Aantekeningen Economics of Innovation
Lecture 1 - Introduction to course & stories about airplanes to
impress your engineering friends.
Paper: in pairs (2500 - 3000) woorden. Deadline: march 23rd 23.59u
Selected country/region
Problems with neoclassical theory:
● Don’t explain what the (economic)’terms’ stands for, what exactly is the technological
change, which made something more/less efficient?
● Instead: evolutionary theory
● Evolutionary economics:
● Focus on dynamics and change
● Bounded rationality & local search
● Path dependency
● Paradigms & Trajectories
● Sub-optimality
NK-model (Frenken 2006) (sorry, still no airplanes)
● Technologies are complex systems: its components are interdependent
● Improvements in one component may damage other components (trade-offs)
● Design is thus a complex search process, and not a rational process that leads to the
optimal solution
● Recall:
- Bounded rationality & local search
- Path dependency
- Sub-optimality
The possibility Space
● N is the amount of components in a technology (N=1,2…)
● An is the amount of possible states for component N
● The amount of possible designs S (=possibility space) of a system is:
, ● S = A1 * A2 * … * An =
● If every component has two possible states (An = 2), the possibility space is binary and
has size:
● S = 2^n
● If it has all 3 it is: 3^n
NK fitness landscape
● zie dia’s met voorbeelden
● You can follow only certain tracks within for example the 3 (n=3) characteristics
● Trial and error: Adjust one or more thing and see what happens (evaluating)
● Bounded rationality
● Local search
● Path dependence (irreversabiltiy)
● sub-optimality
,The example of Northrop
• Two local optima
– Northrop: Fixed landing gear; Compartment wings
– Competitors: Retractable landing gear; Hollow wing
While in hindsight we may think of Northrop’s as a bad design, its development was just as
rational as the development of its ‘brilliant’ alternative
Decomposability
● Comparable to fitness landscape
● Some systems may be divided into subsystems in a way that there is no
interdependence between the subsystems (=‘modularity’)
● A table with black hokjes, with options (characteristics) you can choose from.
Every time you try something, this effects also something else, hard to optimize.
In the case where the zwarte hokjes are more equally divided, you can focus on
these parts and put a certain number of people on it, which makes it easier to
optimize you can just add/substract certain characteristics more easy, without
major effects on other components. This is less the case when people are
working on many things at the same time when there are zwarte hokjes all over
the table and thus many more combinations (=decomposing), here you change
one thing and have to evaluate the whole, because other changes might have
occur (more easily).
● This makes finding the global optimum a whole lot easier!
Architectural innovation:
● Change how things relate to each other, but not necessarily the things itself. Not the lego
pieces, but how they are put together
Component innovation:
● Change the things itself and not how they relate to each other. Not the lego construction,
but only the colors of the stones.
Modular innovation:
, ● Change ‘upgrade’ something, such as change your radio for a navigation system in your
car.
Lecture 1 - Introduction to course & stories about airplanes to
impress your engineering friends.
Paper: in pairs (2500 - 3000) woorden. Deadline: march 23rd 23.59u
Selected country/region
Problems with neoclassical theory:
● Don’t explain what the (economic)’terms’ stands for, what exactly is the technological
change, which made something more/less efficient?
● Instead: evolutionary theory
● Evolutionary economics:
● Focus on dynamics and change
● Bounded rationality & local search
● Path dependency
● Paradigms & Trajectories
● Sub-optimality
NK-model (Frenken 2006) (sorry, still no airplanes)
● Technologies are complex systems: its components are interdependent
● Improvements in one component may damage other components (trade-offs)
● Design is thus a complex search process, and not a rational process that leads to the
optimal solution
● Recall:
- Bounded rationality & local search
- Path dependency
- Sub-optimality
The possibility Space
● N is the amount of components in a technology (N=1,2…)
● An is the amount of possible states for component N
● The amount of possible designs S (=possibility space) of a system is:
, ● S = A1 * A2 * … * An =
● If every component has two possible states (An = 2), the possibility space is binary and
has size:
● S = 2^n
● If it has all 3 it is: 3^n
NK fitness landscape
● zie dia’s met voorbeelden
● You can follow only certain tracks within for example the 3 (n=3) characteristics
● Trial and error: Adjust one or more thing and see what happens (evaluating)
● Bounded rationality
● Local search
● Path dependence (irreversabiltiy)
● sub-optimality
,The example of Northrop
• Two local optima
– Northrop: Fixed landing gear; Compartment wings
– Competitors: Retractable landing gear; Hollow wing
While in hindsight we may think of Northrop’s as a bad design, its development was just as
rational as the development of its ‘brilliant’ alternative
Decomposability
● Comparable to fitness landscape
● Some systems may be divided into subsystems in a way that there is no
interdependence between the subsystems (=‘modularity’)
● A table with black hokjes, with options (characteristics) you can choose from.
Every time you try something, this effects also something else, hard to optimize.
In the case where the zwarte hokjes are more equally divided, you can focus on
these parts and put a certain number of people on it, which makes it easier to
optimize you can just add/substract certain characteristics more easy, without
major effects on other components. This is less the case when people are
working on many things at the same time when there are zwarte hokjes all over
the table and thus many more combinations (=decomposing), here you change
one thing and have to evaluate the whole, because other changes might have
occur (more easily).
● This makes finding the global optimum a whole lot easier!
Architectural innovation:
● Change how things relate to each other, but not necessarily the things itself. Not the lego
pieces, but how they are put together
Component innovation:
● Change the things itself and not how they relate to each other. Not the lego construction,
but only the colors of the stones.
Modular innovation:
, ● Change ‘upgrade’ something, such as change your radio for a navigation system in your
car.
