Riccardo Boero, Marco Castellani,
Flaminio Squazzoni
GROWING BEHAVIORAL ATTITUDES,
REFLEXIVE TYPIFICATION OF SOCIAL
CONTEXTS AND TECHNOLOGICAL CHANGE
IN A COMPUTATIONAL INDUSTRIAL
DISTRICT PROTOTYPE
DSS PAPERS SOC 6-02
To be presented at the 8th International Conference of the Society for
Computational Economics on “Computing in Economics and Finance”,
Aix-en-Provence, France, 27-29th June 2002
INDEX
1.
From districts to “districtualized” firms: behavioral
attitudes and social reflexivity of localized firms ................... Pag. 07
2.
How ID prototype works .................................................................. 10
3.
How ID agent works ......................................................................... 17
3.1 Cognitive step 1: from information to "rough indexes" ............. 19
3.2 Cognitive step 2 : from "rough indexex" to "macro indexes" .... 22
3.3 Cognitive step 3 : indexes evaluation ......................................... 24
3.3.1 Behavioral attitude states ................................................. 25
3.3.2 Behavioral attitude morphogenetics shifts ....................... 27
3.3.3 Behavioral attitude deconstruction shifts ......................... 29
3.4 Cognitive step 3 : actions ............................................................ 31
4.
Indicators and Prototype Settings ................................................... 42
5.
Analysis of outcome and emerging dynamics ................................ 43
6.
Conclusion: how agent-based computational models can put
down stable roots in social sciences? ............................................... 49
References .......................................................................................... 52
Abstract
Industrial districts are complex inter-organizational systems characterized by an
evolutionary network of interactions amongst heterogeneous, localized, functionally
integrated and complementary firms. By creating an industrial district computational
prototype, that is to say simulating an archetype of industrial district through an agentbased computational model, we explore how industrial district dynamics can be
conceived as byproducts of cognitive identity and social identification processes
undertaken by district firms. Rather than study the district just as the effect of emerging
properties of interacting firms, we try to study how firms develop over time more or less
districualized behavioral attitudes based on reflexive typification of social contexts upon
which firms experience and towards which they develop more or less deep
“identification”. The question is: have such cognitive processes a great impact on
technological learning and economic performance of firms over time?
Key Words: Agent-Based Computational Models, Industrial Districts, Cognitive
Architecture, Behavioral Attitudes, Technological Change.
Are industrial districts nothing but the outcome of the “complexity
effect”? Or do you need to think more deeply about “district agents” and
how they cognitively reflect upon such “complexity effect”? and again, is it
better to speak about “industrial districts”, or to speak about dynamics of
firms’ “districtualization”? Are districts nothing but the product of some
emergent properties due to externalities-driven economies of proximityrelated firms agglomeration, or are they as well what can be trivially called a
“state of agent’s mind”?
This paper rises from a first attempt to explore such kind of questions.
To do this, we have created an industrial district agent-based computational
prototype, that is to say we have reproduced a “theoretical idealtype” of
district into a computer, simulating some relevant processes. The aim is to
explore how behavioral attitudes of the industrial district firms evolve over
time, how such evolution is both affected and sustained by a continuos
dynamic of expansion and contraction of the social context experienced by
firms, and how this has relevant impact on the technological learning
undertaken by firms.
The paper is organized as follows:
-
the first section shows some theoretical standpoints on districts
that pushed us towards the idea of exploring district phenomena using
agent-based computational techniques; rather than assuming right from
the start that district firms have a prototypical and homogeneous
behavioral attitude, as the traditional literature on district does (i.e.,
automatic and natural commitment, cooperation, and trust amongst
firms), we study the evolution of different behavioral attitudes, in a broad
Growing Behavioral Attitudes…
5
sense more or less “districtualized”, within firms’ paths of “day-to-day”
experience and action;
-
the second section shows how the industrial district (ID)
prototype works, from the point of view of its “structural properties”, that
is to say classes of firms, division of labor amongst them, spatial
localization of firms, evolution of the technology and market
environment, and some different technology and market challenges upon
which firms need to be able of learning;
-
the third section shows how ID cognitive agents work and by
which kind of computational “building blocks” they are composed; they
refer to what we call “information/action loop”, which shows a general
framework of computational cognitive processes undertaken by district
firms;
-
the fourth section shows some indicators we used to control the
most relevant processes emerging by the prototype;
-
the fifth section shows the analysis of simulation outcomes,
with a focus on the most relevant emerging dynamics, above all, the
relation amongst changing behavioral attitudes, technological learning,
and economic performance of firms over time;
-
finally, the sixth section shows some conclusion about how
agent-based computational models can put down more stable and rich
roots in social sciences and towards which kind of direction we shall
develop the district prototype.
6
Growing Behavioral Attitudes…
1.
From districts to “districtualized” firms: behavioral attitudes and
social reflexivity of localized firms.
In recent years, the literature on industrial districts has moved several
steps towards the analysis of the firm-based “cognitive mechanisms” of
“individual identity” and “social (group) identification”, as a way of
studying more deeply the roots of the individual action within localized
social contexts. For instance, rather than taking a “system as a whole”
perspective and assuming right from the start a natural, homogenous and
prototypical behavioral attitude of the district firms, such analyses have
focused on how “district members perceive and evaluate the district and
how these perceptions affect the actual behaviors adopted by district
members”, and, therefore, on how “identification” of firms towards their
social contexts of experience is “an extreme form of relational modelling
allowing the individual to define his/her identity in relation to the
characteristics of perceived social groups” (i.e., see: Sammarra A. and
Biggiero L., 2001).
By taking a perspective oriented to topics such as the capacity of ID
agents to develop forms of “identity”, using capabilities of “social
reflexivity” and producing processes of “institutional identification”, the
analysis starts to shift quietly its attention from the district as a “systemic
mechanism” with specific geo-spatial boundaries sketched out by the interfirm division of labor, to the dynamic of “districtualization” and even of
“dis-districtualization” developed continuously by firms with respect to their
social context of practices (i.e., see: Becattini G., 2002). From “district” as a
well-defined system to the “districtualization” as a “state of mind” of locally
Growing Behavioral Attitudes…
7
interacting heterogeneous agents, this is the theoretical shift of the recent
literature on district. Clearly, such analytical perspectives are nothing but a
dichotomy in search of integration and synthesis.
This means that we need to study the relation amongst:
a) the aggregate dynamics emerging from the “bottom up” by the
interaction amongst many heterogeneous localized firms, that is to say
what complex system theorists call “the complexity effect” (Holland
J.H., 1995); heterogeneous firms engaged in recurring patterns of
interaction, but embedded within specific local context, give rise to what
can be called an “aggregate composite system” (the district) (Auyang S.
Y., 1998), on which they have different visions and about which none of
them have neither property, nor possibility of completely control and
management (i.e., see: Lane D., 2001);
b) the cognitive capability of firms to develop “reflexivity” upon the
fundamental characteristics of the context they experience over time;
firms, even if localized, develop more or less keen antennas oriented to
environment and contexts within which they move (i.e., see the cognitive
and evolutionary approach suggested by Belussi F. and Gottardi G.,
2000); over time, such monitoring capabilities affect their behavioral
attitudes creating a continuos tension between “cognitive identification”
(“districtualization” of behavior) and “cognitive distance” (“disdistrictualization” of behavior); such cognitive continuum needs to be
conceived as different behavioral states of the cognitive evolutionary
paths of firms, rather than a static “anthropological prototype” of the
district firm, or a byproduct of the district mechanism, avoiding what
8
Growing Behavioral Attitudes…
sociologists call the paradox of the over-socialized conception of agency
(i.e., see: Uzzi B., 1996, 1997; Staber U., 2001);
c) with this respect, the complexity of the district processes stands in the
“circular loop” between emergent properties of the interaction context
and evolutionary reflexivity of agents upon it.
