### The Simple Analytics of Innovation and Learning in Complex Social Production Systems

Reflection on the question of how a complex tri-sector social production system like a national educational system could learn over time to improve its performance suggests an overly simplified model. In this model, the rate of learning by the system can be seen as a function of:

- 1) the number of innovations tried
- 2) the size, character, and (system) location of the tried innovations
- 3) the supply, capacity, and (institutional) position of potential innovators
- 4) the scope of social authorization and financial support for innovation at different levels
- 5) the evaluative terms to be used in assessing the social or public value of a proposed idea
- 6) the ex ante assessed potential of the innovative ideas that were authorized and supported
- 7)
- 7) the ex post evaluation of the empirical results produced
- 8) the capacity of the system that provides financial and social support for new ideas to distinguish good ideas from bad, and to provide sufficient financing and support for the spread of ideas of proven value
- 8) the capacity of the system that provides financial and social support for new ideas to distinguish good ideas from bad, and to provide sufficient financing and support for the spread of ideas of proven value
- 9) the capacity of the system that provides financial and social support of the status quo to recognize and act on the potential for replacing the old with the new

At the heart of this system are the processes that are the focus of the principles outlines above: the criteria to be used to evaluate innovations, the generation of variation in the system, the ability to distinguish the valuable innovations from those that are less valuable, and to grow the good and shrink the bad in terms of their market share in the ed sector as a whole. One could think of this as the “innovation system” as it seeks to challenge existing operations, and to enable good ideas rise to the top and worse ideas sink. How best to manage the innovation and system change that must support and guide national level educational systems is essentially the issue that is contested in the comparison between the centralized R and D systems, and the Schumpeterian system. The GSCRD system imagines that the best source of ideas for change at all levels of the system might come from politicians looking for results, or educational professionals seeking to achieve their aspirations for social impact, or policy analysts, program evaluators and social scientists who want to improve the performance of public sector production systems. This system also imagines the best methods for vetting new ideas is through scientific and professional knowledge and peer assessment of proposals. The best way to know whether a proposal works is through the methods of program evaluation, or (even better) through randomized controlled experiments where those are possible. The CDHDMD system imagines that the best motivator for good ideas is the potential financial returns that could come from patenting a better mousetrap, and that the best test of whether an idea is producing good results, and valuable or not is to ask the person who is using the new good or service. Knowledge and expertise is useful in developing value creating innovations, but it is the person who can see the “market potential” of a practical application of new knowledge who provides the spur to value creating innovations. Table 5 presents a simple table that makes the key distinction between: 1) what might be viewed as the current, steady state operations of the national educational system on one hand, and 2)