Risk

Science is undergoing a profound transformation, driven by the challenges it faces within a globally industrialized world, a world fundamentally shaped by science itself. Today, scientific problem-solving increasingly operates in a “post-normal” context, where facts are uncertain, values are in dispute, stakes are high, and decisions are urgent. The COVID-19 pandemic illustrated this reality: policymakers had to act rapidly despite severe data scarcity, navigating agonizing value conflicts, such as balancing the immediate protection of the elderly against the long-term well-being of the young. Because these contemporary crises are defined by such uncertainty, they inevitably challenge the traditional authority and quality assurance of scientific conclusions. To conceptualize this shift, Funtowicz and Ravetz (1993) introduced the framework of “post-normal science” to navigate the complexities of decision-making when orthodox scientific methods reach their limits. ¹⁾

According to Funtowicz and Ravetz, the interaction between decision-making stakes and system uncertainties determines which problem-solving strategy is appropriate. They distinguish three distinct types of science: applied science, professional consultancy, and post-normal science. Applied Science: This operates when both system uncertainties and decision-making stakes are low. Here, problems are managed using routine, standardized procedures; the uncertainties are purely technical, and the outcomes do not carry immediate, high-stakes political or societal risks. Professional Consultancy: This applies to intermediate conditions where both uncertainty and stakes are higher. A key factor here is the introduction of the client, which can create conflicting values between stakeholders and scientists. Because the findings directly influence human lives or corporate interests, risk assessments are required. Furthermore, since the focus shifts toward solving a unique, specific problem, universal reproducibility is no longer the central priority. Post-Normal Science: This strategy becomes essential when both system uncertainties and decision stakes are high, conditions common to environmental and public health crises. In this realm, urgent decisions must be made despite severe data gaps, and the conflicting values of diverse groups mean that traditional scientific consensus is impossible. ²⁾