1. Engineering Emergent Social Phenomena Laszlo Gulyas AITIA International Inc. lgulyas@aitia.ai

2. 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.

3. 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.

4. Generating robust networks

5. 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

6. 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

7. Social System:  Complex interaction of  a high number of  complex actors.

8. 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

9. 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.

10. 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.

11. 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

12. 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.

13. 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.

14. Main IT tools for ABM  Open-Source versus Proprietary.  Generality versus Ease of Use.  Component-based versus Custom code.  Major general-purpose OSS tools: SwarmSanta Fe Institute, NM, USA Multi-Agent Modeling Language (MAML)Central European University, Budapest, Hungary RePastUniversity of Chicago, Argonne National Lab, IL, USA

15. 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.

16. 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”.)

17. 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.

18. 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

19. 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.

20. 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.

21. 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.

22. 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.

23. 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.

24. 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)

25. 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.

26. 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

27. AITIA’s Multi-Agent Simulation Suite Participatory Extension (PET) The FABLES Simulation Definition Language* Integrated Modeling Environment** Multi-Agent Core (MAC)

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

29. 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.

30. Thank you! Comments are welcome at lgulyas@aitia.ai