US Coast Guard Modeling/Simulation Efforts: Foreign Fishing Vessel (FFV) Incursions

Transcription

1 US Coast Guard Modeling/Simulation Efforts: Foreign Fishing Vessel (FFV) Incursions LCDR D. Blair Sweigart LCDR E. Taquechel USCG Atlantic Area, Operations Analysis Division 30 June 2015

2 Objective Problem: We will discuss ongoing efforts to: analyze FFV activity level and impact Possible solution: We will also discuss a USCG RDC initiative to intelligently randomize USCG enforcement efforts Game Theoretic Fish Patrol Schedule Model (GTFM) USC CREATE assisted in development

3 Overview - Problem Unknown Denominator We know what we saw, what didn t we see? Common in law enforcement / security missions Counter Drug Migrant Interdiction Foreign Fisheries Most applicable when no good use exists

4 Mission Background OLE (Other Law Enforcement) - Mission Background FFVs illegally fishing in US waters NOAA is lead agency USCG conducts majority of enforcement activity

5 Study Objective Provide insight into the quantity and economic impact of illegal fishing activity in areas of interest Gauge the unknown, based on the known What % of FFV activity do we detect? All models are wrong, some are useful.

6 Overview - Observed Incursions 100% - 50% - 10% - Likelihood of Discovery - 0 Number of Incursions Dashed lines represent hypothetical relationships and are not model based

7 Key Factors FFV locations CG asset locations Probability of Detection

8 P( Detection ) p x W 4x 1 e Sweep Width (W) 2 2 Predefined for asset/target combinations Monte Carlo (30K) to develop an overall POD for each asset type in a grid box.

9 P( Detection ) con t POD sj A j i s is m ij m p ij i POD sj average grid POD for month j, class s s asset class (surface, air) A j combined asset availability for month j m ij minutes in grid by asset type i p i POD for asset type i The factor on the far right is the weighted, average POD

10 Key Factors (revisited) FFV locations (SME input) CG asset locations (known) Probability of Detection (calculated) Each time step (t): Bernoulli Trial LOD = 1 P(complete trip undetected) 1 P 1 n t1 (1 p t )

11 Methodology Likelihood of Discovery CG Asset Locations FFV Location Preferences FFV Behavior Simulation Arrival Rate as Control Knob Number of Incursions Biomass Impact Illicit Economic Gains

12 Methodology Discrete Event Simulation (VBA in Excel) 30 minute time steps FFV subject to detection at each step Repeat until all fishing stop complete Arrival Transit to Fishing Area Fish Exit US Waters Tally Stats Detected by Coast Guard? (POD) Legend System Block Decision Block Modeling Block Intercepted Interdicted Tally Stats as Seized

13 CG Asset Presence Surface Assets Blue Force Data for CY Presence Factor Weighted Average POD Air Assets Hours on Mission / Month Location based on Assigned Patrol Boxes CG Surface Asset Presence Grid

14 Utility Grids Probabilistic Location Preferences Favorable conditions for fish Desirable locations for FFVs Observed locations for FFVs over CY

15 Assumptions / Limitations No day/night modeling Weather independent FFV location preferences from SME input No seasonal location preference variation Only basic FFV behavior modeled No evasion efforts or counter-intel based actions

16 Way Ahead Refinements Working through current limitations Modeling more advanced behavior Improved utility grid computations Validation Comparison of known vs. modeled detection info Continue model runs for subsequent years Trend analysis Work with partners Commercial and recreational quotas Model improvements Extension to other applicable mission areas

17 Overview GTFM: Possible Solution Game Theoretic Fish Patrol Schedule Model Being developed by CG Research and Development Center Applies game theory and robust optimization to produce USCG patrol schedule Models a repeated Stackelberg game Assumes bounded rationality Models heterogeneous FFV population Available data used to update model

18 Stackelberg Game USCG = defender, moves first Using optimal strategy reflected by Strong Stackelberg Equilibrium solution FFV = attacker, observes defender strategy and moves second Fishes in certain locations Iterated game, both players adapt to each other Model can be updated with new FFV preferences, equilibrium solution recalculated May yield new USCG patrol tactics

19 Payoffs Defender expected utility (DEU) before attacker strategy considered: where: U ( x) x R (1 x ) P d t t x t = patrol density or strategy (probability of presence in a location t ) d R t d t = defender reward for interdiction (value of seized boat) = defender penalty for failing to interdict (value of illegally taken catch) d P t t d t

20 Bounded Rationality Subjective Utility Quantal Response (SUQR) a SUt, x) 1xt 2Rt 3 ( P a t This is attacker expected utility (AEU) where: x t = weight vector over three factors = first factor: defender patrol density = second factor: attacker reward if avoid interdiction (value of illegal catch) a R t a P t = third factor: attacker penalty if interdicted (value of seized boat)

21 Bounded Rationality con t AEU is used to calculate the probability FFV will select a certain area: q t (, x) e t' SU e t (, x) SU t ' (, x) Ratio of attacker expected utility from one area, to total expected utility over all areas considered

22 Zero Sum Attacker reward = defender penalty Defender reward = attacker penalty

23 Objective Function Maximize DEU: max x min t U ( x) q (, x) Attacker wants to minimize DEU (since zerosum); we want to maximize it Robust optimization - across all possible FFV types in heterogeneous population Initial weight vector reflects historical FFV preference data d t t

24 Constraints (nonexhaustive) Number of CG assets (aviation) Patrol duration Search capabilities CG asset competing priorities Ratio of FFV to CG assets in certain locations

25 Model Output Intelligent randomization: output is mixed strategy probability distribution Patrol strategy for USCG aviation assets Initial solution was optimized over fishing locations Subsequent solutions optimized over patrols

26 Learning and Weight Calibration Model leverages publicly available fisheries data Execute model output collect new data: Weights updated Equilibrium output recalculated Intelligence informing operations additional intelligence

27 Assumptions/Limitations CG presence in a location guarantees successful interdiction Aviation asset notifies interdicting asset (boat, cutter) guaranteeing end game Boundedly rational attacker(selects fishing locations based on weights) Previous CG game theoretic approaches: attacker assumed to select pure strategy Fishing farther from maritime boundary line increases detection probability Zero-sum

28 Testing and Evaluation The model was tested in a Coast Guard District Feedback from the test informed the model s development

29 Way Ahead - GTFM Collect additional data Update weights, test model s learning algorithm Account for FFV observation of our absence from pier (SUQR weights?) Verification and validation Verified during initial field test Additional data may help validate (extent to which model reflects real world)

30 Summary The Coast Guard is analyzing the FFV illegal fishing problem by modeling incursions and estimating economic impact The Coast Guard is also exploring possible solutions by evaluating game theoretic models that intelligently randomize CG assets Intent is to improve interdiction and deterrence Explore potential for model confluence There is still work to be done!