They utilized game refinement, the motion-in-mind model, and artificial intelligence simulations to determine the influence of jerks in card games with incomplete information
-- A jerk is a physical quantity that represents a sudden change of acceleration. It is widely used as a parameter in engineering, manufacturing, sports science, and other industries. Now, researchers suggest that studying the effect of jerks can provide further information about gameplay too. The game refinement theory postulates that acceleration—i.e., the rate of change of information speed—is the balance between certainty and uncertainty in a game. This determines game refinement value, denoted as GR, and is a measure of a gamer’s engagement.
A new perspective, the motion-in-mind model, measures the uncertainty of progress in a game relative to two physical measures—velocity, which represents the win rate, and mass, which represents how hard it is to win. These physical values can be translated to psychological reactions. A jerk—denoted as AD, an abbreviation for addictive—can thus be interpreted as unpredictability or surprise. Games with a higher AD value are highly unpredictable and full of surprises, making them addictive.
Recently, a group of researchers led by Assistant Professor Mohd. Nor Akmal Khalid from the School of Information Science, Japan Advanced Institute of Science and Technology (JAIST), has investigated the influence of jerks on game addiction through several popular card games—these included suits-irrelevant (Wakeng and Doudizhu) and suits-relevant (Winner, Big Two, and Tien Len) games. The study, which was co-authored by Professor Hiroyuki Iida of JAIST, was published in Volume 10 of IEEE Access on 26 December 2022
Prof. Khalid discusses the motivation behind the research. “Card games are typical incomplete information games. Short, repeatable rounds, chances, and strategizing make them among the most entertaining, even addictive, games. We wanted to understand why this was so.”
The researchers first explored the rules, designs, and complexities of these games, using game refinement and the motion-in-mind model. Next, they performed two simulations with self-playing artificial intelligence (AI) agents. In the first experiment, the AI mimicked a fixed game played by contestants with different skill levels (weak, fair, and strong). In contrast, the second experiment comprised games of various sophistications played by a fixed AI level. The differences between two parameters were observed—first, the odds of winning (as seen in games with deterministic versus random odds), and second, the difficulty level (as seen in simple versus complex games). These analyses enabled researchers to compare the different card games.
The results demonstrate that skill and sophistication must match for reasonable GR (correlated with attractiveness) and AD (correlated with surprise) values. In addition, the games must also be balanced and fair enough, so that winning is not interpreted as just good luck. Take Doudizhu for example, which has nearly equal GR and AD values. This balance between uncertainty and unpredictability leads to a fast-paced game with frequent rewards and surprises. As a result, people want to play repeatedly, making Doudizhu the most popular and addictive card game.
Through the above investigation, the researchers discerned the principles of play for addictive entertainment. The four measures of the game progress model—game length, velocity, acceleration, and jerk—correspond respectively to reward cost, reward frequency, uncertainty, and unpredictability. Further, they determine game fairness, reinforcement, attractiveness, and surprise, respectively.
“These components highlight the potential of GR and AD measures as powerful tools to understand gameplay. They will prove useful in making games more attractive and educational. Not just games, the findings of this study can be extended to help make any normal and mundane activity engaging, enjoyable, surprising, and even addictive. In essence, the boundary between work and play can get blurred, leading to an ultimate sense of achievement and passion,”
concludes Prof. Khalid.
About Japan Advanced Institute of Science and Technology, Japan
|Title of original paper:
||Implications of Jerk's On the Measure of Game's Entertainment: Discovering Potentially Addictive Games
Founded in 1990 in Ishikawa prefecture, the Japan Advanced Institute of Science and Technology (JAIST) was the first independent national graduate school in Japan. Now, after 30 years of steady progress, JAIST has become one of Japan’s top-ranking universities. JAIST counts with multiple satellite campuses and strives to foster capable leaders with a state-of-the-art education system where diversity is key; about 40% of its alumni are international students. The university has a unique style of graduate education based on a carefully designed coursework-oriented curriculum to ensure that its students have a solid foundation on which to carry out cutting-edge research. JAIST also works closely both with local and overseas communities by promoting industry–academia collaborative research.
About Assistant Professor Mohd. Nor Akmal Khalid of JAIST, Japan
Dr. Mohd Nor Akmal Khalid is an Assistant Professor at the School of Information Science, Japan Advanced Institute of Science and Technology, a member of Research Center for Entertainment Science, and a member of the International Research Center for Artificial Intelligence and Entertainment Science. He obtained his B.Sc., M.Sc., and a Ph.D. degree from the University of Science, Malaysia. His work focuses on the methods for optimization and game informatics in the fields of operation research and entertainment technology. His topics of interest include artificial intelligence techniques, manufacturing systems, search algorithms, evolutionary computing, advancement in scheduling and planning, and machine learning.
This work was supported by the Japan Society for the Promotion of Science, in the Framework of the Grant-in-Aid for Challenging Exploratory Research under Grant 19K22893.