1. Modelling a Simulation-Based Decision Support System for Effects-Based Planning Farshad Moradi, Johan Schubert farshad.moradi@foi.se Johan.schubert@foi.se Farshad Moradi, Johan Schubert farshad.moradi@foi.se Johan.schubert@foi.se

2. Outline Project Simulation-Based Decision Support EBAO/EBP Decision Support Operational plan, input and output interface, finding indicators Model Plan, activity/action/event, actor, environment, scenario Simulation A*-search algorithm Conclusions and future work Project Simulation-Based Decision Support EBAO/EBP Decision Support Operational plan, input and output interface, finding indicators Model Plan, activity/action/event, actor, environment, scenario Simulation A*-search algorithm Conclusions and future work

3. Project Background “Real-time Simulation for Supporting Effects-Based Planning” A three-years project commissioned by Swedish Armed Forces, started in 2008 Objectives Design and develop a simulation-based decision support system for supporting EBP. Enabling a decision maker to test and evaluate a number of feasible plans against possible courses of events (through simulation) and decide which of these plans are capable of achieving the desired military end state. Deliver results, indicating the (so far) best sequence of activities (plan option), at each point of time. Background “Real-time Simulation for Supporting Effects-Based Planning” A three-years project commissioned by Swedish Armed Forces, started in 2008 Objectives Design and develop a simulation-based decision support system for supporting EBP. Enabling a decision maker to test and evaluate a number of feasible plans against possible courses of events (through simulation) and decide which of these plans are capable of achieving the desired military end state. Deliver results, indicating the (so far) best sequence of activities (plan option), at each point of time.

4. Simulation-Based Decision Support Physical System Physical System Input Data fusion Optimizer Input Data fusion Optimizer Collect data Measure What if? Experiments Simulate Implement - Control - Decision support Automatic Validation Output Analysis Output Analysis

5. Effects-based Approach to Operations A process for obtaining a desired strategic outcome or “effect” on the enemy, through the synergistic, multiplicative, and cumulative application of the full range of military and non- military capabilities at the tactical, operational, and strategic levels [USJFCOM, 2005]

6. Effects-Based Analysis: Which effects are caused by actions? (Causal) Mechanisms Hostile Actions Own Actions Effects Effect Other Actor’s Actions

7. Decision Support: The operational plan Analyzing and simulating the operation plan at any time. Analyze and simulate several alternative plans that are in the main direction of interest. The goal is to find robust groups of plans that have similar implications. Analyzing and simulating the operation plan at any time. Analyze and simulate several alternative plans that are in the main direction of interest. The goal is to find robust groups of plans that have similar implications.

8. Decision Support: Input Interface The user may select area of interest

9. Decision Support: Output Interface Decision support is given as the most robust operational plans (left).

10. Decision Support: Output Interface Intelligence indicators are found by the systems (right).

11. Decision Support: finding indicators We can support the intelligence service by finding indicators through simulations. The hypothesis is that there are groups of plans with similar consequences. These indicators describe the dividing line between groups of different plans. If plans cross these lines consequences will be drastic. We can support the intelligence service by finding indicators through simulations. The hypothesis is that there are groups of plans with similar consequences. These indicators describe the dividing line between groups of different plans. If plans cross these lines consequences will be drastic.

12. Model Modelling based on EBAO and its concepts Plan A sequence of activities that together lead to a desired end-state which is set by a military force Activity An event initiated by own forces, which requires different types of resources in order to be executed Event 1)Initiated by our own actor (activity), 2)initiated by other actors (could be either planned or responsive), 3)spontaneous/natural events (unpredicted incidents, such as weather conditions, natural catastrophes, an unprovoked attack or an accident) Modelling based on EBAO and its concepts Plan A sequence of activities that together lead to a desired end-state which is set by a military force Activity An event initiated by own forces, which requires different types of resources in order to be executed Event 1)Initiated by our own actor (activity), 2)initiated by other actors (could be either planned or responsive), 3)spontaneous/natural events (unpredicted incidents, such as weather conditions, natural catastrophes, an unprovoked attack or an accident)