The standpoint of our paper is to study how behavioral attitudes of
firms change over time, how they are driven by the firms’ capabilities of
monitoring the characteristics of their relational contexts and operational
environments; how firms, as “bounded rationality” cognitive agents, develop
a continuos theoretical “typification” of contexts and environments, trying
an understanding of what they have done and what they need to do, and
which kinds of effects on technological leaning and economic adaptation
such “cognitive typification” has, too.
Growing Behavioral Attitudes…
9
2.
How ID prototype works1
In our intentions, speaking about a district prototype it means to
translate a “general” and “abstract” representation of a district “archetype”
into an agent-based computational architecture. According to the well
known “prototype principle” suggested by Douglass R. Hofstadter (1979),
almost twenty-five years ago, an observer can classify and “typify” an
observed phenomenon, just synthesizing into a “hierarchy of generality” its
spatial and temporal “collocation”, “specificity” and “manifestation”.
Generality means the need to identify a class of phenomena and
fundamental underlying mechanisms and processes, in order to be able to
explain something. By using the term “computational prototype”, we
reinforce the evidence both that our prototype is not a modeling of a “casedistrict”, and that our computational architecture has been thought as a
framework to be computationally developed, both by applying it to casestudy-based modeling and by further building blocks refining.
We start from a very broad and accepted definition of what is an
industrial district archetype: district is a decentralized complex system
characterized by an evolutionary network of interactions amongst
heterogeneous, localized, functionally integrated and complementary firms.
Firms are embedded within an integrated geographical area, they produce
goods on market according to a division of labor mechanism, they have
more or less rich proximity relation, and they move within specific
1
The ID computational prototype has been created using Swarm libraries and Java
programming language. Swarm is a toolkit for agent-based computational simulation
developed by Santa Fe Institute (see:www.swarm.org) and used by a more and more
growing community of social scientists. For descriptions and applications of Swarm to
economic phenomena, see: Terna P. (1998), Luna F. and Stefansson B. (2001), and
Luna F. and Perrone A. (2001).
10
Growing Behavioral Attitudes…
technology and market environment. Such district structural features are not
as pieces of a “social totality”, but rather as byproducts of what Giddens
should call “structuring properties” of the district (Giddens A., 1984). As it
is stressed by Giddens, “structure is regarded as rules and resources” not
“brought into being by social actors, but continually recreated by them via
the very means whereby they express themselves as actions”, in terms of
recursively oriented “social practices ordered in time and space”. We think
about “prototype district structural features”, such as for instance
mechanisms of division (specialization), coordination and integration of
labor (production chains), in a sense like this, namely as recursive timespace processes that “exhibit structuring properties” developed and
reproduced by agents which develop and reproduce at the same time
“conditions that make their activities possible”. Our first operation has been
to translate such distinctive features in specific computational “building
blocks”, designing an agent-based prototypic architecture.
Right from the start, we assume that prototype agents are firms. They
are 400, divided in two different classes, final firms, having functions of
organizing production and selling goods on market, and sub contracted
firms, having specialized functions related to the whole production process.
The class of the sub contracted firms is further divided into three subclasses, sub firms A, B and C. In order to produce a good on market, firms
interact give rising to production chains. Here, final firms have a focal and
innovative role because of their interstitial position at the edge of market and
district (i.e., see: Albino V., Garavelli A. C., Schiuma G., 1999; Belussi F.
and Gottardi G., 2000; Boari C. and Lipparini A, 1999; Lazerson M. H. and
Lorenzoni G., 1999).
Growing Behavioral Attitudes…
11
We assume that a production chain must be composed by: 1 final firm
+ 1 sub firms A + 1 sub firm B + 1 sub firms C. Firm is located within an
environment populated by other firms, namely the district, with given spatial
neighborhood positions (for details, see: Boero R. and Squazzoni F., 2001).
Firms have three basic features: technology (input), organizational
asset (throughput), and economic performance (output). The relation
amongst such three basic features is shown in figure 1, which represents the
evolution of technology and market environment towards which firms need
to adapt. Firms need to undergo 2000 simulation/production cycles, during
which they face three phases of technological continuity and two phases of
technological discontinuity. In short, over time market causes two
technology breaking off (cycle 500 and 1000). Market is conceived as an
“institution” collecting and distributing information about performance and
technology evolution for firms.
Firms need to absorb technology and to learn the way by which adapt
their organizational assets trying to reach the fixed best technological
practice level. We assume that technology (T1, T2, T3) implies an
investment of internal organizational factors. Technology is composed by a
set of four numbers (i.e., 0, 3, 7, 2). Every number can be viewed as an
organizational factor, such as labor, physical capital, human capital, and
information and communication internal architecture. Firms start the
simulation from a combination as follows: T1 (-1, -1, -1, -1), that is to say a
situation of complete ignorance about technology factors. The best
technological practice level of T1 is randomly fixed at the start of the
simulation, and that of T2 and T3 is randomly fixed over time. This implies
that firms can improve their technological effectiveness, both decreasing or
12
Growing Behavioral Attitudes…
increasing number/factors. Firms do not know the “best technological
practice level” and can change number/factors just by turns.
Therefore, we assume that experimental learning of firms is
characterized by “path dependence”, that is to say technological innovation
of firms is affected by the technological position they have. In short, when
firms take technological jumps from T1 to T2, or from T2 to T3, they start
to explore the new combination of number/factors by their previous
combination (i.e., previous combination: T1 3, 4, 7, 8 /jump from T1 to
T2/initial combination: T2 3, 4, 7, 8).
According to the effectiveness of their organizational assets, in terms of
distance/nearness of their combination of number/factors with respect to the
“best technological practice level”, firms have specific costs and reach
specific performance levels, as it is shown in Matrix 3.
To adapt step-by-step their organizational asset, firms have two
strategies of experimental exploration within the state of technological
possibilities: radical innovation (with a possibility fixed on 80% to obtain a
new value, namely a new number/factor); or imitation by exploitation of
information coming from neighborhood (firms is able to look into the
combination of factors of neighboring firms, to compare specific
number/factors, to discover possible differences, and to imitate them) (for
details, see Squazzoni F. and Boero R., 2002).
Figure 1. Evolution of technology and market environment:  is the number of
production/simulation cycles. T1, T2 and T3 are the three technological regimes impacting
district firms over time. Phases of technology breaking-off are about cycles 500 and 1000. Grey
areas show technological positions and related achievable performance levels of firms with
respect to technology standard and market evolution. We assume that technological evolution is
irreversible (from T1 to T3).
Growing Behavioral Attitudes…
13
C (0.84 , T3)
B (0.59 , T2)
T2
A (0.34, T1)
T1
0.25 
0.5 
T3
0.75 

The concept of neighborhood calls for the problem of proximity
relations amongst firms. We introduce different metrics of proximity viewed
as different sources of information for firms. Over time, and with respect to
“behavioral attitudes” of agents described afterwards, firms develop a
dynamic overlapping web of proximity relations with others, namely spatial,
organizational and “social group” forms of proximity (Bellet M., Kirat T.
and Largeron C., 1998; Oerlemans L. A. G., Meuss M. T. H., and Boekema
F. W. M., 2001; Torre A. and Gilly J.- P., 2000). As it will be outlined in the
next paragraph, proximity matters because it produces as byproducts sources
of information, possibility of monitoring of the social context, and
possibility of comparing individual and social context features. Proximity
can be more or less spatial enlarged, more or less geography-dependent,
more or less organizational relation-dependent, or more or less “social
group”-oriented.
