Engineering Emergent Social Phenomena uva Startpagina by sanmelody

VIEWS: 4 PAGES: 30

									Engineering Emergent
 Social Phenomena
            Laszlo Gulyas
        AITIA International Inc.
           lgulyas@aitia.ai
                       Motivation
 Software is not as it used to be.
    Traditional methodologies are aimed at a single, monolithic
     program with well-defined and controllable input streams.
    Today‟s software is almost always situated in a dynamic
     environments.
       Computers are networked, but even on a single computer,
         many programs are running simultaneously.
        The software designer/engineer can no longer enumerate or
         control the state(s) of the environment.
    More importantly, the expected behavior of the software is
     most often not independent of the non-controlled
     components.
       For example, the success of an autonomous agent negotiating
         a deal on an auction site clearly depends on the performance
         of other similar agents, programmed by unknown parties.

 We need methods, techniques and tools for
  engineering emergent complex (software) systems.
Engineering from the Bottom-Up
 Example: Generating robust networks
  L. Gulyas: “GENERATION OF ROBUST NETWORKS: A BOTTOM-UP MODEL
  WITH OPTIMIZATION UNDER BUDGET CONSTRAINTS “, 5th International
  Workshop on Emergent Synthesis (IWES‟04).

 The problem: generating networks that are robust against random
  failures.
 An agent-based model.
    Agents connect to one another aiming to maximize their connectivity.
    Each agent can build a fixed number of links.
    Information about the existing network is costly, the agents optimize
     under budget constraints (i.e., only based on information about a limited
     number of nodes).
    Generates robust networks under a wide range of conditions.
    The pattern of information access (determined by information pricing) is
     pivotal.
Generating robust networks
    Gaining Inspiration from
    Complex Social Systems
 Complex Social Systems
 IT Tools for Social Science Modeling
   Agent-Based Modeling and Simulation
   Participatory Simulation
 Novel Tools: MASS/FABLES
    Gaining Inspiration from
    Complex Social Systems
 Complex Social Systems
 IT Tools for Social Science Modeling
   Agent-Based Modeling and Simulation
   Participatory Simulation
 Novel Tools: MASS/FABLES
Social System:

 Complex interaction of
 a high number of
 complex actors.
   Statistical Physics versus
        Social Sciences

 People are not as simple as molecules, but
  molecules are also much more complex than
  suggested by thermodynamics…

 Scientific Thinking 
  Methodological simplification 
  Modeling
    On Social Science Methods I.
 Herbert Simon:
  “The social sciences are, in fact, the »hard«
  sciences.”
   Problems with experiments
      Human subjects
      Unique events.
   Problem Complexity (e.g., in GT)
      The number of actors.
      Interaction/communications topologies.
       (Everybody knows it all.)
      Dynamic populations. (Cannot exist.)
      Unlimited rationality.
   Methodology
      Equilibrium versus Trajectory.
   On Social Science Methods II.
 Developments in IT technology enables novel
  approaches.

 “In Silico” models and experiments
   „If you didn’t grow it, you didn’t explain it.”
    (J. M. Epstein)


 Numerical simulations
   Grounded in mathematics.
    Gaining Inspiration from
    Complex Social Systems
 Complex Social Systems
 IT Tools for Social Science Modeling
   Agent-Based Modeling and Simulation
   Participatory Simulation
 Novel Tools: MASS/FABLES
   Agent-Based Modeling (ABM)
 One of the novel (in silico) methods.

 Aims at creating complex (social) behavior “from the
  bottom up”.
    Complex interactions of
    A high number of
    (Complex) individuals.

 A generative and
  mostly theoretical approach:
    Computational “thought experiments”,
    Existence proofs, etc.
  Agent-Based Modeling (ABM)
 Capable of
   Studying trajectories.
   Heterogeneous populations.
   Dynamic populations.

   Bottom-up approach 
    cognitive limitations to rationality.
   Explicit modeling of interaction topologies.

   No explicit model for cognitive abilities & interaction
    topologies, no model.
           Main IT tools for ABM
 Open-Source versus Proprietary.
 Generality versus Ease of Use.
 Component-based versus Custom code.

 Major general-purpose OSS tools:
  Swarm                                       Santa Fe Institute, NM, USA
  Multi-Agent Modeling Language (MAML)          Central European University,
                                                    Budapest, Hungary

  RePast                                 University of Chicago, Argonne National
                                                       Lab, IL, USA
                Swarm, 1996
 “Father of all ABM tools”.
   Simulation package.
   Object-oriented, discrete-event design.
   Introduces the main concepts and
    “ABM design patterns”.

 Experimental, hard-to-use system.

