# Enhancing User Acceptance of an AI Agent's Recommendation in Information-Sharing Environments
## 3 The Empirical Study
### Empirical Study Environment  
The selected information-sharing environment was a simulator that mimics the real Facebook OSN platform. While the functionality of the simulated environment was similar to the real environment, conducting an experiment on this platform provides better control over the process. The Facebook simulator was based on time beats, each representing a round of the process. In each round, each user can carry out one of the four following operations: (a) publish a post; (b) like a post (same as the actual Facebook \"Like\") for posts that were categorized as safe or unsafe (e.g., phishing); (c) make a friendship request; and (d) accept a friendship request. The simulator enables the participation of a few users simultaneously who interact with each other. For example, suppose a user chooses to publish a post. In that case, the probability of it appearing in other users' feeds is proportional to previous interactions (e.g., clicking the \"Like\" button) between them. In our framework, the AI agent presented to the participants operated within the simulator to analyze data and generate strategic recommendations by leveraging a combination of heuristic game-theory algorithms. For this specific empirical study, the AI agent utilized an enhanced version of the Monte Carlo Tree Search (MCTS) algorithm, adapted to suit the complexities of the information-sharing environment. The integration of the MCTS in the framework was designed to address the strategic decision-making challenges inherent in the trade-offs between the benefits and risks of information sharing. Rather than relying on large datasets or traditional machine learning (ML) techniques, based on the provided parameters, the possible scenarios were presented as a probability tree diagram, which is a directed graph. Given the high complexity of finding the optimal solution out of the strategy space spanned by this tree, a heuristic search algorithm inspired by the MCTS was applied, as described in [19]. This approach enables the system to perform strategic reasoning in uncertain environments, offering adaptive and near-optimal strategies. The entire mechanism and algorithm selection were not introduced to the participants, who were only informed that an AI agent assistant was available to offer recommendations on how to act in the information-sharing environment. This way, the research main index, the acceptance rate of the user recommendations, could be measured with minimal bias resulting from the adapted technology. The environment was created using the Python programming language (v. 3.10), the Django web framework was applied on the server side, and the database was based on PostgreSQL. Apache was used to implement the HTTP server, and Bootstrap plus CSS and JS for the user interface. The entire environment was hosted on a Google Cloud virtual machine. The experiment was conducted with human participants in a simulated Facebook environment, where the AI agent provided recommendations to the participants (each one independently) regarding their information-sharing decisions. Each participant was allocated one of the 64 experiment layouts, and the participants were grouped into sessions of four participants each, for a total of 16 sessions. The platform was created digitally, thus the participants did not interact outside the simulated environment. For each participant, the experiment simulated a series of 4 decision-making rounds, each with potential losses and rewards. Participants were presented with a choice of an information-sharing action in each round (e.g., posting, like, friendship request, etc.). According to the experiment design, the AI agents' recommendations were presented to each participant with a mixture of components that was assigned to him/her. The acceptance rate of each participant was recorded simply by comparing the AI recommendation to the action that was actually performed by the participant.