Current guidance in the UK and elsewhere indicate upper and target risk limits for the operation of nuclear plant in terms of individual risk per annum. 'As low as reasonably practicable' (ALARP) arguments are used to justify the acceptance or rejection of policies that lead to risk changes between these limits. The suitability of cost-benefit analysis (CBA) and multiattribute utility theory (MAUT) are assessed for performing ALARP ('as low as reasonably possible') assessments, in particular within the nuclear industry. Four problems stand out in current CBA applications to ALARP, concerning the determination of prices of safety gains or detriments, the valuation of group and individual risk, calculations using 'disproportionality', and the use of discounting to trade-off risks through time. This last point has received less attention in the past but is important because of the growing interest in risk-informed regulation in which policies extend over several timeframes and distribute the risk unevenly over these, or in policies that lead to a nonuniform risk within a single timeframe (such as maintenance policies). The problems associated with giving quantitative support to such decisions are discussed. It is argued that multiattribute utility methods (MAUT) provide an alternative methodology to CBA which enable the four problems described above to be addressed in a more satisfactory way. Through sensitivity analysis MAUT can address the perceptions of all stakeholder groups, facilitating constructive discussion and elucidating the key points of disagreement. It is also argued that by being explicitly subjective it provides an open, auditable and clear analysis in contrast to the illusory objectivity of CBA. CBA seeks to justify a decision by using a common basis for weights (prices), while MAUT recognizes that different parties may want to give different valuations. It then allows the analyst to explore the ways in which different parties might (or might not) come to the same conclusion even when weighting items differently.