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The influence of innovation and imitation
on economic performance
Sylvie Geisendorf
section environmental and innovation economics
university of kassel
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Abstract:
The importance of innovation and imitation for the economy is discussed in different branches of
economic theory. Some of them study the macro, others the micro level. Macroeconomic theories,
concerned with technological progress usually do not explicitly distinguish between innovation
and imitation. Microeconomic case studies, examine the advantage of one strategy over the other
for individual firms, but do not study the macroeconomic effect of these individual behaviours. The
present paper attempts to close this gap by proposing a model that is capturing the innovative and
imitative activity on the individual level and the resulting performance on the macro level. This is
done on the basis of a Multi-Agent simulation. The model gives a comprehensive picture of an
evolving economy over time, first because it depicts the interplay of innovation and imitation and
second because the agents are placed in a changing economic landscape, forcing them to discover
new products. Apart from detecting a predominant strategy, the model also shows to what extend
the strategies depend on each other, if the economy should develop as a whole. A main result is
that the significance of innovation is overemphasized in some parts of the literature. Imitation is
the more important strategy, but as it needs a sound innovative base, it actually is the right
mixture between the two with a large proportion of imitation that is moving an economy ahead.
Keywords: Innovation, Learning, Multi-Agent-Systems, Evolutionary Economics, Genetic
Algorithms, Modelling
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I. Introduction
There is an ongoing debate about the determinants of technological progress – the main
source of economic growth. Standard economic growth theory, as in the Solow Model, uses
an exogenously given rate at which technology advances, whereas the newer endogenous
growth theories try to explain the reasons for this advance. Progress is attributed to local
spillover effects (Romer 1994), historical differences of the technological background
(Barro/Sala i Martin 1992) or human capital (Mankiew/Romer/Weil 1992). On closer
inspection, some of these aspects can be identified as imitation and some of them rather as
innovation. However, endogenous growth theory does not explicitly distinguish between
innovation and imitation or discuss, to what extend one is based on the other or more
profitable than the other. This debate is led apart from mathematical models of
macroeconomics in a mostly empirical literature. In a geographical economics approach,
Florida (2002) argues that creativity – i.e. innovations of a particularly creative labour force –
is the main factor of success. On the micro level, case studies examine the advantage of being
pioneer or imitator. The results are ambiguous. A lot of studies affirm that innovation is the
prevalent strategy, but as Schnaars (1994) illustrates, they have a strong “survivor bias”, in
that they are based on data of successful innovations. Schnaars himself sustains the view that
imitation is the far more profitable strategy, if the high costs of unsuccessful inventive
attempts are considered. These studies however, are concerned with individual firms and do
not discuss the effects of the two strategies on macroeconomic performance.
Often situated in between micro and macro approaches but not concerned with the
aforementioned are evolutionary models of economic change, considering innovation and
imitation as driving forces of its evolution – particularly those which are based on learning
algorithms, comprising these two strategies. But they usually focus on other questions than a
comparison of the advantages of one strategy over the other. They are e.g. interested in
returns to investments in R&D (Nelson/Winter 1982) or in a distinction between situations in
which firms are likely to undertake one or the other strategy. Silverberg/Verspagen (1998)
e.g. assume, imitation is more likely the more unsatisfactory a firms performance was. A lot
of studies are concerned with the diffusion of innovations and do therefore not distinguish
between innovation and imitation but between innovation and different mechanisms, rates or
conditions of its diffusion (Metcalfe 1988 or Silverberg/Dosi/Orsenigo 1988). It is a
somewhat general observation that evolutionary models are mostly either interested in the
generation of novelty or in its diffusion (although rarely in the former).
The present paper attempts to compare the effects of innovation and imitation and to close the
gap between micro and macro approaches by integrating both. It proposes a model capturing
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the innovative and imitative activity on the individual level and the resulting performance on
the macro level. 1 It seems more fruitful to study these effects from such an integrated
perspective for several reasons. First, no innovative effort of the firms is lost. Failures are
comprised in the overall results of this model economy, as well as successful innovations.
Thus the advantages of a leading firm are balanced against the drawback of another and the
net effect becomes visible. Second, the model, although quite simple, gives a more
comprehensive picture of an evolving economy over time, in that it depicts the interplay of
innovation and imitation. It does so even more, as the agents are placed in a changing
economic landscape, where products formerly in demand disappear and new production
opportunities have to be discovered.
Apart from detecting a predominant strategy, the model shows to what extend the strategies
depend on each other, if the economy should develop as a whole. A main result is that the
significance of innovation is overemphasized in some parts of the literature. The possible
advantage of being first mover comes with a high price, because most innovative effort does
not lead to profitable products. Firms or countries with the opportunity to imitate others are at
a clear advantage, which confirms Schnaar´s results, not only for single firms, but for national
economies. By attempting to discover profitable products exclusively by own innovations,
without a parallel effort to take up high-quality background of others, an economy is
performing on a much lower level than one that is able to profit from spillover effects, even if
there are some leading firms in the former. On the other hand, the model demonstrates how
important a sound base of innovation is for overall growth, especially in a changing
environment where products only sell well for some periods before being outdated by newly
emerging requirements. Studies concentrating on the advantages of imitation should not
forget to mention, that an economy comprised solely of imitating firms would evolve
nowhere. Finally the importance – but also the difficulties – of innovations are also illustrated
by a comparison between economies starting with some or even one good example to imitate
and such economies that have to invent everything from scratch.
