1 / 48

jES

Pietro Terna pietro.terna@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/jes. jES. _jVE->JES.

orlando-butler
Download Presentation

jES

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Pietro Ternapietro.terna@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/jes jES SwarmFest, Notre Dame

  2. _jVE->JES _______________________________________ jVE jES _______________________________________ SwarmFest, Notre Dame

  3. jVE->jES 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 SwarmFest, Notre Dame

  4. _overview _______________________________________ Overview _______________________________________ SwarmFest, Notre Dame

  5. 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 http://web.econ.unito.it/terna/jes SwarmFest, Notre Dame

  6. 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 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”. SwarmFest, Notre Dame

  7. _jES principles _______________________________________ jESprinciples _______________________________________ SwarmFest, Notre Dame

  8. 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. SwarmFest, Notre Dame

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

  10. jES principles 3 jES principles 3/3 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 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. SwarmFest, Notre Dame

  11. _WD, DW, WDW _______________________________________ WD, DW, WDW _______________________________________ SwarmFest, Notre Dame

  12. WD, DW, WDW WD side or formalism: What to Do DW side or formalism:whichisDoingWhat WDW formalism: When Doing What SwarmFest, Notre Dame

  13. A dictionary 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 SwarmFest, Notre Dame

  14. _DW: a flexible scheme _______________________________________ DW: a flexible scheme _______________________________________ SwarmFest, Notre Dame

  15. DW: a flexible scheme 1 DW 1 Units … 1,3,4 5 3 3 3 1,2,5 1 2 1 2 SwarmFest, Notre Dame

  16. DW: a flexible scheme 2 DW 1 Units and Firms … 1,3,4 5 3 3 3 1,2,5 1 2 1 2 SwarmFest, Notre Dame

  17. DW: a flexible scheme 3 DW 1 … in a district … 1,3,4 5 3 3 3 1,2,5 1 2 1 2 SwarmFest, Notre Dame

  18. 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 SwarmFest, Notre Dame

  19. _WD: recipes _______________________________________ WD: recipes _______________________________________ SwarmFest, Notre Dame

  20. WD: recipes WD SwarmFest, Notre Dame

  21. _a simple example with WD, DW and WDW _______________________________________ A simple example with WD, DW and WDW _______________________________________ SwarmFest, Notre Dame

  22. a simple example 0 the recipes WD the starting sequence WDW the continuous sequence (empty) t=0 100 100 100 101 Building a sequential batch DW 1 2 3 10 a production unit an end unit SwarmFest, Notre Dame

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

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

  25. a simple example 3 the recipes WD the starting sequence WDW the continuous sequence (empty) t=3 101 100 100 100 DW 1 2 3 10 a production unit an end unit SwarmFest, Notre Dame

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

  27. a simple example 5 the recipes WD the starting sequence WDW the continuous sequence (empty) t=5 100 100 100 101 Building a sequential batch DW 1 2 3 10 a production unit an end unit SwarmFest, Notre Dame

  28. a simple example 6 the recipes WD the starting sequence WDW the continuous sequence (empty) t=6 100 100 100 101 Sequential batch step 1/3 DW 1 2 3 10 a production unit an end unit SwarmFest, Notre Dame

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

  30. a simple example 8 the recipes WD the starting sequence WDW the continuous sequence (empty) t=8 100 101 100 DW 1 2 3 10 a production unit an end unit SwarmFest, Notre Dame

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

  32. a simple example 10 the recipes WD the starting sequence WDW the continuous sequence (empty) t=10 100 100 DW 1 2 3 10 a production unit an end unit SwarmFest, Notre Dame

  33. _a complex example: the VIR case _______________________________________ A complex example: the VIR case _______________________________________ SwarmFest, Notre Dame

  34. VIR 1 VIR (a firm producing valves, to regulate the flow of liquids and gas) Case-2 (with unitCriterion=2) SwarmFest, Notre Dame

  35. VIR 2 VIR Case-3 adding 3 complex units in the lathe sector SwarmFest, Notre Dame

  36. _the decision process _______________________________________ The decision process _______________________________________ SwarmFest, Notre Dame

  37. decision process 1 1 How to decide? 1,3,4 5 3 3 3 1,2,5 1 2 1 2 SwarmFest, Notre Dame

  38. 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? SwarmFest, Notre Dame

  39. _new tools: recipes and layers, computational objects _______________________________________ New tools: recipes and layers, computational objects _______________________________________ SwarmFest, Notre Dame

  40. recipes and layers Recipes and layers SwarmFest, Notre Dame

  41. 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 SwarmFest, Notre Dame

  42. 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) SwarmFest, Notre Dame

  43. 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=(MemoryMatrix) pendingComputationalSpecificationSet. getMemoryMatrixAddress(0); layer=pendingComputationalSpecificationSet. getOrderLayer(); mm0.setValue(layer,0,0,1.0); mm0.print(); done=true; } // end c1998 SwarmFest, Notre Dame

  44. _other tools _______________________________________ Other tools _______________________________________ SwarmFest, Notre Dame

  45. other tools Other tools: Stand alone batches Procurements (as seen above) Parallel paths (AND formalism) Multiple paths (OR formalism) SwarmFest, Notre Dame

  46. _references _______________________________________ References _______________________________________ SwarmFest, Notre Dame

  47. 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. SwarmFest, Notre Dame

  48. pietro.terna@unito.it web.econ.unito.it/terna web.econ.unito.it/terna/jes address again SwarmFest, Notre Dame

More Related