Background: The wisdom of crowds and collective decision-making are important tools for integrating information between individuals, which can exceed the capacity of individual judgments. They are based on different forms of information integration. The wisdom of crowds refers to the aggregation of many independent judgments without deliberation and consensus, while collective decision-making is aggregation with deliberation and consensus. Recent research has shown that collective decision-making outperforms the wisdom of crowds. Additionally, many studies have shown that metacognitive knowledge of subjective confidence is useful for improving aggregation performance. However, because most of these studies have employed relatively simple problems; for example, involving general knowledge and estimating values and quantities of objects, it remains unclear whether their findings can be generalized to real-life situations involving complex information integration. This study explores the performance and process of the wisdom of crowds and collective decision-making by applying the wisdom of crowds with weighted confidence to a survival situation task commonly used in studies of collective decision-making. Results: The wisdom of crowds and collective decision-making outperformed individual judgment. However, collective decision-making did not outperform the wisdom of crowds. Contrary to previous studies, weighted confidence showed no advantage from comparison between confidence-weighted and non-weighted aggregations; a simulation analysis varying in group size and sensitivity of confidence weighting revealed interaction between group size and sensitivity of confidence weighting. This reveals that it is because of small group size and not the peculiarity of the survival task that results in no advantage of weighted confidence. Conclusions: The study's findings suggest that the wisdom of crowds could be applicable to complex problem-solving tasks, and interaction between group size and sensitivity of confidence weighting is important for confidence-weighted aggregation effects.