Optimally generate policy-based evidence before scaling

Social scientists have increasingly turned to the experimental method to understand human behaviour. One critical issue that makes solving social problems difficult is scaling up the idea from a small group to a larger group in more diverse situations. The urgency of scaling policies impacts us every day, whether it is protecting the health and safety of a community or enhancing the opportunities of future generations. Yet, a common result is that, when we scale up ideas, most experience a 'voltage drop'-that is, on scaling, the cost-benefit profile depreciates considerably. Here I argue that, to reduce voltage drops, we must optimally generate policy-based evidence. Optimality requires answering two crucial questions: what information should be generated and in what sequence. The economics underlying the science of scaling provides insights into these questions, which are in some cases at odds with conventional approaches. For example, there are important situations in which I advocate flipping the traditional social science research model to an approach that, from the beginning, produces the type of policy-based evidence that the science of scaling demands. To do so, I propose augmenting efficacy trials by including relevant tests of scale in the original discovery process, which forces the scientist to naturally start with a recognition of the big picture: what information do I need to have scaling confidence?