10,971 research outputs found
Strategy-Proof Facility Location for Concave Cost Functions
We consider k-Facility Location games, where n strategic agents report their
locations on the real line, and a mechanism maps them to k facilities. Each
agent seeks to minimize his connection cost, given by a nonnegative increasing
function of his distance to the nearest facility. Departing from previous work,
that mostly considers the identity cost function, we are interested in
mechanisms without payments that are (group) strategyproof for any given cost
function, and achieve a good approximation ratio for the social cost and/or the
maximum cost of the agents.
We present a randomized mechanism, called Equal Cost, which is group
strategyproof and achieves a bounded approximation ratio for all k and n, for
any given concave cost function. The approximation ratio is at most 2 for Max
Cost and at most n for Social Cost. To the best of our knowledge, this is the
first mechanism with a bounded approximation ratio for instances with k > 2
facilities and any number of agents. Our result implies an interesting
separation between deterministic mechanisms, whose approximation ratio for Max
Cost jumps from 2 to unbounded when k increases from 2 to 3, and randomized
mechanisms, whose approximation ratio remains at most 2 for all k. On the
negative side, we exclude the possibility of a mechanism with the properties of
Equal Cost for strictly convex cost functions. We also present a randomized
mechanism, called Pick the Loser, which applies to instances with k facilities
and n = k+1 agents, and for any given concave cost function, is strongly group
strategyproof and achieves an approximation ratio of 2 for Social Cost
Facility location with double-peaked preference
We study the problem of locating a single facility on a real line based on
the reports of self-interested agents, when agents have double-peaked
preferences, with the peaks being on opposite sides of their locations. We
observe that double-peaked preferences capture real-life scenarios and thus
complement the well-studied notion of single-peaked preferences. We mainly
focus on the case where peaks are equidistant from the agents' locations and
discuss how our results extend to more general settings. We show that most of
the results for single-peaked preferences do not directly apply to this
setting; this makes the problem essentially more challenging. As our main
contribution, we present a simple truthful-in-expectation mechanism that
achieves an approximation ratio of 1+b/c for both the social and the maximum
cost, where b is the distance of the agent from the peak and c is the minimum
cost of an agent. For the latter case, we provide a 3/2 lower bound on the
approximation ratio of any truthful-in-expectation mechanism. We also study
deterministic mechanisms under some natural conditions, proving lower bounds
and approximation guarantees. We prove that among a large class of reasonable
mechanisms, there is no deterministic mechanism that outperforms our
truthful-in-expectation mechanism
Algorithm Instance Games
This paper introduces algorithm instance games (AIGs) as a conceptual
classification applying to games in which outcomes are resolved from joint
strategies algorithmically. For such games, a fundamental question asks: How do
the details of the algorithm's description influence agents' strategic
behavior?
We analyze two versions of an AIG based on the set-cover optimization
problem. In these games, joint strategies correspond to instances of the
set-cover problem, with each subset (of a given universe of elements)
representing the strategy of a single agent. Outcomes are covers computed from
the joint strategies by a set-cover algorithm. In one variant of this game,
outcomes are computed by a deterministic greedy algorithm, and the other
variant utilizes a non-deterministic form of the greedy algorithm. We
characterize Nash equilibrium strategies for both versions of the game, finding
that agents' strategies can vary considerably between the two settings. In
particular, we find that the version of the game based on the deterministic
algorithm only admits Nash equilibrium in which agents choose strategies (i.e.,
subsets) containing at most one element, with no two agents picking the same
element. On the other hand, in the version of the game based on the
non-deterministic algorithm, Nash equilibrium strategies can include agents
with zero, one, or every element, and the same element can appear in the
strategies of multiple agents.Comment: 14 page
Verifiably Truthful Mechanisms
It is typically expected that if a mechanism is truthful, then the agents
would, indeed, truthfully report their private information. But why would an
agent believe that the mechanism is truthful? We wish to design truthful
mechanisms, whose truthfulness can be verified efficiently (in the
computational sense). Our approach involves three steps: (i) specifying the
structure of mechanisms, (ii) constructing a verification algorithm, and (iii)
measuring the quality of verifiably truthful mechanisms. We demonstrate this
approach using a case study: approximate mechanism design without money for
facility location
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