1 For those familiar with synergetic models – stating that the whole is more than the sum of its parts – it
is to be said, that the model is not a synergetic one, although comprising some of its characteristics. The
individual behaviours on the micro level generate the system behaviour on the macro level, but the latter
actually is just the sum of its parts. There is no emergence of higher level structures. However, the
performance reflected on the macro level and the knowledge of the individual strategies constitute the
information base for the micro decisions of the next time step, so that further micro development I clearly
linked to former macro results and vice versa. This direct link is a quality missing in solely micro and
solely macro models.
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II. Innovation and imitation
Before we start to discuss the importance of innovation and imitation we should clarify what
we understand by innovation. For Schumpeter (1961) an innovation does not necessarily
mean the innovator has to invent something new by himself. Schumpeter´s entrepreneurs take
up existing inventions, because they believe in their opportunities and transform them into
marketable products. Innovation therefore is understood as the creation of a product and its
launch on the market. For psychological innovation theories, a novelty is only called an
innovation, if it manages to be accepted by its relevant community (Csikszentmihalyi 1997).
The most profound distinction is one between invention, innovation and diffusion
(Beckenbach 2005), where invention is the actual discovery of something new, 2 innovation is
its transformation into something marketable and the diffusion process depicts whether (or
how) an innovation is actually taken up by society.
Although the last distinction is quite useful and certainly the most reflective one, it is
somewhat too subtle for our purposes. 3 The present paper concentrates on the difference
between own innovative activity of a firm that leads to new products, as opposed to the
imitation of products. For these purposes we choose a simple distinction between innovation
and imitation, where innovation comprises everything from the invention, over the
development of a marketable product to its diffusion. In the present paper innovation means
that a firm tries to develop its own products and launches them on the market. 4 Imitation
means that firms are copying products of these innovative firms. The innovators are the only
ones who introduce novelty into the system. For an individual firm however, a product is
called “new”, if it is has not been produced before by this particular firm, regardless of its
being self developed or copied.
In economic growth theory, which at first look should probably be the main theory concerned
with different ways to introduce new products, innovation and imitation are not clearly
distinguished. The main discussion is taking place around the question, whether the actual
reasons for new products (or technological progress, as it is called there) should be part of the
theoretical framework at all. Standard economic growth theory, as in the Solow Model
(1956), uses an exogenously given rate at which technology advances, whereas the newer
endogenous growth theories try to explain the reasons for this advance. On closer inspection,
some of these reasons can be identified as imitation and others as an increase of the
innovative capabilities of an economy or its agents.
2 As already defined by Usher (1959), an invention brings novelty into the system.
3 The distinction is certainly most important, when a model is designed to explain how novelty is
generated, as has been done by the author elsewhere (Geisendorf 2004).
4 Some of them are profitable and others fail, so Csikszentmihalyi´s (1997) distinction is excluded.
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The theory of local knowledge spillovers e.g. is clearly an imitative one (Romer 1987). It does
not actually explain how the imitation is performed, but it assumes capital investments in
regions with a high technological level increase the level of technology more than in
backward regions. The inclusion of investments in human capital on the other hand seems to
be more of an innovation based explanation for technological progress (Mankiew/ Romer/
Weil 1992). To some extend, the two factors have been intermingled in the discussion,
because it has been rightly stated, that technological spillover depends on a trained labour
force, able to use and construct the copied technology and adapt it to local conditions, as, on
the other hand, an educated population alone does not help, if the country has no access to
international best practice (Pack 1994).
None of the new growth theories has generally substituted neoclassical growth theory so far,
although of course, as Romer (1994: 12) put it: „No economist [...] has ever been willing to
make a serious defense of the position that technological change is literally a function of
elapsed calendar time.” It is certain, that technological change, based on innovation and
imitation, is crucial for economic development. But the critics of endogenous growth theory
state that empirical tests have failed to prove a specific alternative theory for all circumstances
so far (Pack 1994).
An explicit debate about the importance of innovation over imitation or vice versa is led apart
from neoclassical growth theory in a more empirically based literature. Florida (2002) e.g.
refers to the emphasis of endogenous growth theory on the importance of human capital, but
leaves the solely macroeconomic perspective. He is a clear advocate of the prevalent
importance of creativity, i.e. innovative activity performed by particularly creative workers
and tries to prove his assertions by comparing some creativity indices with economic
performance. His research unities are large and medium US cities, where he counts the
numbers of well educated employees, nightlife clubs and homosexuals, reflected in “Talent”,
“Coolness” or “Diversity” indices and compares them with income per capita. His theory is,
that growth is brought about by highly creative people and that they are attracted by the
“cool” ambiance of a city. He states that being attractive for such people is more important for
a city than the usual economic incentives like low income taxes or house prices that attract
“conservative” firms. A main shortcoming of his theory might be that his data is disputable.
