jESevol

1. Pietro Ternapietro.terna@unito.it Department of Economics and Finance “G.Prato” University of Torino - Italy Evolving a simulated system of enterprises with jESevol and Swarm web.econ.unito.it/terna web.econ.unito.it/terna/jes jESevol SwarmFest, CSCS, University of Michigan

2. _jES->jESlet and jESevol _______________________________________ jES jESlet and jESevol _______________________________________ SwarmFest, CSCS, University of Michigan

3. jVE->jES From jES … java Enterprise Simulator … to jESlet (with a didactic goal) and … … to jESevol, to simulate an evolving system of enterprises SwarmFest, CSCS, University of Michigan

4. _overview _______________________________________ Overview _______________________________________ SwarmFest, CSCS, University of Michigan

5. overview 1 Overview 1/2 From jES (our java Enterprise Simulator), we have derived jESevol, or “Evolutionary java Enterprise Simulator”. jES is a large Swarm-based package[1] aimed at building simulation models both of actual enterprises and of virtual ones. jESevol simulates systems of enterprises or production units in an evolutionary context, where new ones arise continuously and some of the old are dropped out. Our environment is a social space with metaphorical distances representing trustiness and cooperation among production units (the social capital). The production is represented by a sequence of orders; each order contains a recipe, i.e. the description of the sequence of activities to be done by several units to complete a specific production. [1] Download last versions of jES, jESlet and jESevol from http://web.econ.unito.it/terna/jes SwarmFest, CSCS, University of Michigan

6. overview 2 Overview 2/2 Two units can cooperate in the production process only if they are mutually visible in our social network. Units that do not receive a sufficient quantity of orders, as well as the ones that cannot send the accomplished orders to successive units, disappear. New enterprises arise, in the attempt of filling the structural holes (Burt, 1992; Walker et al., 1997) of our social network. A complex structure emerges from our environment, with a difficult and instable equilibrium whenever the social capital is not sufficient. References Burt R.S. (1992), Structural Holes – The Social Structure of Competition. Cambridge, MA, Harvard University Press. 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, CSCS, University of Michigan

7. evolving system We look at an incomplete production system continuously adapting itself to the reality coming from the global demand of the market … … while new firms arise and old ones are dropped out To produce goods, supply chains are created and modified, according to the changes in exiting firms SwarmFest, CSCS, University of Michigan

8. _jES basics _______________________________________ jESbasics _______________________________________ SwarmFest, CSCS, University of Michigan

9. WD, DW, WDW Three formalisms WD side or formalism: What to Do DW side or formalism:whichisDoingWhat WDW formalism: When Doing What SwarmFest, CSCS, University of Michigan

10. A dictionary dictionary unit = a productive structure; a unit is able to perform one 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) recipe = a sequence of steps to be executed to produce a good SwarmFest, CSCS, University of Michigan

11. _A flexible scheme _______________________________________ A flexible scheme in jESevol _______________________________________ SwarmFest, CSCS, University of Michigan

12. DW: a flexible scheme DW Units … 2 1 3 1 4 … on a toroidal space 2 1 Each unit is able to do a specific step … 5 (left and right borders and top and bottom ones are close together) 3 SwarmFest, CSCS, University of Michigan

13. WD: recipes WD … of a recipe with the recipes of the orders (what to do) expressed as sequences of numbers; orders with recipes are randomly generated with different lengths and structures 1 3 2 4 5 3 4 3 5 1 1 … SwarmFest, CSCS, University of Michigan

14. moving recipes DW and WD moving around among units 2 13 2 4 1 3 1 132 4 4 2 1 324 how to choose ? 1 1 3 24 5 3 lack of visibility Visibility is a metaphorical representation of trustiness and cooperation in a social network; when global visibility increases, we have more “social capital” SwarmFest, CSCS, University of Michigan

15. visibility and … Visibility increases with the time (initial visibility is randomly chosen) • new units appear randomly (enterprise creation) • with strategic relationships … • … or alone visibility changes some units are dropped out SwarmFest, CSCS, University of Michigan

16. … bars The left (blue) bar of each unit reports the number of waiting orders (do be done) The right (red) bar of each unit reports the number of unsent products, due to the fact that a unit able to do the required step does not exist or is not visible The down (grey) bar of each unit reports the number of consecutive clock ticks in which the unit has been idle If >maxInactivity the unit is dropped out and all unsent products are lost If >maxUnsentProducts the unit is dropped out and all unsent and waiting products are lost SwarmFest, CSCS, University of Michigan

17. _an introductory case _______________________________________ An introductory case, robust and fragile _______________________________________ SwarmFest, CSCS, University of Michigan

18. Introductory robust case the parameters, robust introductory case potentialUnitTypes 5 unitGenerationInitialP 1 potentialUnitNumberPerType 2 newUnitGenerationP 0.0 interVisibilityMinLevel 0 increasingVisibilityStep 0.0 maxInactivity 10 maxUnsentProducts 10 max n. of types and max presence per type, here 5 * 2 with p=1 p of a new unit in each cycle, with a random type in this basic case all units are visible and visibility does not change we assume that an actual firm is dropped out from the market after three months of inactivity, so 10 ticks = 3 months of history Why 10? Our recipes have here maxStepNumber =5 and maxStepLength=2; potentially, in 10 ticks, each unit can receive an order, but only as a limit case; with this parameters the system can be exposed to a complete crash similarly … SwarmFest, CSCS, University of Michigan

