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Context and Issues
Introduction to simulation
Multi-agent simulation
Business process modelling and simulation
Simweb perspective
Modelling exercise
Agenda
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Context and Issues
Introduction to simulation
Business process modelling and simulation
Multi-agent simulation
Simweb perspective
Modelling exercise
Agenda
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This seminar is a deliverable of work package 1, “Participatory Problem Analysis”
The objective of is to1. Refine the definition of the problem and the user requirements
2. Establish a common knowledge base among all participants in the project
We should add “model specifications” to the first point When talking about building models in SimWeb, we should
distinguish two aspects1. Creating the model infrastructure
2. Implementing the sector model(s)
Today we would like to gain a better understanding of what we can expect as input from the other partner activities and how the modelling process takes account of the information
Context and IssuesObjective of the Training Seminar: The Modeler’s View
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Context and IssuesThe Modelling Process
“No-Model”
Real-World
Description
Market
Analysis
Simulation
System
EvaluationFeedback
Stakeholders
Mo
del
Infr
astr
uct
ure
Calibration
How?
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... the strategy of the market analysis – what are you looking for and how is this decided?
... how the results of the market analysis can be used to build the sector models
... how the choice of model (MA simulation in our case) constrains the market analysis methodology – e.g. aggregate vs. agent model
... how MA models are designed and adjusted in other areas of social science (e.g. Firma) – the process
... if, how, and when the participatory evaluation methodology influences the modelling process
We propose to set up a work package spanning task to address this problem
Context and IssuesWe would like to (better) understand...
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Context and Issues
Introduction to simulation
Business process modelling and simulation
Multi-agent simulation
Simweb perspective
Modelling exercise
Agenda
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Introduction to simulationFormal modelling
mathematical analysismathematical analysis computer simulationcomputer simulation
formal modellingformal modelling
physical sciencesphysical sciences social sciencessocial sciences
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Understanding of features in the social world Prediction: e.g. business forecasting, demography over the
years Tools: expert systems (simulate expertise of professionals) Training: flight simulators, simulators of national economies Entertainment: SimCity, Sims, flight simulators... Formalisation: theory (precise, coherent, complete) Discovery of consequences in the artificial society
Introduction to simulationThe uses of simulation
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parameter estimationsimulation
predicted datapredicted data
Introduction to simulationThe logic of simulation
modelmodel simulated datasimulated data
targettarget collected datacollected data
abstraction similarity
data gathering
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Introduction to simulationStages of simulation-based research
designing a modeldesigning a model building the modelbuilding the model verification and validation
verification and validation publicationpublication
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Introduction to simulationModel accuracy
simplicity accuracy
understanding prediction
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Introduction to simulationSocial simulation: special features
simple behaviour and simple rules -> complex organisation (sand pile, ants...)
mathematical analysis impossible
simple behaviour and simple rules -> complex organisation (sand pile, ants...)
mathematical analysis impossible
complexitycomplexity
local information
limited capacity to process information
local information
limited capacity to process information
bounded-rationalitybounded-rationality
appears without central planning from the actions of individual agents
flocks, markets, Internet...
appears without central planning from the actions of individual agents
flocks, markets, Internet...
self-organisationself-organisation
interactions among objects at one level give rise to different types of objects at another level
interactions among objects at one level give rise to different types of objects at another level
emergenceemergence
linear: proportional
non-linear: chaotic
linear: proportional
non-linear: chaotic
non-linearitynon-linearity
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Discrete event• event: distinct points of time
• between two consecutive events nothing happens
• number of events are finite
Continuous• the state changes all the time (e.g. water
level in a reservoir)
• discrete event simulation can serve as an approximation.
Introduction to simulationTypes of simulation
t
t
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Introduction to simulation
Business process modelling and simulation
Multi-agent simulation
Simweb perspective
Modelling exercise
Agenda
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Business Process Modelling and Simulation
BPM commercial software packages facilitate business process improvements through modelling and simulation.
