CONDITIONS FOR LONG LASTING SUSTAINABLE
INNOVATION IN AN AGENT-BASED MODEL
Luca Ansaloni
1
, Marco Villani
1,2
, Roberto Serra
1,2
and David Lane
1
1
Modena and Reggio Emilia University, via Allegri 9, I-42100 Reggio Emilia, Italy
2
European Centre for Living Technology, Calle del Clero, 2940, 30124 Venice, Italy
Keywords: Innovation model, Novelty creation, Agent-based model, Economic agent-based models.
Abstract: During the last decades, innovation has become a hot topic in a variety of socio-technological contexts: in
particular, a key problem is that of understanding its origins. Moreover, scientists are not able to evaluate
the sustainability of innovation processes, and it is difficult to discover what sort of conditions might lead to
their crisis and even collapse. In this paper we present a model where agents are able to create new artifacts
and can develop and enact strategies able to sustain innovation for very long periods. We discuss some
results and make observations useful for understanding the processes and the strategies that sustain the
growth of diversity in social and technological organizations.
1 INTRODUCTION
During the last several decades, innovation has
become a hot topic in a variety of social and
technological contexts, including technology itself,
commerce, social systems, economic development,
and policy construction. There is therefore a wide
range of approaches to innovation in the literature
(Fagerberg et al. 2004). In this paper we focus our
attention on socio-technological systems where the
changes are deliberately introduced by agents, which
design artifacts on the basis of specific goals. In the
systems we consider innovation is typically
understood as the successful introduction of
something new, as for example new objects,
methods, techniques, or practices or new or modified
products and services, whose functionality is
determined endogenously, that is, within the system
itself.
At this level of abstraction, the agents could be
software agents interacting in artificial environments
or (groups of) human beings or organizations in the
real world. What is important is that the agents have
the capacity, supported by their internal
sophisticated cognitive and communication
structures, of creating and modifying artifacts. Aim
of this work is that of identifying the minimal
structures and the strategies (if any) that the agents
need in order to achieve a long lasting sustainable
growth of the system.
Modeling such innovation processes is a difficult
challenge, involving many non-linearly interacting
elements. Indeed, human societies consist of large
numbers of agents (human beings or organizations
composed of human beings) involved in distributed
sparse interactions, mediated by the presence of
artifacts (tangible, as chairs and cars, or intangible,
as languages and services). These interactions give
rise to macroscopic regularities such as trading
relationships, protocols, widely accepted duties or
technological innovations, which in turn feed back
into the structure of agents
interactions. The result is
a complex dynamical system composed of recurrent
causal chains connecting agent behaviors,
interaction networks, and collective outcomes.
Similar patterns of interaction may emerge also in
artificial worlds, in which sophisticated software
agents engage in autonomous interaction streams,
through which they seek to invent new kinds of
artifact.
In order to integrate a new kind of artifact into
the already existing patterns of interaction there
must be a certain degree of convergence of agents
attributions about the new artifact’s identity (that is,
about its properties and functionalities). Several
agents have to align themselves around its use, by
building or modifying other artifacts in order to
combine with it and in such a way support the new
functionalities. If this happens, the invention
becomes an effective innovation (that is, an object
410
Ansaloni L., Villani M., Serra R. and Lane D..
CONDITIONS FOR LONG LASTING SUSTAINABLE INNOVATION IN AN AGENT-BASED MODEL.
DOI: 10.5220/0003175104100417
In Proceedings of the 3rd International Conference on Agents and Artificial Intelligence (ICAART-2011), pages 410-417
ISBN: 978-989-8425-41-6
Copyright
c
2011 SCITEPRESS (Science and Technology Publications, Lda.)
embedded in patterns of use, potentially able to
foster the growth of new active zones in artifact
space). The agents can use artifacts created by other
agents: this fact allows the occurrence of so-called
exaptation processes (Gould and Verba, 1982), very
often observed in socio-technological systems
(Villani et al. 2007) (Villani et al. 2009) (Villani et
al. 2010).
