Pietro Terna pietro ternaunito it j ES Department

Pietro Terna pietro. terna@unito. it j. ES Department of Economics and Finance “G. Prato” University of Torino - Italy Decision making and enterprise simulation with j. ES and Swarm web. econ. unito. it/terna/jes April 13 -15, 2003 Swarm. Fest, Notre Dame 1

_j. VE->JES ____________________ j. VE j. ES ____________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 2

j. VE->j. ES From j. VE … Virtual Enterprise ? ? … to j. ES With j. ES we can simulate: Enterprise Simulator • actual enterprises • virtual enterprises (as “would be” enterprises or in the direction of the NIIIP project, see below) www. flightgear. org April 13 -15, 2003 Swarm. Fest, Notre Dame 3

_overview ____________________ Overview ____________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 4

Overview 1/2 overview 1 j. ES, java Enterprise Simulator (formerly j. VE, 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 j. ES – represent the most appropriate tool to be used in this direction. [1] Download last version from http: //web. econ. unito. it/terna/jes April 13 -15, 2003 Swarm. Fest, Notre Dame 5

overview 2 Overview 2/2 Agents, in j. ES, are objects like the orders to be produced and the production units able to deal with the orders. In the same context, there 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 j. ES 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”. April 13 -15, 2003 Swarm. Fest, Notre Dame 6

_j. ES principles _______________________________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 7

j. ES 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. April 13 -15, 2003 Swarm. Fest, Notre Dame 8

j. ES 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 enterprise’s data and for that we want to develop easy to use interfaces based on the XML formalism. April 13 -15, 2003 Swarm. Fest, Notre Dame 9

j. ES principles 3/3 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. April 13 -15, 2003 Swarm. Fest, Notre Dame 10

_WD, DW, WDW _______________________________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 11

WD, DW, WDW WD side or formalism: What to Do DW side or formalism: which is Doing What WDW formalism: When Doing What April 13 -15, 2003 Swarm. Fest, Notre Dame 12

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 = 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 April 13 -15, 2003 Swarm. Fest, Notre Dame 13

_DW: a flexible scheme _______________________________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 14

… DW its 1 DW: a flexible scheme 1 Un 1, 3, 4 5 3 3 3 1, 2, 5 1 2 April 13 -15, 2003 2 Swarm. Fest, Notre Dame 1 15

irm s… DW d. F 1 its 5 Un DW: a flexible scheme 2 an 1, 3, 4 3 3 3 1, 2, 5 1 2 April 13 -15, 2003 2 Swarm. Fest, Notre Dame 1 16

t… DW ist ric 1 in 5 … DW: a flexible scheme 3 ad 1, 3, 4 3 3 3 1, 2, 5 1 2 April 13 -15, 2003 2 Swarm. Fest, Notre Dame 1 17

DW: a flexible scheme 4 … vir or b tua uil l e din nte g u rp p a ris e DW 1 The NIIIP project (National Industrial Information Infrastructure Protocols ) http: //www. niiip. org/ 1, 3, 4 5 3 3 3 1, 2, 5 1 2 April 13 -15, 2003 2 Swarm. Fest, Notre Dame 1 18

_WD: recipes _______________________________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 19

WD: recipes WD April 13 -15, 2003 Swarm. Fest, Notre Dame 20

_a simple example with WD, DW and WDW _______________________________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 21

the recipes the starting sequence a simple example 0 WDW the continuous sequence (empty) t=0 100 Building a 100 sequential batch 101 DW 1 2 3 10 a production unit April 13 -15, 2003 Swarm. Fest, Notre Dame an end unit 22

the recipes WD the starting sequence a simple example 1 WDW the continuous sequence (empty) t=1 100 Sequential batch 100 step 1/3 101 DW 1 2 3 10 a production unit April 13 -15, 2003 Swarm. Fest, Notre Dame an end unit 23

the recipes WD the starting sequence a simple example 2 WDW the continuous sequence (empty) t=2 100 Sequential batch 100 step 2/3 101 DW 1 2 3 10 a production unit April 13 -15, 2003 Swarm. Fest, Notre Dame an end unit 24

the recipes WD the starting sequence a simple example 3 WDW the continuous sequence (empty) t=3 DW 101 100 100 1 2 3 10 a production unit April 13 -15, 2003 Swarm. Fest, Notre Dame an end unit 25

