Integrated Assessment Modelling (IAM) also referred to as Integrated Modelling. IAM is a type of scientific modeling mostly used by environmental policy analysts. Integrated Assessment models are commonly used as tools to aid decision makers, and researchers understand complex environmental problems. Over the recent years, there has been an increase in the number of researchers who are turning to IAMs for advice and necessary input on options for dealing with issues such as climate change, which help them detect and prepare for natural disasters that may emanate from a change in climate.

There is no doubt that IAMs have played a considerable part in aiding in climate research. However, it has its flaws and weaknesses too; one of them is the high complexity of the models which is likely to cause low credibility by the end users. This means that most users may tend to misunderstand information provided by the Integrated Assessment Models due to its complicated nature. This may make some users misinterpret or discredit the models, hence losing their usefulness. It is due to such flaws that current IAM activities are being used mostly for improving the next generation of IAMs rather than contributing directly to the formulation of policy responses.

Given the rise in popularity of IAMs, one can guess that they have strengths that probably overshadow their flaws. One of the most notable advantages of IAMs is its consistent integration between multiple areas such as climate, economy, and biosphere.  This is the reason they are described as integrated since they present a more extensive set of information than what is usually extracted from the standard research activity. This makes IAMs extremely useful in illustrating areas where less research has been done, and there is insufficient knowledge. It is due to the diversity of the fields of study that IAMs obtain information from that researchers and decision makers today appreciate the integrated modeling as a crucial tool in dealing with matters concerning climate change. By improving on the future models, more accurate and helpful information can be obtained from IAMs hence aiding in the prevention of natural disasters.