Accession Number ADA564006
Title Comparison of Strategies Used in the Game of RISK via Markovian Analysis and Monte-Carlo Simulation.
Publication Date Jun 2012
Media Count 82p
Personal Author J. D. Lee
Abstract This paper analyzes strategies of the boardgame RISK using Markov chain analysis and Monte-Carlo simulation in order to compare state-based strategies against sequentially dependent or non-memoryless strategy policies. Previous work had focused on calculating the probability of winning based on using all available engagement strategies and battling until either the attacker is unable to continue engaging the enemy or until the defender is annihilated. This research project applied decision analysis methods to look at alternate strategy policies. Two primary models were utilized to analyze these strategy policies. First, a computer model was developed that would build a Markov chain with the associated transition probabilities based on an initial set of conditions and a specified set of rolling strategies. Second, a Monte- Carlo simulation was developed that would simulate rolling the dice in order to analyze sequentially dependent strategy policies that cannot be modeled via Markov chains. These strategies were then compared based on the attacker's probability of winning and the expected difference between force strengths at the end of a series of engagements.
Keywords Boardgame
Game theory
Markov processes
Markov-chain
Monte carlo method
Monte-carlo simulation
Policies
Probability
Strategy
War games


 
Source Agency Non Paid ADAS
NTIS Subject Category 72E - Operations Research
Corporate Author Air Force Inst. of Tech., Wright-Patterson AFB, OH. Graduate School of Engineering and Management.
Document Type Technical report
Title Note Graduate research paper Jun 2011-Jun 2012.
NTIS Issue Number 1302
Contract Number N/A

Science and Technology Highlights

See a sampling of the latest scientific, technical and engineering information from NTIS in the NTIS Technical Reports Newsletter

Acrobat Reader Mobile    Acrobat Reader