the recipes WD the starting sequence a simple example 4 WDW the continuous sequence (empty) t=4 DW 1 100 101 100 2 3 10 a production unit April 13 -15, 2003 Swarm. Fest, Notre Dame an end unit 26

the recipes WD the starting sequence a simple example 5 WDW the continuous sequence (empty) t=5 DW 1 100 100 2 3 10 a production unit April 13 -15, 2003 Swarm. Fest, Notre Dame an end unit 27

the recipes WD the starting sequence a simple example 6 WDW the continuous sequence (empty) t=6 101 DW 1 2 100 Building a 100 sequential batch 100 3 10 a production unit April 13 -15, 2003 Swarm. Fest, Notre Dame an end unit 28

the recipes WD the starting sequence a simple example 7 WDW the continuous sequence (empty) t=7 101 DW 1 2 100 Sequential batch 100 step 1/2 100 3 10 a production unit April 13 -15, 2003 Swarm. Fest, Notre Dame an end unit 29

the recipes WD the starting sequence a simple example 8 WDW the continuous sequence (empty) t=8 DW 1 100 2 3 100 10 a production unit April 13 -15, 2003 Swarm. Fest, Notre Dame an end unit 30

the recipes WD the starting sequence a simple example 9 WDW the continuous sequence (empty) t=9 100 101 DW 1 2 3 100 10 a production unit April 13 -15, 2003 Swarm. Fest, Notre Dame an end unit 31

the recipes WD the starting sequence a simple example 10 WDW the continuous sequence (empty) t=10 100 DW 1 2 3 100 10 a production unit April 13 -15, 2003 Swarm. Fest, Notre Dame an end unit 32

_a complex example: the VIR case _______________________________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 33

VIR (a firm producing valves, to regulate the flow of liquids and gas) VIR 1 Basic case (with unit. Criterion=2) April 13 -15, 2003 Swarm. Fest, Notre Dame 34

VIR 2 Adding 3 complex units in the lathe sector April 13 -15, 2003 Swarm. Fest, Notre Dame 35

_the decision process ____________________ The decision process ____________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 36

e? cid de 1 to 1, 3, 4 Ho decision process 1 w 5 3 3 3 1, 2, 5 1 2 April 13 -15, 2003 2 Swarm. Fest, Notre Dame 1 37

e? cid de to w Ho 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) April 13 -15, 2003 Swarm. Fest, Notre Dame 38

_new tools: recipes and layers, computational objects ____________________ New tools: recipes and layers, computational objects ____________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 39

recipes and layers Recipes and layers April 13 -15, 2003 Swarm. Fest, Notre Dame 40

Computational objects computational objects 1 Memory matrixes data are reported in a text file (unit. Data/memory. Matrixes. 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 April 13 -15, 2003 Swarm. Fest, Notre Dame 41

Computational objects 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 computational objects 2 time specification: seconds step in recipe 1 s 1 c 1998 1 0 5 s 2 time in seconds 1 c 1998 1 1 6 s 2 1 s 1 c 1998 1 3 7 s 2 April 13 -15, 2003 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) Swarm. Fest, Notre Dame 42

Computational objects computational objects 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 c 1998(){ mm 0=(Memory. Matrix) pending. Computational. Specification. Set. get. Memory. Matrix. Address(0); layer=pending. Computational. Specification. Set. get. Order. Layer(); mm 0. set. Value(layer, 0, 0, 1. 0); mm 0. print(); done=true; } // end c 1998 April 13 -15, 2003 Swarm. Fest, Notre Dame 43

_other tools ____________________ Other tools ____________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 44

Other tools: other tools Stand alone batches Procurements (as seen above) Parallel paths (AND formalism) Multiple paths (OR formalism) April 13 -15, 2003 Swarm. Fest, Notre Dame 45

_references ____________________ References ____________________ April 13 -15, 2003 Swarm. Fest, Notre Dame 46

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/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. April 13 -15, 2003 Swarm. Fest, Notre Dame 47

pietro. terna@unito. it web. econ. unito. it/terna address again web. econ. unito. it/terna/jes April 13 -15, 2003 Swarm. Fest, Notre Dame 48