abs course slides

Upload: sully-zia

Post on 09-Apr-2018

220 views

Category:

Documents


0 download

TRANSCRIPT

  • 8/8/2019 ABS COURSE SLIDES

    1/89

    Agent-based SimulationSocial Science Simulation and Beyond

    Franziska Klgl

    Universitt WrzburgLehrstuhl fr Knstliche Intelligenz

    und Angewandte Informatik

    [email protected]

    Emma Norling

    Centre for Policy Modelling

    Manchester Metropolitan University

    [email protected]

  • 8/8/2019 ABS COURSE SLIDES

    2/89

    Outline

    Introduction

    Application domains

    Example (pedestrian movements)

    Example (cooperation between selfish individuals) Example (food webs)

    Conceptual Issues

    Technical Issues

    Tools

    Wrap up

  • 8/8/2019 ABS COURSE SLIDES

    3/89

    Agent-based Simulation

  • 8/8/2019 ABS COURSE SLIDES

    4/89

    Problem

    Real System

    ? ??? ! ! ! !

    Model

    Modelling

    Answers

  • 8/8/2019 ABS COURSE SLIDES

    5/89

    Why Simulation?

    the part of the real world that we want to examine is notaccessible, e.g. as the original system is not existing anymore or not yet

    Experimenting with the real system is prohibited due to un-desired disturbances

    Time scale of behaviour or system size is too small or toolarge for observation

    Especially for scientific applications: the system and allinfluences from the environment are completelycontrollable

    Modelling as a tool for understanding formalization of a

    hypothesis that otherwise would have remained veryvague focus on relevant aspects for theory building

  • 8/8/2019 ABS COURSE SLIDES

    6/89

    General Notions

    Simulation = Experimenting with a model

    Model = any image which can be considered as a systemand is used by a subject to obtain information aboutanother system

    System = a set of objects with a structure any regularform of interactions may be taken as a structure

  • 8/8/2019 ABS COURSE SLIDES

    7/89

    Relations between original and model

    Original (Real)System

    Model-System

    New Data fromObservation

    PredictionExplanation

    AbstractionFocus

    New Data fromSimulation Experiments

    ... Real World ...

    ... Real Time ...

    ... Simulated World ...

    ... Virtual Time ...

  • 8/8/2019 ABS COURSE SLIDES

    8/89

    Abstract View onto a Model System

    StateInput Variables Output Variables

    Time Advance Function

    Parameter

  • 8/8/2019 ABS COURSE SLIDES

    9/89

    Different Forms of General Simulation

    Depending on system properties and simulation goals:

    Time advance

    Granularity of simulation elements

    Goal of the model

    Dynamics Stochasticity

  • 8/8/2019 ABS COURSE SLIDES

    10/89

    Time Advance Function

    Continuous Simulation

    Differential Equation Models

    Time step can be reduced to arbitrary small intervals

    Event-based Simulation

    Event list is administrated by simulator:

    The time of the head is set and the event executedproduces other events that are inserted into event list

    Time is set to the next event

    Activity-based simulation and process-orientedsimulation as special forms of event-based simulation

    Established Formalism: DEVS

    Time-stepped, round-based

    Orthogonal concept to multi-agent simulation!

  • 8/8/2019 ABS COURSE SLIDES

    11/89

    Granularity of simulation elements

    Dimension for determining the level of detail that is possible in themodel.

    Two basic forms

    Macrosimulation

    Complete system perspective

    Microsimulation

    Smaller Entities with distinct state and behaviour

    (Im NOT talking about social science microsimulationbefore 90ies)

    Intermediate Forms: Multi-Level Models

    Distinction sometimes difficult, e.g. econometric models andsimulation

    Agent-Based Simulation is a special form of Microsimulation

  • 8/8/2019 ABS COURSE SLIDES

    12/89

    Goal of the modelling effort

    Decisions about the model depend on its potential usage Level of necessary validity and empirical grounding

    Two extreme forms

    Prediction Models mostly Case StudiesModel should produce (quantitatively correct)

    predictions depending on its input values Explanation/Understanding Abstract Models