Malanga (2004) alleges, that a lot of Florida´s leading cities have not done as good as Florida
states and that his theory is based to a large extend on the period of the rapidly emerging
internet enterprises with their stock exchange success, that later on proved to be exaggerated
in many cases.
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Thoroughly on the micro level are case studies in which the advantage of being pioneer or
imitator in newly emerging markets has been examined. A lot of them are cited and
commented in Schnaars (1994). The problem of these studies however, is their solely
individual perspective. They compare advantages of innovation or imitation for sometimes
very specific markets, not exemplary for the whole economy, like the highly protected
pharmaceutical market (Schnaars 1994). And most studies talking about the advantages of
pioneering a new market have a strong “survivor bias”, in that they are based on data bases of
successful innovations (ibid: 25). Schnaars himself sustains the view that imitation is the far
more profitable strategy, if all aspects are considered, especially the high costs of
unsuccessful innovative attempts. Such analysis might give interesting insights into the
conditions under which particular firms can gain advantages by either innovating or imitating
and following Schnaars, with his more profound consideration of macroeconomic facts, a lot
seems to speak for the lead of imitation as a strategy for the individual firm, but the overall
importance of these mechanisms for the growth of the whole economy remains unclear.
This is where the present paper sets in. It attempts to capture the amount of innovative and
imitative activity on the individual level and the resulting performance on the macro level.
This is done by a computer model comprising several heterogeneous economic agents or
firms which are producing goods of different qualities with different economic success, i.e.
benefit. The amount of innovation and imitation is changed in a number of simulations, in
order to examine their effect. It seems more fruitful to study these effects from such an
integrated perspective for several reasons. First, no innovative effort of the firms is lost.
Failures are comprised in the overall results of this model economy as well as successful
innovations. Thus the advantages of a leading firm are balanced against the misfortune of
another and the net effect becomes visible. Second, the model, although quite simple, gives a
complete picture of an evolving economy over time, in that it depicts the interplay of
innovation and imitation. It does so even more, as the agents are placed in a changing
economic landscape, where products formerly in demand disappear and new production
opportunities have to be discovered.
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III. Modelling the process of innovation and imitation
III.1 A complex product landscape
The individual firms are placed in a common product landscape, depicted in Fig. 1. It is their
economic environment. The hills in the landscape are profitable products. Each point on these
product hills can be produced by a certain combination of two production inputs. Optimal
product quality in terms of benefits is reached on top of the hills, but the sloops are profitable
too. Nothing is to be gained on the plain in-between the hills. Firms located on this plain have
attempted an unsuccessful innovation.
The smaller product hills in front and in the middle of the landscape have both a maximum of
50, corresponding to the maximum benefit attainable on them. The rightmost cone has a
maximum of 80 and the global maximum of the landscape is the cylinder with an altitude of
120. Although the cylinder has a maximum 1/3 higher than the cone, it is far more difficult to
detect as the most profitable product hill, because of its steeper slope and smaller diameter.
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Fig. 1: Initial product landscape
The smaller product hills in front and in the middle of the landscape have both a maximum of
50, corresponding to the maximum benefit attainable on them. The rightmost cone has a
maximum of 80 and the global maximum of the landscape is the cylinder with an altitude of
120. Although the cylinder has a maximum 1/3 higher than the cone, it is far more difficult to
detect as the most profitable product hill, because of its steeper slope and smaller diameter.
III.2 Change of the economic environment over time
The economic landscape changes over time. It remains stable for some periods (1/3 of the
observed time steps), then changes into a different economic environment, in which some of
the product hills disappear and others emerge and finally remains stable again for the last 1/3
of observation time. Altogether the economy is observed for 20 time steps. The change of the
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landscape is to be interpreted as a shift in demand or production possibilities. 5 Two kinds of
changes from the same initial landscape (Fig. 1) are studied in the simulations. In both
landscapes the product represented by the middle hill disappears and the small front product
hill is stable.
Landscape 1 (Fig. 2) makes performance for the economic agents easier for two reasons.
First, during the periods of change, the leftmost cylinder transforms into a lower cone, but the
profit of the corresponding production opportunity never drops to really low levels. Second, a
cylinder (the new global maximum) emerges in the back. This product however, does not
differ in all respects from former products. In one input factor it is similar to the former cone
and in the other to the former cylinder. The adaptation and even a switch from the former
second best product to the new optimum are more likely than for an entirely novel product.
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Fig. 3 Landscape 2 after change
In the changed landscape 2 the optimum is harder to discern. The initial optimum is less
discernable than the second best product and change is also in favour of the second best
alternative. The new second best can be found by varying only one of the two production
inputs, whereas the new optimum requires an almost complete innovation, because it is only
based on a formerly minor production function. During the periods of change, the economic
landscape looks like something in between the two states. Fig. 4 shows this for landscape 1.
