Abstract

We propose the 'Seinhorst Research Program', derived from Seinhorst's empirical philosophy. All theories of the 'Seinhorst Research Program' are developed by searching for recurring regularities (patterns) in a collection of observations, named 'the empirical base'. To prevent 'ghost theories from sloppy data', all assumptions underlying the empirical base are carefully described in theories with respect to methodology and technology, including statistics. The patterns to be recognised are summarised by mathematical equations, which must be connected with biological processes to bridge the gap between 'normal' language and mathematical language for the description of biological theories. Often, the patterns result from more than one biological process. If so, the basic patterns are disentangled from one another using a method of pattern analysis. The procedure is best carried out when only a limited number of more or less congruent patterns are involved. Therefore, attention must be given to the choice of the hierarchic level and the complexity of the investigated system. Investigations proceed from simple experimental systems to complex natural systems at a hierarchic level that is neither so high that manifesting processes are very dissimilar nor so low that one runs the risk of describing processes irrelevant for the purpose of the investigation. In the 'Seinhorst Research Program', this purpose is finding methods for improvement of financial returns of host crops attacked by plant-parasitic nematodes through calculating risks of nematode population development and subsequent yield reduction. Pattern analysis yields theories about causes of phenomena observed at the investigated hierarchic level and about properties of processes at the nearest lower hierarchic level. Predictions at the next higher hierarchic level are made by synthesising several patterns in (stochastic) simulation models. Synthesis is also applied to compound patterns of processes in simple experimental systems, with the objective of explaining complicated patterns in complex systems.