14
Growing Behavioral Attitudes…
To regulate all such computational operations, we introduce three
matrixes, called “Info Matrix”, “Tech Matrix”, and “Change Matrix”, where
all actions are transformed in costs and values.
Matrix 1. “Change Matrix”
Technological Change
Organizational Asset Change
T1
T2
T3
50
200
100
400
200
“Change Matrix” shows costs needed to implement a new technology (first line) or to
improve organizational asset, that is to say to change number/factors combination
(second line). Along the column, there are all the three “technological regimes”
impacting firms over time. Costs gradually increase over time with the gradual growth of
market requests and performance needed.
Matrix 2. “Info Matrix”
T1
Technology Imitation
Organizational Asset
Imitation
Technology Innovation
Organizational Asset
Innovation
Best Sub on Performance
or on Investment on
Organizational Asset
T2
T3
40
70
30
100
20
250
10
80
50
30
5
5
5
“Info Matrix” shows costs which firms must pay in order to achieve different type of
information. Information concerns both technological strategies (innovation and
imitation), and partnership selection mechanisms. The second case refers to different
information criteria by which final firms organize their production chains, aggregating a
team of sub contracted firms. Final firms need continuously information about economic,
technology and organizational features of sub contracted firms in order to choose
between stabilizing or destabilizing their inter-organizational contexts (chains).
Growing Behavioral Attitudes…
15
Matrix 3. “Tech Matrix”
T1
Organizational
Asset
Worst
Best
A
B
5
7.32
6
10.49
T2
C
0.01
0.01
A
6.65
9.74
T3
B
9.12
15.96
C
0.01
0.01
A
8.86
12.97
B
13.87
24.26
C
“Tech Matrix” shows data about costs and performance of firms in all the different
learning steps undertaken by firms. As it is mentioned above, technology costs and
economic performance gradually increase as well as the market requests over time.
Column A shows technology costs, B shows levels of achievable performance, and C
shows decreasing costs for the use of the same combination of number/factors for more
than one simulation/production cycle. All costs and performance values are expressed by
a continuum between “worst” and “best” technological practice levels, with an average
calculus on the degree of distance/nearness of the combination of number/factors
implemented by firms with respect to the “best” and “worst” levels.
Finally, we introduce a double metrics of the firm’s profit. Firms have
their individual level or profit, due to the difference between costs and levels
of economic performance, as it is shown in column A and B of “Tech
Matrix”. But, at aggregate level of chains, the “total profit” is not the simple
sum of the individual “profit” of interacting firms. We introduce an “extra
profit” which mirrors the
“technological compatibility” level of firms
involved into the same chain. In order to produce quickly and to reach the
possible highest level of quality of the good on market, firms need to
“speak” the same technological language. In short, such “extra profit”
emerging by the production-oriented aggregation of firms, is what we call in
our computational codes, “time compression” value (see for details:
Squazzoni F. and Boero R., 2002).
16
Growing Behavioral Attitudes…
0.01
0.01
3.
How ID agent works
The foundation of the cognitive architecture of ID agents is based on
the hypothesis that agents are able to process information about technology
and market environment, district environment, context of partnership,
organizational and economic features, and to transform information into
possible courses of “appropriate” situated action. We refer to “cognitive
architecture” in a very broad sense, that is to say the way by which agents
perceive and process information in order to maintain or modify a particular
routine of action. “Routinizing” action, agents are able to develop what can
be called a “reflexivity monitoring” of “day-to-day” operations in specific
fields of action (Giddens A., 1984). Computationally speaking, agents
realize their tasks by means of what we call the “information/action loop”
(see Figure 2). The information towards action mechanism is driven by a
continuos loop which relates data to “rough” indexes, “rough” indexes to
“macro indexes”, “macro indexes” to “evaluations”, and “evaluations” to
actions. We set up an information set with different data concerning “day-today” activities of agents, as it is shown in table 1, and different cognitive
steps through which agents use, monitor and transform information into
decision. Such data are built both on temporal and spatial dimensions, and
even on their interrelation.
As in a “cycle of cognition” proceeded from Neisser essential works
(Handlbauer G., 2000), the “information/action loop” causes learning
characters. According to the “simonian grand theme” (Simon H. A., 1987),
such learning characters rely more on “procedural” perspective about
information processing undertaken by agents, than on “structural”
perspective, as it is pointed out in various classical studies (Craik, 1980).
Growing Behavioral Attitudes…
17
Learning is based on the capacity of agents to typify both their social
experience and their routines of action over time. Agents are able to
elaborate day-to day ordered evaluations about what they have done, and
day-to-day monitoring evaluations about which kind of social context they
are moving in. In a sense, action has here what Emirbayer and Mische call a
“practical-evaluative dimension” associated with a “relational dimension”
(Emirbayer M. and Mische A., 1998). As it is stressed by Giddens,
“routinization” of actions is important because of “a sense of trust in the
continuity of the object world and in the fabric of social activities […]
depends upon certain specifiable connections between the individual agent
and the social contexts through which that agent moves in the course of dayto-day life” (1984, p. 60). As it will be outlined, individual experience,
behavioral attitude development, and reflexive monitoring of social contexts
by means of information processing, cognitive transformation of
information into routines, practice of evaluation and monitoring are
fundamentals of the cognitive architecture of ID agents.
We assume that computational capabilities of agents are bounded, and
that time, memory and attention of agents are finite and selective resources
(March J., 1994). We assume that agents cannot act cognitively with parallel
processing mechanisms, namely they cannot control, manage and face the
entire set of information with the same level of “cognitive attention”.
Moreover, we assume that there is a tradeoff between “width” and “depth”
of the cognitive process.
As it will be outlined, all the cognitive steps undertaken by agents
imply an information processing activity based on approximation,
abstraction and synthesis of the “relevant attributes” belonging to the
information. In conclusion, the “information/action loop” starts with a
18
Growing Behavioral Attitudes…
“domain specific” information and it ends, by means of specific cognitive
procedural processes, with a “broad generic” information upon which the
decision process of agents is based.
3.1
Cognitive step 1: from information to “rough indexes”.
The first cognitive step is the transformation of information into “rough
indexes” of “attribution”. Information is about all the topics faced by firms.
“Rough indexes” allow agents to assign a “positive” or “negative” judgment
to information, which is expressed by a computational dichotomy of 0 and 1
values.
Agents cluster, synthesize, and categorize information belonging to the
same topics, transforming “numbers” into “evaluations”, even if through a
“first inference” on a “rough information”. “Rough indexes” are as follows:
-
“sold” (index allows a first inference on market effectiveness of firms
and their neighborhoods and a comparison amongst such values)
-
“time compression” (index allows to final firms a first inference on
“technological compatibility” of their production chains and their
neighboring chains, and a comparison amongst such values);
-
“performance” (index allows an inference on the effectiveness on market
and a comparison with the neighborhood);
-
“number of chains” (index allows an inference on the degree of
“stability” and “good relations” amongst firms);
-
“selling firms” (index allows an inference to the effectiveness of the
system as a whole);
Growing Behavioral Attitudes…
19
Figure 2. From information to indexes by means of the “approximation-abstraction-synthesis
mechanism”. In order to transform information data (the lightest solid), into rough indexes (the
middle one) and then into macro indexes (the darkest solid), agent-based cognitive operations
meet a tradeoff between increase of the degree of the three dimensions (abstraction, synthesis,
approximation) and a decrease of the volume of information to be considered.