    Qualitatively significant results are sufficient forunderstanding the reaction of the system to inputvalues

    in principal orthogonal dimensions for agent-basedsimulation

  • 8/8/2019 ABS COURSE SLIDES

    13/89

    Where Standard Simulation Fails

    Appropriate Modelling of (intelligent) Human Behaviour as necessary for socio-technical Systems, also with explicit

    treatment of sociality

    Systems, that draw their dynamics from flexible local

    interaction

    Systems, where individuality and/or locality is important

    Multi-Level Systems

    Emergent Phenomena and self-organizing systems

    Biology

    Traffic Systems

  • 8/8/2019 ABS COURSE SLIDES

    14/89

    Agent-based Simulation is the Solution

    = Multi-Agent System in a simulated environmentand in virtual time

    Model concept based on Multi-Agent Systems

    Active entities = Agents

    Environment for an agent consists of other agents and explicitenvironmental entities: e.g. resources, world Frame foragent system, in particular important for adaptive agents

    Interactions among the agents are central point

    Agent-based Simulation: Agents+Interaction+Environment

    Conceptual level Implementation level?

  • 8/8/2019 ABS COURSE SLIDES

    15/89

    Alternative Views

    Simulation System itself is consisting of agents

    Simulator, Optimizer, etc. form a Multi-Agent System

    Some researchers request stronger notion of agents

    required for multi-agent (based) simulation

    Some researcher see origin of multi-agent simulation inArtificial Life

    Here: Agent-based simulation = Multi-agent simulation =Modelling a real Multi-Agent System/Agent as a Multi-

    Agent System/Agent in the Model Using the concept of

    multi-agent systems in conceptualization, specificationand implementation of the model

  • 8/8/2019 ABS COURSE SLIDES

    16/89

    Agent-based Simulationversus Traditional Approaches

    For argumentation, why agent-based simulation should beused knowledge about the weak points of otherparadigms is necessary.

    Macro Simulation

    Process-oriented Models Queuing Networks

    Petri-Nets

    Object-oriented Simulation

    Cellular Automata

    No need to contrast Agent-based Simulation with Event-based Simulation

  • 8/8/2019 ABS COURSE SLIDES

    17/89

    Why Multi-Agent Simulation?

    New paradigm that allows more appropriate modelling Social science Biology Modelling of socio-technical systems Modelling test environments for agent-based software

    Allows to treat systems that were not or hardly treatable by

    simulation before Emergent phenomena Variable structure models

    Allows to bring more details into models more realism, micro-validity

    Provides a more intuitive way of modelling

    Facilitates communication Enables more researcher to use simulation

  • 8/8/2019 ABS COURSE SLIDES

    18/89

    Characteristics

    Agents = autonomous entities that are able to react toenvironmental change, but also to proactively changetheir behavior

    Typically >>1 agent

    At least two levels of observation

    Locality of perception/action Heterogeneity(most time spatially explicit, but not necessary)

    Variable structure models

    Flexible interactions between the agents

    Non-linear feedback loops

    Adaptive agents / systems

  • 8/8/2019 ABS COURSE SLIDES

    19/89

    Remarks on Autonomy in Simulation?

    Autonomy seems to be a problematic notion

    Two levels of autonomy of the simulated agents

    Autonomy within the model in relation to other

    modelled entities Autonomy in relation to the modeller

  • 8/8/2019 ABS COURSE SLIDES

    20/89

    Agent-based Systems Agent-based Simulation?

    Agent-based Models are used as substitutes for anothersystem, the original multi-agent system

