An Introduction to Multi-agent Simulation

An Introduction to Multi-agent Simulation 31-05-2002

Agenda Context and Issues Introduction to simulation Multi-agent simulation Business process modelling and simulation Simweb perspective Modelling exercise

Agenda Context and Issues Introduction to simulation Business process modelling and simulation Multi-agent simulation Simweb perspective Modelling exercise

Context and IssuesObjective of the Training Seminar: The Modeler’s View • This seminar is a deliverable of work package 1, “Participatory Problem Analysis” • The objective of is to • Refine the definition of the problem and the user requirements • 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 aspects • Creating the model infrastructure • 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

How? Model Infrastructure Stakeholders Market Analysis Context and IssuesThe Modelling Process Simulation System “No-Model” Calibration Evaluation Feedback Real-World Description

Context and IssuesWe would like to (better) understand... • ... 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

Agenda Context and Issues Introduction to simulation Business process modelling and simulation Multi-agent simulation Simweb perspective Modelling exercise

mathematical analysis computer simulation physical sciences social sciences Introduction to simulationFormal modelling formal modelling

Introduction to simulationThe uses of simulation • Understandingof 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) • Discoveryof consequences in the artificial society

parameter estimation simulation abstraction similarity data gathering Introduction to simulationThe logic of simulation predicted data model simulated data target collected data

Introduction to simulationStages of simulation-based research designing a model building the model verification and validation publication

simplicity accuracy prediction understanding Introduction to simulationModel accuracy

non-linearity bounded-rationality • linear: proportional • non-linear: chaotic • local information • limited capacity to process information complexity self-organisation • simple behaviour and simple rules -> complex organisation (sand pile, ants...) • mathematical analysis impossible • appears without central planning from the actions of individual agents • flocks, markets, Internet... emergence • interactions among objects at one level give rise to different types of objects at another level Introduction to simulationSocial simulation: special features

Introduction to simulationTypes of simulation • 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. t t

Agenda Introduction to simulation Business process modelling and simulation Multi-agent simulation Simweb perspective Modelling exercise

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.

Business Process Modelling and SimulationExamples • 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.

Mortgage ApplicationSimulation Run

Mortgage ApplicationReports

Truck AssemblySimulation Run

Flexible ManufacturingSimulation Run

Multi-agent simulationWhat is an agent? A weak definition • “Computational system situated in some environment, that is capable of flexibleautonomous 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 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 hardor impossible

Multi-agent simulationWhy multi-agent simulation? • 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 simulation Why multi-agent simulation? Synthesis and Analysis • 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 simulationHow to If <cond> then <action1> else <action2> Inanimate agents Observer Animate agents Data Organizations of agents Artificial world

Multi-agent simulationDiscrete event simulation • Simulation proceeds in discrete time steps • Interaction between agents or procedures within simulation may have own event schedule

Multi-agents simulationSoftware • 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

Objective C, Java… Object-oriented (not agent-oriented) Structure: Schedule The Model Swarm Agent Probes Sub-Swarm The Interface Multi-agent simulationAn implementation: Swarm

A computer’s CPU executes program instructions Swarm kernel is virtual CPU running model and GUI events Nested Swarms merge activity schedules into one Model GUI Swarm kernel A Swarm as a virtual computer Multi-agent models An implementation: Swarm Operating System CPU

Multi-agents simulationExamples • Examples: • Sugarscape • MANTA • Evolution of organised society (EOS) • SFI Artificial Stock Market • Fish Market

Multi-agent simulationArtificial stock market: why agent-based simulation? 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 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.

Multi-agent simulationArtificial stock market: multi-agent model • 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 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.

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.

Multi-agent simulationArtificial stock market: multi-agent simulation output

Multi-agent simulationFish market (I)

Multi-agent simulationFish market (II)

Multi-agent simulationFish market (III): tournament setting

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)

Simweb perspectiveDigital content distribution model consumers • <20 years old • <10 €/month • high bandwidth • >30 conn/week • 20-35 years old • 15-30 €/month • high bandwidth • <5 conn/week • pay-per-download: 0,10€ • max: 1 Mb/s • month subscription: 20€ • max: 10 downlds providers

Simweb perspective Multi-agent simulation for supporting strategical decisions Expected Market Share Decision Maker NEW MARKET STRATEGY Simweb is meant to be a strategic decision support tool to help select the most succesful business models in the digital contents sector.

Modelling exerciseFish market

Modelling exerciseTechnical Issues • 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

An Introduction to Multi-agent Simulation