Validation of an Agent-based Civil Violence Model Michael Jaye, Ph.D. COL Robert Burks, Ph.D.
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Transcript of Validation of an Agent-based Civil Violence Model Michael Jaye, Ph.D. COL Robert Burks, Ph.D.
![Page 1: Validation of an Agent-based Civil Violence Model Michael Jaye, Ph.D. COL Robert Burks, Ph.D.](https://reader035.fdocuments.in/reader035/viewer/2022062407/56649e4c5503460f94b421af/html5/thumbnails/1.jpg)
Validation of an Agent-based Civil Violence Model
Michael Jaye, Ph.D.COL Robert Burks, Ph.D.
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Model Validation
• Validation: ascertains whether or not a model represents and correctly reproduces behaviors of the intended real-world system
• Required by some agencies
• Agent-based model (ABM) validation:– Is the process of ABM validation different than that for any other model?– Defense-related validation work:
• Appleget, et.al, “Irregular Warfare Model Validation Best Practices Guide” • USMC “Agent-Based Simulation Verification, Validation, and Accreditation
Framework Study”
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Model ValidationConceptual Validation• Establishes credibility of model
– Underlying theories– Structure, logic– Sufficiently accurate for intended purpose– Includes submodel(s)
• Must be well-documented
Operational Validation• Establishes that output behavior has accuracy required for model’s
intended purpose
• Techniques vary – depend on system of interest (observable) and approach (subjective/objective); include statistical comparison, face validation (SME), docking, etc.
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Conceptual Validation• Establishes credibility of model
– Underlying theories– Structure, logic– Sufficiently accurate for intended purpose– Includes submodel(s)
• Must be well-documented • Must include the rules by which agents behave & operate
Operational Validation• Establishes that output behavior has accuracy required for model’s
intended purpose
• Techniques vary – depend on system of interest (observable) and approach (subjective/objective); include statistical comparison, face validation (SME), docking, etc.
Agent-Based Model Validation
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Insurgencies
• Dynamic interaction between competing sides over contested political space.
• Theories include– Gurr, Why Men Rebel (Relative Deprivation & Cognitive Dissonance)
– Leites & Wolf, “Rebellion and Authority: An analytic essay on insurgent conflicts” (Insurgency analyzed as a system)
– Kuran, “Sparks and prairie fires: A theory of unanticipated political revolution” (Preference Falsification)
– McCormick, “Revolutionary origins and conditional mobilization” (Expected Cost)
– Epstein, “Modeling civil violence: An agent-based computational
approach” (Legitimacy, Hardship, Repression)
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Two Conceptual Models
• Epstein’s Civil Violence agent-based model (ABM) implementation, with extensions
– A member of the general population considers grievance, government (il-) legitimacy, “momentum”, risk aversion in the determination of whether or not to rebel
• S-I-R-S Ordinary Differential Equation (ODE) model
– Likens insurgency spread to that of an infectious disease
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Civil Violence Model Agent Specification
• Members of fixed (no births/deaths) population may be:– Inactive (not rebelling)– Actively rebelling– Jailed– Moving (randomly) to open space (Moore neighborhood) within “vision”– At end of jail term, agents return to population as inactive
• Political grievance a function of hardship and regime illegitimacy
• Agents decide to rebel based on:– Risk aversion– Hardship– Government illegitimacy– Arrest probability– Some threshold value
Two Types of Agents State (“Cop”) General Population
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Civil Violence Model State Agent Specification
• Number of state agents (“cops”) specified and fixed
• Move randomly (Moore neighborhood)
• Can arrest active agents within some “vision” radius
‒ Arrested agent’s jail time is random, up to some max‒ Jailed agent location frozen‒ Freed agent re-enters as inactive agent
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ABM Conceptual Model Agent Rules
Agent Rules
• If (G – N) > T, become active (join rebellion); otherwise remain inactive.
– G = H(1 – L)
– N = R(1 – exp[-k(C/A)v]
– H and R are assigned from U[0, 1]– T is a threshold value, v is the agent “vision” radius, and k is a
constant, each user prescribed
• Cop arrest rule: each turn, arrest one active agent chosen randomly from those within cop “vision” radius
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S-I-R-S Conceptual Model
• Inactive - or Susceptible ( S ) - agents interact and can become part of insurgency, thereby Infected ( I )
• Infected agents can be Removed ( R ); e.g. jail
• After serving jail term, agents revert back to Susceptible
• Insurgency spreads from S to I to R, then back to S
• Assume fixed population (no births, deaths)
Yields a system of ordinary differential equations (ODEs)
Many published works consider insurgency growth as the spread of an infectious disease
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S-I-R-S System of ODEs
RIdt
dR
IISdt
dI
RISdt
dS
With initial conditions:
0)0(;1)0(;)0( 0 RISS
Now Rebelling
Released from Jail
Actively Rebelling
Arrested
Susceptible
Infected
Removed
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S-I-R-S Analysis
0 200 400 600 800
0
200
400
600
800 I(t)
Solution Trajectories
Implications‒ Revolutionary ideas persist‒ Herd immunity can be achieved due to indoctrination/repression
S(t)
Stable equilibrium condition in first quadrant (only meaningful quadrant)
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0 20 40 60 80 100 120 140 160 180 2000
100
200
300
400
500
600
700
800
900
S(n)
I(n)
R(n)
S-I-R-S Analysis (Cont.)
Susceptible Population
Actively Rebelling
Currently Arrested
Solution Trajectories
Slight oscillation, equilibrium conditions attained
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Civil Violence ABM Implementation
NETLOGO implementation (Initial Setup)
User Interface Screen
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Civil Violence ABM (Cont.)
NETLOGO implementation (65 Time Steps)
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Civil Violence ABM (Cont.)
Slight oscillation, equilibrium conditions attained
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ODE Model Solution ABM Implementation
“Docking” results of two models is a means toward establishing operational validity.
“Docking”
Civil Violence ABM & S-I-R-S Model Result
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Additional ABM Results ABM results
- not attainable from the ODE model- observed behavior - riots
Punctuated Equilibrium
Agent vision 8Cop vision 8
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Additional ABM Results (Cont.)
Average activeagents ≈ 6
Agent vision 10Cop vision 1
ABM results - not attainable from the ODE model- observed behavior - revolutionary war
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New equilibrium: average activeAgents ≈ 120
Additional ABM Results (Cont.)
“Sparks and Prairie Fires”A Spark set off a sudden rebellion
Equilibrium shift =Revolution!
Mechanism: Threshold is a functionOf agent vision radius
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Further establishing operational (face) validity
“When Do Institutions Suddenly Collapse? Zones of Knowledge and the Likelihood of Political Cascades”
– Ian Lustick, Dan Miodownik
• Considers “zones of knowledge”, small worlds (flash mobs)
• Neighborhood size – akin to “vision” radius
• Cascades and threshold behaviors related
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Further establishing operational (statistical) validity
“Empirical Performance of a Decentralized Civil Violence Model” – Klemens, Epstein, Hammond, Raifman
• Examines Hardship, Legitimacy, Repression
• Draws from Political Instability Task Force data set
• Findings:- model’s explanatory power supported at high significance
- results robust across a variety of statistical instruments
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Future Work
• Stressing other insurgency theories through ABM implementation. – Kuran: agent preference falsification– McCormick: effect of insurgent violence on agent expectation
• Validation ‘best practices’
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Questions? Comments?