September 25th, 2007Real Collegio Carlo Alberto1 Agent based simulation and electricity market Pietro TERNA, Department of Economic and Financial Science, University of Torino Politecnico di Torino - Ceris – Hermes - AEIT Electricity market performance under physical constraints

September 25th, 2007Real Collegio Carlo Alberto2 _______________________________________ Building models: three ways _______________________________________

September 25th, 2007Real Collegio Carlo Alberto3 Three different symbol systems: verbal argumentations mathematics computer simulation (agent based)

September 25th, 2007Real Collegio Carlo Alberto4 _______________________________________ How to use agents in simulation models: a radical view _______________________________________

September 25th, 2007Real Collegio Carlo Alberto5 The radical characterization of an ABM must be found into the possibility of real – direct or indirect (via the environment) – interaction amid the agents … avoiding simplifications coming from the use aggregate (simultaneous) equations (going back to the Walrasian auctioneer)

September 25th, 2007Real Collegio Carlo Alberto6 _______________________________________ Agent based simulation and real world representation _______________________________________

September 25th, 2007Real Collegio Carlo Alberto7 Social simulation as a computer based way to execute complex mental experiments, but also as a via to represent the complexity of real world simulation = agent-based models

September 25th, 2007Real Collegio Carlo Alberto8 _______________________________________ A dictionary _______________________________________

September 25th, 2007Real Collegio Carlo Alberto9 A dictionary, from Conte R, Edmonds B, Moss S., Sawyer R.K., Sociology and Social Theory in Agent Based Social Simulation: A Symposium Computational & Mathematical Organization Theory 7, ,2001 “1. The purpose of Agent Based Social Simulation (ABSS) is to analyse the properties of social systems defined by dense patterns of interaction among autonomous, cognitive individuals. 2. The same modelling techniques that are intended to represent real social systems can also represent software systems such as the Internet and large federated data bases populated by intelligent information agents or, indeed, any other large, complex multi agent system. Multi agent based simulations (MABS) of such systems share the techniques of ABSS.” My note: we use frequently the name of Agent Based Model (ABM) or Agent Computational Ecomics (ACE) instead of ABSS; in computer science the attention is devoted to Multi Agent Systems, MAS; adding “simulation” we have MABS and, in some way, ABSS.

September 25th, 2007Real Collegio Carlo Alberto10 _______________________________________ A general structure for agent-based simulation models, the ERA scheme _______________________________________

September 25th, 2007Real Collegio Carlo Alberto11 NN CS GA Avatar ERA, Environment, Rules, Agents

September 25th, 2007Real Collegio Carlo Alberto12 _______________________________________ Tools _______________________________________

September 25th, 2007Real Collegio Carlo Alberto13 Swarm, SLAPP, Swarm-Like Agent Protocol in Python, temporary at ; Python at JAS, Ascape, Repast, StarLogo, StarLogo TNG, NetLogo, SDML (based upon SmallTalk, as a declarative programming tool): See also ABLE, JADE, or DAML, didactical perspective nearly videogames

September 25th, 2007Real Collegio Carlo Alberto14 We have also specialized agent based simulators, like jES and jESOF a simulator useful to reproduce enterprises behavior

September 25th, 2007Real Collegio Carlo Alberto15 _______________________________________ The surprising world of the Chameleons, with SLAPP From an idea of Marco Lamieri, a project work with Riccardo Taormina _______________________________________

September 25th, 2007Real Collegio Carlo Alberto16 The reinforcement learning algorithm A direct tool: the reinforcement learning. We have a set of states S, related to an environment; a set of possible actions A; a set of scalar rewards, in R. At any time t we have an agent in a state s t of S and we can chose the action a in A(s t ). After the action it will be in s t+1 with a reward r t+1. Reward are summed over time with a discount rate factor. Our agent develops the capability of mapping all the possible actions A in a state S to all the related rewards.

September 25th, 2007Real Collegio Carlo Alberto17 The metaphorical models we use here is that of the changing color chameleons We have chameleons of three colors: red, green and blue When two chameleons of different colors meet, they both change their color, assuming the third one (If all chameleons get the same color, we have a steady state situation) The metaphor is interpreted in the following way: an agent diffusing innovation or ideas (or political ideas) can change itself via the interaction with other agents: as an example think about an academic scholar working in a completely isolated context or interacting with other scholars or with private entrepreneurs to apply the results of her work

September 25th, 2007Real Collegio Carlo Alberto18 A simple rule for a complex environment Let play

September 25th, 2007Real Collegio Carlo Alberto19 Running or chasing for identity! Reinforcement learning and pattern recognition, with bounded rationality Agent brain built upon 9 Artificial Neural Networks

September 25th, 2007Real Collegio Carlo Alberto20 _______________________________________ The surprising world of the Chameleons, with NetLogo _______________________________________

September 25th, 2007Real Collegio Carlo Alberto21

September 25th, 2007Real Collegio Carlo Alberto22 _______________________________________ Electricity market _______________________________________

September 25th, 2007Real Collegio Carlo Alberto23 Leigh Tesfatsion work

September 25th, 2007Real Collegio Carlo Alberto24 Leigh Tesfatsion work

September 25th, 2007Real Collegio Carlo Alberto25 Leigh Tesfatsion work

September 25th, 2007Real Collegio Carlo Alberto26 Leigh Tesfatsion work

September 25th, 2007Real Collegio Carlo Alberto27 Leigh Tesfatsion work

September 25th, 2007Real Collegio Carlo Alberto28 Leigh Tesfatsion work

September 25th, 2007Real Collegio Carlo Alberto29 Leigh Tesfatsion work

September 25th, 2007Real Collegio Carlo Alberto30 Leigh Tesfatsion work

September 25th, 2007Real Collegio Carlo Alberto31 _______________________________________ From spin glasses to videogames: representing our worlds and their complexity via the use of simple simulation tools, with agents _______________________________________

September 25th, 2007Real Collegio Carlo Alberto32 _______________________________________ Scratch _______________________________________

September 25th, 2007Real Collegio Carlo Alberto33

September 25th, 2007Real Collegio Carlo Alberto34 The credit multiplier (money_supply.sb)

September 25th, 2007Real Collegio Carlo Alberto35 _______________________________________ StarLogo TNG _______________________________________

September 25th, 2007Real Collegio Carlo Alberto36 StarLogo TNG

September 25th, 2007Real Collegio Carlo Alberto37 Sperimentiamo la complessità con StarLogo TNG termites2

September 25th, 2007Real Collegio Carlo Alberto38 Sperimentiamo la complessità con StarLogo TNG Fish and Plankton

September 25th, 2007Real Collegio Carlo Alberto39 _______________________________________ Squeak _______________________________________

September 25th, 2007Real Collegio Carlo Alberto40

September 25th, 2007Real Collegio Carlo Alberto41 _______________________________________ Second Life _______________________________________

September 25th, 2007Real Collegio Carlo Alberto42