Note: Experiments for the hackathon were run on the andy_6pm_exps branch.

We chose the following set of metrics. Since rewards and items differ between rounds, we normalize all metrics to the maximum total utility possible in that round.

1. Welfare [aka "Net Utility"]: The sum of all agents' utility. How much value did we capture in total compared to what could have been captured?
2. Inequality [aka "Fairness"]: on average, how big was the utility gap between agents? i.e. utility_agent_one - utility_agent_two [Gandhi et a1., 2023]
3. Reward: On average, how much utility did each agent end up with? We experiment with agents with different initial moral mappings: baseline agents are not given a specific set of values, but simply prompted to think strategically. Machiavellian agents are prompted to maximize their own rewards by any means possible. Prosocial agents are prompted to look for a fair compromise, while deceptive agents are specifically encouraged that they can misrepresent their own rewards.