Abstraction
Synthesis
Approximation
20
Growing Behavioral Attitudes…
- “technological change” (index allows an inference to the degree of
“technological instability” of the system as a whole);
- “searching for new sub firms” (index allows an inference of the
instability of inter-firm relations and the tendency to the emergence of
new partnership assets within the system as a whole);
- “technology” (index allows a comparison amongst the “technology level”
of firms and neighborhood, standard);
- “organizational asset effectiveness” (index allows a comparison between
the level of effectiveness of the “organizational assets” of firms and
neighborhood);
- “ homogeneity of criterions for keeping sub firms” (index allows to
evaluate the degree of uniformity of the “inter-organizational assets”
within the neighborhood);
- “homogeneity of criterions for searching sub firms” (index allows to
evaluate the degree of diffusion of changes in the “inter-organizational
assets” within the neighborhood);
- “profit over time” (index allows an inference on the relation of level of
profit of firms and neighborhood over time, namely using an inference
with temporal retrospective dimension);
- “resources over time” (index allows an inference on the relation between
level of resources of firms and neighborhood over time, namely using an
inference with temporal retrospective dimension);
- “performance over time” (index allows an inference on the relation
between performance of firms and performance of neighborhood over
time, namely using an inference with temporal retrospective dimension);
Growing Behavioral Attitudes…
21
- “investment on technology over time” (index allows to compare the
average of the technology investment of firms and neighborhood over
time, namely starting from data on the last 20 simulation cycles);
- “investment on organizational asset over time” (index allows to compare
the average of the investment on organizational assets of firms and
neighborhood over time, namely starting from data on the last 20
simulation cycles).
3.2
Cognitive step 2: from “rough indexes” to “macro indexes”.
Agents are able to extrapolate on such “rough indexes” five macro
aggregated indexes, developing a level of “cognitive abstraction” even more
synthetic in respect to the previous step. Such macro indexes are both
external and internal ones, that is to say that they allow to monitor both
technology and market environment, social context, and individual features
of agents’ activity. As it is shown in Figure 3, we call “PETOE Scheme” the
structure of the macro indexes. Indexes are five: “partnership”,
environment”, “technology”, “organization” and “economic”. The former
two refer to “external dimensions” of the firm, while the latter three refer to
its “internal dimension”. Macro indexes are as follows:
- “partnership” (index allows to synthesize “rough indexes” on “sold”,
“time compression”, “performance”, and “number of chains”, in a macroinference of the “positive” or “negative” nature of the features of the
“partnership context”);
- “environment” (index allows to synthesize “rough indexes” on “selling
firms”, “technological change”, and “searching for new sub firms”, in a
22
Growing Behavioral Attitudes…
macro-inference on the “stable” or “unstable”, or “fair” and “unfair”
nature of technology and market environment);
- “technology” (index allows to synthesize “rough indexes” on
“technology” and “organizational asset effectiveness” in a macroinference on the individual degree of technological effectiveness);
- “organization” (index allows to synthesize “rough indexes” on
“homogeneity of criterions for keeping sub firms”, “homogeneity of
criterions for searching sub firms”, and “homogeneity of criterions for
searching sub firms” in a macro-inference of the “positive” or “negative”
nature of the organizational fundamentals of the firm);
- “economic” (index allows to synthesize “rough indexes” on “profit over
time”, “resources over time”, “investment on technology over time”, and
“investment on organizational asset over time” in a macro-reference on
the “positive” or “negative” nature of the economic fundamentals of the
firm).
Macro indexes synthesize and cluster “rough indexes” at a higher level
of cognitive abstraction. The relation between “macro indexes” and “rough
indexes” conforms to a general rule as follows:
M a  Wa1 Ra1  Wa 2 Ra 2  ...  Wan Ran where Ma , Wa1 , Ra1 , etc..  [0,1] and Ma
represents a macro index, Wa1 is the weight of the first rough index Ra1 and
so on.
The shift from rough to macro indexes is based on a computational
procedure called “Weighted Average” performed by agents over time. Such
procedure is characterized by a heterogeneous assignment of relevancedriven attention undertaken by agents on specific indexes.
Growing Behavioral Attitudes…
23
3.3
Cognitive step 3: indexes evaluation
According to the “information/action” loop shown in figure 2, before
acting, agents need to be able to evaluate such indexes. The evaluation
process calls for the problem of which kind of “behavioral attitudes” agents
develop over time. Attitudes are conceived as different possible “states” of
agent’s behaviors emerging from a continuum between more or less degree
of “districtualization”. Agents can be more or less ‘districtualized’, in the
sense that their behavior can be more or less affected by the characteristics
of their social context. An agent less districtualized is pushed to think more
in terms of “individual centered self”. It can set aside the features of its
context of interaction and social experience. Its decisions are not particularly
bounded by social neighborhood influences. Otherwise, an agent more
districtualized is pushed to think more in terms of “social group self”. Its
attitude is characterized by a more active “identification” with other agents
belonging to the same context of experience. In short, we assume that the
features of the social context have a deep influence on the individual
cognitive process when “social reflexivity” of agents grows over time.
Thus, over time, agents develop different behavioral attitudes. They
can conceive themselves as “isolated” atoms, or as “parts of a microcosm
more socially enlarged”, or as members of a “social group”. Behavioral
attitudes change over time with the growth of “reflexive capabilities” of
agents towards the macro-characteristics of their context of experiences, by
means of their “monitoring activities” concerning “global” features of their
context of relations. In our perspective, such processes do not work in a kind
of simple linear and irreversible progress, from the “state” of “individual
self” to the “state” of “social group member”. The growth of the “reflexive
24
Growing Behavioral Attitudes…
capabilities” of agents implies even their capability to “dis-districtualize”
themselves, to escape from a social group, to “come back” and become once
again “isolated atoms”. Agents can choose to have “rich” or “poor”, “longtime” or “one-shot”, closed” or “open” relations with others, and
neighborhood relations can be more or less wide and deep. Agents can be
interest in interacting with other firms just to produce goods but without
founding sound and stable cooperation relations, that is to say they can act in
a pure “market-like” style.
3.3.1 Behavioral attitude states
The possible behavioral attitudes of an agent are as follows:
a) state 0, or the “self-centered” attitude
- Agent is located in a context, it has a position within the space, that is to
say it has specific neighboring agents;
- Agent enacts production relations with other agents, produces and sells
products, tries to increase its economic performance, its technological
profile, its organizational asset, and so on;
- Agent is not interested in establishing stable and rich relations with other
agents, that is to say it seeks for “one-shot interactions”, focusing
continuously on imperatives of the “economic performance” (Squazzoni
F. and Boero R., 2002);
b) state 1, or the “chain-management” attitude
- Agent is interested in maintaining stable and rich relations with other
interacting agents;
Growing Behavioral Attitudes…
25
- Agent thinks about chain as an “unit”, that is to say as a locus of
organizational relations and relevant information, and as a source of
technological
learning
coordination;
agents
start
to
conceive
“complementarity” relations with others;
- Agent enlarges the microcosm of the “state 0” to five other agents
(spatial and organizational neighborhood);
c) state 2, or the “clustering” attitude
- Agent with “chain management attitude” meets other agents with “chain
management attitude” which put “trust” on the importance of what can
be called “social horizon enlargement” policies;
- Agent belonging to stable chains enlarges its microcosm to other agent
belonging to stable neighboring chains (spatial and organizational
neighborhood plus neighboring chains);
- Agent exchanges information with other agents without having direct
interactions;
d) state 3, or the “grouping” attitude
- Agent starts to reflect upon the collective properties of the “cluster” and
tries to improve the “collective effectiveness” of the “cluster”;
- Agent recognizes all the other agents as member of the “cluster” and
interacts with them;
- Agent can exchange information and partners within all the group and
makes social distribution policy of the “extra profit”.
