A Research Agenda for the Modeling and Simulation of Conflict Systems

© Yilmaz-Ören - 2004-07-25 “Conflict Systems” 1

A Research Agenda for theModeling and Simulation of Conflict Systems

Levent YilmazM&SNet: Auburn M&S LaboratoryComputer Science & Engineering

Auburn University, Auburn, AL 36849http://www.eng.auburn.edu/~yilmaz

M&SNet Consortium Meeting 2004 Summer Computer Simulation Conference

San Jose, California, July 24-29, 2004


GoalsGoals

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.


PlanPlan

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


Improving the Modeling, Simulation, Improving the Modeling, Simulation, and Analysis of Conflictsand 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.


Conflict Analysis, Resolution, and Peacekeeping Studies Conflict Analysis, Resolution, and Peacekeeping Studies

Associations: IACM - International Association for Conflict Management http://www.iacm-conflict.org/

Computer-Aid in Conflict Analysis Conflict Datasets http://www.pcr.uu.se/research/UCDP/conflict_dataset_catalog/data_list.htm AI Methods in Conflict Avoidance and Prevention of Crises and Wars http://www.oefai.at/oefai/aisoc/peace.html CASCON: http://web.mit.edu/cascon/ World Bank - Conflict Prevention and Reconstruction – Conflict Analysis Framework

Conflict Forecasting http://www-marketing.wharton.upenn.edu /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 http://www.eng.auburn.edu/~yilmaz/ADS-Conflict.htm


Toward Toward Simulation-Based Simulation-Based Problem Solving Environments Problem Solving Environments for Conflict and Peace-Keeping Studies 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.


Conflict SystemsConflict 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.

ConflictStructure 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.


Conflict Systems - The StructureConflict 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


PlanPlan

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


A Three-Tier Agenda for Advancing A Three-Tier Agenda for Advancing Computational Peace and Conflict StudiesComputational 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).

Conflict Theory

Agent Theory

Conflict System

Domain Theory

Agent-BasedSimulation

Agent-SupportedSimulation

AgentSimulation

i.e., Multisimulation(Next Generation

PSEs)

Agent-Directed Simulation

i.e., Simulating Anticipatory Systems(Advanced Methodologies)Behavioral

Sciences

Computational Modeling Theory


I- A Systemic View for Peace and Conflict StudiesI- A Systemic View for Peace and Conflict Studies((Advancing the Modeling of Conflict Systems)Advancing the Modeling of Conflict Systems)

Conflict Data &Knowledge-Base

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Extending and Realizing the Peace Science Vision of Isard and Smith (1982) with Agent-Directed Simulation

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Deliberation and Operational Units for ConflictsDeliberation and Operational Units for Conflicts

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The Role of Perception in Decision-Making during ConflictsThe Role of Perception in Decision-Making during Conflicts

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Conflict Theories Conflict Theories (i.e., power transition, lateral pressure theories)(i.e., power transition, lateral pressure theories)

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Cognitive Deliberation Subsystem and Cognitive Deliberation Subsystem and Its Interface to Operational Level Its Interface to Operational Level

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II- Behavioral Realism in the Simulation of Conflict SystemsII- 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



AgentSimulation


PSEs)



Sciences



Simulating Anticipatory SystemsSimulating 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 - http://www.oefai.at/oefai/aisoc/peace.html.

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?


The Social Psychology Factor in ConflictsThe 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.

InterpretationSubsystem

PhysicalEnv.

SocialEnv.

Interdependence Structure

CONTEXT

PersonalityOrientaton Conflict Style

CognitiveOrientation

EmotionalOrientation

MotivationalOrientation

NormativeOrientation

SOCIALORIENTATION

PSYCHOLOGICALORIENTATION

Intention -Behavior


III- Using Agents as Simulator Design MetaphorsIII- 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



AgentSimulation


PSEs)



Sciences



Dynamic Model and Simulation Updating Dynamic Model and Simulation Updating for Exploring Complex Conflict Phenomenafor 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.


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 Motivation: Adaptive Experience Management in Strategic Leadership Training with Contingency Models Leadership Training with Contingency Models

Contingency Contingency ModelsModels

ScenarioLineScenarioLine

• 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?


WhyWhy and and When When Dynamic Model/Scenario Update is Needed?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.


A type of multimodel: metamorphic model - (e.g., egg, larva, pupa, butterfly)

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There is a predefined sequence for the alternate models.


Another type of multimodel: multiaspect model - (e.g., ice, water, vapor)

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More than one alternate model can exist at the same time with possible flows of entities (e.g., mass) between submodels


Taxonomy of Multimodels (Submodel Structure)Taxonomy of Multimodels (Submodel Structure)


Taxonomy of Multimodels (Submodel Activation Behavior)Taxonomy of Multimodels (Submodel Activation Behavior)


Toward Multisimulation with Dynamic Simulation UpdatingToward 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


Simulation Branching with MultisimulationSimulation Branching with Multisimulation

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(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.


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 email at: [email protected].

Contact:Dr. Levent Yilmazhttp://www.eng.auburn.edu/~yilmazAuburn Modeling and Simulation Laboratory of the M&SNETComputer Science & EngineeringCollege of EngineeringAuburn UniversityAuburn, AL 36849 USA

* On these topics, AMSL is already cooperating with Dr. Tuncer Ören of the OC-MISS of M&SNet


Thank you for your attention !

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A Research Agenda for the Modeling and Simulation of Conflict Systems

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