jES

1
jES
Pietro Terna
pietro.terna_at_unito.it   Department of
Economics and Finance G.Prato University of
Torino - Italy Decision making and enterprise
simulation with jES and Swarm
web.econ.unito.it/terna web.econ.unito.it/terna/je
s
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_jVE-gtJES
_______________________________________ jVE
jES _______________________________________
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jVE-gtjES
From jVE
Virtual Enterprise ??
to jES
Enterprise Simulator

• With jES we can simulate
• actual enterprises
• virtual enterprises
• (as would be enterprises or in the direction of
  the NIIIP project, see below)

www.flightgear.org
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_overview
_______________________________________ Overview _
______________________________________
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overview 1
Overview 1/2 jES, java Enterprise Simulator
(formerly jVE, java Virtual Enterprise), is a
large Swarm-based package1 aimed at building
simulation models both of actual enterprises and
of virtual ones. On the first side, the
simulation of actual enterprises, i.e. the
creation of computational models of those
realities, is useful for the understanding of
their behavior, mainly in order to optimize the
related decisional processes. On the other side,
through virtual enterprises we can investigate
how firms originate and how they interact in
social networks (Burt, 1992 Walker et al., 1997)
of production units and structures, also in
would be situations. In both cases, following
Gibbons (2000), we have to overcome the basic
economic model of the firm, i.e. a black box with
labor and physical inputs in one end and output
on the other, operating under the hypothesis of
minimum cost and maximum profit. Simulation
models such as jES represent the most
appropriate tool to be used in this
direction. 1 Download last version from
http//web.econ.unito.it/terna/jes
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overview 2
Overview 2/2 Agents, in jES, are objects like the
orders to be produced and the production units
able to deal with the orders. In the same
context, there are also agents representing the
decision nodes, where rules and algorithms (like
GA or CS), or avatars1 of actual people, act.
In the case of avatars, decisions are taken
asking actual people what to do in this way we
can simulate the effects of actual choices we
can also use the simulator as a training tool
and, simultaneously, as a way to run economic
experiments to understand how people behave and
decide in organizations. This is the big Simons
(1997) question. Some recent improvements of jES
are outlined in the presentation. 1 From
www.babylon.com s. avatar (Hindu mythology)
earthly incarnation of a god, human embodiment of
a deity (Internet) online image that represents
a user in chat rooms or in a virtual space.
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_jES principles
_______________________________________ jES
principles _______________________________________
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jES principles 1
jES principles 1/3
The goals With the simulator we want to reproduce
in a detailed way the behavior of a firm into a
computer. The basis of the method has to be found
into agent based simulation techniques, i.e. the
reconstruction of a phenomenon via the action and
interaction of minded or no minded agents within
a specific environment, with its rules and
characteristics. In our cases, we have both no
minded agents - as things to be done (orders) or
units able to work with them - and minded - as
the agents who have to express decisions within
the model -. Simulating a single enterprise or a
system of enterprises (e.g. within a district or
within a virtual enterprise system) we can apply
in a direct way the what if analysis
introducing changes into the simulation, while
fully preserving the complexity of our context.
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jES principles 2
jES principles 2/3
Why agents and what kind of tool? Only in a true
agent based context, with independent pieces of
software expressing the different behavior of all
the components of our environment (a firm), we
can overtake the traditional limitation of models
founded on equations (differential equations or
recursive ones) where the granularity of the
description is strongly compelled by the
limitations of the method. We are interested in
using a plurality of tools, with Swarm at the
first place, to build our models. We must also
interact in a correct way with actual
enterprises data and for that we want to develop
easy to use interfaces based on the XML formalism.
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jES principles 3
jES principles 3/3
Perspectives and results Perspectives and results
of our models are along three directions. Enterpri
se optimization, also via soft computing tools as
genetic algorithms and classifier systems, and
what-if analysis when we use a genetic algorithm
or a classifier system in a simulation framework,
the fitness of the evolved genotype or of the
evolved rules is evaluated running the
simulator. Interaction between people and the
model, through artificial agents representing the
actual ones, with two purposes to study how
people behave in organizations, with experiments
built using the simulator to train people about
the consequences of their decision within an
organization. Theoretical analysis of would be
situations of enterprises and their interactions,
to increase the knowledge about how firms start,
behave and decline.
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_WD, DW, WDW
_______________________________________ WD, DW,
WDW _______________________________________
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WD, DW, WDW
WD side or formalism What to Do DW side or
formalism which is Doing What WDW formalism
When Doing What
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dictionary
A dictionary
unit a productive structure within or outside
our enterprise a unit is able to perform one
or more of the steps required to accomplish an
order order the object representing a good
to be produced an order contains technical
information (the recipe describing the
production steps) and accounting
data recipe a sequence of steps to be executed
to produce a good
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_DW a flexible scheme
_______________________________________ DW a
flexible scheme __________________________________
_____
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DW a flexible scheme 1
DW
1
Units
1,3,4
5
3
3
3
1,2,5
1
2
1
2
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DW a flexible scheme 2
DW
1
Units and Firms
1,3,4
5
3
3
3
1,2,5
1
2
1
2
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DW a flexible scheme 3
DW
1
in a district
1,3,4
5
3
3
3
1,2,5
1
2
1
2
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DW a flexible scheme 4
DW
The NIIIP project (National Industrial
Information Infrastructure Protocols
) http//www.niiip.org/
1
or building up a virtual enterprise
1,3,4
5
3
3
3
1,2,5
1
2
1
2
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_WD recipes
_______________________________________ WD
recipes _______________________________________
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WD recipes
WD
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_a simple example with WD, DW and WDW
_______________________________________ A simple
example with WD, DW and WDW ______________________
_________________
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a simple example 0
the recipes
WD
the starting sequence
WDW
the continuous sequence (empty)
t0
100 100 100 101
Building a sequential batch
DW
1
2
3
10
a production unit
an end unit
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a simple example 1
the recipes
WD
the starting sequence
WDW
the continuous sequence (empty)
t1
100 100 100 101
Sequential batch step 1/3
DW
1
2
3
10
a production unit
an end unit
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a simple example 2
the recipes
WD
the starting sequence
WDW
the continuous sequence (empty)
t2
100 100 100 101
Sequential batch step 2/3
DW
1
2
3
10
a production unit
an end unit
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a simple example 3
the recipes
WD
the starting sequence
WDW
the continuous sequence (empty)
t3
101
100 100 100
DW
1
2
3
10
a production unit
an end unit
26
a simple example 4
the recipes
WD
the starting sequence
WDW
the continuous sequence (empty)
t4
100
100 100 101
DW
1
2
3
10
a production unit
an end unit
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a simple example 5
the recipes
WD
the starting sequence
WDW
the continuous sequence (empty)
t5
100 100
100 101
Building a sequential batch
DW
1
2
3
10
a production unit
an end unit
28
a simple example 6
the recipes
WD
the starting sequence
WDW
the continuous sequence (empty)
t6
100 100 100
101
Sequential batch step 1/3
DW
1
2
3
10
a production unit
an end unit
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a simple example 7
the recipes
WD
the starting sequence
WDW
the continuous sequence (empty)
t7
100 100 100
101
Sequential batch step 2/3
DW
1
2
3
10
a production unit
an end unit
30
a simple example 8
the recipes
WD
the starting sequence
WDW
the continuous sequence (empty)
t8
100
101
100
DW
1
2
3
10
a production unit
an end unit
31
a simple example 9
the recipes
WD
the starting sequence
WDW
the continuous sequence (empty)
t9
100 101

