THE USE OF MODELING TOOLS FOR POLICY IN

EVOLUTIONARY ENVIRONMENTS

Bart VerspagenEindhoven Centre for Innovation

Studies (Ecis)b.verspagen@tm.tue.nl

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

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

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

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

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

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

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

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)

Model and outcomes

• Core of the model is an adoption decision based on comparative costs

Table 1. Adoption years (take off – complete diffusion) produced by the model for different energy prices Scaling factor price of electricity Scale factor price of

natural gas ½ 1 1½

½ 2021 – 2027 2010 – 2012 1 2025 – 2034 2011 – 2015

1½

Hydrogen does not take off before 2050

2032 - 2045 2013 - 2018

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