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Fig. 4 Change of landscape 1 over time/ time steps 8, 10 and 11
5 Note that in the present stage of the model this shift is exogenous. The economy therefore is demand
side driven.
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III.3 The economic agents and their strategies
There are ten individual firms searching for profitable products in the economy. Each of them
produces one good. 6 In the model they are represented by their production function for this
good. 7 The production is a function of two inputs that are combined to form either a sellable
product or something no one would buy. Benefit depends on how high a firm gets on the
product hills. Production inputs for each firm are represented by a string, where the first part
is decoded for the amount of input one and the second part for input two.
Both inputs can be varied by the firm. To do so, the firms can pursue different strategies:
Innovation, imitation of whole production functions or imitation of parts of production
functions of others. An innovation occurs when a firm changes one or several bits of its string.
Imitation implies the firm drops its own production function in favour of the one of someone
else. When imitating parts of production functions, the firms keep part of their own former
input scheme but exchange some of it for the method of others.
The amount – and possible combination – of innovation and imitation activity of the agents is
changed in a number of simulations, in order to examine their effect. In each time step all the
firms can attempt to create new products or product improvements with a certain probability.
If they copy parts or whole production schemes of others, they are more likely to copy from
successful ones. The bigger the advance of a firm over others is, the more likely it is imitated
by others if they are allowed so. The imitation process in this economy is designed in a one
way manner. One is copying parts or whole production functions from others, because less
successful firms try to improve and successful ones either remain the way they are or copy of
other successful ones.
All these activities influence the amount of one or both of the production inputs either slightly
or significantly. The inputs can be interpreted as aggregate input factors like capital and
labour. Different amounts and combinations of them therefore not only imply different
productivity levels for the same production but can be interpreted as employment of different
kinds of capital and labour. Consequently innovation either leads to another – preferably
higher – point on the product hill or the firm ends up producing a different good.
6 Of course, firms producing a single good aren’t very sophisticated entities. But whole countries doing
the same thing – as common in macroeconomic growth models – are even less so. So it might be
justifiable to make this simplification. It is not a necessary one in terms of computability, but makes the
interesting effects more discernible.
7 I don’t want to contribute to the discussion about the justification of firms, their production schemes or
products as entities of selection in an evolutionary model. This is done elsewhere (Geisendorf 2004) and
by others in abundance (e.g. Mokyr 2003).
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IV. Results – The relation of innovation and imitation
IV.1 Product introduction strategies and initial conditions
In the following simulation runs, four kinds of product introduction strategies have been
compared:
•
Individual and independent innovation without exchange of ideas with others.
•
Innovation and imitation of parts of successful production functions
•
Innovation and imitation of whole successful production functions
•
Pure Imitation of production functions or parts of them
Pure imitation or imitation of parts has only been regarded in exemplary runs for
demonstration, because it does not lead anywhere. Depending on initial production
performance of the best firms, the economy quickly converges to a mostly not very
sophisticated level and once the economic environment changes it remains stuck – even in the
zero-benefit plain – if virtually no one is coming up with something new (i.e. innovations).
This possibility has therefore not been included in the below Monte-Carlo-Simulations.
All the firms are principally allowed to attempt new product launches every period. This
assumption is disputable as well as defendable. The reader might decide for himself. In
evolutionary models like Genetic Algorithms it is argued that successful individuals are more
likely to be selected to improve their strategies (or more precisely to be the basis for new
strategies by mixing up theirs in their offspring). 8 Simon (1982) on the other hand argues
successful individuals have less incentive to change, because they are satisfied. Change, he
states, only occurs when the individual’s aspiration level is unfulfilled. Not wanting to decide
this dispute here, the present model excludes neither assumption and lets the successful as
well as the unsatisfied firms try to improve. However, as the activities of innovation and
imitation are only performed with certain probabilities, not every firm actually develops a
new product each time step.
In simulations in which only innovation takes place, every firm changes each part of its string
with a certain probability. 2% and 20% are tested. That amounts to a total innovation
propensity by changing at least one bit of the string of 32% and 320% if multiplied by the 16
bits. The second innovation probability is unrealistically high, but serves to examine, whether
the possibility to invent “for free” might be favourable for the economy under some
8 For those familiar with Genetic Algorithms, it can be said, that the model has similarities with these
models, but also deviates in some crucial aspects, the most important one probably being this deviation
from natural selection in several respects.
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conditions. Note that for the first probability the product remains unchanged with a
probability of 68%, i.e. for two thirds of the firms.
In simulations in which innovation and imitation are allowed, a mix of both as well as just one
of the activities is possible. Every firm changes each part of its string with the given
innovation probability. It then copies parts or whole production functions of others with a
probability of 75%. In 25% of cases it remains unchanged (meaning it remains in the state it
was after undergoing a possible innovation activity). When only parts of other production
functions are copied, it is randomly decided whether the imitation concerns both inputs or
only one of them and whether they are only altered or completely imitated from the example.
