1. Sistemi per la Gestione Aziendale - Proff Giuseppe Zollo Cristina Ponsiglione 1 Sistemi per la Gestione Aziendale. AA. 2015-16 Ingegneria Gestionale (LM) SGA LEZ02 Introduzione ai Complex Adaptive Systems e alla Simulazione ad agenti

2. 2 Sommario della lezione 02 Modelli e simulazione ad agenti Sistemi adattivi complessi (CAS) e il laboratorio computazionale Come si costruisce un modello basato su agenti e come si implementa Introduzione al laboratorio Netlogo

3. 3 Models Typology Physical Models (i.e. reproduction of an object to be studied); Descriptive/Linguistic Models (i.e. reproduction of a phenomenon through natural language); Analytical Models ( i.e. models using differential equations ) Simulative Models (i.e. models for calculation, system dynamics models, agent-based models)

4. 4 Models Typology Physical Models (not usable in social sciences and in the presence of high complexity); Descriptive/Linguistic Models (flexibility, not usable to calculate); Analytical Models (possibility of calculation, need of simplification) Simulative Models (flexibility and possibility of calculation)

5. 5 The Use of Simulation Simulation as a speculative complement to a descriptive model; Simulation as a tool to calculate numerical solutions of a mathematical model (operations research, system dynamics); Simulation as a research approach in the presence of complex adaptive systems

6. 6 Complexity Is the property of a system in which the agents local actions and interactions produce aggregate behaviors different from the sum of individuals behaviors. ( Holldobrer and Wilson, 1997) The use of simulation in a descriptive or in a pure calculative way does not permit to consider a complex phenomenon in its parts and at all at the same moment.

7. 7 Complicated and Complex A complicated system is a system in which is simple to deduce the global behavior knowing the behavior of its parts. A complex system is a system in which the simple analysis of isolated parts does not produce the understanding of collective-aggregate behaviors.

8. 8 Agent-Based simulations Representation of behaviors of individuals populating the system through informatics routines and algorithms; Focus on descriptive aspects of reality (not equations); Possibility of calculus offered by computers; Useful to conduct generative experiments in a virtual laboratory.

9. 9 The Complexity Approach Six features of Economy that make difficult the use of mathematics and analytical models (Arthur, Durlauf, Lane, 1997- Santa Fe Institute) Dispersed interaction (autonomous and heterogeneous agents interact in a parallel way); No-global controller (no global entity controls interactions, control is provided by local mechanisms of cooperation and competition); Cross-Cutting Hierarchical Organization (many levels of organization with many communications between levels-the parts of a level make the building block of another level); Continual Adaptation (agents continuously adapt) ; Perpetual Novelty (new markets, new technologies and so on…); Out-of-Equilibrium Dynamics (the Economy operates far from any global equilibrium- improvements are always possible).

10. 10 Examples of CAS New York City (buyers, sellers, administrators, buildings, streets, bridges – No single constituent remains in place, but the city persists over time- It is a pattern in time); The human immune system (community of cells named antibodies that repel or destroy antigens – Antigens change over time and antibodies have to adapt and learn – the antibodies change over time as a consequence of their adaptation, but the system preserves its choerence and your identity); Central Nervous System (many neurons of different forms that interact in a complex network – the network adapts and learns- the behavior of the CNS depends on the interactions among neurons instead of the individual actions – billions of interactions among neurons create the capability of mammalians to anticipate – The network of interactions produces an internal model)

11. 11 CAS: a summary Choerence in the face of changes Extensive interactions…………… ……… among many different constituents …………………. ………….that produce aggregate behaviors different from the sum of local behaviors……………… …………and that adapt or learn

12. 12 The generative approach The generativist’s question: How could the decentralized local interactions of heterogeneous and autonomous agents generate the given macroscopic regularity? The ABM is well suited to the study of this question due to the specific characteristics:  Heterogeneity (diversity)  Autonomy (no top-down control)  Local Interaction (agents interact with their neighbors)  Bounded rationality (bounded information, limited computing power)  Space of action (a space of interconnected resources) (Epstein and Axtell, 1996) The generativist’s question: How could the decentralized local interactions of heterogeneous and autonomous agents generate the given macroscopic regularity? The ABM is well suited to the study of this question due to the specific characteristics:  Heterogeneity (diversity)  Autonomy (no top-down control)  Local Interaction (agents interact with their neighbors)  Bounded rationality (bounded information, limited computing power)  Space of action (a space of interconnected resources) (Epstein and Axtell, 1996)

13. 13 The generative approach A generative experiment consists in (Epstein and Axtell, 1996):  placing an initial population of heterogeneous and autonomous cognitive agents in a virtual environment;  let them interact and evolve according to some individual behavioral rules (micro-specifications);  observe if such micro-specifications are sufficient to generate expected or plausible macroscopic regularities. (Shelling, 1978; Axelrod, 1995; Conte, 1999; Epstein and Axtell, 1996; Holland and Miller, 1991; Tesfation, 2001) A generative experiment consists in (Epstein and Axtell, 1996):  placing an initial population of heterogeneous and autonomous cognitive agents in a virtual environment;  let them interact and evolve according to some individual behavioral rules (micro-specifications);  observe if such micro-specifications are sufficient to generate expected or plausible macroscopic regularities. (Shelling, 1978; Axelrod, 1995; Conte, 1999; Epstein and Axtell, 1996; Holland and Miller, 1991; Tesfation, 2001)

14. 14 The generative approach An ABM provides a computational demonstration that a given micro-specification is sufficient to generate a macroscopic regularity; The generativist wants to know how such macroscopic configuration can be reached by a decentralized system of heterogeneous autonomous agents; If a micro-specification is able to generate a macroscopic regularity then it is a good candidate to explain it.

