The next stage is therefore to attempt to link adoption with some properties of farms. As we have already remarked, it is not possible to get access to representative data about adopters and non adopters at present. The third stage involves using the questionnaire and interview data being collected to build models of decision making for different types of farmers. It is at this point that we can correlate particular properties of farms and farmers with the adoption decision and learn most about the design of improved AEMs. Even for very simple diffusion models, realistic assumptions about differing willingness to adopt, spatial distribution and so on, make analytical solutions extremely difficult.
Simulation provides a testbed where different assumptions can be compared very easily.
For more complex models, simulation is used as a theory building tool, to investigate the sensitivity of adoption to different assumptions about parameters. This focuses the task of data collection on the aspects of behaviour that are most important. For complex models of innovation diffusion where agents may have different models of the world and transfer knowledge and preferences, simulation is the only viable technique for describing the emergent behaviour of the population. An additional advantage is that the same simulation framework can be used for very simple agents as for complex ones, thus ensuring that the results are comparable. Profiles generated by various simulations can be used to cast light on observed patterns of adoption.