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© Yilmaz-Ören - 2004-07-25 “Conflict Systems” 1 A Research Agenda for the Modeling and Simulation of Conflict Systems Levent Yilmaz M&SNet: Auburn M&S.

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Presentation on theme: "© Yilmaz-Ören - 2004-07-25 “Conflict Systems” 1 A Research Agenda for the Modeling and Simulation of Conflict Systems Levent Yilmaz M&SNet: Auburn M&S."— Presentation transcript:

1 © Yilmaz-Ören “Conflict Systems” 1 A Research Agenda for the Modeling and Simulation of Conflict Systems Levent Yilmaz M&SNet: Auburn M&S Laboratory Computer Science & Engineering Auburn University, Auburn, AL M&SNet Consortium Meeting 2004 Summer Computer Simulation Conference San Jose, California, July 24-29, 2004

2 © Yilmaz-Ören “Conflict Systems” 2 Goals  to overview the common characteristics, structure, and processes involved in conflict systems.  to present challenges in computational modeling of conflicts.  to outline a comprehensive research framework to facilitate  advancement of the state of the art in modeling of conflicts by gleaning useful information from conflict theory, behavioral sciences, and agent theory and  development of new advanced simulation methodologies to perceive, conceive, and explore realistic conflict scenarios.

3 © Yilmaz-Ören “Conflict Systems” 3 Plan  Motivation  Conflict Systems: An Overview  A Research Framework for Simulation-Based Study of Conflicts  Conceptualization: Improving the Use of Conflict Theory as well as Behavioral Sciences in Model Formulation and Design  Realization: Agent-Directed Simulation  Augmenting agent interfaces with domain theories.  Advancing realism with agent-based simulation of conflicts  Agent-supported methodologies for next generation PSEs for conflict analysis and peacekeeping studies.  Conclusions

4 © Yilmaz-Ören “Conflict Systems” 4 Improving the Modeling, Simulation, and Analysis of Conflicts Motivation: Political, economic, military as well as terrorist conflicts are the most destructive elements of the modern world. –Yet, there is a lack of simulation-based computational frameworks and PSEs that are able to express realistic assumptions, postulates, theories, and dynamics that are explicitly drawn from conflict theory and behavioral sciences. Need to view conflicts as complex social interaction systems  to understand the characteristics, parameters, and conditions of various types of conflicts to facilitate their computational modeling.  to expand our horizons in modeling and simulation of conflicts from basic dilemmas to realistic complex conflict processes.  to advance the state of the art of simulation science and methodology to better deal with conflicts.

5 © Yilmaz-Ören “Conflict Systems” 5 Conflict Analysis, Resolution, and Peacekeeping Studies Associations: IACM - International Association for Conflict Management Computer-Aid in Conflict Analysis Conflict Datasets ataset_catalog/data_list.htm  AI Methods in Conflict Avoidance and Prevention of Crises and Wars  CASCON:  World Bank - Conflict Prevention and Reconstruction – Conflict Analysis Framework Conflict Forecasting /forecast/Conflicts/index.html Selected Organizations - USA:  CRC- Conflict Research Consortium - University of Colorado  ICAR - Institute for Conflict Analysis & Resolution  IGCC - University of California Institute on Global Conflict and Cooperation  PI - Prevention Institute  USIP - US Institute of Peace For a more comprehensive list of resources on conflict and peace keeping studies

6 © Yilmaz-Ören “Conflict Systems” 6 Toward Simulation-Based Problem Solving Environments for Conflict and Peace-Keeping Studies  Yilmaz L. and T. Ören (2004). “Enriching Computer-Aided Conflict and Peace Studies with Anticipation and Agent-Directed Simulation,” submitted to Agent 2004 Conference on: Social Dynamics: Interaction, Reflexivity and Emergence.  Yilmaz L. and T. Ören (2004- in press). “Towards Simulation-Based Problem Solving Environments for Conflict Management in Computational Social Science,” In Proceedings of the Agent2003: Challenges in Social Simulation,  Yilmaz L. (2004). “Advancing the Theory and Methodology of Modeling and Simulation to Explore Understanding and Managing Social Conflicts,” to appear in Modeling & Simulation Magazine.  Ören T. and L. Yilmaz (2004). “Behavioral Anticipation in Agent Simulation,” submitted to 2004 Winter Simulation Conference.  Yilmaz L and T. Ören (2004). “Dynamic Model Updating in Simulation with Multimodels: A Taxonomy and Generic Agent-Based Architecture,” to appear in Proceedings of the SCSC'04.