13. Model Actor An entity with resources, an action repertoire, an agenda and an internal state Entity: group of people, who somehow have a common identity and purpose organized such as police forces, relief agencies, well-organized militia units, and state administrative bodies loosely coupled groups and social clusters, which are only held together by one common interest a single individual, such as a prominent opinion maker, a political leaders or a financial potentate Action repertoire a set of possible actions that an entity is capable of performing, determined by its resources and knowledge Each action has a probability of being executed, which is dynamic Actor An entity with resources, an action repertoire, an agenda and an internal state Entity: group of people, who somehow have a common identity and purpose organized such as police forces, relief agencies, well-organized militia units, and state administrative bodies loosely coupled groups and social clusters, which are only held together by one common interest a single individual, such as a prominent opinion maker, a political leaders or a financial potentate Action repertoire a set of possible actions that an entity is capable of performing, determined by its resources and knowledge Each action has a probability of being executed, which is dynamic

14. Model Actor Agenda the plan that an actor is supposed to follow in order to achieve its goals State a combination of resources, such as weapon strength, no of soldiers, etc., and internal state, such as mood, solidarity, short-term agenda, etc. The states of the actors changes as a response to the activities and events, together with the probability of performing different actions Actor Agenda the plan that an actor is supposed to follow in order to achieve its goals State a combination of resources, such as weapon strength, no of soldiers, etc., and internal state, such as mood, solidarity, short-term agenda, etc. The states of the actors changes as a response to the activities and events, together with the probability of performing different actions

15. Model Actor State attributes, an example: Resources: Weapon Strength firepower movement Crew number capable of bearing arms number of sympathizers location Economy scale stability spatial dominance Logistical capacity to use resources optimally infrastructure propaganda channels Soft power contacts reputation Resources: Weapon Strength firepower movement Crew number capable of bearing arms number of sympathizers location Economy scale stability spatial dominance Logistical capacity to use resources optimally infrastructure propaganda channels Soft power contacts reputation Internal State: Discontent - experienced distance to the ideal desired end state Relationships - the degree of aversion to each of the other players Teamwork – cohesion Ideological conviction Purposefulness Cunning - wisdom Internal State: Discontent - experienced distance to the ideal desired end state Relationships - the degree of aversion to each of the other players Teamwork – cohesion Ideological conviction Purposefulness Cunning - wisdom

16. Model Actor action repertoire, an example: Military actions: Bomb plants Bomb transports Insulate and tie the opponent's resources Regroup Eliminate opponents positions Secure transport corridor Secure storage area Secure area Search an area Prevent view Sniper Capitulation Actor action repertoire, an example: Military actions: Bomb plants Bomb transports Insulate and tie the opponent's resources Regroup Eliminate opponents positions Secure transport corridor Secure storage area Secure area Search an area Prevent view Sniper Capitulation

17. Model Scenario consists of participating actors, their initial state and probability distribution for different actions, environmental data, as well as the plan that is to be evaluated and an event list which consists of actions derived from the other actors’ agendas, and spontaneous/natural events Environment consists of various facilities and sites with symbolic value. Functional buildings, such as hospitals, schools, housing, management centres, etc. Transportation routes and transfer points, such as roads, bridges, pipelines, ports, airports, etc. Utilities such as natural resources like arable land, mines, etc. and processing facilities such as power plants, factories, warehouses, etc. Information channels such as radio and TV stations, networks, transmission masts, etc. The symbolical sites can be geographical areas, statues or other memorials, religious buildings, etc. Scenario consists of participating actors, their initial state and probability distribution for different actions, environmental data, as well as the plan that is to be evaluated and an event list which consists of actions derived from the other actors’ agendas, and spontaneous/natural events Environment consists of various facilities and sites with symbolic value. Functional buildings, such as hospitals, schools, housing, management centres, etc. Transportation routes and transfer points, such as roads, bridges, pipelines, ports, airports, etc. Utilities such as natural resources like arable land, mines, etc. and processing facilities such as power plants, factories, warehouses, etc. Information channels such as radio and TV stations, networks, transmission masts, etc. The symbolical sites can be geographical areas, statues or other memorials, religious buildings, etc.