The reciprocal feedbacks between microstructure
(agents and artifacts) and macrostructure
(organizations) has long been explicitly recognized
as of fundamental importance for social sciences
(Hayek, 1948) (Olsen, 1975) (Schelling, 1978)
(Smith, 1937), but they are relatively new topics in
artificial agents research area. For long time
scientists have lacked the tools to quantitatively
model these feedbacks, nor could they deal with
their complexity. The most salient characteristic of
traditional quantitative models on these topics,
derived from economic or physical researches, is
their top-down construction: frameworks such as
fixed decision rules, common knowledge, mean field
and equilibrium assumptions occupied the greatest
part of the researches. Face-to-face interactions
among heterogeneous economic agents typically
play no role, with the only exception of the highly
stylized game tournaments (Fudenberg and Tirole,
1991) (Dutta 1999).
A major advance was the introduction of agent-
based models (Lane 1993a) (Lane 1993b) (Epstein
and Axtell, 1996) (Gilbert and Terna, 2000)
(C.Cioffi-Revilla 2002) (Ormerod et al. 2002)
(Axelrod and Tesfatsion, 2006). These models deal
with the topic of coordination and cooperation
among heterogeneous agents, often lacking a
complete knowledge of the whole system; the
models aim to bridge the gap between micro-level
interactions and emerging patterns at the macro-
level, avoiding the misleading “representative agent”
micro-macro link.
Agent-based models are very useful tools, but
many of them in the innovation context
underestimate the specific role of agents and, even
more, the attributions that agents make about artifact
identity, as noted above (Lane and Maxfield 1997)
(Lane et al. 2005). Agents and artifacts interact in
complex ways, giving rise to the so called socio-
technological systems.
In fact, one of the most intriguing observations
on these kinds of system is the growth of the
quantity and diversity of artifacts that agents use:
over time, not only the quantity and the diversity of
artifacts has grown, but also the number and kinds of
organizations has increased. These two phenomena
are in reciprocal relationship (van der Leeuw et al.
2009); both phenomena contribute in important
ways to the system’s information coordination and
processing capabilities. In particular, in the actual
world, a high rate of innovation seems to be a
peculiar and fundamental key to sustain the systems
itself.
But can the current explosion in number of
artifacts and organizational forms continue
indefinitely? How can agents lacking a global vision
of the whole system coordinate their actions, in
order to cooperate in building a coherent system?
Are there agents’ strategies that favor a sustainable
growth, and others that lead to system collapse? In
order to address this question, we need to understand
the dynamics of innovation processes.
In this paper we make use of an agent-based
model, where the relationships among agents are
mediated by the presence of artifacts. Agents
endowed with a suitable internal structure survey the
opportunities offered by their social and material
environment to create new (kinds of) artifacts, which
in turn change and shape the present pattern of
interactions heavily influencing the emergence of
new agents-artifacts (sub)systems. This kind of
approach has already provided some interesting
results highlighting the importance of relationships
among agents, which can influence the information
flows through the system (Lane et al. 2005) (Serra et
al. 2009) (Villani et al. 2007) (Villani et al. 2008). In
this paper, we describe four scenarios, which taken
together indicate that the conditions enabling a long
lasting sustainable growth are neither simple nor
widespread. The paper is organized as follows. The
second section provides a detailed introduction to
the basic innovation model. The third section
describes results obtained by exploring four different
innovation theoretic scenarios; the fourth section
presents some conclusions derived from simulations
based upon these scenarios.
2 THE MODEL
2.1 Agents and Artifacts
There are numerous approaches to studying
innovation dynamics, but few of them attempt to
construct models in which the reciprocal causality
between transformations in the space of artifacts and
organization in the space of agents plays an essential
role. Rather, most models assume that only artifacts
matter (for example, theories of technological
trajectories), whereas others are agent-centric, based
CONDITIONS FOR LONG LASTING SUSTAINABLE INNOVATION IN AN AGENT-BASED MODEL
411
on the idea that creativity or knowledge is the key
factor underlying innovation dynamics (Dosi 1982)
(Schumpeter 1934) (March 1991).