Firms start the simulation as self-centered attitude agents (state 0). We
assume that all the shifts amongst states depends on the presence of different
26
Growing Behavioral Attitudes…
mechanisms of regulation of the inter-firm relations. Agents develop the
perception of possible economic benefits which can emerge by the
cooperation with others and are pushed to define better, and in a more stable
way, their contexts of interaction. Agents start to conceive their context of
interaction as a tool of learning and information. They start to explore
actively the social environment because it is perceived as a source of
information, comparison and mutual monitoring with other contextualized
agents. Exploring the context, agents create with others a kind of “relational
tie”, where information is exchanged, learning takes mutual directions, and
resources are less or more shared. As it will be outlined, this implies that
agents develop a cognitive representation of their tasks above all in terms of
“relationship” (Bickhard M. H., 2000).
3.3.2 Behavioral attitude morphogenetic shifts
We define the agent-based process of elaboration and change of
“behavioral states” from the “bottom-up” (from state 0 to state 3) over time
as “morphogenetic shifts”. The shift from state 0 to state 1 depends on the
emergence of a relative stability of a production chains (five cycles of
recurring interactions with the same team of sub contracted firms) and on
specific conditions of the macro indexes on “partnership” and “economic
fundamentals” (macro index of “partnership” ≥ 0.75; macro index of
“economic” ≥ 0.75). According to such conditions, agents can develop a
“behavioral attitude” towards the transformation of the previous “recurring
interactions” in “stable partnership relation”, loosing their previous “selfcentered attitude”. We assume that agents, facing a state of good economic
performance and perceiving a potential good context of interactions, are
Growing Behavioral Attitudes…
27
pushed to define, in a more binding way, their organizational relations. In a
sense, agents put “trust” in their organizational neighborhood contexts.
The shift from state 1 to state 2 depends on specific condition of the
“partnership index” (value of 0.95). A “clustering behavioral attitude”
implies the interest about, and a sharing of the information contained within
the whole spatial neighboring firms. The next step is the diffusion of the
“clustering behavioral attitude” within spatial neighboring chains, when
similar condition of “trust” in the partnership mutually grows amongst
agents. This is the mechanism which allows the diffusion of the state 2
amongst firms. This implies that spatial proximity relations start to develop
cluster proximity relations.
The shift from state 2 to state 3 depends on conditions as follows: if
“partnership index” or “environment index”, or both ones are > 0.75, then at
least two of three others (“technology”, “organization” and “economic
index”) ≥ 0.75; if the spatial neighboring agents are already in the state 3.
“Partnership index” and “environment index” give to agents the “trust” on
the positive global state of the industry as a whole. The other indexes show a
positive combination related to the individual state of the agent. We assume
that, in this condition, agents are pushed to reflect in a more global way and
to conceive the problem of the relation between individual effectiveness and
collective effectiveness of the “group” as a whole.
It is worth to notice that the growth of such “morphogenetic shifts”
over time implies an expansion of the neighborhood relation of firms and an
increase of information “deep on the ground”, that is to say an individual
information more compared to information experienced by other agents. On
the state 0, agents put “trust” on macro indexes and “antennas” which allow
a broad vision of the industry as a whole. The nature of the information is
28
Growing Behavioral Attitudes…
very general and not so rich, deep and “domain specific”. In short, the
“morphogenetic shifts” of behaviors are evolutionary mechanism through
which agents can improve their learning on what is to be done, by means of
a more deep comparison both between “macro indexes” and specific actions,
and between what an agents does and what others are to do.
3.3.3 Behavioral attitude deconstruction shifts
“Bottom-up” shifts mentioned above are not equated with linear and
irreversible processes. In fact, agents can develop, change and destroy
continuously their “behavioral attitudes”, over time. It is a matter of
cognitive adaptation in respect to contexts and environments. Facing some
“positive” cognitive configuration, agents develop a bottom-up process of
elaboration of their behavioral attitudes (from 0 to 1, and so on), while
facing some “negative” cognitive configuration, agents destroy their
“behavioral attitudes” turning back to previous steps, in a top-down process.
This “turning back” process does not works as a simple mirror of the
“bottom up” in steps as follows: from state 3 to state 1 or 0, from state 2 to
state 0, and from state 0 to state 1.
The process of deconstruction of the behavioral attitudes conforms to a
general computational rule as follows: if all the macro-indexes, both
external and internal, are ≤ 0.5, then agents shift from state 1, 2, or 3 to state
0. Moreover, we assume other specific conditions for deconstructing
behavioral attitudes as follows:
a) deconstruction from state 3 to state 1, or the “group exit option”
Growing Behavioral Attitudes…
29
- conditions are: if “technology”, “organization” and “economic index” <
0.25, and if “partnership” and “environment index” > 0.5
b) deconstruction from state 2 to state 0, or the “cluster exit option”
- conditions are: if production chain asset is broken; a production chain is
broken if one of these four actions is “true”: “sold=0”, “profit t < profit t5”, “resources t < resources t-5”, “time compression t < time
compression t-5”; if
c) deconstruction from state 1 to state 0, or the “free hands option”
- conditions are: if production chain asset is broken; a production chain is
broken if one of these four actions is “true”: “sold=0”, “profit t < profit t5”, “resources t < resources t-5”, “time compression t < time
compression t-5”.
The deconstruction process which pushes agents to shift from “group
attitude” to “chain-management attitude” is enacted when the membership
implies a deep decreasing of benefits for individual agents and external
conditions of environment are perceived as “positive”. Agents are pushed to
perceive their exit from the group as a source of possible benefits. Thus,
agents shelter in their chain-microcosm. This is what we call the “group exit
option”.
The deconstruction process which push agents to shift from “clustering
attitude” and “chain-management attitude” to “self-centered attitude” is
based on the emergence of “negative” indexes about individual features.
These are what we call “cluster exit option” and “free hands options”.
30
Growing Behavioral Attitudes…
In conclusion, on one hand the dynamics of “morphogenetic shifts”
hides what can be called the growing emergence of “districtualization” of
agents. On the other hand, the dynamics of “deconstruction shifts” hides
what can be called a “self-centered re-organization” of agents. It is a matter
of an organizational schumpeterian “creative destruction” process.
With this respect, because of the “structuring properties” of the
prototype, it is worth to notice that final firms have a focal role. They are
what Douglass R. Hofstadter (1979) calls “catalyst enzymes”, that is to say,
in our words, agents endowed with the capacity to continuously grow,
select, destroy and redefine the ID cognitive and relational architecture of
the context within which they move.