18. Simulation An activity An transforms the system state Sn according to Sn = f (Sn-1, An), in the time interval (tn-1, tn) Sn is the sum of the actors’ and environment states f (Sn-1, An) is implemented as an event-driven, stochastic simulation Simulates interactions between our own activity, other actors’ agendas and response operations, and other external events Monte Carlo simulations are used in order to obtain frequency functions of the entire outcome space An activity An transforms the system state Sn according to Sn = f (Sn-1, An), in the time interval (tn-1, tn) Sn is the sum of the actors’ and environment states f (Sn-1, An) is implemented as an event-driven, stochastic simulation Simulates interactions between our own activity, other actors’ agendas and response operations, and other external events Monte Carlo simulations are used in order to obtain frequency functions of the entire outcome space

19. Simulation For each round of the Monte Carlo loop: Initialize event list with our activity A Randomly draw the external events and add them to the event list Randomly draw a starting state for each state parameter from resp. distribution. For each actor: Randomly draw the next action from the current agenda and add to the event list. For each event in the event list as long as time is less than tn: Environmental parameters may change (which could generate new events). For each actor (including "our own" operator "): –Note directly or indirectly through filtered or biased information. –Analyse the information → internal state and resources are changing. –Action repertoire is updated with new probabilities –Randomly generate the next action –Add a new action to the event list. Save the results for each state parameter. Create a summary of results for each state parameter in the form of a histogram, which serves as an approximation for resp. output distribution For each round of the Monte Carlo loop: Initialize event list with our activity A Randomly draw the external events and add them to the event list Randomly draw a starting state for each state parameter from resp. distribution. For each actor: Randomly draw the next action from the current agenda and add to the event list. For each event in the event list as long as time is less than tn: Environmental parameters may change (which could generate new events). For each actor (including "our own" operator "): –Note directly or indirectly through filtered or biased information. –Analyse the information → internal state and resources are changing. –Action repertoire is updated with new probabilities –Randomly generate the next action –Add a new action to the event list. Save the results for each state parameter. Create a summary of results for each state parameter in the form of a histogram, which serves as an approximation for resp. output distribution

20. A*-search algorithm One of the main requirements of the simulation system is: At any moment in time, suggest an alternative sequence of activities that best suits the decision maker’s desired end- state Requires an algorithm that searches through the activity tree in an efficient manner so that there is always a so far ”best option” available for presentation Neither “breadth first search” nor “depth first search” can meet this requirement. A*-search is the solution One of the main requirements of the simulation system is: At any moment in time, suggest an alternative sequence of activities that best suits the decision maker’s desired end- state Requires an algorithm that searches through the activity tree in an efficient manner so that there is always a so far ”best option” available for presentation Neither “breadth first search” nor “depth first search” can meet this requirement. A*-search is the solution

21. A*-search algorithm

22. Conclusions and future work We have designed of a simulation-based decision support methodology with which we can test operational plans as to their robustness We have suggested a methodology that can find important indicators, towards which the intelligence service may put intelligence questions The system is still under development hence there are no experimental results obtained so far The current version is being tested at the moment Future work includes testing the system with actual operational plans, a more precise actor profiles, and detailed functions for calculating probabilities of actions in the action repertoires We have designed of a simulation-based decision support methodology with which we can test operational plans as to their robustness We have suggested a methodology that can find important indicators, towards which the intelligence service may put intelligence questions The system is still under development hence there are no experimental results obtained so far The current version is being tested at the moment Future work includes testing the system with actual operational plans, a more precise actor profiles, and detailed functions for calculating probabilities of actions in the action repertoires