This study explores the optimal settings for a prevalent simulation in which agents powered by large language models (LLMs) make decisions without predetermined actions as a substitute for economic experiments. Economic experiments are essential methods in economics where the behaviors of participants are observed under controlled conditions to test hypotheses and theories, involving significant time, effort, and cost. If a simulation can supplant economic experiments, researchers can overcome these limitations by utilizing it. We focused on three essential factors for applying the simulation using LLMs to economic experiments and conducted simulations for both the proposer and responder sides of the one-shot ultimatum game in various settings of the three factors. The sensitivity analysis revealed that, on the proposer side, there was a setting that produced results similar to actual human-centered experiments. However, on the responder side, none of the simulation setups yielded results consistent with real-world experimental data. These findings suggest both the potential and limitations of the novel simulation with LLMs as a substitute for economic experiments.

In this study, we evaluated the impact of introducing a new transportation system, bus rapid transit (BRT), on traffic congestion using a multi-agent simulation (MAS). The rapid urbanisation accompanying the development of cities worldwide, with a growing population of over one million people living in cities, is expected to result in increased traffic congestion. Therefore, addressing this problem has become increasingly urgent. One of the potential solutions is the BRT system, which has attracted considerable interest. However, research on its effectiveness in alleviating congestion has not been sufficiently conducted, which was the focus of this study.We conducted MAS using the tool Simulation of Urban MObility (SUMO), focusing on the Eastern Saitama region as a case study and evaluated the impact of congestion relief resulting from the introduction of BRT.We observed that the introduction of BRT had a positive impact on increasing the maximum traffic flow rate and contributed to congestion relief through the adjustment of BRT headways. Additionally, we obtained suggestions for further congestion relief by designing of dedicated BRT lanes. The outcomes of this study can be leveraged to provide strategic recommendations and decision-making support for road construction and BRT introduction plans.

It is becoming common to apply location information for actual problems such as automatic routing, creating tour, and the prediction of user activity. In many of these cases, they use a geodata that is based on a user's self check-in. In this study, to focus on a user's daily behavior deeply, we use a large volume of geospatial data, which is detected regularly and automatically from a cell phone's GPS system and the volume of data is totally different from the self check-in based. We can comprehend users activity in more detail than self check-in based data. The main purpose of this research is to extract and interpret user preferences from big geospatial data. Because the auto-detected data is big and spatial, it is necessary to process and analyze it for transformation to a value. Firstly, we extract the spots where people visited and stayed from raw geodata. Next, we categorize spots using the spot information to reduce the dimension. Finally, we interpret user preferences by applying a topic model to the matrix of users and the categorized spot. Our results show that we can interpret user preferences as topics from big geospatial data using dimension reduction and a topic model. In addition, we verify a performance of methods that use the topics to predict purchase. We found that the models that use topics outperforms others on the real prediction problem.