 Strong user community.
   Major impact in spreading the methodology.
                MAML, 1999
 First special-purpose programming language for
  ABM.
 Layered over Swarm.
   Thus following the main design and concepts.
 Easier to use system.
   Aspect-Oriented: separation of modeling and
    observational concerns.
   Still, unfortunate “borrowing” of many problems from
    Swarm. (E.g., installation's “hard way to heaven”.)
               RePast, 2001
 Re-designed and re-worked version of Swarm.
   Maintains all the major concepts and patterns.
   Simulation package in Java.

 Easy to use, but still general system.

 Growing user community
   Major impact in showing the „maturity‟ of ABM
    technology.
    Gaining Inspiration from
    Complex Social Systems
 Complex Social Systems
 IT Tools for Social Science Modeling
   Agent-Based Modeling and Simulation
   Participatory Simulation
 Novel Tools: MASS/FABLES
      Experimental Economics
 Controlled laboratory experiments with human
  subjects.
    The effect of human cognition on economic behavior.
    Learning and adaptation.
    Social traps (Tragedy of Commons, etc.)
 Typical tools:
    Observation (Videotaping)
    Questionnaires, etc.
 An experimental approach.
   Participatory Simulation (PS)
 A computer simulation, in which human subjects
  also take part.
 Agent-based simulations are well suited:
   Individuals are explicitly modeled, with
   Strict Agent-Environment and Agent-Agent
    boundaries.
 Bridges the theoretical and experimental
  approaches. Can help both of them:
   Testing assumptions and results of an ABM.
   Generating specific scenarios (e.g., crowd behavior)
    for laboratory experiments.
    General Purpose Participatory
   Architecture for RePast (GPPAR)
 First toolset for participatory ABM.
   Developed in 2003 at AITIA, Inc., Budapest,
    Hungary.
   Supports the transformation of any RePast
    model into a participatory simulation.
   Distributed, web-based user interfaces.
   Example Application of GPPAR
 Replication of a famous ABM in finance.
    Replication of results is a most important step in science!

 Conversion to a PS.
    Partly as a demonstration of our General-Purpose Participatory
     Architecture for RePast (GPPAR).

 Initial Experiments, testing:
    Original results‟ sensitivity to human trading strategies.
    Human versus computational economic performance.
    The effect of human learning between runs.
     Practices of ABSS
REPLICATION above everything
 Scientific experiments (tests and replicas)
    True (uncontrolled) parallelism is ruled out.
 Probabilistic models:
    Pseudo RNGs
    Control over the seed
    Independent variables, Separate RNGs
 Full specification
    E.g. Standard practice of random choice among
     equal maxima.
              Practices of ABSS
  Generating and Handling of Results
 Statistical nature of results:
    One go is „no go‟.
    Sensitivity Analysis and Confidence Intervals.
 Parameter Sweep
    Non-Linear Dependencies
    Tricks like Active Non-Linear Tests (ANTs)
       Practices of ABSS
Separating Model and Observer(s)
  Basic idea in science,
    but in computational practice it‟s only been
     around since Swarm (1994)
  Several observers
      GUI
      Batch1
      Batch2
      …
  Independence of the Observers‟ RNGs from
   the Model‟s RNGs.
    Gaining Inspiration from
    Complex Social Systems
 Complex Social Systems
 IT Tools for Social Science Modeling
   Agent-Based Modeling and Simulation
   Participatory Simulation
 Novel Tools: MASS/FABLES
          AITIA‟s
Multi-Agent Simulation Suite


        Participatory Extension (PET)




           Multi-Agent Core (MAC)



                The FABLES
       Simulation Definition Language*

      Integrated Modeling Environment**
      The Functional Agent-Based
   Language for Simulation (FABLES)
                                      • An executable formalism close to the
 Interactive tools for observation   language of publications.
   (in IME – planned).                • Building on the knowledge of
                                      mathematical calculus.
                                      • Standardization among ABM tools?
 Functional definitions for
     relations,
     sets, and
                                                  Participatory Extension (PET)
     state-transitions.


 Objects for agents.                                Multi-Agent Core (MAC)



 Imperative language for                                 The FABLES
                                                 Simulation Definition Language*
     Scheduling and
     Agent creation/destruction.               Integrated Modeling Environment**
                 Summary
 Towards engineering complex (emergent)
  phenomena.
 Inspiration from the practice of agent-based
  social simulation.
 Overview of agent-based modeling & simulation
   As a means to engineer emergent phenomena in
    complex software systems.
 Older and Novel tools for ABM/S.
   Thank you!
Comments are welcome at
   lgulyas@aitia.ai

								
To top