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Traffic management problems provide a unique environment to study how multi-agent systems promote desired system level behavior. In particular, they represent a special class of problems where the individual actions of the agents are neither intrinsically “good” nor “bad” for the system. Instead, it is the combinations of actions among agents that lead to desirable or undesirable outcomes. As a consequence, agents need to learn how to coordinate their actions with those of other agents, rather than learn a particular set of “good” actions. In this chapter, the authors focus on problems where there is no communication among the drivers, which puts the burden of coordination on the principled selection of the agent reward functions. They explore the impact of agent reward functions on two types of traffic problems. In the first problem, the authors study how agents learn the best departure times in a daily commuting environment and how following those departure times alleviates congestion. In the second problem, the authors study how agents learn to select desirable lanes to improve traffic flow and minimize delays for all drivers. In both cases, they focus on having an agent select the most suitable action for each driver using reinforcement learning, and explore the impact of different reward functions on system behavior. Their results show that agent rewards that are both aligned with and sensitive to, the system reward lead to significantly better results than purely local or global agent rewards. They conclude this chapter by discussing how changing the way in which the system performance is measured affects the relative performance of these rewards functions, and how agent rewards derived for one setting (timely arrivals) can be modified to meet a new system setting (maximize throughput). Document type: Part of book or chapter of book

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The "prospect of upward mobility" (POUM) hypothesis formalised by Benabou and Ok (2001a) finds explicit assumptions under which some individuals that are poorer than the average optimally choose to oppose redistribution policies. The underlying intuition is that these individuals rationally expect to be richer than average in the future. This result holds provided the mobility process is concave in expectations, redistribution policies are expected to last for a sufficiently long period and individuals are not too risk averse. This paper tests the POUM hypothesis by means of a within subjects experiment where the concavity of the mobility process, the degree of social mobility, the knowledge of personal income and the degree of inequality are used as treatments. Other determinants of the demand for redistribution, such as risk aversion and inequality aversion are (partially) controlled for via either the experiment design or the information collected during the experiment. We find that the POUM hypothesis holds under alternative specifications, even when we control for individual fixed effects. Document type: Part of book or chapter of book

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