THE SOCIAL IMPACT MODEL: A TOOL FOR IRREGULAR WARFARE ADJUDICATION, ANALYSIS AND VALIDATION TRADOC Analysis Center – Monterey February 2011

Purpose & Agenda Agenda Background. IW Adjudication. IW Analysis. IW Validation. Purpose: To describe the methodology by which Social Impact Model performs Irregular Warfare Adjudication, Analysis and Validation. 12/24/2015 Strategic Data Farming 2

Background The Social Impact Model (SIM) performs adjudication, analysis and validation of social impact in US Army TRADOC Analysis Center (TRAC) ‘s Tactical Wargame (TWG). The TWG and the models within the SIM are focal points of TRAC’s Irregular Warfare Analysis Capability (IWAC) initiative IWAC is the one of the largest IW analysis efforts in the Department of Defense The SIM is a federation of stand alone models and tools The Cultural Geography (CG) model at the Population level (TRAC-Monterey) The Nexus Network Learner Model (NNL) at the Individual level (OSD,TRAC-Monterey) The eXtensible Behavioral Model Framework (XBM) analysis tool to integrate the models (OUSDI, TRAC-Monterey, CTTSO) More models are to be added in 2011 The models and tools of the SIM are designed to address gaps in IW adjudication, analysis and validation Each component leverages advanced artificial intelligence technologies to address the gaps 12/24/ Strategic Data Farming

GAP 1: IW Adjudication Conventional warfare simulations assume human factors are symmetrical and wash out, leaving the only force upon force –Conventional warfare adjudication is physics-based, and therefore well understood. In Irregular Warfare, the population is the center of gravity –Irregular Warfare adjudication is based on computational social science, and less agreed upon The models that comprise the SIM take a principled approach to scientific rigor in IW adjudication, enabled by advanced technologies CG and NNL both include: –Cognitive Agent Based Modeling, so that social phenomena is computed from first principles and emergent rather than hard coded –Bayesian Networks, so that data can be read into and kept track of in the model in a flexible manner –Reinforcement Learning, so that agents may learn new behaviors from motivations over time, the true causes of new social structure, rather than being pre-rigged towards desired structures –Social Networks to emphasize the relational aspect of social structure –Representation of the major schools of social theory (interpretive, materialist) 12/24/ Strategic Data Farming

SIM IW Adjudication in TWG CG models population attitude responses to events –Survey responses, both Subject Matter Expert (SME) and Gallup, are entered into Bayesian Networks that model population responses -To the direct events of the wargame -To the endogenous infrastructure satisfaction events –Incorporates the narrative paradigm from the Interpretive social paradigm – “beliefs matter” -Issue stances change as agents beliefs change as a result of events that they experience and hear of through their social networks NNL models tactical level interactions in HVI and Corruption, and Key Leader Networks –Social Structure is input through an ontology of roles, role relation rules, and actual individuals in Afghanistan that fall into these roles –Demographic data is entered through a Bayesian Network, that tells initial likelihood of role based behaviors such as role partner choice and transaction behaviors such as bribery –Players engage directly with Key Leaders, with transactions such as bribery and attrition –NNL adjudicates outcomes based on rules and replaces leaders based on roles 12/24/ Strategic Data Farming

Gap 2: IW Analysis The Basic Mission of Analysis The mission of the analysis community is to describe the uncertainties inherit in Courses of Action as accurately as possible for rational risk based analysis The nature of uncertainty is different in Irregular Warfare (IW), and so new methodologies are needed to perform the basic mission of analysis of IW analysis The Social Impact Model (SIM) uses advanced simulation technologies to perform the basic mission of analysis through proportionate characterization of the space of outcomes. 12/24/ Strategic Data Farming

The Basic Mission of Analysis: Conventional Warfare Simulation Single models are often sufficient, and uncertainty lies in random variate pulls from internal distributions. –It is often sufficient to vary parameters in plausible combinations by hand, and models depend on analyst knowledge. –Input is typically scripted. Analysis is typically done by stopping the run midway and changing the script, until it “looks right” Joint and multi-echelon analysis has led to the need for analysis with multiple models and dealing with issues of multiresolutional/ multiperspective model federations Data Farming is state of the art description of relation of parameters to each other, but often they are not in plausible combinations when model runs are automated thousands of times. –For example, scripts are not reactive to the enemy and environment –Model input parameters are designed for convenience of analyst input rather than automatic generation of plausible input combinations. 12/24/ Strategic Data Farming

The Basic Mission of Analysis: Irregular Warfare Simulation More uncertainty –Ok, as long as we take it all into account for risk based analysis Uncertainty from: –Lack of consensus of social scientists, –Credibility of data, –Match of data to study, –Arbitrariness intrinsic to human behavior More uncertainty implies more computational runs for statistical significance BUT, wargames are more trusted for the human aspects of warfare –Irregular Warfare measures of effectiveness such as popular support, and moves and counter moves, are human aspects. However, wargames yield only one or very few runs – They are not statistically significant The SIM is designed to use apply a Wargame to Analysis 12/24/ Strategic Data Farming