The model with which this paper is concerned is
based on a theory of innovation developed in (Lane
and Maxfield 1997) (Lane and Maxfield 2005). It
represents a simplified world, inhabited by highly
abstract representations of real world agents,
artifacts, and attributions. The aim of the model
therefore is not to describe in detail a real innovation
context providing quantitative predictions: rather, its
purpose is that of identifying the feedbacks and the
causal connections implicit in the theory and useful
in describing certain kinds of qualitative behaviours
of real innovation contexts.
A claim of the theory is that agents and artifacts
are both important for innovation, because artifacts
mediate interactions between agents, who in turn
actively produce and manipulate the knowledge
needed to make effective the artifacts’
functionalities.
A key point is the representation of artifacts and
their combinations. For modelling purposes, we
have considered different alternatives: binary coding
as in classifier systems; -calculus as in the
Alchemy model (Fontana, 1992) (Fontana and Buss
1994); or simply numbers, either natural or real,
with functions to describe interactions. What is
required is that the space has an algebraic structure,
and that suitable constructors can be defined to build
new artifacts by combining existing ones. We
concentrated on the integer number representation
and the use of arithmetic or other simple functions as
operators, because it is more compact than the
binary representation and simpler than the -
calculus.
Despite this very simple representation, the real
meaning of an artifact is not trivial, since it is
determined not by the thing “in itself,” but by which
agents use it, and for what. For that reason, in the
model the same entity is representing:
a type of artifact, i.e., the “idea” – or archetype –
of the article the producer is making (for example,
the platonic idea of a chair – or of the number “12”
in a particular model run): the artifact “name” in the
following;
the artifact(s) a particular producer is making
(the article a particular producer is making and
offering to other agents): “article” in the following;
a single artifact token (a single chair present in
the stock of an article): “item” in the following.
The entities manipulated by our algorithm are the
articles, which in the model have a unique identifier
and a stock.
The intelligent part of the system is embedded in
the agents, endowed with sophisticated cognitive
and communication capabilities. In particular, agents
1. can explore their environment (composed of
articles and other agents);
2. can manipulate articles (in order to build other
articles);
3. can choose their goal (a particular name);
4. can use their knowledge in order to reach their
goals.
Agents’ capabilities are finite; therefore, they are
not manipulating all the articles present in their
world, nor know the goals of other agents. The role
of agents is defined by what they do, and by the
other agents with whom they interact. Agents have
not a complete information, and this situation
heavily influences their behaviour. Agents have to
identify useful goals and pursue them; in so doing,
they may or may not collectively build a sustainable
world.
Note that at this abstract level this description
applies both to living systems and to totally artificial
systems. The topic with which we are dealing
therefore embraces the more general theme of
coordinating many different agents that can
manipulate and interact with their environment, also
by introducing new objects. The new objects could
be tangible or intangible; the model we present here
however explores worlds where these objects (the
artifacts) are countable - the simplest and most
common situation.
Now we can describe the agents’ internal
structure. In this model we aim to identify the
simplest set of structures and strategies needed to
assure the agents’ functionality, so an agent:
1. can detect the presence of (a subset of the)
already existing articles and agents;
2. can manipulate some article by means of
“recipes” (ordered sequences of article identifiers
and production operators), producing other articles;
3. can identify goals, derived by its world
knowledge;
4. can manipulate its recipes in order to build new
recipes, producing the article that match their goals.
The most complicated structure owned by an
agent is the recipe, the tool it uses to process the
items it obtain from other agents in order to produce
the items of its output articles. In the experiments
reported here, recipes employ the arithmetic
operators “+” and “-”.
ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence
412
An agent can possess more than one recipe at the
same time. If the stock of a particular recipe is lower
than a desired level (10 items in the following
simulations) the agent repeats the production action
a finite number of times (whose maximum level here
is set to 10). If the stock exceeds this level, the agent
decreases production; finally, in each step all the
recipes owned by one agent have to be produced at
least once, if inputs are available (the agents need
artifacts in order to survive). A recipe that during the
last 15 time steps is not produced (because the
needed input names are not available in the system)
or not used (because no other agents have taken
items from its stock) is removed from the
simulation: in such a way “useless” articles
disappear.