3.4
Cognitive step 3: actions
Therefore, agents develop different “behavioral attitudes” over time,
and they act into different “operation fields” having finite “action recipes”,
as it is shown in table 2. The “operation fields” are what we call
“technology”, “keep”, “search”, and “share”. “Technology” refers to the
need of agents to exploit context-based local information to improve their
“technology” and “organizational assets”. “Keep” and “Search” refer to how
agents manage their partnership relations, between needs of stabilization and
thrusts of de-stabilization of their relational contexts. “Share” refers to what
policy of chain profit management agents do. Behavioral attitudes have the
properties to relate such fields to specific “action recipes”. As it is shown in
table 2, different behavioral attitudes imply the use of specific “action
recipes” in specific “operation fields”.
Growing Behavioral Attitudes…
31
The principle which rules table 2 is that higher the behavioral attitude
of agents towards social environment, that is to say higher the level of
embeddedness of agents within the social context, then higher the “width”
and the “depth” of the information it has and more developed and keen are
the receptivity of their antennas oriented to environment. The differences of
the “action recipes” conforms to this rules.
Another principle is that agents develop routines, that is iterations of
the same “action recipe” within an “operational field”. In our sense, what
Giddens calls “routinization” works by means of what Douglass R.
Hofstadter calls the “prototype principle”, and what Marvin Minsky calls the
“analogy” mechanism, that is to say by means of capacity and tendency of
agents to “represent each new thing as though it resembles to something
[they] already know” (Minsky M., 1985). With this respect, figure 4 shows
the “actions code” of agents. In short, at the start of the simulation, agents
use a specific “recipe” assigned in a random way. Over time, they carry on
using it, that is to say transform the “recipe” into routine. The routine is
broken when macro indexes push the agent to change it. Thus, the agent
starts a phase of trial and error process trying to define a new routine within
“action recipes”. Thus, routines can be maintained or changed, and this is a
focal phase of the agent’s action.
The role of macro indexes and their configuration is fundamental for
understanding why
and how agents change or maintain their routines.
Macro indexes configuration is conceived as the adaptation mechanism
which force agents towards learning about routines. We set a fixed number
of indexes configurations and the presence of a kind of “ringing bell”
mechanism which represent the capacity of agents to perceive the presence
of an unsatisfactory routine. Specific configurations of macro-indexes cause
32
Growing Behavioral Attitudes…
the activation of the “ringing bell” mechanism, driving the attention of the
agent on a specific topic. But, macro-indexes contain just a “synthetic”
information about agent’s problem.
In fact, the “ringing bell” means just that agent has some problem with
its routines. According to such fixed combinations, and because of
“cognitive limitations” about “memory”, “time”, “attention” and “selfmonitoring”, the agent can change its routines just within a specific
operation field (Technology, Keep, Search, Share), that is to say that it has
limitations in proceeding to estimate the routines value (goodness) and in
identifying the “critical” routine. The agent starts to perceive a problem on a
specific “operational field” and develop a phase of evaluation and learning
based on the exploration of other possible “action recipes” based on a
“memory function” which collects data on the last “five time period” where
a specific routine has been used. The agent needs to learn how to solve the
problem within an “operational field” defining new routines. The “ringing
bell” mechanism works as follows:
a) in the case of sub firms, if “technology index” and “economic index” <
0.25, the “ringing bell” focuses on “technology”; the agent perceives the
necessity of change its routine on such “operational field”;
b) in the case of final firms:
- if all the indexes ≤ 0.5, the agent falls in a “panic condition” and starts to
change randomly its routines on one or two different “operation fields”;
- otherwise, if “technology index” < 0.25, the agent starts to change its
routine in the “technology operation field”; if “organization index”
<0.25, the agent starts to change its routine with equal probability in
Growing Behavioral Attitudes…
33
“keep” or “search” operation fields, while if “organization index” < 0.25
≥ 0.5, the agent starts to change its routine in “share” operation field; if
“economic index” < 0.25, the agent starts to change its routine in
“technology” operation field and with equal probability its routine in
“keep” or “share” operation field.
The “ringing bell” mechanism allows us to computationally manage the
relation between representation of contexts and environments developed by
agents and the specific actions they undertake. The mechanism is based on
the hypothesis that selective attention of agents is oriented towards fixed
“operation fields”, and directed to specific significant areas of the ‘problem
space’, by means of a sort of distinctiveness (Lockhart R. S. and Craik F. I.
M., 1990).
The principle of the change of routines is that agent facing the
perception of a problem within an “operation field” can use its memory on
past routines, that is to say the last five periods of time during which a
routine has been used, to support its routine definition process. Agent can
relate routines to macro-indexes in order to define “positive” or “negative”
associated values. According to the “memory function”, it changes,
evaluates and chooses routines. Here, the cognitive process of routine
definition is based on steps as follows:
- the agent has memory of routines implemented in the past, even if
concentrated upon macro-indexes and to bounded time periods (last five
time cycles of implementation);
- its space of possible routines is limited by its “behavioral attitude”, as it
is shown in table 2;
34
Growing Behavioral Attitudes…
- the agent uses continuously memory function to developing data about
all routines used;
- if within the space of all the possible routines, there is a routine not yet
explored, the agent chooses this last one;
- the agent creates an average of collected data on past routines;
- in the case of complete exploration of all possible routines, using data
referring to the past, the agent defines its new routine according to an
evaluation about the relation between routines and macro-indexes.
It is worth to notice that the link between memory and information
processing has a procedural cognitive nature, focused on a “frugal” design
by which cognitive limitations of agents imply a restricted possibility of
items recalling. Agents explore, maintain and change routines of actions by
means of a step-by-step adjustment mechanism by which agents develop
information according to specific search rules within specific behavioral
attitudes (Gigerenzer G. and Selten R., 2001). In short, as it is shown in
table 2, behavioral attitudes imply search rules towards “operational fields”oriented problem solving activities which are monitored by agents. Search
rules act within different “repertoires of routines”, which are composed by
different “action recipes”, according to “behavioral attitude” developed by
agents over time.
In conclusion, the cognitive architecture of ID agents is based on
“cognitive typification” activities which relate continuously individual
experience and social contexts. Macro-indexes evaluation is a cognitive step
through which agents try to incorporate information and to develop
“attribution” about state of technology and market environment,
characteristics of their relational social contexts and control of their own
Growing Behavioral Attitudes…
35
individual features, in order to find “appropriate” strategies of technological
learning. “Reflexive typification” works through the capacity of agents to
assign “objective” characteristics both on their experiences, their social
context, and their operation environment.
Table 1: Information Processing of ID Agents
Information
Selling Result
Neighbors Selling Result
Time Compression
Neighbors Time Compression
Performance
Neighbors Performance
Number of Stable Production
Chains in the Industry
Percentage of Firms Selling to
the Market
Rough Indexes
Macro Indexes
Sold Index
Time Compression Index
Partnership Index
Performance Index
Number of Chains Index
Selling Firms Index
Percentage of Technological
Technological Changes
Environment
Jumps
Index
Index
Percentage of Brand New
Searching for New Subs
Production Chains
Index
Technology Level
Neighbors Technology Level
Technology Index
Organizational Asset
Technology Index
Effectiveness Level
Organizational Asset
Neighbors Organizational
Effectiveness Index
Asset Effectiveness Level
Percentage of Extra-Profit
36
Homogeneity of Extra-
Organization
Growing Behavioral Attitudes…
Neighbors Percentage of
Profit Sharing Policies
Extra-Profit
Index
Index
Routine for Choosing if Keep
Actual Sub Firms
Homogeneity of Criterions
Neighbors Routine for
for Keeping Sub Firms
Choosing if Keep Actual Sub Index
Producers
Routine for Choosing New
Sub Firms
Neighbors Routine for
Choosing New Sub Firms
Homogeneity of Criterions
for Searching New Sub
Firms Index
 Profit
Neighbors  Profit
Profit over Time Index
 Resources
Resources over Time
Neighbors  Resources
Index
 Performance
Performance over Time
Neighbors  Performance
Index
(Investment on
Technologyt,..,t-20)/20
Neighbors (Investment on
Investment on Technology Economic Index
over time
Index
Technologyt,..,t-20)/20
(Investment on
Organizational Assett,..,t-5)/5
Investment on
Neighbors (Investment on
Organizational Asset Index
Organizational Assett,..,t-5)/5
Growing Behavioral Attitudes…
37
Figure 2: Information/Action Loop
actions
information
rough indexes
indexes
evaluation
macro indexes
Figure 3 The “PETOE” Scheme: Macro Indexes Refer to Different Topics and
Environments
World
Partnership I.