100
DW
1
2
3
10
a production unit
an end unit
32
a simple example 10
the recipes
WD
the starting sequence
WDW
the continuous sequence (empty)
t10
100

100
DW
1
2
3
10
a production unit
an end unit
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_a complex example the VIR case
_______________________________________ A complex
example the VIR case ____________________________
___________
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VIR 1
VIR (a firm producing valves, to regulate the
flow of liquids and gas) Case-2 (with
unitCriterion2)
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VIR 2
VIR Case-3 adding 3 complex units in the lathe
sector
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_the decision process
_______________________________________ The
decision process _________________________________
______
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decision process 1
1
How to decide?
1,3,4
5
3
3
3
1,2,5
1
2
1
2
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decision process 2

• In a random way
• Using fixed rules
• Using an expert system
• Via soft computing techniques (GA CS)
• Asking to an actual agent what to do (training
  and monitoring actual agents behavior)

How to decide?
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_new tools recipes and layers, computational
objects
_______________________________________ New
tools recipes and layers, computational
objects _______________________________________
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recipes and layers
Recipes and layers
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computational objects 1
Computational objects
Memory matrixes data are reported in a text file
(unitData/memoryMatrixes.txt)
number(from_0_ordered_negative_if_insensitive_to_
layers)_rows_cols 0 2 3 -1
3 5 2 4 1 3 3
1
Mandatory first line
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computational objects 2
Computational objects
Recipes with computations (recipes are reported
in external and intermediate format)
time specification seconds
External format (remember step, time
specification, time)
1 s 1 c 1999 3 0 1 3 2 s 2 3 s 2 1 s 1 c 1998 1
0 5 s 2 1 s 1 c 1998 1 1 6 s 2 1 s 1 c 1998 1 3
7 s 2
step in recipe
a step with computation step 2, requiring 2
seconds, involve computation 1999 with 3 matrixes
(those numbered 0, 1, 3 in the previous Figure)
time in seconds
a step with computation step 7, requiring 2
seconds, involve computation 1998 with 1 matrix
(that numbered 3 in the previous Figure)
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computational objects 3
Computational objects
The Java Swarm code used by the recipes with
computations of this example
/ computational operations with code -1998 (a
code for the checking phase of the
program) this computational code
place a number in position 0,0 of the
unique received matrix and set the status to
done / public void c1998() mm0(Memor
yMatrix) pendingComputationalSpecificationSet.
getMemoryMatrixAddress(0) layerpendingComputa
tionalSpecificationSet. getOrderLayer() mm
0.setValue(layer,0,0,1.0) mm0.print() donetr
ue // end c1998
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_other tools
_______________________________________ Other
tools _______________________________________
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other tools
Other tools Stand alone batches Procurements
(as seen above) Parallel paths (AND
formalism) Multiple paths (OR formalism)
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_references
_______________________________________ References
_______________________________________
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references
References Burt R.S. (1992), Structural Holes
The Social Structure of Competition. Cambridge,
MA, Harvard University Press. Gibbons R. (2000),
Why Organizations Are Such a Mess (and What an
Economist Might Do About It). A draft of the
first Charter is at http//web.mit.edu/rgibbons/ww
w/ Simon H.A. (1997), Administrative Behavior A
Study of Decision-Making Processes in
Administrative Organizations. Simon Schuster,
New York. Walker G., Kogut B., Shan W. (1997),
Social Capital, Structural Holes and the
Formation of an Industry Network, in Organization
Science. Vol. 8, No. 2, pp.109-25.
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address again

pietro.terna_at_unito.it  
web.econ.unito.it/terna web.econ.unito.it/t
erna/jes