In order to achieve their goals, the agents can
create new recipes. Once the goal is set, with
probability p
inn
the agents try to build a recipe able
to produce an article having a name close (in some
metric) to the goal. Several optimization strategies
can be applied to this aim: in the implementation
used here, the agents combine their existing recipes
by means of genetic algorithms, whose fitness
function is the inverse of the distance between the
goal and the realized name. If the genetic algorithm
is not able to create a recipe building an article
whose name is within a given distance from the
goal, the invention process fails and the agent
doesn’t reach the goal (see (Serra et al. 2009) for
further details).
The goal is the name the agent is trying to
realize; it can be maintained until the building of an
article with the same name succeeds, or it can be
changed at each step with a given probability Pgoal.
The agents could use several strategies in order to
set their goal: in the following simulations the agents
randomly keep the name of one of the already
existing articles and occasionally mutate it (by
multiplying it by the value C_jump the 30% of the
times a new goal is set – an action that correspond to
a “jump” in the artifacts’ space). Note that in such a
way the choice of the goal is influenced by the
number of articles that have the same name: replicas
therefore are not negligible.
The systems’ environment is very simple, and is
constituted by a set of articles (the “raw materials”)
whose stock is unlimited, the interesting study of
systems in which raw materials have a production
limit being postponed to further works.
2.2 Dynamics and Novelty Generation
A typical run (see also (Serra et al. 2009) and (Vil-
lani et al 2007)):
1. creates of a set of initial conditions (N agents
having 2 recipes each)
2. repeats until n_passimax_passi
a. sets Count=0
b. repeats until CountN
i. the random choice of an agent (add 1 to
Count)
ii. the determination of a new goal (with
probability P
inn
) and its realization
iii. the production of the actual agent’s
recipes
iv. the increase of Count by one unit
3. final visualizations
It is possible that some stocks become empty,
since very often several recipes make us of the same
articles; in this case these recipes have to change
provider, by finding a new one producing an article
with the same name and a non-empty stock. This
process has several interesting consequences:
new articles, just built, have the possibility of
being used (so allowing their inclusion on the
already existing patterns of interaction);
articles having the same name could be realized
by different recipes, combining different set of
articles; a frequent change of providers allows
therefore the existence of a highly heterogeneous
mixture of artifacts, favouring high diversity in the
systems;
cycles composed of articles and agents can be
formed, and can become the source of long-lasting
patterns of interaction (each article of value for the
next one).
The continuous creation of new recipes making
use of the already existing articles, combined with
the change of providers, allows the formation of new
(groups of) cycles, stabilizing in this way these new
parts of the system. As a first conclusion, the change
of provider seems the key feature enabling (directly
or indirectly) the stabilization of the innovations, by
means of the consequent formation of cycles.
2.3 Typical Behaviors
The model provides the basic elements for a suitable
description of the creation and stabilization of
innovations. Table 1 and fig.1 show the parameters
and a portrait of a typical scenario, where the
number of artifacts (fig.1a), the diversity (the
number of different names present at a given step -
fig.1b), the typical recipes’ production level (fig.1c)
CONDITIONS FOR LONG LASTING SUSTAINABLE INNOVATION IN AN AGENT-BASED MODEL
413
and the diameter (the difference between the
maximum and the minimum names - fig.1d) reach a
stable situation.
Table 1: The main model parameters and their values in
the standard case.
(a) (b)
(c) (d)
Figure 1: Behaviour of 10 systems initially composed of
40 agents, each agent having 2 recipes. Each plot reports
the smallest, average, median and biggest value of each
variable vs. time. (a) Number of articles; (b) diversity; (c)
production levels; (d) diameter in the artifact space.