Environment I.
Firm/Group
Technology I.
Organization I.
Economic I.
38
Growing Behavioral Attitudes…
Table 2: Relations amongst “operation fields”, “behavioral attitudes” and “action
recipes”.
Action Recipes
Behavior
Operation
Attitudes
Fields
look at the first agent with different
technology/organizational asset you meet
look at the first agent with different
technology/organizational asset you meet, which has
sold its product
look at the agent with different
Self Centered
technology/organizational asset you meet, which has
a percentage of extra-profit better than yours and the
highest available
Technology
look at the agent with different
technology/organizational asset you meet, which has
(imitation in
a behavioral attitude higher than yours and the
the sub-
highest available
fields of
look at the agent with different
technology
technology/organizational asset you meet, which has
a level of resources better than yours and the highest
available
look at the agent with different
and
Chain
Management
organization
asset)
Clustering
technology/organizational asset you meet, which has
a level of profit better than yours and the highest
available
look at the agent with different
technology/organizational asset you meet, which has
a level of cost higher than yours and the highest
Grouping
available
Growing Behavioral Attitudes…
39
look at the agent with different
technology/organizational asset you meet, which has
a level of effectiveness of organizational asset better
than yours and the highest available
look at the agent with different
technology/organizational asset you meet, which has
a level of investment on technology/organizational
asset better than yours and the highest available
look at the first agent with different
technology/organizational asset you meet, which has
a level of performance better than yours and the
highest available
keep your team of sub firms if time compression t,
t-1 >= 0
keep your team of sub firms if profit t, t-1 >= 0
Self Centered
Keep
keep your team of sub firms if resources t, t-1 >= 0
keep your team of sub firms if you have sold your
(strategy of
product
keep your team of sub firms if time compression t,
t-5 >= 0
keep your team of sub firms if profit t, t-5 >= 0
keep your team of sub firms if resources t, t-5 >= 0
Chain
partnership
Management
stabilization)
Clustering
Grouping
search for a new team of sub firms randomly
search for a new team of sub firms focusing on who
has the highest investment on organizational asset
search for a new team of sub firms focusing on who
has the highest performance
search for a new team of sub firms focusing on who
Self Centered
Chain
Management
Clustering
Grouping
Search
(strategy of
partnership
definition)
has the most similar technology and organizational
asset configuration
40
Growing Behavioral Attitudes…
give to your partners the 0% extra-profit
give the 5% extra-profit to each partner
Self Centered
give the 10% extra-profit to each partner
Share
give the 13.3% extra-profit to each partner
Chain
give the 16.6% extra-profit to each partner
Management
give the 20% extra-profit to each partner
Clustering
give the 23.3% extra-profit to each partner
(policy of
chain extraprofit
management
and
give 25% extra-profit to each partner
give the 70% extra-profit to partners, distributed
proportionally according to their needs
distribution)
Grouping
distribute proportionally the 100% extra-profit
according to the needs of each member of the chain
Figure 4: Actions Code of the ID Agent (according to structuring properties of the prototype,
just final firms have the complete actions code)
01
11
01
00
Routine for choosing neighbor from
which imitate technology and
organizational asset (“true” for all the
firms).
Routine for choosing if sub firms
need to be changed
Only for
final firms
Routine for finding new sub firms.
Routine for sharing extra-profit.
Growing Behavioral Attitudes…
41
4.
Indicators and Prototype Settings
To test the prototype, we use several indicators. By observing them, it
is possible to grasp fundamental dynamics emerging by the prototype. We
create also different experimental settings in order to reinforce evidences
about how behavioral attitudes and typology of social contexts affect
performance of firms (running the prototype it is possible to choose all the
different combinations of behavioral states, right from the start of the
simulation). Indicators we use here are as follows (running the prototype, it
is possible to observe and produce other kinds of indicators):
- final firms matching market requests over time;
- final firms performance and behavioral attitudes over time;
- final firms performance in different prototype settings running separately
with behavioral attitudes state 0, state 0 and 1, and with complete
behavioral states;
- weight of the different macro indexes over time;
- dimension of the neighborhood relations over time.
42
Growing Behavioral Attitudes…
5.
Analysis of outcome and emerging dynamics
Before observing the outcome of the simulation on the prototype, we
have set some question to test:
Is there a positive relation between development of behavioral attitudes
more oriented to social contexts and technological leaning of firms, that is to
say are agents able of technological learning just by means of the
enlargement of their social contexts? or are agents facing technological
breaking-off more oriented to perceive social contexts as adaptation
“individual constraints”? in short, have social grouping and cognitive
identification activities towards social contexts a positive or negative impact
on economic performance of district firms? is it better to districtualize
during phase of technological continuity and dis-districtualize during phases
of technological discontinuity?
If we observe the simulation outcome, we can sketch several
inferences. Technological breaking-off phases imply a selection of the
district firms, even if with different dynamics. As it is shown in figure 6, the
first discontinuity phase (about cycle 500) is absorbed by the 88% of firms,
while the second phase causes a strong oscillation in the firms’ performance,
but without implying a further exit of firms from market. This is due to the
fact that firms over time are more effective in technological learning, despite
the growth of costs and request of technological quality of their goods
marked by market.
The evolution of behavioral attitudes states over time shows that firms
facing technological discontinuity and increasing market pressure phases
develop different strategies of response over time, while firms facing
Growing Behavioral Attitudes…
43
technological continuity tend to stabilize their “behavioral attitudes”. As it is
shown in figure 6, the first phase of technology and market stability (until
500 cycle) shows a tendency of agents to lock-in their behavioral attitudes
with a lot of them in state 2 (“clustering attitude”) and few of them in state
0 (“self centered attitude”). The 10% of agents are quickly able to develop
the “grouping attitude” (state 3), while another 10% of agents lock-in their
behavioral attitude right from the start in the state 0. Such stabilization of the
behavioral attitudes goes on until the first technology breaking-off (around
cycle 500). In this phase, agents in more critical technology and market
conditions try to develop their behavioral attitudes, above all shifting from
state 0 to state 2, but without success. They are the first and the only victims
of the market selection. The behavioral state 1 (“chain-management
attitude”) is just a shelter in times of technology and market deeper
challenge, along all the simulation time. Just as before, the second phase of
technological stability shows a “long durée” settlement of behavioral
attitudes of agents.
Certainly, the second phase of technological instability is more
interesting than the previous one (around cycle 1000). As it is shown in
figure 5, here district firms live a deep but quick phase of market crisis. How
firms face such crisis, from the point of view of their behavioral attitudes?
