Hedonic Games and Monte Carlo Simulation

Hedonic games have applications in economics and multi-agent systems where the grouping preferences of an individual is important. Hedonic games look at coalition formation, amongst the players, where players have a preference relation over all the coalition. Hedonic games are also known as coalition formation games, and they are a form of a cooperative game with a non-transferrable utility game. Some examples of hedonic games are stable marriage, stable roommate, and hospital/residence problem. The study of hedonic games is driven by understanding what coalition structures will be stable, i.e., given a coalition structure, no players have an incentive to deviate to or form another coalition. Different solution concepts exist for solving hedonic games; the one that we use in our study is core stability. From the computational perspective, finding any stable coalition structure of a hedonic game is challenging. In this research, we use Monte Carlo methods to find the solution of millions of hedonic with the hope of finding some empirical points of interest. We aim to explore the distribution of the number of stable coalition structures for a given randomly generated hedonic game and to analyze that distribution using Cullen and Frey graph approach

A Discussion on Supplier Selection Modeling Approaches

Supplier selection is a subfield of supply chain management that involves multiple steps in order for decision-makers to find suitable suppliers. Supplier selection is important as it could influence the whole company positively or negatively. It has, recently, become a topic of interest because of the recent pandemic and its effect on the global supply chain, which causes supply shortages. As such, the focus of this paper is on characteristics of decision-making modeling approaches, specifically agent-based modeling and multi-agent systems, in supplier selection, as its modeling has always been a challenge for companies due to its complex nature

Humans and the Core Partition: An Agent-Based Modeling Experiment

Although strategic coalition formation is traditionally modeled using cooperative game theory, behavioral game theorists have repeatedly shown that outcomes predicted by game theory are different from those generated by actual human behavior. To further explore these differences, in a cooperative game theory context, we experiment to compare the outcomes resulting from human participants’ behavior to those generated by a cooperative game theory solution mechanism called the core partition. Our experiment uses an interactive simulation of a glove game, a particular type of cooperative game, to collect the participant’s decision choices and their resultant outcomes. Two different glove games are considered, and the outputs from 62 trial games are analyzed. The experiment’s outcomes show that core coalitions, which are coalitions in a core partition, are found in about 42% of games. Though this number may seem low, a trial’s outcome is more complex than whether the human player finds a core coalition or not. Finding the core coalition depends on factors such as the other possible feasible solutions and the payoffs available from these solutions. These factors, and the complexity they generate, are discussed in the paper

Interactive Agent-Based Simulation for Experimentation: A Case Study with Cooperatve Game Theory

Incorporating human behavior is a current challenge for agent-based modeling and simulation (ABMS). Human behavior includes many different aspects depending on the scenario considered. The scenario context of this paper is strategic coalition formation, which is traditionally modeled using cooperative game theory, but we use ABMS instead; as such, it needs to be validated. One approach to validation is to compare the recorded behavior of humans to what was observed in our simulation. We suggest that using an interactive simulation is a good approach to collecting the necessary human behavior data because the humans would be playing in precisely the same context as the computerized agents. However, such a validation approach may be suspectable to extraneous effects. In this paper, we conducted a correlation research experiment that included an investigation into whether game theory experience, an extraneous variable, affects human behavior in our interactive simulation; our results indicate that it did not make a significant difference. However, in only 42 percent of the trials did the human participants’ behavior result in an outcome predicted by the underlying theory used in our model, i.e., cooperative game theory. This paper also provides a detailed case study for creating an interactive simulation for experimentation