AI and humans cooperate and compete on shared megabikes in a testbed game
A Serious-Game Testbed for Self-Governing Human-Machine Institutions
Multiagent SystemsHuman-Computer Interaction
Summary
Managing groups where humans and AI work together or compete raises questions about cooperation and rules. The authors made a game called Megabike where players share control of a bike and decide how to steer, pedal, and share resources all while competing with others and avoiding dangers. Both humans and AI can play any role, and the game records all actions to study their interactions. The authors tested AI players with different behaviors and found that their mixes could lead to surprising and sometimes bad results. They suggest Megabike could help explore how humans and machines can govern themselves together.
What this means in practice
- •For human-computer interaction teams: Use Megabike to test how different AI and human combinations affect group decision-making and governance in mixed teams.
- •For game developers: Incorporate Megabike’s multi-agent governance scenarios to create engaging multiplayer games that explore cooperation and resource sharing.
Authors
Callum Berger, Jeremy Pitt, Stefan Sarkadi
Abstract
AI agents no longer act alone: they cooperate, coordinate and compete within and between groups, increasingly alongside humans who are in, on or out of ``the loop''. Whether such mixed groups can govern themselves is an open question about machine behaviour and human-computer interaction that needs a testbed for hybrid institutions. We present Megabike, a multi-agent simulator and serious game with interdependent arenas for common-pool resource management, cooperative survival and constitutional choice. Riders sharing a megabike must agree their arrangements for steering, pedalling and distributing resources, and their political regime, while negotiating with rival bikes and avoiding an existential threat. Every rule is enforced by the simulator, every action is logged, and any seat can be taken by a human or a machine, so governance can be studied under well-defined metrics. We demonstrate Megabike with forty sessions in which language-model riders with cooperative, lazy, subversive or opposing dispositions chose regimes, elected leaders and expelled one another. The results show that different combinations of agent archetypes could produce unexpected and counterproductive outcomes. We conclude with extensions for systematic experiments mixing human and computational agents.