In pragmatic science, the goal is to develop knowledge that can be used to improve a situation. This we can call prescriptive knowledge. Prescriptive knowledge, according to van Aken (2004, 2004b, 2005) can take the form of a technological rule. A technological rule is "... a chunk of general knowledge linking an intervention or artifact with an expected outcome or performance in a certain field of application" (van Aken, 2005: p23). This rule can be formulated much the same way as my earlier example of a causal statement; 'if you perform action X to subject Y, then Z happens' (Note the cause and effect formulation). This type of algorithmic formulation is called a design solution (vanAken and Romme 2005). A design solution is usually a statistically proven quantitative model that can be taken as specific instruction (van Aken & Romme, 2005). On the other hand, there are more abstract technological rules that are used for designing solutions. These are heuristics that guide, but do not determine, the design process and are called design solutions (van Aken 2005; van Aken and Romme 2005). Design solutions are formulated in the following way; " If you want to achieve Y in situation Z, then you perform something like X" (van Aken & Romme, 2005; p. 6). In short, the resulting artifacts of pragmatic research can also be causal relationships, just typically not as specific or reductionist as those resulting from positivist research. The words 'something like' in the statement implicitly refer to the complexity in which the causal relationship is enacted. The causal agent (X, in the statement above) can also be seen as complex and multivariate (Cook, 1983). Testing these causal agents is done in context, much the same way as evaluation research tests social or economic programs (van Aken 2003) .