7 © Yilmaz-Ören “Conflict Systems” 7 Conflict Systems Structure: Conflict requires at least two parties or two analytically distinct units or entities with mutually exclusive and/or mutually incompatible values, goals, and objectives. Dynamics: Conflict requires interaction among parties. As such, conflict relations constitute a fundamental social interaction process. Conflict Structure Dynamics Context Context is the environment within which the conflict occurs (i.e., task structure, situation, institutions, culture, spatial model of the physical environment) influence the way conflict unfolds toward increasing escalation or de-escalation.

8 © Yilmaz-Ören “Conflict Systems” 8 Conflict Systems - The Structure Structural Components of Conflict Systems –Parties and their characteristics – their values, motivations; aspirations, objectives; their beliefs about conflicts, including their conceptions of strategy and tactics. –Relations between parties – their attitudes, beliefs, and expectations about one another –The structure of the issue – scope and formulation. –The strategy and tactics employed by the parties in the conflict in assessing the one another’s utilities, subjective probabilities; use of promises, rewards, incentives. Third Party Party A Norms, Motives Goals etc. Party B Norms, Motives Goals etc. Interests, constituents, audiences WIDER CONFLICT ENVIRONMENT CONFLICT SYSTEM

9 © Yilmaz-Ören “Conflict Systems” 9 Plan  Motivation  Conflict Systems: An Overview  A Research Framework for Simulation-Based Study of Conflicts  Conceptualization: Improving the Use of Conflict Theory as well as Behavioral Sciences in Model Formulation and Design  Realization: Agent-Directed Simulation  Augmenting agent interfaces with domain theories.  Advancing realism with agent-based simulation of conflicts  Next generation PSEs and training environments for strategic conflict analysis and peacekeeping studies.  Conclusions

10 © Yilmaz-Ören “Conflict Systems” 10 A Three-Tier Agenda for Advancing Computational Peace and Conflict Studies (1) Advancing the Computational Modeling of Conflict Systems: Agent theory enables modeling actions, dynamics involved in changing preferences and utility, attitudes, objectives, goal-driven behavior, as well as social guiding principles (i.e., norms, culture). (2) Advancing Behavioral Realism in the Simulation of Conflicts ( i.e., Conflicts entail anticipatory behavior: Human behavior and third party interventions are predicated on the perceptions about the plausible future states of conflicts). (3) Advanced Simulation Methodologies for Conflict Systems (Conventional methodologies lack the flexibility and adaptivity to appropriately deal with multi- aspect, multi-stage, and uncertain social phenomena).

11 © Yilmaz-Ören “Conflict Systems” 11 I- A Systemic View for Peace and Conflict Studies ( Advancing the Modeling of Conflict Systems) Conflict Data &Knowledge-Base DADA OAOA Deliberation Subsystem Operational Interaction Subsystem DBDB OBOB AA AA AA BB BB BB vAvA vBvB e nvironment dAdA dBdB df yAyA yByB     AA BB eAeA eBeB uAuA uAuA uBuB uBuB vAvA vBvB AA BB vAvA vBvB Extending and Realizing the Peace Science Vision of Isard and Smith (1982) with Agent-Directed Simulation yAyA yByB

12 © Yilmaz-Ören “Conflict Systems” 12 Conflict Data &Knowledge-Base DADA OAOA DBDB OBOB vBvB e dAdA vAvA   dBdB Deliberation and Operational Units for Conflicts yByB yAyA

13 © Yilmaz-Ören “Conflict Systems” 13 Conflict Data &Knowledge-Base DADA OAOA DBDB OBOB AA AA AA BB BB BB vAvA vBvB e dAdA dBdB   AA BB eAeA eBeB uAuA uAuA uBuB uBuB vAvA vBvB The Role of Perception in Decision-Making during Conflicts yAyA yByB

14 © Yilmaz-Ören “Conflict Systems” 14 Conflict Data &Knowledge-Base DADA DBDB AA AA AA BB BB BB e dAdA dBdB   AA BB eAeA eBeB uAuA uAuA uBuB uBuB vAvA vBvB OAOA Operational Interaction Subsystem OBOB vAvA vBvB yAyA yByB Conflict Theories (i.e., power transition, lateral pressure theories) yAyA yByB