They allow to:• Model your processes to define, document, and communicate.
• Simulate the future performance of your business to understand complex relationships and identify opportunities for improvement.
• Visualise your operations with dynamic animation graphics.
• Analyse how your system will perform in its “as-is” configuration and under a myriad of possible “to-be” alternatives so that you an confidently choose the best way to run your business.
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Mortgage Applications• Simple mortgage application review process.
Truck Assembly• This model demonstrates the operations of a truck assembly line. A
new truck chassis enters the line every 9.5 minutes. It is then conveyed down the line from work position to work position, whereby each work position adds a part or performs an operation.The model animation includes a display of utilization statistics for each work position, as well as the number of trucks produced.
Flexible Manufacturing• This model shows a detailed operation of a typical factory. The Factory
includes an Injection molding area, machining centre, foam gasket assembly, painting area, and warehouse. Detailed statistics are kept for performance measurements at each area.
Business Process Modelling and SimulationExamples
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Introduction to simulation
Business process modelling and simulation
Multi-agent simulation
Simweb perspective
Modelling exercise
Agenda
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“Computational system situated in some environment, that is capable of flexible autonomous action in order to meet its design objectives” [Jennings et al. 1998]
By flexible, it is meant that the system is:• Responsive. Agents perceive and respond to their environment.
• Pro-active. Goal-directed behaviour; agents take the initiative.
• Social. Capability of interacting with artificial agents and humans.
Multi-agent simulationWhat is an agent? A weak definition
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Multi-agent simulationSimulation and the environment
Collective behavioiurs of a system’s components can have dynamics that influence the environment. If so
• constraints on components are not fixed• attempts to analyze the system must provide ongoing
mutable parameterization of the environment
Even when a clean formulation is possible, analysis often involves concurrent expansion of recursive functions -> analytical compression is hard or impossible
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To study complex nonlinear systems e.g.• Organisms Cell behavior drives and is driven-by
metabolism, hormones• Ecologies The viability of a certain species being
dependent on a complex of other species• Economies Consumer behaviour drives economy.
Economy places constraints on consumers.• Transportation Automobiles make up traffic patterns.
Traffic jams constrain drivers
Multi-agent simulationWhy multi-agent simulation?
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With synthesis, the modeller aims to accurately describe a systems' components and plausible interactions, and then use a realization of that description as an empirical basis for study of the systems' global dynamics.
This bottom up approach is called Agent-Based Modelling (ABM). ABM complements and enhances, rather than supplants, traditional
approaches.
Agent-based models allow us to study • Spatial interaction• Adaptive, heterogeneous agents• Agents which face costs of information acquisition and
processing• Nested subsystems - economy, markets, firms, plants, employees• etc.
Multi-agent simulation Why multi-agent simulation? Synthesis and Analysis
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Organizations of agents
Animate agents
Data
Artificial world
Observer
Inanimate agents
If <cond>
then <action1>
else <action2>
If <cond>
then <action1>
else <action2>
Multi-agent simulationHow to
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Simulation proceeds in discrete time steps
Interaction between agents or procedures within simulation may have own event schedule
Multi-agent simulationDiscrete event simulation
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Software:• AgentSheets
• ARVA (cellular automata)
• Ascape
• CABLE
• CORMAS
• EVO (Swarm-based)
• MAGSY
• Multi-agent modelling language – MAML (Swarm-based)
• REPAST (Java, Swarm-like)
• Sim_Agent
• Swarm
• XRaptor
Multi-agents simulationSoftware
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Swarm
Sub-Swarm
Agent
Schedule The Model
Multi-agent simulationAn implementation: Swarm
The Interface
Probes
Objective C, Java… Object-oriented (not agent-oriented) Structure:
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A Swarm as a virtual computer
A computer’s CPU executes program instructions
Swarm kernel is virtual CPU running model and GUI events
Nested Swarms merge activity schedules into one
Operating System
CPU
Swarm kernel
GUI Model
Multi-agent modelsAn implementation: Swarm
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Examples:• Sugarscape
• MANTA
• Evolution of organised society (EOS)
• SFI Artificial Stock Market
• Fish Market
Multi-agents simulationExamples
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Multi-agent simulation Artificial stock market: why agent-based simulation?
