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

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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

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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

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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

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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

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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

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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

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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

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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)

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

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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

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