Multiple authorities have introduced an anti-price-gouging law to prevent sellers from raising prices higher than what is considered reasonable. Effectiveness of the law has been heatedly debated in various disciplines such as economics, ethics and politics. In this article, we investigate its effectiveness by developing a model that simulates a post-earthquake situation and apply the model to San Francisco, CA, USA. The model accounts for various competing forces, i.e. post-disaster increase in production cost and demands, assets damage, donation and hoarding. Thereby, it returns multiple decision metrics, i.e. unfulfilled needs in basic goods, repair periods and well-being loss caused by insufficient supplies and increased prices. The result shows that the optimal level of a price cap depends on a decision metric and local conditions. This indicates that the problem does not have a single optimal decision, but rather a compromise needs to be made between conflicting decision metrics. Generalising this observation, we propose a narrative numeric (NN) method as a new social discourse method. The objective of the NN method does not lie in concluding the most truthful argument, but rather in identifying a decision scenario that yields an agreeable compromise to (hopefully) all stakeholder groups.