Using deep reinforcement learning to promote sustainable human behaviour on a common pool resource problem (2404.15059v1)

Abstract: A canonical social dilemma arises when finite resources are allocated to a group of people, who can choose to either reciprocate with interest, or keep the proceeds for themselves. What resource allocation mechanisms will encourage levels of reciprocation that sustain the commons? Here, in an iterated multiplayer trust game, we use deep reinforcement learning (RL) to design an allocation mechanism that endogenously promotes sustainable contributions from human participants to a common pool resource. We first trained neural networks to behave like human players, creating a stimulated economy that allowed us to study how different mechanisms influenced the dynamics of receipt and reciprocation. We then used RL to train a social planner to maximise aggregate return to players. The social planner discovered a redistributive policy that led to a large surplus and an inclusive economy, in which players made roughly equal gains. The RL agent increased human surplus over baseline mechanisms based on unrestricted welfare or conditional cooperation, by conditioning its generosity on available resources and temporarily sanctioning defectors by allocating fewer resources to them. Examining the AI policy allowed us to develop an explainable mechanism that performed similarly and was more popular among players. Deep reinforcement learning can be used to discover mechanisms that promote sustainable human behaviour.

Deep Reinforcement Learning Enhances Sustainable Human Cooperation in Resource Allocation

Introduction and Background

Historically, the design of mechanisms that encourage cooperative behavior in situations where individual incentives might lead to resource depletion (the so-called "common pool resource" problem) has challenged economists and social scientists. Existing solutions often rely on participants' ability to communicate and penalize non-cooperators, an approach not always feasible in real-world scenarios. This paper introduces a novel approach utilizing artificial intelligence to address this gap.

Methodology

The researchers employed a multiplayer trust game simulating a scenario where participants, tasked with maintaining a common resource, decide on their contribution levels round-by-round. Crucially, a deep reinforcement learning (RL) model acted as a social planner, dynamically allocating resources based on a comprehensive set of player behaviors and game states, aiming to maximize overall player surplus—a measure of collective benefit.

Key Experiments and Findings

Initial testing compared the RL-designed mechanism against baseline mechanisms varying from equal to proportional allocations based on past contributions. Traditional methods either encouraged free-riding or led to resource monopoly by a single player due to their inability to adapt allocation strategies dynamically based on the resource pool's status.

The RL mechanism, however, adapted its strategy to maximize long-term benefits dynamically. Key findings were:

• Unlike static mechanisms, the RL model could incentivize sustainable contributions without preset rules for communication or penalties.
• It successfully prevented the free-rider problem and avoided the monopolization risks associated with proportional allocation strategies.
• Remarkably, the RL model outperformed all baseline scenarios in maintaining a higher resource pool and ensuring a fair distribution of resources among players.

Implications and Future Directions

This research showcases the potential of integrating advanced AI techniques with economic theory to solve complex societal challenges like sustainable resource management. The RL mechanism not only adapted allocations based on available resources and player actions effectively but also operated transparently, enhancing participant satisfaction.

The scalability of this approach to broader applications, such as public policy around natural resources, corporate resource allocation, or global economic planning, is promising. Future studies could explore the integration of such AI-driven mechanisms into real-world economic systems, testing adaptability and effectiveness across diverse and large-scale settings.

Conclusion

The utilization of deep reinforcement learning in designing resource allocation mechanisms offers a novel way to promote sustainable behavior in settings characterized by shared resources. By dynamically adjusting allocations based on real-time conditions and behaviors, AI-driven models can significantly enhance cooperation and sustainability, highlighting a promising intersection between AI technology and economic theory.