    Agent-based Simulations mostly use virtual time

    Simulated Multi-Agent Systems live a in a simulatedenvironment

    Social space

    2d/3d virtual geometric space

    Thus, time and environment are controlled by the modeller

  • 8/8/2019 ABS COURSE SLIDES

    21/89

    Main Steps in a Simulation Study

    Concept Model

    Phase

    Specification

    Phase

    Implementation

    Phase

    Experimentation

    Phase

    Mistakes or Misconceptions Repeat different steps

    In some domains iterative modelling is advisable

    Play around with model prototypes

    Starting with the most simple model adding pattern

    of behavior

  • 8/8/2019 ABS COURSE SLIDES

    22/89

    Details

    1. Formulate the goal of the simulation study / research question

    2. Design of the model

    Coarse level concept model

    Detailed, formal level specified model

    3. Justification of assumptions

    4. Selection of output values and measurements

    5. Selection of simulation software

    6. Implementation of the model

    7. Verification

    8. Experiment Design

    9. Validation, Sensitivity Analysis10.Interpretation and Presentation of results

  • 8/8/2019 ABS COURSE SLIDES

    23/89

    Validation and Verification

    Concept Model Specified ModelImplemented

    Model

    Simulation

    Output Data

    Original (Real) System

    ValidationValidation Validation Validation

    Verification VerificationVerification

  • 8/8/2019 ABS COURSE SLIDES

    24/89

    Do-nots for Simulation in General

    1. Failure to have a well-defined set of objectives at thebeginning of the simulation study

    2. Inappropriate level of model detailminimal model

    3. Treat simulation simply as programming exercise

    4. Failure to collect appropriate data from the original system5. Inappropriate simulation software

    6. Failure to account correctly for sources of randomness

    7. Inappropriate output data analysis

  • 8/8/2019 ABS COURSE SLIDES

    25/89

    Agent-based Simulation - Summing Up

  • 8/8/2019 ABS COURSE SLIDES

    26/89

    Advantages

    Agent-based Simulation is a powerful paradigm:

    Dealing with real Multi-Agent Systems directly:

    real Agent = simulated Agent

    Elegant treatment of variable structures

    Allows modelling of adaptation and evolution

    Modelling of heterogeneous space and population

    Different levels of observation, but also of modelling

    High-level, intelligent, social behaviour treatable

  • 8/8/2019 ABS COURSE SLIDES

    27/89

    A Glance on Issues

    High requirements on modelers and time

    Which type of model to select?

    What level of abstraction / detail reasonable?

    Dependence on initial situation?

    Emergence and non-linearity Stochastic processes

    Size of simulation and scalability

    Sensitivity, but huge parameter space

    Implementation details

    Problems in validation?

  • 8/8/2019 ABS COURSE SLIDES

    28/89

    Categories and Application Domains

  • 8/8/2019 ABS COURSE SLIDES

    29/89

    Identification of Dimension for Categories:Case Studies - Abstractions

    Marseilles Fish Market Abstract Auction Properties

    Case Studies aim at reproducing one particular andsystem instance

    Empirical data plays central role

    Predictions may be possible

    Abstractions: generalized models that reproduce on aprinciple level

    Stand for a category of system, not one particularinstance

    Aim at understanding

    Intermediate forms (Typification in the terminology of

    Boero&Squazzoni)

  • 8/8/2019 ABS COURSE SLIDES

    30/89

    Additional Dimensions

    Gilbert 2004:

    artificial versus realistic - what original system should bereproduced?

    positive versus normative

    spatial versus network - refers to the topology of the environment

    complex versus simple agents - from SOAR to simple reflexHare & Dreadman, 2004 (Environmental Management)

    Coupling of social and environmental model: spatially explicit orspatially non-explicit

    Social interaction: none, local social adaptation, global socialadaptation and group based tasks

    Intrinsic adaptation: None, fine tuning, multiple strategies

  • 8/8/2019 ABS COURSE SLIDES

    31/89

    Application Areas

    Biological/Ecological Systems:active, heterogeneous entities (e.g animals) exert influences ontheir local environment reacting on local stimuli

    Traffic Simulation:Microscopic Models are more and more enhanced by intelligentabilities of the (single) simulated drivers

    Social Science Simulation:Artificial Societies for testing scientific hypothesis

    Military Simulations (TacAirSoar System)

    Industrial Simulation with relevant non-technical parts, likehumans, etc, but also Cargo Routing Optimization (South WestAirline)

    Simulation for Entertainment (MASSIVE and Lord or the Ring Two

    Towers) Testbeds for Multi Agent Systems and other complex control

    systems

  • 8/8/2019 ABS COURSE SLIDES

    32/89

    Three Illustrative Examples

  • 8/8/2019 ABS COURSE SLIDES

    33/89

    Pedestrian Simulation

    Wide area of related approaches based on CellularAutomata, Particle Models, etc.

