1,131 research outputs found
Uncertainty in Multi-Commodity Routing Networks: When does it help?
We study the equilibrium behavior in a multi-commodity selfish routing game
with many types of uncertain users where each user over- or under-estimates
their congestion costs by a multiplicative factor. Surprisingly, we find that
uncertainties in different directions have qualitatively distinct impacts on
equilibria. Namely, contrary to the usual notion that uncertainty increases
inefficiencies, network congestion actually decreases when users over-estimate
their costs. On the other hand, under-estimation of costs leads to increased
congestion. We apply these results to urban transportation networks, where
drivers have different estimates about the cost of congestion. In light of the
dynamic pricing policies aimed at tackling congestion, our results indicate
that users' perception of these prices can significantly impact the policy's
efficacy, and "caution in the face of uncertainty" leads to favorable network
conditions.Comment: Currently under revie
Information Design in Large Anonymous Games
We consider anonymous Bayesian cost games with a large number of players,
i.e., games where each player aims at minimizing a cost function that depends
on the action chosen by the player, the distribution of the other players'
actions and an unknown parameter. We study the nonatomic limit versions of
these games. In particular, we introduce the concepts of correlated and Bayes
correlated Wardrop equilibria, which extend the concepts of correlated and
Bayes correlated equilibria to nonatomic games. We prove that (Bayes)
correlated Wardrop equilibria are indeed limits of action flow distributions
induced by (Bayes) correlated equilibria of the game with a large finite set of
small players. For nonatomic games with complete information admitting a convex
potential, we show that the set of correlated Wardrop equilibria is the set of
probability distributions over Wardrop equilibria. Then, we study how to
implement optimal Bayes correlated Wardrop equilibria and show that in games
with a convex potential, every Bayes correlated Wardrop equilibrium can be
fully implemented.Comment: 53 page
Information Design for Congested Social Services: Optimal Need-Based Persuasion
We study the effectiveness of information design in reducing congestion in
social services catering to users with varied levels of need. In the absence of
price discrimination and centralized admission, the provider relies on sharing
information about wait times to improve welfare. We consider a stylized model
with heterogeneous users who differ in their private outside options: low-need
users have an acceptable outside option to the social service, whereas
high-need users have no viable outside option. Upon arrival, a user decides to
wait for the service by joining an unobservable first-come-first-serve queue,
or leave and seek her outside option. To reduce congestion and improve social
outcomes, the service provider seeks to persuade more low-need users to avail
their outside option, and thus better serve high-need users. We characterize
the Pareto-optimal signaling mechanisms and compare their welfare outcomes
against several benchmarks. We show that if either type is the overwhelming
majority of the population, information design does not provide improvement
over sharing full information or no information. On the other hand, when the
population is a mixture of the two types, information design not only Pareto
dominates full-information and no-information mechanisms, in some regimes it
also achieves the same welfare as the "first-best", i.e., the Pareto-optimal
centralized admission policy with knowledge of users' types.Comment: Accepted for publication in the 21st ACM Conference on Economics and
Computation (EC'20). 40 pages, 6 figure
Optimal information in Bayesian routing games
We study optimal information provision in transportation networks when users
are strategic and the network state is uncertain. An omniscient planner
observes the network state and discloses information to the users with the goal
of minimizing the expected travel time at the user equilibrium. Public signal
policies, including full-information disclosure, are known to be inefficient in
achieving optimality. For this reason, we focus on private signals and restrict
without loss of generality the analysis to signals that coincide with path
recommendations that satisfy obedience constraints, namely users have no
incentive in deviating from the received recommendation according to their
posterior belief. We first formulate the general problem and analyze its
properties for arbitrary network topologies and delay functions. Then, we
consider the case of two parallel links with affine delay functions, and
provide sufficient conditions under which optimality can be achieved by
information design. Interestingly, we observe that the system benefits from
uncertainty, namely it is easier for the planner to achieve optimality when the
variance of the uncertain parameters is large. We then provide an example where
optimality can be achieved even if the sufficient conditions for optimality are
not met.Comment: 8 pages, 3 figures. Full version of accepted paper for the 2023 62th
IEEE Conference on Decision and Control (CDC
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