After the changes have been performed the new – as well as the unchanged – products are
launched on the market and receive their corresponding benefits. This assumption again is
disputable and defendable as well. A lot of research and development activities lead to
nothing marketable and it is plausible to assume, that especially the innovative firms – as
opposed to imitators – try to asses the success of their possible products before actually
putting them on the market. Although this seems to be a reasonable assumption, its inclusion
into the model is not straightforward. The agents are placed in a landscape unknown to
themselves, except for the few products performances they and their competitors have
achieved. If they develop a new product, there is probably nothing in the known economic
data, indicating whether it might be accepted by the market or not. 9 Especially in a changing
environment it is far from obvious, whether a new product will succeed or fail. As
Beckenbach (2005: 10) has put it: “Novelty creating processes can be interpreted as processes
solving ill-defined problems. Normally, there is no possibility for an unambiguous testing
before really selecting and practicing an option.” However, as a reference scenario for the
above setting, where all new products are launched on the market – called NEXT – a scenario
BEST has been calculated. In this setting old products are only substituted for the new ones, if
they would have performed better under the former economic conditions. 10 Such a test might
be imaginable for the firms if we assume that they are able to determine a products potential
by performing market studies. But note that this is still a somewhat unrealistic scenario
9 No one knew in advance if monoskies or snowboards would succeed, the former being a sort of variant
of skies, the latter a mixture of skies and skateboards or surfboards. Both propagated at first. After some
years only snowboards prevailed and even outnumbered traditional skies for the next generation of
customers.
10 See Nelson/ Winter (1982) for this kind of internal selection, or Arifovic (1994), who used something
similar as an “election operator” in a Genetic Algorithm.
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because it would imply that the agents have a more complete knowledge of the economic
landscape than they actually have. 11
Besides, the better such a test would be, the more it would cost. Costs however, are not
explicitly included in the model for several reasons. First, the model should be kept as simple
as possible. Usually it is agreed upon that innovation is the more cost intensive strategy
(Schnaars 1994). As the model will find that imitation is the prevalent strategy, cost inclusion
would only emphasize this fact. Second, cost effects and the inclusion of other monetary
effects like capital stock, starting capital, payments for patent rights, insolvency or a variation
of costs according to the complexity of the action (incremental innovations would have to be
less expensive than fundamental ones etc.) might superpose the basic comparison of the two
strategies and blur the findings. However, after having established this basic relation, it might
be interesting to start testing their influence. We will come back to this point after the
discussion of the results. The BEST scenario is certainly overoptimistic, because it does not
account for the costs of such a sophisticated product placement, whereas NEXT – although it
does not consider costs either – does reflect the larger costs of innovations implicitly. A main
reason for the higher development costs of innovative firms is the large amount of
unsuccessful innovative attempts. In the scenario NEXT, the higher costs of failure are
included in the form of their economic counterpart in terms of forgone benefits that are
present in the overall performance of this economy.
The performance of the model economy has been observed under different conditions, each of
which have undergone a Monte Carlo simulation over 1.000 runs to get the statistical average
of performance. The economy has been started with four different initial conditions in terms
of initial production functions. Two randomly generated ones and two in which the first or the
first two production functions of one of the random initialisations have been substituted for
the optimal input mix. We can observe the same initial landscape, changing over time in two
different final landscapes. The performance is tested for two innovation intensities, one of
which is rather high. And performance was observed for the first three of the four
abovementioned product development strategies or strategy mixes in all these settings,
whereas pure imitation has only been tested for reference and not been included in the Monte
Carlo simulations, because its performance is quite straightforward. Results for scenario
NEXT, in which all invented or imitated products are launched on the market, are shown in
Table 1 in the Appendix. Results for the reference scenario BEST, where a firm only launches
11 If they can find out how some products would perform, why not just having a look at the whole
landscape to find out the global optimum straight away? Of course we could furthermore assume, that the
landscape is so large, that this would take ages and that one is only capable of assessing the potential of
products one knows about already.
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its new product, if it is superior to the old one under the conditions known so far, are given in
Table 2.
IV.2 Main results
The performance indicator for the model economy is total benefit over time. For reference: if
all the firms would spend all the time on the topmost product hill, total benefit over time
would be 24.000. All the results are considerably lower, 12 which is an interesting finding in
comparison to standard economic theory, where optimization is assumed. Even a privileged
economy under scenario BEST, that only launches tested products and gets innovations and
their tests for free, remains far from a long term optimum. It is able to locate profitable and
even optimal solutions quite quickly, but still, innovations take time and the changing
environment requires a continuous adaptation. 13
The second interesting observation and the main focus of the paper is the effect of innovation
and imitation on economic performance. As all individual attempts to place new products are
comprised in the overall success of the model economy in terms of the benefit they generate –
or fail to generate – the odds of developing new product opportunities become visible. 14
Therefore, the disadvantage of economies with too many fruitless innovations or an
unsuitable mixture of innovation and imitation becomes visible without considering costs
explicitly.