XBM Services 9 Leverage XBM COA services: –Explicitly incorporates operational goals, strategy, and doctrinal constraints, along with player perception, into the COA analysis process. –Strategically focuses on portions of COA space that are practically feasible, doctrinally tenable, and best suited to achieve specified operational goals. –Dynamically revises COA by evaluating the situation on the ground (based on model outputs) to approximate nature of real life in the simulation. Each player has the following as part of its COA strategy: –Decision Points: Points at which players will consider a change to its COA. –COA Options: Options to consider at each decision point, specifying conditions under which it will be exercised and possible moves. –Goals: Ways to evaluate the situation within the move selection algorithm. –Mental Models: Presumed strategy of other players (i.e., belief levels, decision points, COA options, move selection). 12/24/2015 Strategic Data Farming

Model Integration Loosely couple federates through Bayesian Inference (Sequential Iterative Hub and Spoke design). –Hub probabilistic ontology holds population’s attribute relations. –Bayesian inference generates populations for spoke models. –BayesOwl propositional logic bridges operational/tactical resolution boundary. –Models change attribute relations in population, which is re-learned back into the hub. –Run each spoke model in sequence and iterate. 12/24/ Strategic Data Farming

Model Integration Design Using Probabilistic Ontologies 11 12/24/2015 Strategic Data Farming

Probabilistic Ontology Development Survey players to elicit intent, goals, rules, and strategies. Express player intent in a probabilistic ontology with strategies, decision points, and goal states. Formulate a conceptual model of TWG 2010 in a probabilistic ontology that categorizes actual moves. Develop rudimentary Markov processes to model move sequences. The ontology holds statistical information on actual next moves for move-based Strategic Data Farming. Ontology provides input to simulation runs. 12/24/ Strategic Data Farming

Probabilistic Ontology Benefits Probabilistic ontology holds plausible combinations of parameters for proportionate data farming. Output may be expressed in the probabilistic ontology in the form of a Markov process. –Enables tipping point and path dependence analysis. Facilitates validation. Probability distributions may be probabilistically matched to another “gold standard” probability distribution process, such as AckSys, to compute a “Validation Score”. 12/24/ Strategic Data Farming

Probabilistic Ontology Plausible Combinations 12/24/ Strategic Data Farming Joint probability distributions help define the experimental region for Data Farming. 14 Speed of Boat When visibility is low, the boat is slow.

Execution – Strategic Data Farming SDF is the use of combinatorial game theory to optimize player moves in wargames, scripted simulations, and agent based simulations, according to player’s goals and strategies. Players become automated agents that look ahead to results of moves assuming players are trying to achieve goals, in a simple, general cognitive model. Uses “Game Trees”, a common Artificial Intelligence technique. 12/24/ Strategic Data Farming

Execution – Strategic Data Farming Players apply the game tree technique throughout the simulation by dynamically employing strategy as part of the move selection process. 12/24/ Strategic Data Farming

12/24/ Strategic Data Farming Green Action Red Reaction Strategic Data Farming – Africa Use Case Popular support levels (from Nexus): G = 0.57 R = 1.0 Evaluation Function GE – (each side attempts to maximize their evaluation function): GE = ((1-R)+G)/2 = 0.28 RE = 1-GE = 0.72 GE: 0.5 GE: 0.35 (after looking ahead) GE: 0.5 GE: 0.25 (after looking ahead) RE: 0.35RE: 0.65 RE: 0.75 RE: 0.5 Disrupt alliance between tribe J and tribe D. Conduct Civil Affairs. Make tribe O, a green ally, appear to harm tribe J. Make green appear to harm tribe J. Without looking ahead, Green’s actions seem the same (both are.5). But by looking ahead to how Red would react, he finds action Disruption (action 1) (GE=1-.65=.35) is better than CA (action 2) (GE=1-.75=.25).

Strategic Data Farming – Game Tree Trace 12/24/ Blue Red Blue {260, 250, 210, 300, 190} Blue player attempting to maximize blue support and minimize red support. Calculate “best” move by expanding the tree forward through the duration of the simulation using CONOPS and assessment of evaluation/function criteria. CONOPS do not have to be precise, as tree pruning will still add value and efficiency. {120, 180, 150, 175, 140} {90, 170, 145, 160, 130} {90, 110, 100, 115, 120} {120, 100, 150, 175, 180} {185, 130, 155, 180, 100} Actual Simulation Run. Blue Cognition during game. Strategic Data Farming

Analysis – Tipping Point Analysis 1 Data farm Markov probabilities to expose tipping points based both on data mined and hypothesis-driven causal variables. Presented in the form of Markov processes to visually capture the complexity of dynamic systems. Depicts states and transition probabilities to new states. Records all observed dynamics. Identifies sensitivities, critical behavior, and landmark regions. 12/24/ Strategic Data Farming 1. Bramson, Aaron. “Measures of Tipping Points, Robustness, and Path Dependencies”, AAAI Fall Symposium, Strategic Data Farming

Gap 3: IW Validation 20 Social Impact Model 12/24/2015 Once Data is put into a Markov Process, it can be compared to other Gold Standard Markov Processes for Validation. Distance of Probability Distributions computation derives a Validation Score

Summary US Army TRAC-Monterey’s SIM is part of a major TRAC and DoD Analysis Community effort to develop an IW Analysis Capability, The Social Impact Model (SIM) is designed to address Gaps in IW adjudication, analysis and validation SIM uses advanced technologies such as Cognitive Agents, Combinatorial Game Theory, and Markov Processes to address these gaps in a scientifically rigorous manner 12/24/ Strategic Data Farming

22 Comments / Discussion 12/24/2015 Debbie Duong TRADOC Analysis Center – Monterey, CA Strategic Data Farming