THE USE OF MODELING TOOLS FOR POLICY IN EVOLUTIONARY ENVIRONMENTS

1. THE USE OF MODELING TOOLS FOR POLICY INEVOLUTIONARY ENVIRONMENTS Bart Verspagen Eindhoven Centre for Innovation Studies (Ecis) b.verspagen@tm.tue.nl

2. What makes an evolutionary view different? • Mainstream (economic) policy models assume a simple world: • One dynamic equilibrium path that can be influenced by policy • The (average) consequences of (policy) actions can be identified, uncertainty takes the form of risk • An evolutionary world is fundamentally different • Multiple equilibria • Strong uncertainty

3. What are the relevant notions in evolutionary analysis? • Bounded rationality, heterogeneity, selection & mutation, co-evolution are all ingredients into the complex world view • Their relevance is derived: they are important because they lead to a complex world • We should take the complex world view as the starting point of a policy discussion, not its ingredients

4. World views • The “clockwork Newtonian” view against “chance and necessity” • Chance (contingency) has a major impact in evolutionary processes • This means that building a policy model is possibly problematic: we cannot predict contingencies • If chance plays a decisive role, the conclusion would be that evolutionary processes are not steerable

6. Is evolution on earth a “magnificent accident”? • Perhaps in the biological realm it is • But in socio-economic environments there are subsystems that are more influenced by necessity than by chance • Problems of the “intermediate range” (Merton): large-scale systems with many interactions are indeed “magnificent accidents”, micro-systems are essentially random at the level that we are interested in

7. Intermediate range problems • Multiple equilibria are a good example of an intermediate range problem • If we can define the two equilibria in a clear way, we can build an evolutionary policy model of the potential transition between them • But we can only build a model for a specific transition, not for transitions in general

8. Modeling guidelines • Foresight studies as a major input into defining the two transitions • Pay attention to user population and strategies (evaluate strategies using notions from evolutionary game theory) • Use the model for exploration of the equilibria and possible transition paths, not as a prediction

9. An example: a model for the emergence of the H-economy • Mattijs Taanman (2004), MSc thesis Eindhoven • Taanman/de Groot/Kemp/Verspagen (2005) • Micro cogeneration • Conclusion: in some of the contexts that were analyzed, the H-economy will be a fact before 2050

10. Modeling preliminaries • Foresight studies to generate “technological contexts” (e.g., centralized/decentralized H-production, mixing H with natural gas, different types of fuel cells) and their potential learning paths • Detailed description of user population (buildings)

11. Model and outcomes • Core of the model is an adoption decision based on comparative costs

12. Policy lessons from the model • It provides an exploration of the feasibility of the H-economy • It provides a platform to evaluate the different effectiveness of aspects of policies (stimulating demand, stimulating technology, etc.), e.g., • What learning rate do we need to make fuel cells effective? • How much do we need to help users by subsidies? • But implementing policy remains an art (the model does not capture this), e.g., • Mix of firms vs. PRIs in R&D