Imitation pays
The prevalent result of the model is that whatever else the agents do it is most profitable, if
there is a fair amount of imitation in the market. The economy as a whole generates the
highest income when new ideas are propagated. This does not imply, that some first movers
might not be at a clear advantage over followers, but as Schnaars (1994) recognizes, studies
emphasising this advantage often sweep the huge amount of innovative activity necessary
until a sellable product is found under the rug.15
12 The highest one being close to 18.000 with a large advance on most other results (Table 2 in the
Appendix).
13 This is an interesting result in itself, speaking for a more widespread use of evolutionary models to
verify the results of general equilibrium models in more realistic settings, taking adaptation time into
account, but that is not the concern of the present paper.
14 This advantage is lost to some extend in scenario BEST. As mentioned above, this scenario is therefore
only used for comparison. Most examples are taken from scenario NEXT.
15 This becomes quite obvious when comparing simulations with and without at least one ideal initial
product. Discovering possible new bestsellers takes a lot of time, and when the market changes before
someone detects an opportunity it is lost.
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Fig. 5: Initial conditions and time step 7 with and without imitation (L1, Inv.int. 0.02, 2/4, NEXT)
Success makes lazy
Although contributing largely to economic performance, imitation has its dangers. It takes
heterogeneity out of the market. This does not matter, if a quick adaptation to given
conditions is attempted, but becomes a problem in a changing environment. As Fig. 6 shows,
the economic agents can get quite resistant against change, if they already are in a good
position, especially, if new products are only launched, when presumably performing better
than accomplished ones. In the long run, this can pose a problem for an economy, because
often new products do not sell better than former ones right from the start, although they have
the potential to overtake the established products when given the chance to be ameliorated in
use. 16
-20
0
-10
x
0
10
20
20
10
0 y -20
0
-10
-10
0
x
-20
10
20
20
10
0 y -20
0
-10
-10
0
x
-20
10
20
20
10
0 y
-10
-20
Fig. 6: Production schemes in time steps 1, 8 and 20 with imitation (L1, Inv.int. 0.02, 2/4, BEST)
It is an interesting observation that an economy that has not yet converged to the global
optimum at the beginning is better positioned to reach the optimum after change. This effect
also becomes obvious when comparing the two landscapes. Landscape 2 undergoes more
fundamental changes over time than landscape 1. This brings about, that the global maximum
after and during change is harder to find than in landscape 1. The importance of this difficulty
gets quite observable when comparing results for simulations started with one or two
pioneering firms with optimal product qualities, that others are allowed to imitate (imitation
rows in 1/4 and 2/4 columns in tables in the Appendix). Benefits of this economy get quite
16 The model incorporates this fact by making the global maximum hard to discover, because the region
for which the product does better than the second best is rather small.
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high in a first time, where laggards are catching up with the market leaders. After the
transformation of marketable product qualities however, it gets much harder to adapt to the
new requirements, when fundamental innovations are necessary. 17
The larger difficulties to detect the global maximum after its shift are not always reflected in
the summed up benefits over time however. But the seemingly indifference of the economy
towards this complication under some conditions is deceptive. These findings are a result of
the fact, that in many cases the agents do not detect the global maximum at all in the first
stable period of time. When settling on a local maximum or still roaming search space, they
are more likely to discover a newly emerging opportunity than formerly optimizing agents.
Obviously – as probably in all evolving systems – there exists a trade of between good
adaptability and openness to further change.
Complete imitation pays best
There is a considerable difference in performance between the strategy of partial and
complete imitation. 18 This does not need to be so under all circumstances, but it certainly is
so in a complex environment, where the inclusion of parts of others production functions
often does not lead to a nice innovative mixture but to a strange muddle instead. The more
complex the environment, the less obvious this might be for the imitating firm in advance.
Recombination of successful elements is a strong procedure compared to pure trial and error,
but on average profitability hills are climbed faster, if there is a possibility to directly follow
leading firms. Fig. 7 gives an example of what can happen, if a firm incorporates just some
aspects of a successful production function, e.g. just the technological production process,
without considering the importance of an effectively trained labour force or adequate material.
-20
0
-10
x
0
10
20
20
10
0 y -20
0
-10
-10
0
x
-20
10
20
20
10
0 y
-10
-20
Fig. 7: Initial conditions and time step 6 with partial imitation (L2, Inv. int. 0.02, 2/4, NEXT)
17 E.g. total benefits under a reasonable amount of innovation of 0.02, initially two optimal performing
firms and an allowance to imitate is 14.425 in landscape 1 compared to 9.693 in landscape 2 (see Table 1
in the Appendix).