    Compared to Standard Traffic Simulation more degreesof freedom

    Less inertia in Movement and Change of direction

    Not bound to lanes Available data and prediction models based on a

    concrete, often real-world layout

    Validation based

    on counts

    plausibility

    feature-based comparing

  • 8/8/2019 ABS COURSE SLIDES

    34/89

    Agent-based Pedestrian Simulation

    Activity Level Location Selection Module

    Planing Level Path Generation Module

    Actual Movement Level Collision Avoidance

  • 8/8/2019 ABS COURSE SLIDES

    35/89

    One particular example:Pedestrians in Railway Stations

    Restricted Activity Module:Pedestrians with two goals: Entering trains / leaving station

    (changing trains)

    Planning Module:

    Predefined paths from station entrance to trains

    stopping areas

    Some heuristics/rules for high-level replanning

    Collision Avoidance

    Cellular Automaton

    Social Force Model

    Simple Rule-based System

  • 8/8/2019 ABS COURSE SLIDES

    36/89

    Coffee Break

  • 8/8/2019 ABS COURSE SLIDES

    37/89

    Example: Cooperation in Selfish Agents

    There are certain areas in which individuals maybe inherently selfish, but tasks requirecooperation in order to be solved

    Societies in general

    Specific areas such as file sharing networks

  • 8/8/2019 ABS COURSE SLIDES

    38/89

  • 8/8/2019 ABS COURSE SLIDES

    39/89

    Model Outline

    There are N resource types, each requiring aspecific skill for harvesting

    Agents require all resources to survive

    Agents have only one skill, so must rely upon

    other agents for the balance of resources An agent will respond to a request for resources

    if the requesting agent is perceived as similarenough

  • 8/8/2019 ABS COURSE SLIDES

    40/89

    Each agent has

    One fixed skill

    A tag in the range [0.0, 1.0]

    A tolerance in the range [0.0, 1.0]

    Stores for each resource type

    A maximum number of steps that it will live

  • 8/8/2019 ABS COURSE SLIDES

    41/89

    At each time step

    A random amount of each resource type isdistributed between the agents with thecorresponding skill

    Each agent is randomly paired with p other

    agents Each partner whose tag is within tolerance of

    the agents own tag, receives a proportion ofany excess resources

    All stores are taxed

  • 8/8/2019 ABS COURSE SLIDES

    42/89

    Births, Deaths and Arrivals

    A small number of random individuals areinserted into the population at each time step

    An agent will die if any store reaches 0

    An agent will die if it reaches its maximum age

    An agent will reproduce if all stores are higherthan some thresholdX

    Offsprings skill is copied directly, but tag andtolerance are subject to mutation

  • 8/8/2019 ABS COURSE SLIDES

    43/89

    Donation mechanism

  • 8/8/2019 ABS COURSE SLIDES

    44/89

  • 8/8/2019 ABS COURSE SLIDES

    45/89

    Processing jobs

    4

    1

    3

    12

    1

    = altruist

    = selfish

    Job(1)

    Job(3)

    Job(1)

    -0.25

    +1

    +1

    0

    Job(2)+1

    Job(2)

    0

    Job(

    4)

  • 8/8/2019 ABS COURSE SLIDES

    46/89

    SLAC

    Agent A1 processes job (if possible)

    If cannot process, tries to find a neighbour who will

    Mutation step:

    A1 randomly selects another agent, A2

    If wealth A2 > wealth A1, A1 copies A2:

    Drops existing links Links to A2

    Copies links of A2

    Copies altruism of A2

    With small chance, mutates link (random rewire)

    With smaller chance, mutates behaviour

  • 8/8/2019 ABS COURSE SLIDES

    47/89

  • 8/8/2019 ABS COURSE SLIDES

    48/89

    Typical Results

  • 8/8/2019 ABS COURSE SLIDES

    49/89

    SLACER

    As with SLAC, but instead of dropping all existinglinks, probabilistically drops links (typical p = 0.95;p = 1.0 SLAC)

    Leads to a small world network

  • 8/8/2019 ABS COURSE SLIDES

    50/89

    Typical Results (SLACER)

  • 8/8/2019 ABS COURSE SLIDES

    51/89

    Trying to be smart

    What if, instead of probabilistically dropping links,drop links that have not been helpful?

    Nave approach:

    Keep track of who did / did not assist with

    requests, drop those who did not assist. Surprising outcome

  • 8/8/2019 ABS COURSE SLIDES

    52/89

    Typical Results

  • 8/8/2019 ABS COURSE SLIDES

    53/89

    Example: Food Webs

    What is a food web?