15 © Yilmaz-Ören “Conflict Systems” 15 Conflict Data &Knowledge-Base DADA OAOA Deliberation Subsystem Operational Interaction Subsystem DBDB OBOB AA AA AA BB BB BB vAvA vBvB e dAdA dBdB df yAyA yByB     AA BB eAeA eBeB uAuA uAuA uBuB uBuB vAvA vBvB AA BB vAvA vBvB Cognitive Deliberation Subsystem and Its Interface to Operational Level yAyA yByB

16 © Yilmaz-Ören “Conflict Systems” 16 II- Behavioral Realism in the Simulation of Conflict Systems Improving Behavioral Realism in the Simulation of Conflicts (adaptivity, pro-activity): –Anticipation is a pervasive factor that surrounds many realistic and interesting intelligent processes embedded in social systems, as well as symbolic systems. Conflict are driven by perception and anticipation. Conflict Theory Agent Theory Conflict System Domain Theory Agent-Based Simulation Agent- Supported Simulation Agent Simulation i.e., Multisimulation (Next Generation PSEs) Agent-Directed Simulation i.e., Simulating Anticipatory Systems (Advanced Methodologies) Behavioral Sciences Computational Modeling Theory

17 © Yilmaz-Ören “Conflict Systems” 17 Simulating Anticipatory Systems Conflicts entail anticipatory behavior: Third party interventions are predicated on the perceptions about the future (undesirable) states of conflict. Predictive models for the avoidance of wars and crises are already available: AI Methods in Conflict Avoidance and Prevention of Crises and Wars - Issues: How can anticipatory behavior improve social agent interaction? How do anticipations influence attention? How can perception and forecasting be used to realize anticipatory behavior? What is the role of anticipations on motivations and emotions?

18 © Yilmaz-Ören “Conflict Systems” 18 The Social Psychology Factor in Conflicts Behavioral orientations are based on the uncertainties with respect to social environment, cognitive schemas and attribution mechanisms, the state of emotional arousal, and current moods. Adapting option preferences and utilities based on emergent cognitive and emotional states would be critical to allocate utilities and payoffs for the action tendencies of agents. The action tendencies are based on the cognitive expectancies, norm values, trust, and motivational orientations leading to cooperation or competition. Interpretation Subsystem Physical Env. Social Env. Interdependence Structure CONTEXT Personality Orientaton Conflict Style Cognitive Orientation Emotional Orientation Motivational Orientation Normative Orientation SOCIAL ORIENTATION PSYCHOLOGICAL ORIENTATION Intention- Behavior

19 © Yilmaz-Ören “Conflict Systems” 19 III- Using Agents as Simulator Design Metaphors Advanced Simulation Methodologies for Next Generation PSTEs: –using agents as simulator and model design metaphors will enable dynamic model and simulation update facilities for multisimulation and multimodel simulators. Conflict Theory Agent Theory Conflict System Domain Theory Agent-Based Simulation Agent- Supported Simulation Agent Simulation i.e., Multisimulation (Next Generation PSEs) Agent-Directed Simulation i.e., Simulating Anticipatory Systems (Advanced Methodologies) Behavioral Sciences Computational Modeling Theory

20 © Yilmaz-Ören “Conflict Systems” 20 Dynamic Model and Simulation Updating for Exploring Complex Conflict Phenomena For most realistic complex phenomena the nature of the problem changes as the simulation unfolds. –Conventional methodologies lack the flexibility and adaptivity to appropriately deal with multi-aspect and multi-stage phenomena. –Need to view available knowledge as being contained in the collection of modeling experiments that become plausible and viable given what is known or learned. Problem: Run-time switching of models based on –interpretation of emergent, potentially unforeseen conditions to facilitate dynamic run-time model update and replacement for (simultaneous) experimentation with multiple simulation models.