18 In only one of the simulated 32 cases partial imitation did better than pure imitation.
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Imitation needs innovation
As strong as imitation is in advancing an economy, it needs a sound base of innovation. This
must not be forgotten when long term economic development – e.g. for a country – is
considered and when facing changing economic environments like real markets. Firms or
countries relying utterly on imitation, will find themselves at a point, where no first mover has
breached way anymore – perhaps because the imitator already took over all markets by being
able to sell copied products at a lower price. If they have not contributed to the common
knowledge pool so far, their labour force probably does not have the intellectual capacity to
start being creative of their own all of a sudden. We don’t want to go into much detail here,
but psychology tells, that creativity needs certain character traits (Guilford 1976 or
Csikszentmihalyi 1997), that are possibly not easy to acquire on demand, if not trained. 19
-20
0
-10
x
0
10
20
-20
0
-10
x
0
10
20
20
10
0 y -20
0
-10
-10
0
x
-20
20
10
0 y -20
0
-10
-10
0
x
-20
10
20
20
10
0 y
-10
-20
benefit
120
100
80
60
40
20
5
10
15
20
t
benefit
120
100
20 80
10
60
0 y
40
-10
20
10
20
-20
5
10
15
20
t
Fig. 8: Initial conditions, production schemes at time step 20 and average benefit over time with imitation,
but with and without innovation (L1, Inv. int. 0.02, 1, NEXT)
As important as innovations are, under most conditions it is not favourable to experiment as
much as possible. This holds even, if search and development cost are not explicitly regarded,
as in this model. They are however included in the form of forgone benefits in scenario
NEXT, where innovations often substitute products that have performed better than the
newcomer. In this context it is quite interesting to have a closer look at the economy, where
new innovations are only launched when superior to former practice. Somewhat surprisingly,
even there an exceedingly high creative activity for free is only favourable under very
19 This problem was e.g. realized by China. Chinese economy is based on catching up by imitation to
large extend and it works tremendously well. Chinese are very motivated to commit themselves to
economic growth and labour force is comparatively cheap. Chinese mentality however has not a strong
critical tradition- an important prerequisite for creative thinking- a problem that starts being realized by
the educational system (Hirn 2005).
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restrictive conditions. In a purely innovative economy such an activity leads to considerably
higher results, but as soon as imitation is allowed to some extend, the results get ambiguous,
depending on initial conditions. And when successful products can be imitated entirely, it gets
safer to stop experimenting this exuberantly (compare the rows for an invention intensity of
0.2 and 0.02 for the different strategies in Table 2 in the Appendix). Inventions are a crucial
base of a sound economic development but they have to be adjusted adequately.
Innovation needs imitation
Innovations are the base on which an economy can grow, but it won’t do so, if everybody is
just experimenting on their own. The performance of a solely inventive economy is very low.
This is particularly true if all new ideas are tried out on the market (see Fig. 8 and the
invention rows of Table 1), but it is still very low, if we assume, that new ideas have to
undergo a test (see innovation rows in Table 2 for an innovation intensity of 0.02). Only if we
assume an unrealistically high propensity to innovate and only keep the real breakthroughs,
the pure innovation strategy becomes acceptable, although still considerably inferior to ones
including imitation (see paragraph above). We have to keep in mind however, that this setting
does not reflect the tremendous costs of such an intensive creative activity at all. So the
overall result is that inventing from scratch takes a long time and reduces performance
considerably.
-20
0
-10
x
0
10
20
-20
0
-10
x
0
10
20
20
10
0 y -20
0
-10
-10
0
x
-20
20
10
0 y -20
0
-10
-10
0
x
-20
benefit
120
100
20 80
10
60
0 y
40
-10
20
10
20
-20
5
10
15
20
5
10
15
20
t
benefit
120
100
20 80
10
60
0 y
40
-10
20
10
20
-20
t
Fig. 9: Initial conditions, production schemes at time step 20 and average benefit over time with
innovation, but with and without imitation (L1, Inv. int. 0.o,02, 4, NEXT)
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V. Conclusions
The paper proposed a computer simulation model to compare the macroeconomic effect of
innovation and imitation performed by individual firms of the model economy. The model
also served to show how the two depend on each other. It thereby goes a step further than
traditional macroeconomic growth models in which not much individual differentiation is
possible and than empirical case studies which disregard the overall impact of their findings
for macroeconomic performance. Such an endeavour is all the more interesting as the
macroeconomic models do not clearly distinguish between the innovative procedures of
innovation and imitation and the results of the case studies are ambiguous, some founding for
the prevalence of innovation and some for imitation.
The performance of the model economy has been tested for a changing economic
environment, with product demands disappearing and new market opportunities emerging to
be discovered by the firms. The performance of the model economy has been tested in several
simulated settings, where different innovation strategies, research and development activity
and market launch procedures were available to the agents. It has also been tested for several
initial conditions and been run in a Monte Carlo simulation to get reliable averages.
The integrated perspective of the proposed model was able to show which strategy is
preferable for the economy but as well, how much it relies on the other. Imitation is a very
profitable strategy, not only for the imitator, but for the economy in general, because it shifts
technological know how of the whole society towards a higher level, thereby making it easier
– even for the already advanced firms – to develop further innovations. Innovation thus being
a less important aspect for economic performance then first suspected. On the other hand, the
model also displays the key role invention plays for overall development for several reasons.
