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Samenvatting Literatuur Economics of Innovation (GEO2-2211)
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Literature Economics of InnovationWeek 1
Architectural Innovation - Henderson & Clark (1994)
Distinction between incremental and radical innovation isn’t enough
There is evidence that there are numerous technical innovations that involve
apparently modest changes to the existing technology but have quite dramatic
competitive consequences (mix of incremental and radical?)
This paper is to explain how minor innovations can have great competitive
consequences
Products architecture : lays out how the components will work together
2 different types of knowledge about a product:
- Component knowledge: knowledge about each of the core design concepts and the
way in which they are implemented in a particular component
- Architectural knowledge: knowledge about the ways in which the components are
integrated and linked together into a coherent whole
Framework for defining innovation
- Radical innovation establishes a new dominant design, so a new set of core design
concepts embodied in components that are linked together in a new architecture
- Incremental innovation defines and extends an established design; improvement
occurs in individual components, but the underlying core design concepts and links
remain the same
- Modular innovation only changes the core design concepts of a technology
- The essence of architectural innovation is the reconfiguration of an established
system to link together existing components in a new way (this does not mean the
components themselves are untouched by architectural innovation) core design
concept behind each component stays the same
Periods of experimentation are brought to an end by the emergence of a dominant design (=
characterized both by a set of core design concepts that correspond to the major functions
performed by the product and that are embodied in components and by a product
architecture that defines the ways in which these components are integrated)
, A dominant design incorporates a range of basic choices about the design that are
revisited in every subsequent design
When an industry is characterized by a dominant design, architectural knowledge is
stable, and it become embedded in the practices and procedures of the organization
Communication channels, information filters, and problem-solving strategies in managing
architectural knowledge:
- Communication channels: relationships around which the organization builds
architectural knowledge; it will embody its architectural knowledge of the linkages
between components that are critical to effective design
- Information filters: the organization develops filters that allow it to identify immediately
what is most crucial in its information stream; they will create information filters about
the dominant design, and know what to know
- Problem-solving strategies: summarizes what an organization has learned about
fruitful ways to solve problems in its immediate environment; reflect architectural
knowledge as they are likely to express part of an organization’s knowledge about the
component linkages that are crucial to the solution of routine problems
When architectural knowledge is stable once a dominant design has been accepted,
it can be encoded in these forms and thus become implicit
Problems created by architectural innovation: differences in the way architectural and
component knowledge are managed: 2 sources:
- Radical innovation tend to be obviously radical, and there is a quick need for new
modes of learning and new skills. However information that might warn the
organization the organization that a particular innovation is architectural may be
screened out by the information filters and communication channels
- The need to build and to apply new architectural knowledge effectively; only
recognizing is not enough; they should switch to a new mode of learning and then
invest time and resources in learning about the new architecture
Architectural innovation may thus have very significant competitive implications. Established
organizations may invest heavily in the new innovation, interpreting it as an incremental
extension of the existing technology or underestimating its impact on their embedded
architectural knowledge. But new entrants to the industry may exploit its potential much more
effectively, since they are not handicapped by a legacy of embedded and partially irrelevant
architectural knowledge.
Innovation, evolution and complexity theory – Koen Frenken
Differences biological and technological evolution:
- Scope for change: human search is not ‘by nature’ (myopia) there is local search in
technological evolution
Processes of technological substitution cannot be understood from prices of inputs alone:
Elements are complementary: replacing one element can have a negative impact on
another element
Because components are very related and intertwined, strategy is needed for evolution for
possible combinations:
- Complex systems: Global trial-and-error
- Simple systems: local trial-and-error
Innovation can be seen as formally equivalent to a mutation in biology (changing a gene from
0 to 1 vs replacing a steam engine by a gasoline engine)
,The NK model is a tool to simulate the effects of interdependencies on the fitness of complex
systems
- Dependencies between the functioning of elements in a complex system are called
epistatic relations : this means that when you change an element, the change affects
both functioning of the elements that it epistatically affects; the ensemble of epistatic
relations among elements in a technological system is called a technology’s
architecture
K-value of an NK system indicates by how many elements each element is affected
Fitness landscape: refers to efficiency
The fitness landscape metaphor refers to the distribution of fitness values of different designs
in design space. Each string has N neighbouring strings in design space, which is the set of
strings that have the same alleles except one. For a peak in the landscape it holds that all its
N neighbouring strings have a lower fitness value. Local optima can thus be considered as
combinations of complementary states.
In the simulation, string 100 is a global optimum since its fitness is the highest of all strings,
while string 111 is a local optimum since its fitness is higher than the fitness of its
neighbouring strings, but lower than the fitness of the global optimum.
- The number of local optima (0.5 usually) indicates the ruggedness of a fitness
landscape and is directly related to the degree of epistasis K of a complex system
- The higher the complexity of a system and the larger the size of a system, the more
likely agents get trapped in a local optima
The path dependent nature of myopic search implies that most adaptive walks will end up in
local optima with sub-optimal fitness. Myopic agents simply climb up the nearest hill without
knowledge of other peaks with higher fitness. Only agents whose initial conditions are, by
chance, within the basin of attraction of a global optimum (meaning a set of sequential
moves that monotonically increases fitness), may end up in the global optimum.
A number of insights from the NK model
- The number of local optima increases exponentially with K. This means that for
systems with higher complexity K, it becomes increasingly more likely that local trial-
and-error leads to a local optimum rather than the global optimum
- The correlation between local optima as measured by the number of states of
elements two local optima have in common, decreases for increases in K. This
means that the higher the complexity of a system, the more randomly spread the
local optima are in design space.
, - The mean fitness of local optima is highest for systems with a positive but moderate
complexity (around K=2, K=3 or K=4 depending on N). This can be understood as
reflecting that small K-values create, on average, positive complementarities. When
K-values get larger, they pose increasingly more incompatible design constraints on
the design resulting in poorer performance of local optima (with expected fitness
equal to 0.5 for K approaching infinity).
- The higher the fitness value of a local optimum in a given fitness landscape, the
larger its basin of attraction17, which is the number of strings starting from which trial-
and-error can end up the local optimum. A local optimum with a larger basin of
attraction has, on average, a higher probability to be found by trial-and-error.
- The variance of fitness levels of local optima falls as K increases
Conclusion; moderate complexity produce the highest fitness value of local optima, but also
a higher change on finding these optima
& the NK-model of complex system suggests that designers will generally not be able to find
globally optimal designs and more often end up in locally optimal designs
Reproduction rate of design s
Alpha: strength of competition
- High? : selection instantaneously wipes out the less fit variants, and therefore a
population will be dominated by one design at each moment in time
- Low? design variety will be present
For systems with high complexity, fitness levels of local optima have a low variance implying
that selection operates slowly in eliminating less fit technologies. By contrast, systems with
low complexity have a high variance in the fitness values of local optima
The speed of evolution is inversely related to the complexity of a system’s
architecture
Long jump : usually there is one mutation at a time, but with long jumps, there is a radical
innovation, which means there is a transition of one complex technological system to a new
and very different. The location of the new peak cannot be know beforehand. (search
distance is long in this case)
- This implies a global optimum is always found
Computational complexity
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