Heuristics, optimization, and equilibrium analysis for automated wargames

Due to the complexity of the wargaming structure, it may be difficult to completely solve some wargames involving large number of players, player options, system states, mission types, and uncertainties of operation/campaign results. Thus it is important to develop and improve heuristics, which is studied in this paper. In particular, we transform a paper-based six-player wargame into a computer-based one using Matlab programming and its graphical user interface (GUI). We design 2-4 heuristics for each of the players, develop interfaces with user inputs and automation, and run the simulation 1,000 times for each possible combinations of different players' heuristics. In particular, based on the two original heuristics for Player 1 (prioritization based on location and population types), we use human experiences to improve his heuristics (e.g., not only maximizing his short-term payoffs by assigning faction to population cards, but also preventing other players from winning by destroying their resources, especially when the other players are close to their victory conditions). Our results show that the improved heuristics would: (a) increase Player 1's equilibrium winning frequencies from 25% to 94%; and (b) decrease the ending periods, leading Player 1 to win the game faster. Our research provides some novel insights for advancing automatve wargaming.