19. introductory case: robust case Introductory robust case only 5 units kept alive 1,000 ticks = 25 years of actual time PRODUCTION global/potential final/potential final/global SwarmFest, CSCS, University of Michigan

20. Introductory fragile case the parameters, fragile introductory case potentialUnitTypes 10 unitGenerationInitialP 1 potentialUnitNumberPerType 1 newUnitGenerationP 0.0 interVisibilityMinLevel 0 increasingVisibilityStep 0.0 maxInactivity 10 maxUnsentProducts 10 max n. of types and max presence per type, here 10 * 1 with p=1 Our recipes have here maxStepNumber 10 and maxStepLength 1 SwarmFest, CSCS, University of Michigan

21. introductory case: fragile case Basic fragile case no units kept alive 150 ticks < 4 years of actual time PRODUCTION global/potential final/potential final/global SwarmFest, CSCS, University of Michigan

22. _a study case __________________________________________________ A study case, with 3 versions: (i) basic, (ii) increasing social capital, (iii) with greater financial intervention of the banking system __________________________________________________ SwarmFest, CSCS, University of Michigan

23. (i) basic study case, starter file 5 in jESevol 0.3.00 the parameters, basic study case potentialUnitTypes 5 unitGenerationInitialP 0.8 potentialUnitNumberPerType 2 newUnitGenerationP 0.8 interVisibilityMinLevel 1 increasingVisibilityStep 5 maxInactivity 10 maxUnsentProducts 10 max n. of types and max presence per type, here 5 * 2 with p=0.8 p of a new unit in each cycle, with a random type in this study case, min visibility is 1, i.e. at least one common patch; visibility increases of 5 patches in each tick Our recipes have here maxStepNumber 5 and maxStepLength 2 SwarmFest, CSCS, University of Michigan

24. study case: basic (i) basic study case, starter file 5 in jESevol 0.3.00 a relevant variability in the number of units (social costs), with the trace of a cycle a medium performance in term of potential production 1,000 ticks = 25 years of actual time PRODUCTION global/potential final/potential final/global some form of structure seems to emerge SwarmFest, CSCS, University of Michigan

25. (ii) Increasing social capital study case, starter file 5.2 in jESevol 0.3.00 the parameters, increasing social capital study case potentialUnitTypes 5 unitGenerationInitialP 0.8 potentialUnitNumberPerType 2 newUnitGenerationP 0.8 interVisibilityMinLevel 1 increasingVisibilityStep 10 maxInactivity 10 maxUnsentProducts 10 max n. of types and max presence per type, here 5 * 2 with p=0.8 p of a new unit in each cycle, with a random type in this study case, min visibility is 1, i.e. at least one common patch; visibility increases of 10 patches in each tick Our recipes have here maxStepNumber 5 and maxStepLength 2 SwarmFest, CSCS, University of Michigan

26. study case: increasing social capital (ii) Increasing social capital study case, starter file 5.2 in jESevol 0.3.00 a relevant variability in the number of units (social costs), but now with an evident cycle a good (and increasing) performance in term of potential production 1,000 ticks = 25 years of actual time PRODUCTION global/potential final/potential final/global evident structures emerge SwarmFest, CSCS, University of Michigan

27. (iii) Greater financial intervention of the banking system study case,starter file 5.3 in jESevol 0.3.00 the parameters, bank system study case potentialUnitTypes 5 unitGenerationInitialP 0.8 potentialUnitNumberPerType 2 newUnitGenerationP 0.8 interVisibilityMinLevel 1 increasingVisibilityStep 5 maxInactivity 15 maxUnsentProducts 10 in this study case, min visibility is 1, i.e. at least one common patch; visibility is increases of 5 patches in each tick we assume that an actual firm is dropped out from the market after 15 ticks of inactivity, instead of 10 Our recipes have here maxStepNumber 5 and maxStepLength 2 SwarmFest, CSCS, University of Michigan

28. study case: bank system study case (iii) Greater financial intervention of the banking system study case,starter file 5.3 in jESevol 0.3.00 a less relevant variability in the number of units (reduced social costs), always with an evident cycle a good performance in term of potential production 1,000 ticks = 25 years of actual time PRODUCTION global/potential final/potential final/global evident structures emerge SwarmFest, CSCS, University of Michigan

29. Stability Cases i, ii and iii are stable also running them for 4,000 ticks (one century)! Stability; perspectives Short term enhancements A lot of investigation is necessary on cases (i), (ii) and (iii) modelling explicitly the banking system, with the concurrent effects of the cases of bankruptcy in firms and banks Using a Genetic Algorithm tool to choose units to be created at each tick and where to place them; the fitness will be generated by jESevol itself, from different points of view: the whole economic system, a specific unit, a cluster of units, … SwarmFest, CSCS, University of Michigan

30. address again Let run case 5.2 or 5.3 at the question time! pietro.terna@unito.it web.econ.unito.it/terna web.econ.unito.it/terna/jes SwarmFest, CSCS, University of Michigan