Selection and Transformation in the Innovation Process: A Genetic Algorithm Modeling.
Manuel Cartier
manuel.cartier@dauphine.fr


     What is the impact of selection and adaptation on the generation of innovations in corporations? Many researches argue that one of the Darwinian process and the Lamarkian process is more present in evolution of population. But in an intraorganizational ecological point of view, these processes does not come from the environment but from the firm. New questions could be : which one of these processes in the most efficient to create viable and performing projects? Does a higher level of selection or adaptation always lead to better performance? Are they competing path of evolution or complementary ones?
      We think theses issues depend on many factors linked to complexity theory and complex adaptive systems: number of competing projects, initial diversity in projects characteristics, exchanges between platforms, environment characteristics,…
In this contingent approach, gaps emerge from the crossing of evolutionary theory and product development literature.
     - Do firms need to stop failing projects in complex technological environment, allowing to reallocate funds to alternative projects?
    - Does cooperation in product development can allow the decrease of research efforts?
    - Does diversity increase the effect of selection on performance?
    - Does internal selection prevent from getting stuck into a pattern of low performance in rugged landscapes?
    We'll use an agent modeling methodology based on genetic algorithms, running on MATLAB 6.1 combined with a GEAT Toolbox. Global behavior at the organization level emerges in simulation from basic interactions between individual projects.
Genetic Algorithms allow the use of inputs such as diversity, number of agents, search rules or specific landscapes (as in the NK model) but also crucial evolutionary concepts: selection rate on each generation and crossovers between successful agents.
Model Robustness' to changes in basic variables will be tested. The validity will be approached by analytical adequacy (comparison to admitted theories) whereas ontological adequacy will be let for future contributions.
     Our computational model tends to be a useful building block in theory about the role of internal selection and transformation in firm evolution and to contribute to agent-based modeling in organization science.