21 © Yilmaz-Ören “Conflict Systems” 21 Scenario: An inspection team under the command of the team of participants is at a weapons storage site in a fictional city. The inspection team discovers that weapons from the site are missing and that a hostile crowd is forming around them. As the inspection team radios for help, the members of the command staff must prepare and launch a rescue operation. Evidence begins to mount that the weapons were stolen by paramilitary troops who are motivating the hostile crowd. As additional paramilitary troops stream into the town, the command staff must overcome a series of obstacles in order to rescue the inspection team without incident or injury – A scenario from (Gordon and Iuppa 2003). Can we foresee all moves in a conflict? What if the original training scenario did not foresee a trainee decision that can result in civilian casualty? Motivation: Adaptive Experience Management in Strategic Leadership Training with Contingency Models ContingencyModels ScenarioLine How can we provide to trainers as much freedom as possible, while assuring that the training goals are achieved by exerting control on the scenario flows?

22 © Yilmaz-Ören “Conflict Systems” 22 Why and When Dynamic Model/Scenario Update is Needed? Changing Scenarios: For most realistic social dilemmas, the nature of the problem changes as the simulation unfolds. Ensembles of Models: Our knowledge about the problem (i.e., conflict) being studied may not be captured by any single model or experiment. Uncertainty: Adaptivity in simulations and scenarios is necessary to deal with emergent conditions for evolving systems in a flexible manner. Exploration: As simulations of complex phenomena are used to aid intuition, dynamic run-time simulation composition will help identify strategies that are flexible and adaptive.

23 © Yilmaz-Ören “Conflict Systems” 23 A type of multimodel: metamorphic model - (e.g., egg, larva, pupa, butterfly) M M1M1 M2M2 MnMn There is a predefined sequence for the alternate models.

24 © Yilmaz-Ören “Conflict Systems” 24 Another type of multimodel: multiaspect model - (e.g., ice, water, vapor) M M1M1 M2M2 M3M3 More than one alternate model can exist at the same time with possible flows of entities (e.g., mass) between submodels

25 © Yilmaz-Ören “Conflict Systems” 25 Taxonomy of Multimodels (Submodel Structure)

26 © Yilmaz-Ören “Conflict Systems” 26 Taxonomy of Multimodels (Submodel Activation Behavior)

27 © Yilmaz-Ören “Conflict Systems” 27 Toward Multisimulation with Dynamic Simulation Updating Multisimulation (or multisim, for short) is simulation of several aspects of reality in a study. It includes simulation with single aspect multimodels, simulation with multiaspect models, and simulation with multistage models. Simulation with sequential multimodels allows computational experimentation with several aspects of reality. Simulation with multiaspect models (or multiaspect simulation) allows computational experimentation with more than one aspect of reality simultaneously. Simulation with multistage models allows branching of a simulation study into several simulation studies

28 © Yilmaz-Ören “Conflict Systems” 28 Simulation Branching with Multisimulation CS MS AS CS MS AS CS MS AS frame 1 frame 1.1 frame 1.2 (1) Select one of them and ignore others. This approach is similar to many cases in traditional simulation where implicit assumptions are not brought to the users attention. (This alternative is not a good one and definitely is not our choice.) (2) Perform an ordered simulation in breadth or depth first manner with alternative contingency models. (In this case, the user cannot easily and intuitively follow the consequences of alternative simulation studies.) (3) Select multisimulation branching (for all or some of them) and observe (visually or through metrics) behavioral and/or structural developments in simulation studies executed in parallel.

29 © Yilmaz-Ören “Conflict Systems” 29 RESEARCH and COOPERATION OPPORTUNITIES IN CONFLICT SYSTEMS MODELING & SIMULATION at The Auburn Modeling and Simulation Laboratory (AMSL) of the M&SNet Topics*: Conflicts are social phenomena that are worth studying because they affect quality of life everywhere. New advanced simulation techniques may offer proper means to model and explore alternative and unforeseen consequences of conflicts. Positions: Auburn Modeling and Simulation Laboratory is seeking graduate students (M.S. and/or Ph.D. level) and visitors within M&SNet organizations to collaborate on a wide range of methodological, theoretical, and applied simulation modeling problems regarding conflict analysis, resolution, and management. Applicants with interest and knowledge in agent-directed simulation and applications of simulation modeling to social science problems as well as to human behavior and conflicts are encouraged to contact Dr. Levent Yilmaz by at: Contact: Dr. Levent Yilmaz Auburn Modeling and Simulation Laboratory of the M&SNET Computer Science & Engineering College of Engineering Auburn University Auburn, AL USA * On these topics, AMSL is already cooperating with Dr. Tuncer Ören of the OC-MISS of M&SNet

30 © Yilmaz-Ören “Conflict Systems” 30 Thank you for your attention ! Questions ?


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