3 RESULTS
Fig.1 shows that the systems’ diversity can undergo
a considerable growth. However, there are strategies
that can be adopted in order to significantly increase
the number of articles and their diversity. We
propose here four different scenarios, able to support
different diversity levels. Besides the standard one
(P_stand scenario, with p
inn
=0.2), we can
significantly increase the agents’ innovation
probability (p
inn
=1.0, P1_0 scenario), enable a
feedback between a measure of the agents’ size (as
for example the number of their recipes) and the
innovation probability (P_chang scenario); or finally
we can compare these scenarios with a situation
where the goal setting is random (p
inn
=0.2, P_rand
scenario).
(a) (b)
(c) (d)
Figure 2: Median on 10 different runs of variables
characterizing the 4 scenarios described in the text. (a)
Number of articles; (b) diversity - the number of different
names existing in the system; (c) the production levels; (d)
the diameter in the artifact space (note the use of
logarithmic scale in this plot).
As we can see in fig.2, the different scenarios:
produce significantly different quantities of
articles;
support very different diversity levels;
are able to maintain different recipes’ production
levels;
explore different portion of the artifacts’ space.
To these differences there correspond very different
structures of the artifact space, as it is shown in fig.3
for typical runs.
(a) (b)
(c) (d)
Figure 3: Artifact space of a typical run of the 4 scenarios
described in the text. (a) random goals (b) standard
situation; (c) p
inn
=1.0 scenario; (d) p
inn
dependent on the
number of recipes owned by each agent. Note the use of
different scales in (a) and (c) cases.
ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence
414
From data in fig.2 and fig.3 we can draw some
significant observations:
the random system
o has high diameter size,
o uniformly covers the explored area,
o support high diversity levels;
o has low recipe production levels;
the introduction of an imitative goal setting, with
respect to the random system
o strongly reduces the explored area;
o reduces the diversity level;
o allows high recipe production levels,
o allows high number of articles;
a very strong innovation
o restores the previous diameter, diversity and
recipes production levels
o by maintaining a small covered area and
shrinking it to some restricted “levels”;
the feedback between agents’ size and innovation
probability allows
o long lasting periods of diversity growth;
o long lasting periods of growth of the number of
articles;
o high numbers of articles
o very high levels of article production.
Further clues can be inferred from table 2, which
shows the efficiency of the scenarios in integrating
the novelties into the already existing patterns of
interactions. As we can see, in systems with high
diameters, the innovation processes have high
percentages of failure and produce a great number of
articles that are not useful: artifacts too dispersed
likely lead to the creation of other “outlier” artifacts
not easily fitting with others. The systems where
agents set the goal randomly have a very low article
production, almost to the point that each article is a
unique exemplar; the diversity is high, but so is the
article turnover, because of the great difficulty in
replacing inputs that have disappeared. The system
wanders through artifact space. The goal setting
strategy of P1_0 systems leads them to reduce the
covered area despite the high diameter, but once the
structure of artifact space reaches a kind of
stationary condition, lots of agents’ innovation trials
fail, because of the increased difficulty in realizing
suitable recipes starting from a very sparse situation
(note the emergence of levels, as partial reaction to
this situation).
The combination of the diameter and the covered
area in the artifact space seems therefore to play a
significant role in determining the main system
behaviours. The strategies the agents adopt can
influence the diameter: agents able to tune the
diameter in the artifacts’ space are able to force the
systems’ global behaviour, driving it toward high
diversity or high production levels.
Table 2: Some results about the four scenarios presented
in the text (averages on 10 runs). The “failures” column
show the percentages of the unsuccessful innovation trials
(there are no failures in P_rand scenario, because of in
absence of definite goals each single innovation is
produced). The “unsuccessful successes” are articles that
the agents were able to build, but that once made are not
used by other agents and therefore disappear; the
“successful successes” are articles that once made never
disappears; the “medium successes” covers the remaining
cases (the values in these last columns are percentages on
the total number of articles made during the simulation).
The table shows also the average final diversity of the
scenarios, and the corresponding fraction on the total
number of articles.
