jES

April 13-15, 2003 SwarmFest, Notre Dame 1

jES

Pietro Terna [email protected]

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/jes


_jV

E->

JES

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jVE jES

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From jVE …

… to jES

jVE

->jE

S

Virtual Enterprise ??

Enterprise Simulator

www.flightgear.org

With jES we can simulate:

•actual enterprises

•virtual enterprises

(as “would be” enterprises or in the direction of the NIIIP project, see below)


_ove

rvie

w

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Overview

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over

view

1

Overview 1/2

jES, java Enterprise Simulator (formerly jVE, java Virtual Enterprise), is a large Swarm-based package[1] 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


over

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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 avatars[1] 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 Simon’s (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”.


_jE

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rinc

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s

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jES principles

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jES

pri

ncip

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1

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.

jES principles 1/3


jES

pri

ncip

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2

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 enterprise’s data and for that we want to develop easy to use interfaces based on the XML formalism.

jES principles 2/3


jES

pri

ncip

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3

Perspectives and results

Perspectives and results of our models are along three directions.

Enterprise 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 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 build 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.

jES principles 3/3


_WD

, D

W, W

DW

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WD, DW, WDW

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WD

, D

W, W

DW

WD side or formalism: What to Do

DW side or formalism: which is Doing What

WDW formalism: When Doing What


dict

iona

ry

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

A dictionary


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me _______________________________________

DW: a flexible scheme

_______________________________________


DW

: a f

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me

1

2

1

3

2

1

3

1

5

3

1,3,4

1,2,5

Uni

ts …

DW


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2

2

1

3

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DW


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3

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… in

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4

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… o

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The NIIIP project (National Industrial Information Infrastructure Protocols )

http://www.niiip.org/

DW


_WD

: rec

ipes

_______________________________________

WD: recipes

_______________________________________


WD

: rec

ipes

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

WD


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e w

ith

WD

, DW

and

WD

W

_______________________________________

A simple example with WD, DW and WDW

_______________________________________


1 2

10

3

a production unit

an end unit

a si

mpl

e ex

ampl

e 0

the recipes

DW

1 100 * 3 101 * 1 ;

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

1 ;WDW

the starting sequence

the continuous sequence (empty)

t=0

100100100101

Building a sequential batch


1 2

10

3

a production unit

an end unit

a si

mpl

e ex

ampl

e 1

the recipesWD

1 100 * 3 101 * 1 ;

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

1 ;WDW



t=1

100100100101

Sequential batch step 1/3

DW


1 2

10

3

a production unit

an end unit

a si

mpl

e ex

ampl

e 2

the recipesWD

1 100 * 3 101 * 1 ;

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

1 ;WDW



t=2

100100100101


DW


1 2

10

3

a production unit

an end unit

a si

mpl

e ex

ampl

e 3

the recipesWD

1 100 * 3 101 * 1 ;

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

1 ;WDW



t=3

101

100100100

DW


1 2

10

3

a production unit

an end unit

a si

mpl

e ex

ampl

e 4

the recipesWD

1 100 * 3 101 * 1 ;

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

1 ;WDW



t=4

100

100100101

DW


1 2

10

3

a production unit

an end unit

a si

mpl

e ex

ampl

e 5

the recipesWD

1 100 * 3 101 * 1 ;

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

1 ;WDW



t=5

100100

100101

DW


1 2

10

3

a production unit

an end unit

a si

mpl

e ex

ampl

e 6

the recipesWD

1 100 * 3 101 * 1 ;

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

1 ;WDW



t=6

100100100101

Building a sequential batch

DW


1 2

10

3

a production unit

an end unit

a si

mpl

e ex

ampl

e 7

the recipesWD

1 100 * 3 101 * 1 ;

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

1 ;WDW



t=7

100100100101


DW


1 2

10

3

a production unit

an end unit

a si

mpl

e ex

ampl

e 8

the recipesWD

1 100 * 3 101 * 1 ;

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

1 ;WDW



t=8

100101

100DW


1 2

10

3

a production unit

an end unit

a si

mpl

e ex

ampl

e 9

the recipesWD

1 100 * 3 101 * 1 ;

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

1 ;WDW



t=9

100101

100DW


1 2

10

3

a production unit

an end unit

a si

mpl

e ex

ampl

e 10

the recipesWD

1 100 * 3 101 * 1 ;

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

1 ;WDW



t=10

100

100DW


_a c

ompl

ex e

xam

ple:

the

VIR

cas

e

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A complex example: the VIR case

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VIR

1VIR (a firm producing valves, to regulate the flow of liquids and gas)

Basic case (with unitCriterion=2)


VIR

2VIR

Adding 3 complex units in the lathe sector


_the

dec

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oces

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The decision process

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deci

sion

pro

cess

1

2

1

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2

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5

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1,3,4

1,2,5

How

to d

ecid

e?


deci

sion

pro

cess

2 How

to d

ecid

e?

• 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)


_new

tool

s: r

ecip

es a

nd la

yers

, com

puta

tion

al o

bjec

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New tools: recipes and layers, computational objects

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# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

reci

pes

and

laye

rs

# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;# Recipes ;

recipeA 101 1 s 1 p 1 10 2 s 1 3 s 1 ;

recipeB 100 1 s 3 \ 3 2 s 1 3 s 3 \ 2 e 10 ;

Recipes and layers


com

puta

tion

al o

bjec

ts 1

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

Computational objects


com

puta

tion

al o

bjec

ts 2

Recipes with computations (recipes are reported in external and intermediate format)

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

time specification: seconds

time in seconds

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)

a step with computation: step 7, requiring 2 seconds, involve computation 1998 with 1 matrix (that numbered 3 in the previous Figure)



com

puta

tion

al o

bjec

ts 3

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=(MemoryMatrix) pendingComputationalSpecificationSet. getMemoryMatrixAddress(0);layer=pendingComputationalSpecificationSet. getOrderLayer();

mm0.setValue(layer,0,0,1.0);mm0.print();

done=true; } // end c1998



_oth

er to

ols

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Other tools

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othe

r to

ols

Other tools:

Stand alone batches

Procurements (as seen above)

Parallel paths (AND formalism)

Multiple paths (OR formalism)


_ref

eren

ces

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References

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refe

renc

es

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/www/

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.


addr

ess

agai

n

[email protected]

web.econ.unito.it/terna

web.econ.unito.it/terna/jes

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