    A map of what eats what within anecosystem

    Basal species are at the bottom of the

    food web, getting nutrients directly fromthe environment

    Intermediate species feed on otherspecies, but are in turn preyed upon

    Topspecies have no predators

  • 8/8/2019 ABS COURSE SLIDES

    54/89

    A Stylised Food Web

    10 11

    98765

    4321

  • 8/8/2019 ABS COURSE SLIDES

    55/89

    Two Natural Food Webs

    The Scotian Shelf

  • 8/8/2019 ABS COURSE SLIDES

    56/89

    Modelling Food Webs

    Food webs are dynamic - the previous figuresshowed snapshots only

    As one species declines, its predator(s) may

    switch to more abundant prey

    Species mutation or migration can introducenew species into the web

    Few models look at this dynamic nature of foodwebs

  • 8/8/2019 ABS COURSE SLIDES

    57/89

    The Physicists Model

    An abstract model of food web evolution - aim isto replicate typical food web characteristics

    Each species has 10 characteristics from a

    possible set of 500

    Species A has a score against species B basedon the relative scores of the characteristics

  • 8/8/2019 ABS COURSE SLIDES

    58/89

    The Physicists Model, cont.

    Species A can eat species B if the score ispositive

    The number of any given species at time t+1 is

    equal to:

    The number at time t plus any gains fromfeeding and reproducing, minus any lossesdue to predation

  • 8/8/2019 ABS COURSE SLIDES

    59/89

    An Agent-Based Alternative

    The key shortcoming of this model is that otherthan their defining characteristics, all species areintrinsically the same

    An agent-based alternative offers opportunity

    for heterogeneity, both between species andwithin species

  • 8/8/2019 ABS COURSE SLIDES

    60/89

  • 8/8/2019 ABS COURSE SLIDES

    61/89

    Structure of the ABM

    Non-spatial (initially)

    Uses same species characteristics as EBM

    Uses same parameter settings as EBM

    At each time step, each agent chooses a single

    victim (or the world) to feed upon. If victim canbe eaten, agent feeds.

    Agents reproduce when they gain sufficientresources.

  • 8/8/2019 ABS COURSE SLIDES

    62/89

    Results of the ABM

    Initial results showed promise.

  • 8/8/2019 ABS COURSE SLIDES

    63/89

    but

    Unlike the EBM, a multi-level food web neverreally evolved.

    At most two levels formed, but most commonly

    large numbers of basal species evolved, partly

    preying off each other.

  • 8/8/2019 ABS COURSE SLIDES

    64/89

    What was wrong?

    First thought: something to do with ignoringspace.

    But adding a spatial representation did not

    improve matters

    Anyway, EBM ignored space. Current focus:

    There are some discrepancies in the EBM

    There are also some unpublished rules

  • 8/8/2019 ABS COURSE SLIDES

    65/89

    Conceptual Issues

    Micro-macro link

    Complexity

    Level of detail

    Size and scalability

  • 8/8/2019 ABS COURSE SLIDES

    66/89

    Micro-Macro Link

    In most (but not all) ABS, it is the macro (i.e.system) level behaviour that is measured, butbehaviours are coded at the micro (i.e.

    individual) level

    Need confidence that there is a reliablerelationship between the two results at themacro level should be explicable

  • 8/8/2019 ABS COURSE SLIDES

    67/89

    Complexity

    Complexity is a great buzz-word of themoment

    Much funding targeted at tackling complexity

    No clear definition of what it is

    Commonly linked to emergent behaviour

  • 8/8/2019 ABS COURSE SLIDES

    68/89

    Level of Detail

    Agent-based simulations allow the study ofindividual differences, but the degree ofdifference being considered will depend largely

    on the application

    If you simply use homogeneous agents, ananalytic alternative might be possible

  • 8/8/2019 ABS COURSE SLIDES

    69/89

    Size and Scalability

    Closely linked to previous points, and also atechnical issue

    What size is required to demonstrate your

    point(s)?

    To what extent can your simulation beextended/modified for other (related) purposes?

  • 8/8/2019 ABS COURSE SLIDES

    70/89

    Technical Issues

    Assumptions

    Sensitivity

    Validation and verification

    Size and scalability

    Agent or environment?