Failures Medium
successes
Unsuccessful
successes
Successful
successes
Diversity Diversity/
#artifacts
P_rand ‐‐‐ 39% 60% 2% 320 0.86
P_stan
d
26% 47% 40% 14% 240 0.06
P1_0 72% 27% 59% 14% 350 0.05
P_chan
g
26% 58% 34% 8% 1050 0.16
The only strategy supporting both a high number
of articles and elevated production and diversity
levels is the P_chang scenario, where there is a
feedback between agent size and probability of
innovating, leading to the presence of heterogeneous
agents (in fact, agents having different numbers of
recipes innovate at different rates, increasing the
already existing gap). We have simulated systems
with each of these characteristics separately, but
without obtaining any evident increase in the
number of articles or the diversity. In particular, we
simulated (a) worlds having from the beginning high
heterogeneity in agents’ innovation probabilities
(two groups of agents having p
inn
=0.2 and p
inn
=1.0),
(b) worlds where the innovation probability
increases in time and (c) worlds where the
innovation probability is p
inn
=1.0 from the
beginning. None of these variants sustain long
lasting growth, whereas agents that develop high
innovation activity after a soft growth phase are able
to do so.
These remarks show the presence in the model of
path dependent processes: in fact, agents having
from the beginning p
inn
=1.0 are not able to sustain an
endless growth, despite the high number of produced
innovations, whereas agents whose innovation
probability is linked to their recipes’ number (but
CONDITIONS FOR LONG LASTING SUSTAINABLE INNOVATION IN AN AGENT-BASED MODEL
415
only during the first part of the simulation) are able
to.
P_chang worlds are able to efficiently recruit the
new inventions, making them effective innovations
(8%+58%=66% of the new articles are useful)
note that the superiority of the P_stand systems in
making “successful successes” (14%) is only
apparent: in fact the total number of artifacts in the
P_chang worlds is overwhelming, as is their
absolute number of completely successful
innovations.
4 CONCLUSIONS
In this work we present an agent-based model of
innovation, where agents and artifacts coevolve
giving rise to a system where innovations take place
and become integrated into the already existing
pattern of interactions. The model captures the basic
features of innovation processes, and allows the
search for strategies able to support the system
expansion, both in term of artifact space exploration
and of the artifacts’ quantity and diversity.
Quite unexpectedly, strategies implying high
innovation rates are not able to support long lasting
increases in system diversity. A key aspect is that of
the diameter of the explored area in the artifacts’
space: if too large, the artifacts match poorly,
leading to poor worlds with very low production
levels.
Systems having at the same time high diversity
levels and high production levels require a kind of
balance between exploration and exploitation
processes. A too strong expansion in artifact space
can lead to the building of artifacts that are not
integrated with one another, whereas an intense
propensity to build artifacts not so dissimilar to each
other can limit the exploration range.
Another unexpected feature that strongly
influences the integration of novelties into the
already existing patterns of interaction is the change
of providers, which allows:
the existence of many agents with similar
specializations;
the simultaneous presence of several ways to
build the same kind of objects (supporting in such a
way high diversity levels);
the stabilization of complete chains of integrated
artifacts.
Systems without this peculiar feature cannot
sustain or diffuse innovations.
A strategy able to provide a long lasting
sustainable enrichment is that of varying the agents’
propensity to innovate, as for example by coupling it
with a measure of the agents’ size. This strategy
allows a complex interplay among each single agent
and its environment (the other agents and the
artifacts), bringing to growth path dependent
processes.
We identify therefore several processes able to
sustain diversity, and make some observations about
the behaviour of the number and diversity of
artifacts produced by groups of agents. Further work
is needed to study the influence of artifact coding on
the feedbacks discussed here and to analyze the
formation of the structures in agent-artifact space
that differentiate the proposed scenarios, in order to
develop new strategies able to support long lasting
and sustainable cascades of innovations.
ACKNOWLEDGEMENTS
We would like to thank Andrea Ginzburg, Giovanni
Bonifati, Sander van der Leeuw and Margherita
Russo for valuable discussions and suggestions.
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