April 13-15, 2003SwarmFest, Notre Dame1 jES Pietro Terna 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

April 13-15, 2003SwarmFest, Notre Dame2 _jVE->JES _______________________________________ jVE jES _______________________________________

April 13-15, 2003SwarmFest, Notre Dame3 From jVE … … to jES jVE->jES Virtual Enterprise ?? 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)

April 13-15, 2003SwarmFest, Notre Dame4 _overview _______________________________________ Overview _______________________________________

April 13-15, 2003SwarmFest, Notre Dame5 overview 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

April 13-15, 2003SwarmFest, Notre Dame6 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 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 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, 2003SwarmFest, Notre Dame7 _jES principles _______________________________________ jES principles _______________________________________

April 13-15, 2003SwarmFest, Notre Dame8 jES principles 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

April 13-15, 2003SwarmFest, Notre Dame9 jES principles 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

April 13-15, 2003SwarmFest, Notre Dame10 jES principles 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

April 13-15, 2003SwarmFest, Notre Dame11 _WD, DW, WDW _______________________________________ WD, DW, WDW _______________________________________

April 13-15, 2003SwarmFest, Notre Dame12 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, 2003SwarmFest, Notre Dame13 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 A dictionary

April 13-15, 2003SwarmFest, Notre Dame14 _DW: a flexible scheme _______________________________________ DW: a flexible scheme _______________________________________

April 13-15, 2003SwarmFest, Notre Dame15 DW: a flexible scheme ,3,4 1,2,5 Units … DW

April 13-15, 2003SwarmFest, Notre Dame16 DW: a flexible scheme ,3,4 1,2,5 Units and Firms … DW

April 13-15, 2003SwarmFest, Notre Dame17 DW: a flexible scheme ,3,4 1,2,5 … in a district … DW

April 13-15, 2003SwarmFest, Notre Dame18 DW: a flexible scheme ,3,4 1,2,5 … or building up a virtual enterprise The NIIIP project (National Industrial Information Infrastructure Protocols ) DW

April 13-15, 2003SwarmFest, Notre Dame19 _WD: recipes _______________________________________ WD: recipes _______________________________________

April 13-15, 2003SwarmFest, Notre Dame20 WD: recipes WD

April 13-15, 2003SwarmFest, Notre Dame21 _a simple example with WD, DW and WDW _______________________________________ A simple example with WD, DW and WDW _______________________________________

April 13-15, 2003SwarmFest, Notre Dame a production unit an end unit a simple example 0 the recipes DW WDW the starting sequence the continuous sequence (empty) t= Building a sequential batch

April 13-15, 2003SwarmFest, Notre Dame a production unit an end unit a simple example 1 the recipes WD WDW the starting sequence the continuous sequence (empty) t= Sequential batch step 1/3 DW

April 13-15, 2003SwarmFest, Notre Dame a production unit an end unit a simple example 2 the recipes WD WDW the starting sequence the continuous sequence (empty) t= Sequential batch step 2/3 DW

April 13-15, 2003SwarmFest, Notre Dame a production unit an end unit a simple example 3 the recipes WD WDW the starting sequence the continuous sequence (empty) t= DW

April 13-15, 2003SwarmFest, Notre Dame a production unit an end unit a simple example 4 the recipes WD WDW the starting sequence the continuous sequence (empty) t= DW

April 13-15, 2003SwarmFest, Notre Dame a production unit an end unit a simple example 5 the recipes WD WDW the starting sequence the continuous sequence (empty) t= DW

April 13-15, 2003SwarmFest, Notre Dame a production unit an end unit a simple example 6 the recipes WD WDW the starting sequence the continuous sequence (empty) t= Building a sequential batch DW

April 13-15, 2003SwarmFest, Notre Dame a production unit an end unit a simple example 7 the recipes WD WDW the starting sequence the continuous sequence (empty) t= Sequential batch step 1/2 DW

April 13-15, 2003SwarmFest, Notre Dame a production unit an end unit a simple example 8 the recipes WD WDW the starting sequence the continuous sequence (empty) t= DW

April 13-15, 2003SwarmFest, Notre Dame a production unit an end unit a simple example 9 the recipes WD WDW the starting sequence the continuous sequence (empty) t= DW

April 13-15, 2003SwarmFest, Notre Dame a production unit an end unit a simple example 10 the recipes WD WDW the starting sequence the continuous sequence (empty) t= DW

April 13-15, 2003SwarmFest, Notre Dame33 _a complex example: the VIR case _______________________________________ A complex example: the VIR case _______________________________________

April 13-15, 2003SwarmFest, Notre Dame34 VIR 1 VIR (a firm producing valves, to regulate the flow of liquids and gas) Basic case (with unitCriterion=2)

April 13-15, 2003SwarmFest, Notre Dame35 VIR 2 VIR Adding 3 complex units in the lathe sector

April 13-15, 2003SwarmFest, Notre Dame36 _the decision process _______________________________________ The decision process _______________________________________

April 13-15, 2003SwarmFest, Notre Dame37 decision process ,3,4 1,2,5 How to decide?

April 13-15, 2003SwarmFest, Notre Dame38 decision process 2 How to decide? 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, 2003SwarmFest, Notre Dame39 _new tools: recipes and layers, computational objects _______________________________________ New tools: recipes and layers, computational objects _______________________________________

April 13-15, 2003SwarmFest, Notre Dame40 recipes and layers Recipes and layers

April 13-15, 2003SwarmFest, Notre Dame41 computational objects 1 Memory matrixes data are reported in a text file (unitData/memoryMatrixes.txt) number(from_0_ordered;_negative_if_insensitive_to_layers)_rows_cols Mandatory first line Computational objects

April 13-15, 2003SwarmFest, Notre Dame42 computational objects 2 Recipes with computations (recipes are reported in external and intermediate format) External format (remember: step, time specification, time) : 1 s 1 c s 2 3 s 2 1 s 1 c s 2 1 s 1 c s 2 1 s 1 c 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) Computational objects

April 13-15, 2003SwarmFest, Notre Dame43 computational objects 3 The Java Swarm code used by the recipes with computations of this example /** computational operations with code (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 Computational objects

April 13-15, 2003SwarmFest, Notre Dame44 _other tools _______________________________________ Other tools _______________________________________

April 13-15, 2003SwarmFest, Notre Dame45 other tools Other tools: Stand alone batches Procurements (as seen above) Parallel paths (AND formalism) Multiple paths (OR formalism)

April 13-15, 2003SwarmFest, Notre Dame46 _references _______________________________________ References _______________________________________

April 13-15, 2003SwarmFest, Notre Dame47 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 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

April 13-15, 2003SwarmFest, Notre Dame48 address again web.econ.unito.it/terna web.econ.unito.it/terna/jes