisions based on this knowledge. Nonetheless, the concept of “probabilistic risk assessment” remains an important heuristic for dealing with potential future events. This paper addresses questions of the function of scientific risk assessment in organized risk management. The main argument in this paper is that knowledge alone no longer functions as a mechanism for absorbing uncertainty. Accordingly, the interaction between science and decisions must enable a temporarily stable commitment to manage new threats like products and applications coming from the field of nanoscience and nanomaterials.

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  1. Luhmann used this expression in a similar fashion with regard to the mass media: “Whatever we know about our society, or indeed about the world in which we live, we know through the mass media (…) we know so much about the mass media that we are not able to trust these sources” [2, p. 1].

  2. One example can be found in the NRC report [3]; another example (for an excessively complicated model) can be found in the International Risk Governance Council report [10]. For an overview, see Jahnel [11] in this special section.

  3. The concept of “truth” refers to the theory of “symbolically generalized communication media,” elaborated by Luhmann [12]. This concept cannot be laid out in detail in this paper, so the discussion is limited to the distinct modes of attribution. The specific function, differentiation, and self-validation of the symbolically generalized communication media of truth is discussed in “Die Wissenschaft der Gesellschaft” [The Science of Society] [13]. Unfortunately, the book has not been translated yet.

  4. Other experts concur in this evaluation [17, 18].

  5. One of the most prominent attempts was conducted by Chauncey Starr [21].

  6. See, for example, Zwick and Renn [28]. Following this fashion of more information, the findings of risk perception research have, above all, been incorporated into the disciplines of natural science in hazard research [29], e.g., in many of the contributions in this anthology [30].

  7. Theories on risk, consequently, develop concepts or analyze heuristics which enable the reduction of complexity of future events, i.e., the limitation of expectable events. Considering different disciplines and fields of application, we can identify various approaches to deal with the problem of risk: probabilistic measurements, deterministic methods, and psycho-metric research on individual risk perception. For an overview, see [38].

  8. See Hansen and Baun [42] in this special section.

  9. Pielke’s statement [35, p. 64] that good decisions reduce uncertainty is tautological: The condition for certainty is the absence of unknown (and possibly negative) consequences; yet such absence simultaneously relieves from the necessity to make a precarious decision in the first place.

  10. The mark consists of a vertical line that separates two sides and a horizontal line that points to one side and not the other [48].

  11. For example, the IceCube Neutrino Observatory, where neutrino research is conducted while the particle detector is buried in Antarctic ice. Statements about findings are communicated in statistical terms. The crucial distinction in the cited research is the observation of ‘extraterrestrial’ events, as opposed to events of atmospheric origin [55].

  12. Thus, the observation creates the object observed. Comparable statements are also made in physics (indeterminacy principle) and mathematics (observer theory [56]).

  13. As a result of the scientification of politics, the latter cannot be conducted on ideological grounds alone. Scientific advice serves the purpose of displaying actionability within politics, but with reference to reasoning from outside of politics [60, p. 258].

  14. The precautionary principle is a striking example of problems with operationalizing hypothetic knowledge for legal action: “The [European] Court of Justice and Court of First Instance, as well as the EFTA Courts, reply to this [speculative health risks] is that a preventative measure cannot properly be based on a purely hypothetical consideration of the risk, founded on mere conjecture that has not been scientifically verified. It follows that there must exist a threshold of scientific plausibility” [67].

  15. Examples of other social systems are science, economy, and law.

  16. A topic which cannot be discussed in depth in this paper.

  17. That is true in qualitative and quantitative regard: to regulate earlier and more often, because of the precautionary principle, and to act on the basis of fragile knowledge: “where there is uncertainty as to the existence or extent of risks to human health, protective measures may be taken without having to wait until the reality and seriousness of those risks become fully apparent” [67, p. 142].

  18. For a thorough discussion, see Jahnel [11] in this special section.

  19. Our translation of “Unsicherheitsabsorption ist ein Entscheidungsprozess.”


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I would like to express my gratitude toward the editor and the reviewer for their helpful comments to improve this paper. Also, I would like to thank my colleagues from the Institute of Technology Assessment and Systems Analysis for their contributions in countless discussions. Especially, Jutta Jahnel helped me sharpen the arguments. For her patience and support with linguistic and stylistic issues, I would like to thank Mira Klemm. Any remaining issues are my responsibility.

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Büscher, C. Risk Calculation as Experience and Action—Assessing and Managing the Risks and Opportunities of Nanomaterials. Nanoethics 9, 277–295 (2015).

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  • Risk assessment
  • Risk management
  • Nanotechnology
  • Decision-making
  • Uncertainty absorption
  • Attribution theory