This document discusses different perspectives on science, technology, and innovation policy. It covers neoclassical and evolutionary views of innovation policy, as well as big science projects from World War II. Key topics of technology and innovation policy discussed include which technologies governments should support, intellectual property issues around university research, and whether markets or policies best drive innovation. The document also outlines adaptive innovation policy approaches focused on managing unpredictable innovation outcomes and collaboration across innovation systems.

1. Science, Technology and innovation
policy
Merit course – 2006
The evolution of innovation policy
A neoclassical perspective on innovation
policy
An evolutionary perspective on innovation
policy

2. Big Science and WW II
Francis Bacon (1627), J.D. Bernal (1939) and
V. Bush (1945)
The social and economic utility of science
Large scale projects: Manhattan Project,
nuclear energy, Apollo programme (Big
Science)
Little science and the linear model

3. Science policy
Mobilization of sufficient funds and allocation
to projects / disciplines
Serendipity vs. demand steering
A modern issue: should university knowledge
be patented?

4. Technology policy
Science based technologies as an engine of
growth
Specialization and strategic industries
(Krugman’s strategic trade)
Latecomers and catching-up

5. Technology policy questions
Which technologies?
At what stage of development?
What about competition?
The market knows best?

6. Innovation policy
Innovation as a broad phenomenon (not only
high-tech sectors)
Innovation systems:
– systemic failures (lack of coordination)
– Interaction (public-private)
Learning and education

7. Neoclassical policy: market failure
Spillovers and the lack of incentives
– Basic science (the Ps)
– R&D subsidies (growth models)
But also: negative spillovers
– R&D taxes?

8. The implementation of policies
Tax cuts vs. subsidies
Additionality of R&D subsidies?
“Picking winners”? and generic vs. specific
policies

9. Evolutionary innovation policy
The blind watchmaker revisited
– Does the free market generate enough
experimentation?
No role for optimality
The adaptive policymaker (Metcalfe)

10. Adaptive policy – the basics
A disequilibrium approach: deviant economic
behaviour drives change
Innovation systems are capable of multiple
responses to incentives
Innovation outcome is unpredictable
Brings in resources and capabilities (to
manage innovation processes)

11. Collaboration and systems
Linked to the management of innovation
resources
Missing system elements: are all relevant
knowledge sources present?
– Set up new research organizations
– Entrepreneurship
Missing connections: opportunities and
incentives for collaboration
– Enhancing awareness
– Removing barriers to collaboration

12. Trends
Privatization of (semi-)public research
organizations
Globalization and European integration: the
FWP
Entrepreneurship and innovation