15. 15 Topics and Applications  Physics  Biology  Living Systems  Economics  Social and Organizational Systems (Social Simulation) Journals Journals  Journal of Artificial Societies and Social Simulation (listed in SSCI)  E:CO (Emergence, Complexity and Organizations)  Computational and Mathematical Organization Theory  IEEE Transactions on Evolutionary Computation Topics and Journals

16. 16  Santa Fe Institue (www.santafe.edu)www.santafe.edu  Swarm Development Group Wiki (Center for the study of Complex Systems-Michigan University- www.swarm.org)www.swarm.org  Center for Connected Learning and Computer Based Modeling (Northwestern University- ccl.northwestern.edu)  Computational Finance and Economic Agents (Essex University- ccfea-research@essex.ac.uk) Scientific Associations and Research Groups

17. 17 The steps to build an ABM Identify classes of agents Define the characteristics of the space of action (e.g. topology, resources, constraints) Make hypothesis about individual behavioral rules (microspecifications) –(if then ) Choose a platform to implement your meta-model Write a software code to implement and simulate the model (to perform generative what if analysis and build your own virtual laboratory)

18. 18 Tools for agent-based simulation Main platforms: Swarm ( www.swarm.org); www.swarm.org JAS ( jaslibrary.sourceforge.net); Repast (repast.sourceforge.net); NetLogo (ccl.northwestern.edu/netlogo); Agentsheets (www.agentsheets.com).

19. 19 Main features of Swarm Developed at Santa Fe Institute in ’90; Two possible programming languages: Objective C and Java; The toolkit is made by: a simulation environment, a graphical interface, a library of objects usable to make models.

20. 20 NetLogo Developed at Center for Connected Learning and Computer-Based Modeling at the end of ’90; NetLogo offers an interactive development environment and a simple programming language; the programming language is LOGO.

21. 21 NetLogo NetLogo offers a wide library of developed models (Models Library) useful to: - understand the logic of modeling; - run different simulation settings; - modify the code of a model.

22. 22 Let start with laboratory Download NetLogo 5.0.4 Open NetLogo (an “untitled file” will appear) Give a look to the Models Library (in the “File” menu) Open the User Manual (in the “Help” menu)

23. 23 The world of NetLogo The world is made by agents, that follow instructions and act in different ways, but all simultaneously; Three types of agents: - turtles (they can move around the world); - patches (constitute the ground of the world, they don’t move but are “alive”); - observer (it is a looking over the world); Each turtle is identified by a “who” ID number and by its coordinates (xcor; ycor); Each patch is identified by its coordinates (pxcor; pycor); The coordinates of patches identify the distance of patches from the origin of the world; The total number of patches in the world is determined by the setting of min-pxcor, max-pxcor, min-pycor, max-pycor; The world can have different shapes: torus, box, horizontal cylinder, vertical cylinder (the default setting is a torus, it isn’t bounded, but “wraps”)

24. 24 The NetLogo interface: the menus File: Through the file menu you can start a new file, open an existing one, choose a model in the models library, Import or export plots and files Tabs: This menu offers keyboard shortcuts for each tab: Interface Information Procedures Help: This menu permits to open the user manual in a web browser

25. 25 The NetLogo interface: the tabs Interface Tab: is were you can watch your model going on. When you open a model the interface appears. Just the view is empty at the opening of the interface Information Tab: is were you can read information about a model included in the model library (what is it?, how to use it?, things to try, extending the model, NeTlogo features, related models, references) Procedures Tab: is were you can read the NetLogo code of the selected model

26. 26 The NetLogo interface: main window

27. 27 The elements of the main window of the interface Buttons: once-only buttons perform the related instructions at once. Forever buttons, when clicked, execute instructions over and over until you stop Sliders: are global variables, which are accessible by all turtles. Sliders are used to modify values of variables (parameters) without change the code Switches: represent a true/false global variable that the observer can set in the setup phase Chooser: permits to choose a value for a global variable in a list of choices Monitor: reports the value of a variable or of a complex expression Plot: plots graphs related to the running experiment Input:is an area related to globals variables represented by a string of number Output: is a scrolling area of text that can be used to create a log of activity Note: is a box in which you can add text to the main window of the interface

28. 28 The NetLogo interface: the view Change dimensions of the world Modify the speed of simulationFreeze or unfreeze the view Edit the world of simulation and set the view Switch on the 3D view

29. 29 Two sample models Segregation Ants