Here, the data underlying the article "Reputation Communication from an Information Perspective" is provided. There are two example simulations, one with 3 ordinary agents and one with a dominant agent among two ordinary agents. Each simulation is represented by a .json file in which all events that happened during the simulation are collected. Generally, there are three types of events: communications, self-updates (information that the speaker gained about itself is processed) and updates (information that the receiver gained about the speaker and the topic is processed). Additionally, the first line specifies the parameters of each simulation, and the last few lines summarize the final status of the simulation. In the following all important abbreviations are explained: parameters decpeting: whether or not agents in generally make dishonest statements listening: whether or not agents in listen to their communication partners disturbing: whether or not agents are particularly risk-taking when making dishonest statements x_est: intrinsic honesties of the agents RSeed: the used random seed NA: number of agents NR: number of rounds communication a: speaker b: receiver c: topic J: transmitted message in the form of self_update id: number of agent who is updating knowledge about itself Nl, Nt: number of dishonest/honest statements the agent has observed from itself so far I_<id>: knowledge that the agents has about itself after the update in the form of update id: number of agent who is updating its knowledge I_<id1>: knowledge that the updating agent has about agent <id1> in the form of Jothers_<id1>_<id2>: last statement that the updating agent heared agent <id1> make about agent <id2> Iothers_<id1>_<id2>: what the updating agent believes that agent <id1> thinks about agent <id2> after the update Cothers_<id1>_<id2>: what the updating agent believes after the update that agent <id1> wants it to think about agent <id2> new_friends/enemies: id of the agent, the updating agent after the update considers a friend/enemy new_K: normalized surprise the updating agent experienced in the last communication (used to calculate kappa) kappa: median of the last ten normalized surprises the updating agent experienced final_status id/name: number if the described agent x: the agent's honesty I: the agent's knowledge about all others Nc/Nt/Nl: total number of conversations/honest statements/dishonest statements the agent has made K: the last 10 normalized surprises the agent experienced kappa: the median of K friends/enemies: list of the agent's friends/enemies Jothers/Iothers/Cothers: same as above, now as full array, i.e. the combined information about all others openess/mind/decepting/strategic/egocentric/deceptive/flattering/aggressive/shameless/disturbing: the agent's character traits