As it is possible to observe comparing figure 5 and 6 around cycle 1000,
firms involved in such crisis are above all those in state 2 (“clustering
attitude”). They do not simply destroy their behavioral state, for instance
passing from state 2 to state 0, but someone develops a like-state 3
“behavioral attitude”. It is worth to notice that the so called “grouping”
behavioral attitude of firms stands up despite the two technological and
market crisis, and even strengthens over time.
44
Growing Behavioral Attitudes…
Such strengthening-effect of the “identity towards identification”
attitudes over time can be as well confirmed if one observes figure 7, where
data about dynamics of the average dimension of neighborhood proximity
relations allow to observe how much large is the context of relations enacted
by agents over time. Such enlargement of the social horizons of agents not
only grows over time, but rather it grows during phase of technology and
market adaptation challenges. Such data tell us that “identification”
dynamics developed by agents are not only a fundamental tool of
technological learning and economic performance for firms, but rather that
district firms develop a polarization of “grouping attitudes” over time, and
that they are over time more enhanced and reinforced, when agents face
technology and market adaptation needs.
Figure 5. Final firms matching market requests over time.
Growing Behavioral Attitudes…
45
Figure 6. Final firms matching market requests over time at variance of behavioral attitudes.
Figure 7. Average dimension of neighborhood proximity relation.
If we observe figure 8, where data on the relevance of the different
“macro indexes” over time are shown, it is possible to outline that at the
46
Growing Behavioral Attitudes…
start of the simulation firms develop too much “optimistic confidence” on
the “fair nature” of the technology and market environment. The problem
perceived by agents during the technology breaking-off phase is just the
redefinition of routine towards environment, and the perception of an
“environment complication” is let off on problems on the “technology
indexes”. More than problems on operation fields of “economic” and
“organization” indexes, agents perceive problems on “technology” features.
Finally, we create different prototype settings by changing mechanisms
of behavioral attitudes development. As it is shown in figure 9, we set a
prototype running just with behavioral attitude state 0, and running with
state 0 and 1. The outcome of set with state 0 and 1 confirms that behavioral
attitude state 1 (“chain management attitude”) causes the loss of the
advantage of market-oriented “self centered attitudes” without generating
the advantage of information source and processing typical of a wide
“relational context”, as in the state 2 and 3. Figure 9 shows that at the end of
simulation cycles, levels of firms still on market are as follows: 88% in the
complete set, 76% in state 0, and 66% in state 0 and 1.
Figure 8. Relevance of the different “macro indexes” over time.
Growing Behavioral Attitudes…
47
Figure 9. Final firms matching market requests on different experimental settings.
From the left to the right, outcomes of state “complete”, state 0 and 1, and state 0.
In conclusion, the simulation outcome of the prototype shows that
district agents are able to develop different behavioral attitudes over time,
such attitude development has an positive effect on long-time period
learning of agents, and social relational context and “districtualized”
behavioral attitude are more deeply developed during phases of technology
and market challenge. As in the case of technology breaking-off phases, if
we compare figure 7, 8 and 9, it is possible to outline that social projection
of agents and “districtualization” of firms within their contexts of action are
conceived not as a “constraint” upon the individual economic imperative,
but rather as a source of information and learning about environment
challenge.
48
Growing Behavioral Attitudes…
6.
Conclusion: how agent-based computational models can put down
stable roots in social sciences?
But, how to further develop the district prototype? In this paper, our
intentions were to move some steps towards an useful and integrated way of
using agent-based computational models in social sciences. In fact, our
opinion is that social phenomena need to be studied not only from a bottomup emergent properties perspective, as in the traditional literature on agentbased computational models, but also from a perspective focused on
“reflexive capabilities” of “human and social agents” towards the social
contexts which they live in. Such perspective has been suggested some years
ago by Nigel Gilbert, Rosaria Conte and Cristiano Castelfranchi (1996), and
more recently again by Rosaria Conte (2000). Sociological perspective on
computational models starts by the awareness that agent-based model
mainstream
has
reactive/simple
underestimated
which
respond
the
to
difference
between
environment
modeling
signals
and
proactive/purposeful agents which are able to reflect upon the global
characteristics of contexts within which they move:
“some computer simulations may have oversimplified important
characteristics of specifically human societies, because the actors (agents) in
these societies are capable of, and do routinely reason about the emergent
properties of their own societies. This adds a degree of reflexivity to action
which is not present (for the most part) in societies made up of simpler
agents, and in particular is not a feature of most current computer
simulations” (Gilbert N., 1996; see also: Caldas J. C. and Coelho H., 1999;
Chattoe E., 1998).
Growing Behavioral Attitudes…
49
As it is stressed by Nigel Gilbert, “the complication in the social world
is that individuals can recognise, reason about and react to the institutions
that their actions have created. Understanding this feature of human society,
variously known as second-order emergence, reflexivity, and the double
hermeneutics, is an area where computational modelling shows promise”
(Gilbert N., 1996; Gilbert N., Terna P., 2000)…“not only can we as social
scientists distinguish patterns of collective action, but the agents themselves
cal also do so and therefore their actions can be affected by the existence of
these patterns” (Gilbert N. e Troitzsch K. G:, 1999).
The famous question about the relation between individuals and
aggregate posed some years ago by Thomas Schelling (1979) and the
question about “how does the heterogeneous micro-world of individual
behavior generate the global macroscopic regularities of the society?”, more
recently confirmed by Joshua Epstein and Robert Axtell (1996), are issues
towards which agent-based computational social scientists have been usually
interested. Otherwise, issues towards which agent-based computational
social scientists need to be more interest are about the other side of the
question: “how such emerging regularities are monitored, reflected,
experienced and changed even “intentionally” by heterogeneous agents?”.
Clearly, institutions emerge by interactions amongst heterogeneous agents,
according to distributed artificial intelligence mechanisms, but agents also
reflect upon them, perceive their fundamental features, monitor, typify and
“intentionally” change them. In a top-down way, institutions are produced
and have reinforcing effects, even dysfunctional ones.
In conclusion, to integrate more actively agent-based computational
models in the estate of social sciences, social computational scientists need
50
Growing Behavioral Attitudes…
not only to show that computational models allow to formalize complex
social phenomena in a way that traditional mathematical and statistical tools
can not do (Hanneman R., 1995). They also need to address typical social
science theory problems, as it is stressed by so called “socionists” (Malsh T.,
2001). To do this, they can not simply pursue a “bottom-up reductionism”
way, comparing human agent intelligence to ant “swarm intelligence”
(Bonabeau E., Dorigo M., Theraulaz G., 1999).
According to such ideas, our intentions are to develop the district
prototype towards two interlaced directions:
- introducing a meta-cognitive level in the cognitive architecture of district
agents, by which agents should be able to direct their own actions
considering specific relations between the “ringing bell” mechanism and
the operation fields, not only by means of a strategy based on a preset
rule of searching alternatives; the way to symbolize a meta-cognitive
frame is to show how agent could act as a result of a learning process
which allow them to set up different link amongst specific information
sources and operation fields they choose to control and manage;
- introducing a formal institutional level in the cognitive architecture of
district agents, that is to say the capability of agents to grow formalized
institutional contexts able to reinforce some “behavioral attitudes” of
agents, redefining the cognitive process of agents by means of a set of
macro “cognitive urgency” impacting macro indexes, or the antennas of
agents; by looping top-down and bottom-up levels, what could happen is
what Nigel Gilbert call “second order emergence” (1996).
Growing Behavioral Attitudes…
51
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