一种促进家庭负载均衡中社会行为的机制
A mechanism to promote social behaviour in household load balancing
AI总结：
提出一种基于社会资本的去中心化时段交换机制，通过跟踪智能体间的恩惠激励灵活性与给予行为，提升家庭负载均衡中的整体满意度，并使社会型智能体在收益偏向的社会学习中胜过自私型智能体。
摘要：
降低家庭的峰值能源消耗对于可再生能源的有效利用至关重要，以确保尽可能多的家庭需求能由可再生能源满足。这需要将洗碗机和洗衣机等大功率电器的使用分散到全天各个时段。解决这一问题的传统方法依赖于由集中式公用事业公司制定的差别定价。但这一机制在促进电器使用的广泛转移方面并不有效。在此，我们考虑一种替代性的去中心化机制，其中智能体（agents）获得使用电器的初始时段分配，随后可与其他智能体进行交换。如果智能体愿意在所接受的交换中表现得更加灵活，那么整体满意度（以被满足的智能体时段偏好百分比衡量）将会提高。这需要一种能激励智能体变得更灵活的机制。基于先前的工作，我们表明，一种纳入社会资本（social capital）——即跟踪给予和接受的恩惠（favours）——的机制，能够激励智能体灵活行事，并通过接受那些不能立即使其获益的交换来给予恩惠。我们证明，跟踪恩惠的机制提高了智能体的整体满意度，并且至关重要的是，在基于收益的社会学习（payoff-biased social learning）下，允许给予恩惠的社会型智能体胜过不这样做的自私型智能体。因此，即使是完全自利的智能体，也有望学会产生对社会有益的结果。
英文摘要：
Reducing the peak energy consumption of households is essential for the effective use of renewable energy sources, in order to ensure that as much household demand as possible can be met by renewable sources. This entails spreading out the use of high-powered appliances such as dishwashers and washing machines throughout the day. Traditional approaches to this problem have relied on differential pricing set by a centralised utility company. But this mechanism has not been effective in promoting widespread shifting of appliance usage. Here we consider an alternative decentralised mechanism, where agents receive an initial allocation of time-slots to use their appliances and can then exchange these with other agents. If agents are willing to be more flexible in the exchanges they accept, then overall satisfaction, in terms of the percentage of agents time-slot preferences that are satisfied, will increase. This requires a mechanism that can incentivise agents to be more flexible. Building on previous work, we show that a mechanism incorporating social capital - the tracking of favours given and received - can incentivise agents to act flexibly and give favours by accepting exchanges that do not immediately benefit them. We demonstrate that a mechanism that tracks favours increases the overall satisfaction of agents, and crucially allows social agents that give favours to outcompete selfish agents that do not under payoff-biased social learning. Thus, even completely self-interested agents are expected to learn to produce socially beneficial outcomes.
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