  • 8/8/2019 ABS COURSE SLIDES

    71/89

    Assumptions

    Every detail added to model means newassumptions

    Be explicit about any assumptions, no matter

    how trivial they may seem

    Assumptions may have unexpectedconsequences (particularly interference)

  • 8/8/2019 ABS COURSE SLIDES

    72/89

    Sensitivity

    Chaotic model behaviour means:Small changes in input variables / parameter values resultin large changes in simulation output

    Sensitivity Analysis is systematic testing for effects ofchanges in parameter values large and small variationsin values experimental design techniques

    Relevance for calibration: sensitive models are difficult tocalibrate

    Additional advantage: Identifying parameters withoutinfluence model simplification

    Worst case:model is highly sensitive to an unjustified parameter

  • 8/8/2019 ABS COURSE SLIDES

    73/89

    Validation and Verification

    Validation and verification are useddifferently by different practioners!

    Basically, models must be matched against thephenomenon being modelled, and checked for

    errors at each stage of use

  • 8/8/2019 ABS COURSE SLIDES

    74/89

    Validation and Verification (cont)

    Ideal: There is enough empirical data available Could be realistic for case studies as data from one

    particular system may be collected

    Abstractions?

    Aggregate comparison of input/output behavior(counting passing pedestrians, overall gains, measure for

    food web structure) possible Micro-Validation can hardly be done on concrete level,

    especially with stochastic processes.

    Plausibility Checks, Turing Tests and other forms of intensivecommunication with domain experts

    Participatory simulation / stakeholder involvement

  • 8/8/2019 ABS COURSE SLIDES

    75/89

    Size and Scalability

    As mentioned previously, also a technical issue

    Likely to be a limit on the number of agents youcan simulation in a given time frame

    What size is necessary to demonstrate desired

    phenomena? How does this affect other issues?

  • 8/8/2019 ABS COURSE SLIDES

    76/89

    Agent or Environment?

  • 8/8/2019 ABS COURSE SLIDES

    77/89

    Tools

    There are literally hundreds of tools available foragent-based simulation, providing assistancewith different aspects of the design,

    development and analysis process

    Your choice will depend on your particularneeds

  • 8/8/2019 ABS COURSE SLIDES

    78/89

    E l f T l

  • 8/8/2019 ABS COURSE SLIDES

    79/89

    Examples of Tools

    Three tools are presented here as examples(SeSAm, Repast and JACK)

    All are Java-based; all provide different levels of

    support

    This is not intended to be a representativesample of available tools; it would be impossibleto provide this!

    S SA

  • 8/8/2019 ABS COURSE SLIDES

    80/89

    SeSAm

    http://www.simsesam.de/

    Developed and maintained at the University ofWrzburgs Department for Artificial Intelligence

    and Applied Computer Science

    Free, downloadable from above site, versions forvarious OS

  • 8/8/2019 ABS COURSE SLIDES

    81/89

  • 8/8/2019 ABS COURSE SLIDES

    82/89

    Repast

  • 8/8/2019 ABS COURSE SLIDES

    83/89

    Repast

    http://repast.sourceforge.net/

    Originally from the University of Chicago, nowmaintained by a not-for-profit volunteer

    organisation

    Widely used for social simulation

    Freely available, with full source, from above site

    Repast

  • 8/8/2019 ABS COURSE SLIDES

    84/89

    Repast

    Provides classes to support simulation andanalysis

    No development tools provided, but a library of

    examples

    Large and active user community

    Easy to develop simulations within their scope;experienced Java programmers can do muchmore

  • 8/8/2019 ABS COURSE SLIDES

    85/89

    JACK

  • 8/8/2019 ABS COURSE SLIDES

    86/89

    JACK

    http://www.agent-software.com/shared/products/index.html

    Commercial and academic licences available;

    60 day evaluation available from above site

    Full commercial support and ongoingdevelopment and extensions

  • 8/8/2019 ABS COURSE SLIDES

    87/89

  • 8/8/2019 ABS COURSE SLIDES

    88/89

    Wrap Up

  • 8/8/2019 ABS COURSE SLIDES

    89/89

    Wrap Up

    Agent-based simulation is a key area for agents

    A number of open issues

    Are you going to use ABS as a tool, or is ABS itselfthe focus of your research?

    Contacts:

    Emma Norling, [email protected]

    Franziska Klgl,

    [email protected]