Agent-based
simulation
Agent-based
simulation
Agents are heterogeneous!!! They act and evolve differently (there are signs of heterogeneity in the rules that financial agents apply)
Agents are not hyper-rational utility maximising but rationally bounded
Mathematical analysis too complex. Difficult to obtain the clean solutions found for homogeneous agents.
Agents are heterogeneous!!! They act and evolve differently (there are signs of heterogeneity in the rules that financial agents apply)
Agents are not hyper-rational utility maximising but rationally bounded
Mathematical analysis too complex. Difficult to obtain the clean solutions found for homogeneous agents.
Standard
economic
theory
Standard
economic
theory
Agents are homogeneous
• same demand functions for assets
• rational expectations about prices and dividends There exists unique price that clears the market Researchers can obtain analytical solutions
Agents are homogeneous
• same demand functions for assets
• rational expectations about prices and dividends There exists unique price that clears the market Researchers can obtain analytical solutions
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Standard neo-classical model of asset pricing Agents are risk averse. Agents have heterogeneous demand functions for the asset
that change based on their prediction of next period asset price and dividend.
Each agent employs a number of different ways to forecast prices and dividends.
Agents evolve over time via evolutionary algorithms. Auctioneer calculates the price the most closely cleas the
market.
Multi-agent simulation Artificial stock market: multi-agent model
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Multi-agent simulationArtificial stock market: multi-agent simulation parametrisation
numBFagents – the number of agents in the simulation.
initholding – the initial asset holding of each agent.
initialcash – the initial cash holding of each agent.
minholding – the minimum asset holding of each agent (short selling constraint).
mincash – the minimum cash holding of each agent (borrowing constraint).
intrate – the interest rate.
baseline – the dividend baseline.
mindividend – the minimum dividend.
maxdividend – the maximum dividend.
amplitude – the amplitude of deviations from the baseline.
period – the mean period or auto-correlation time.
exponentialMAs – whether moving averages are exponential or uniform
maxprice – the maximum price.
minprice – the minimum price.
taup – moving average healing time for profit.
sptype – the specialist type
maxiterations – the maximum number of price iterations for iterative specialists.
minexcess – the target for |bids - offers| for iterative cases.
eta – the amount by which price changes per bid or offer for eta specialist.
etamax – maximum value of eta with the adaptive eta specialist.
etamin – minimum value of eta with the adaptive eta specialist.
rea – dividend multiplier for RE price with the re specialist.
reb – constant offset for RE price with the re specialist.
randomSeed – the random number seed for the market and specialist (0 is random).
tauv – moving average healing time for forecaster variances.
numBFagents – the number of agents in the simulation.
initholding – the initial asset holding of each agent.
initialcash – the initial cash holding of each agent.
minholding – the minimum asset holding of each agent (short selling constraint).
mincash – the minimum cash holding of each agent (borrowing constraint).
intrate – the interest rate.
baseline – the dividend baseline.
mindividend – the minimum dividend.
maxdividend – the maximum dividend.
amplitude – the amplitude of deviations from the baseline.
period – the mean period or auto-correlation time.
exponentialMAs – whether moving averages are exponential or uniform
maxprice – the maximum price.
minprice – the minimum price.
taup – moving average healing time for profit.
sptype – the specialist type
maxiterations – the maximum number of price iterations for iterative specialists.
minexcess – the target for |bids - offers| for iterative cases.
eta – the amount by which price changes per bid or offer for eta specialist.
etamax – maximum value of eta with the adaptive eta specialist.
etamin – minimum value of eta with the adaptive eta specialist.
rea – dividend multiplier for RE price with the re specialist.
reb – constant offset for RE price with the re specialist.
randomSeed – the random number seed for the market and specialist (0 is random).
tauv – moving average healing time for forecaster variances.
