Urgency-aware optimal routing in repeated games through artificial currencies

When people choose routes minimizing their individual delay, the aggregate congestion can be much higher compared to that experienced by a centrally-imposed routing. Yet centralized routing is incompatible with the presence of self-interested users. How can we reconcile the two? In this paper we address this question within a repeated game framework and propose a fair incentive mechanism based on artificial currencies that routes selfish users in a system-optimal fashion, while accounting for their temporal preferences. We instantiate the framework in a parallel-network whereby users commute repeatedly (e.g., daily) from a common start node to the end node. Thereafter, we focus on the specific two-arcs case whereby, based on an artificial currency, the users are charged when traveling on the first, fast arc, whilst they are rewarded when traveling on the second, slower arc. We assume the users to be rational and model their choices through a game where each user aims at minimizing a combination of today's discomfort, weighted by their urgency, and the average discomfort encountered for the rest of the period (e.g., a week). We show that, if prices of artificial currencies are judiciously chosen, the routing pattern converges to a system-optimal solution, while accommodating the users’ urgency. We complement our study through numerical simulations. Our results show that it is possible to achieve a system-optimal solution whilst significantly reducing the users’ perceived discomfort when compared to a centralized optimal but urgency-unaware policy

Urgency-aware Optimal Routing in Repeated Games through Artificial Currencies

When people choose routes minimizing their individual delay, the aggregate congestion can be much higher compared to that experienced by a centrally-imposed routing. Yet centralized routing is incompatible with the presence of self-interested agents. How can we reconcile the two? In this paper we address this question within a repeated game framework and propose a fair incentive mechanism based on artificial currencies that routes selfish agents in a system-optimal fashion, while accounting for their temporal preferences. We instantiate the framework in a parallel-network whereby agents commute repeatedly (e.g., daily) from a common start node to the end node. Thereafter, we focus on the specific two-arcs case whereby, based on an artificial currency, the agents are charged when traveling on the first, fast arc, whilst they are rewarded when traveling on the second, slower arc. We assume the agents to be rational and model their choices through a game where each agent aims at minimizing a combination of today's discomfort, weighted by their urgency, and the average discomfort encountered for the rest of the period (e.g., a week). We show that, if prices of artificial currencies are judiciously chosen, the routing pattern converges to a system-optimal solution, while accommodating the agents' urgency. We complement our study through numerical simulations. Our results show that it is possible to achieve a system-optimal solution whilst reducing the agents' perceived discomfort by 14-20% when compared to a centralized optimal but urgency-unaware policy.Comment: Accepted for presentation at the European Control Conference 202

On Combining Stated Preferences and Revealed Preferences Approaches to Evaluate Environmental Resources Having a Recreational Use

This work aims at analysing the value of recreational water uses for the Idro Lake (Lombardy, Northern Italy), which has been experiencing dramatic fluctuations in its levels in recent years, due to excessive productive withdrawal that affected recreational uses. It estimates the economic benefits deriving from recreational uses, by considering the current recreational demand and the hypothetical one obtained by considering an “improved quality” scenario. Through an on-site survey, we built a panel dataset. Following Whitehead et al. (2000) and Hanley et al. (2003) we get welfare estimates by combining SP and RP responses. The present CS is estimated in €134 per individual, whilst the increase in CS is estimated in €173 per individual. These figures can be confronted with the economic value of competitive uses and with the clean up costs, respectively, to infer some policy indications