september 25th, 2007real collegio carlo alberto1 agent based simulation and electricity market...
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September 25th, 2007 Real Collegio Carlo Alberto 1
Agent based simulation and electricity market
Pietro TERNA, Department of Economic and Financial Science,University of Torino
[email protected], http://web.econ.unito.it/terna
Politecnico di Torino - Ceris – Hermes - AEITElectricity market performance under physical constraints
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September 25th, 2007 Real Collegio Carlo Alberto 2
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Building models:
three ways
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September 25th, 2007 Real Collegio Carlo Alberto 3
Three different symbol systems:
• verbal argumentations
• mathematics
• computer simulation (agent based)
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September 25th, 2007 Real Collegio Carlo Alberto 4
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How to use agents in simulation models:
a radical view
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September 25th, 2007 Real Collegio Carlo Alberto 5
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)
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September 25th, 2007 Real Collegio Carlo Alberto 6
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Agent based simulation
and real world representation
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September 25th, 2007 Real Collegio Carlo Alberto 7
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
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September 25th, 2007 Real Collegio Carlo Alberto 8
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A dictionary
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September 25th, 2007 Real Collegio Carlo Alberto 9
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, 183-205,2001
“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.
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September 25th, 2007 Real Collegio Carlo Alberto 10
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A general structure for agent-based simulation models, the ERA scheme
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September 25th, 2007 Real Collegio Carlo Alberto 11
http://web.econ.unito.it/terna/ct-era/ct-era.html
NNCSGA
Avatar
ERA,
Environment,
Rules, Agents
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September 25th, 2007 Real Collegio Carlo Alberto 12
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Tools
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September 25th, 2007 Real Collegio Carlo Alberto 13
Swarm, http://www.swarm.org
SLAPP, Swarm-Like Agent Protocol in Python, temporary at http://eco83.econ.unito.it/terna/slapp ; Python at www.python.org
JAS, http://jaslibrary.sourceforge.net/
Ascape, http://www.brook.edu/dynamics/models/ascape/
Repast, http://repast.sourceforge.net/
StarLogo, http://education.mit.edu/starlogo/
StarLogo TNG, http://education.mit.edu/starlogo-tng/
NetLogo, http://ccl.northwestern.edu/netlogo/
SDML (based upon SmallTalk, as a declarative programming tool): http://www.cpm.mmu.ac.uk/sdml/
See also ABLE, http://www.research.ibm.com/able/
JADE, http://jade.tilab.com/
or DAML, www.daml.org
didactical perspective
nearly videogames
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September 25th, 2007 Real Collegio Carlo Alberto 14
We have also specialized agent based simulators, like
jES and jESOFhttp://web.econ.unito.it/terna/jes/
a simulator useful to reproduce enterprises behavior
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September 25th, 2007 Real Collegio Carlo Alberto 15
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The surprising world of the Chameleons, with SLAPP
From an idea of Marco Lamieri, a project work with Riccardo Taormina
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September 25th, 2007 Real Collegio Carlo Alberto 16
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 .
At any time t we have an agent in a state st of S and we can chose the action a in A(st). After the action it will be in st+1 with a reward rt+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.
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September 25th, 2007 Real Collegio Carlo Alberto 17
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
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September 25th, 2007 Real Collegio Carlo Alberto 18
A simple rule for a complex environment
Let play
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September 25th, 2007 Real Collegio Carlo Alberto 19
Running or chasing for identity!
• Reinforcement learning and pattern recognition, with bounded rationality
• Agent brain built upon 9 Artificial Neural Networks
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September 25th, 2007 Real Collegio Carlo Alberto 20
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The surprising world of the Chameleons, with NetLogo
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September 25th, 2007 Real Collegio Carlo Alberto 21
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September 25th, 2007 Real Collegio Carlo Alberto 22
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Electricity market
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September 25th, 2007 Real Collegio Carlo Alberto 23
Leigh Tesf
atsion work
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September 25th, 2007 Real Collegio Carlo Alberto 24
Leigh Tesf
atsion work
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September 25th, 2007 Real Collegio Carlo Alberto 25
Leigh Tesf
atsion work
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September 25th, 2007 Real Collegio Carlo Alberto 26
Leigh Tesf
atsion work
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September 25th, 2007 Real Collegio Carlo Alberto 27
Leigh Tesf
atsion work
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September 25th, 2007 Real Collegio Carlo Alberto 28
Leigh Tesf
atsion work
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September 25th, 2007 Real Collegio Carlo Alberto 29
Leigh Tesf
atsion work
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September 25th, 2007 Real Collegio Carlo Alberto 30
Leigh Tesf
atsion work
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September 25th, 2007 Real Collegio Carlo Alberto 31
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From spin glasses to videogames:
representing our worlds and their complexity via the use of simple simulation tools, with agents
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September 25th, 2007 Real Collegio Carlo Alberto 32
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Scratch
http://weblogs.media.mit.edu/llk/scratch/
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September 25th, 2007 Real Collegio Carlo Alberto 33
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September 25th, 2007 Real Collegio Carlo Alberto 34
The credit multiplier (money_supply.sb)
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September 25th, 2007 Real Collegio Carlo Alberto 35
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StarLogo TNG
http://education.mit.edu/starlogo/
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StarLogo TNG
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September 25th, 2007 Real Collegio Carlo Alberto 37
Sperimentiamo la complessità con StarLogo TNG
termites2
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September 25th, 2007 Real Collegio Carlo Alberto 38
Sperimentiamo la complessità con StarLogo TNG
Fish and Plankton
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September 25th, 2007 Real Collegio Carlo Alberto 39
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Squeak
http://www.squeak.org/
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September 25th, 2007 Real Collegio Carlo Alberto 41
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Second Life
http://secondlife.com/
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September 25th, 2007 Real Collegio Carlo Alberto 42