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Multi-agent simulationArtificial stock market: multi-agent simulation parametrisation
numfcasts – number of forecasters per agent.
tauv – moving average healing time for forecaster variances.
lambda – the degree of agent risk aversion.
maxbid – the maximum bid or offer.
selectionmethod – the method by which to select between activated rules: best, roulette, or average.
mincount – the minimum number of observations before a forecaster is used.
subrange – this is used for initialization of the forecast parameters a, b, and c.
min and max values of the parameter a ( the coefficient on pt+dt in the prediction equation).
min and max values of b (the coefficient on dt).
min and max values of c (the constant term).
newfcastvar – variance assigned to a new forecaster.
initvar – variance of overall forecast for time steps t < 200.
bitcost – penalty parameter for specificity (i.e., non-hashed bits in a forecaster).
maxdev – max deviation of a forecast in variance estimation.
individual – whether to use individual forecast variances: yes or no.
bitprob – probability each bit is either 0 or 1 (i.e., non-hashed) initially.
numfcasts – number of forecasters per agent.
tauv – moving average healing time for forecaster variances.
lambda – the degree of agent risk aversion.
maxbid – the maximum bid or offer.
selectionmethod – the method by which to select between activated rules: best, roulette, or average.
mincount – the minimum number of observations before a forecaster is used.
subrange – this is used for initialization of the forecast parameters a, b, and c.
min and max values of the parameter a ( the coefficient on pt+dt in the prediction equation).
min and max values of b (the coefficient on dt).
min and max values of c (the constant term).
newfcastvar – variance assigned to a new forecaster.
initvar – variance of overall forecast for time steps t < 200.
bitcost – penalty parameter for specificity (i.e., non-hashed bits in a forecaster).
maxdev – max deviation of a forecast in variance estimation.
individual – whether to use individual forecast variances: yes or no.
bitprob – probability each bit is either 0 or 1 (i.e., non-hashed) initially.
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Multi-agent simulationArtificial stock market: multi-agent simulation output
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Multi-agent simulationFish market (III): tournament setting
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Introduction to simulation
Business process modelling and simulation
Multi-agent simulation
Simweb perspective
Modelling exercise
Agenda
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Simweb perspective Multi-agent simulation for strategical decision
Simweb allows to research or adapt business models to new reality of digital content markets.
Simweb allows to determine business models which minimise the risk over a rank of possible futures, as well as those with the highest success or failure probability.
Simweb uses multi-agent simulation to forecast the reacheable market shares for each scenario.
Simweb is based on market characterisation:• consumers (i.e. bargain hunters, image sensitives...)• products (i.e. low-cost, high marketing investments...)• providers (business models)
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Simweb perspectiveDigital content distribution model
<20 years old <10 €/month high bandwidth >30 conn/week
<20 years old <10 €/month high bandwidth >30 conn/week
20-35 years old 15-30 €/month high bandwidth <5 conn/week
20-35 years old 15-30 €/month high bandwidth <5 conn/week
month subscription: 20€ max: 10 downlds
month subscription: 20€ max: 10 downlds
pay-per-download: 0,10€
max: 1 Mb/s
pay-per-download: 0,10€
max: 1 Mb/s
consumers
providers
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Expected Market Share
Decision MakerNEW
MARKET
STRATEGY
Simweb perspective Multi-agent simulation for supporting strategical decisions
Simweb is meant to be a strategic decision support tool to help select the most succesful business models in the digital contents sector.
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Introduction to simulation
Business process modelling and simulation
Multi-agent simulation
Simweb perspective
Modelling exercise
Agenda
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Agent Description• Why multi-agent simulation?
• Describing agent roles
• Describing agent interactions
How do we implement interaction protocols How do we model the environment? How can we interpret the results? Once the (uncalibrated) model is produced:
• How should we adjust it?
• What elements of the model are affected by the adjustments?- Structural- Parameters
Modelling exerciseTechnical Issues
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