First and quite obviously, imitation needs a base on which to imitate. Second, a mostly
imitative strategy takes variety out of the economic system, making it inflexible to changes
and even to the detection of preferable products under static conditions. Third and finally,
pure imitation is only advisable in an ideal world with an optimal example to copy from – not
exemplary for real economies and not helpful in changing environments. Summing up,
economic performance is best with a well balanced mixture of rather small inputs of novelty
generating innovations and their diffusion by an extended imitation activity.
Although already displaying some effects quite agreeably, the model is still very basic and
offers potential for further research and amelioration. One alteration to be tested might be the
explicit inclusion of innovation costs. However it is not a priori clear whether this would
actually be an improvement or would only complicate the model unduly. Schnaars (1994: 28f)
cites studies that found, that imitation costs were only 65% of innovation costs or that it only
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takes 70% of innovation time to copy the product and those figures are still rather cautious.
This would mean the already found effect of a superiority of imitation would only be
reinforced by making innovation even less attractive, without possibly gaining further
insights. However, the inclusion of costs might cure another problem of the model. So far it
sometimes displays unrealistically high leaps up- or downwards, which can be attributed to
the fact, that innovations can be attempted at all times and for free. If an inclusion of costs
will be found unadvisable for the above mentioned reasons, some other mechanism has to be
found to dim the model economy and adapt it to reasonable growth rates.
An interesting extension could be the inclusion of patent rights, imitation mistakes, e.g.
depending on the technological complication of a product or different costs for production
inputs in different firms. The later obviously leads to another interesting extension: the
explicit distinction between different countries, one e.g. being the technological leader and the
other one trying to catch up by different strategies. Finally the demand side of the model
could be endogenised by including demand curves for the products and thereby making
benefits dependent on the total market supply of a product 20 . The advantages of individual
firms, extending their market share, could thus be made more visible.
20 In Geisendorf/ Weise (2001) this has been done for a simpler economic landscape, in which the agents
search for an optimal production function for just one product and the demand side is modelled by a
demand curve.
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Appendix
Product
landscape
Innovation
intensity
Strategy
1
4
1/4
2/4
0.02
Innovation + imitation
Innovation + imitation of parts
Innovation
6.945
4.373
1.382
5.175
4.116
1.431
13.994
4.981
1.923
14.425
6.248
2.720
0.2
Innovation + imitation
Innovation + imitation of parts
Innovation
2.227
1.794
1.420
2.649
1.758
1.378
3.042
1.919
1.523
3.177
2.069
1.638
0.02
Innovation + imitation
Innovation + imitation of parts
Innovation
6.896
3.179
1.539
5.856
2.670
1.332
9.269
3.732
1.886
9.693
4.503
2.528
0.2
Innovation + imitation
Innovation + imitation of parts
Innovation
2.386
1.648
1.424
2.292
1.609
1.370
2.650
1.778
1.531
2.764
1.928
1.639
L1
L2
Table 1: Average benefit over time of 1.000 runs for different strategies/ setting NEXT (all innovation are
launched) 21
Product
landscape
Innovation
intensity
Strategy
1
4
1/4
2/4
0.02
Innovation + imitation
Innovation + imitation of parts
Innovation
9.016
7.460
2.305
6.206
7.219
2.833
17.563
8.774
3.846
17.925
10.403
5.173
0.2
Innovation + imitation
Innovation + imitation of parts
Innovation
8.534
7.079
5.921
9.095
7.661
6.816
13.062
8.603
7.056
14.196
10.171
8.404
0.02
Innovation + imitation
Innovation + imitation of parts
Innovation
8.100
6.784
3.318
7.831
5.185
2.746
11.443
7.129
3.523
11.807
7.728
4.503
0.2
Innovation + imitation
Innovation + imitation of parts
Innovation
8.090
6.647
6.257
7.338
6.264
5.860
8.645
7.109
6.627
9.308
7.804
7.331
L1
L2
Table 2: Average benefit over time of 1.000 runs for different strategies/ setting BEST (innovations are
launched if performing better than old practice)
21 The numbers over the benefit columns indicate the reproducible starting values, with which the runs
have been initialized. All runs of a column have the same starting conditions in terms of product quality
and choice. 1 and 4 are different starting values. 1/4 and 2/4 are almost the same as 4. Only the first (1/4)
or the first two (2/4) products have been substituted by a production input mix corresponding to the
global optimum.
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Impressum:
papers on agent-based economics
Herausgeber:
Universität Kassel
Fachbereich Wirtschaftswissenschaften (Prof. Dr. Frank Beckenbach)
Fachgebiet Umwelt- und Innovationsökonomik
Nora-Platiel- Str. 4
34127 Kassel
www.ivwl.uni-kassel.de/beckenbach/
ISSN: 1864-5585
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