21,623 research outputs found
The Green Choice: Learning and Influencing Human Decisions on Shared Roads
Autonomous vehicles have the potential to increase the capacity of roads via
platooning, even when human drivers and autonomous vehicles share roads.
However, when users of a road network choose their routes selfishly, the
resulting traffic configuration may be very inefficient. Because of this, we
consider how to influence human decisions so as to decrease congestion on these
roads. We consider a network of parallel roads with two modes of
transportation: (i) human drivers who will choose the quickest route available
to them, and (ii) ride hailing service which provides an array of autonomous
vehicle ride options, each with different prices, to users. In this work, we
seek to design these prices so that when autonomous service users choose from
these options and human drivers selfishly choose their resulting routes, road
usage is maximized and transit delay is minimized. To do so, we formalize a
model of how autonomous service users make choices between routes with
different price/delay values. Developing a preference-based algorithm to learn
the preferences of the users, and using a vehicle flow model related to the
Fundamental Diagram of Traffic, we formulate a planning optimization to
maximize a social objective and demonstrate the benefit of the proposed routing
and learning scheme.Comment: Submitted to CDC 201
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Contextual advertising
Contextual advertising entails the display of relevant ads based on the content that consumers view, exploiting the potential that consumers' content preferences are indicative of their product preferences. This paper studies the strategic aspects of such advertising, considering an intermediary who has access to a content base, sells advertising space to advertisers who compete in the product market, and provides the targeting technology. The results show that contextual targeting impacts advertiser profit in two ways: First, advertising through relevant content topics helps advertisers reach consumers with a strong preference for their product. Second, heterogeneity in consumers' content preferences can be leveraged to reduce product market competition, especially when competition is intense. The intermediary has incentives to strategically design its targeting technology, sometimes at the cost of the advertisers. When product market competition is moderate, the intermediary offers accurate targeting such that the consumers see the most relevant ads. When competition is high, the intermediary lowers the targeting accuracy such that the consumers see less relevant ads. Doing so intensifies competition and encourages advertisers to bid for multiple content topics in order to prevent their competitors from reaching consumers. In some cases, this may lead to an asymmetric equilibrium where one advertiser bids high even for the content topic that is more relevant to its competitor. © 2012 INFORMS
Emergence of Equilibria from Individual Strategies in Online Content Diffusion
Social scientists have observed that human behavior in society can often be
modeled as corresponding to a threshold type policy. A new behavior would
propagate by a procedure in which an individual adopts the new behavior if the
fraction of his neighbors or friends having adopted the new behavior exceeds
some threshold. In this paper we study the question of whether the emergence of
threshold policies may be modeled as a result of some rational process which
would describe the behavior of non-cooperative rational members of some social
network. We focus on situations in which individuals take the decision whether
to access or not some content, based on the number of views that the content
has. Our analysis aims at understanding not only the behavior of individuals,
but also the way in which information about the quality of a given content can
be deduced from view counts when only part of the viewers that access the
content are informed about its quality. In this paper we present a game
formulation for the behavior of individuals using a meanfield model: the number
of individuals is approximated by a continuum of atomless players and for which
the Wardrop equilibrium is the solution concept. We derive conditions on the
problem's parameters that result indeed in the emergence of threshold
equilibria policies. But we also identify some parameters in which other
structures are obtained for the equilibrium behavior of individuals
Joint Head Selection and Airtime Allocation for Data Dissemination in Mobile Social Networks
Mobile social networks (MSNs) enable people with similar interests to
interact without Internet access. By forming a temporary group, users can
disseminate their data to other interested users in proximity with short-range
communication technologies. However, due to user mobility, airtime available
for users in the same group to disseminate data is limited. In addition, for
practical consideration, a star network topology among users in the group is
expected. For the former, unfair airtime allocation among the users will
undermine their willingness to participate in MSNs. For the latter, a group
head is required to connect other users. These two problems have to be properly
addressed to enable real implementation and adoption of MSNs. To this aim, we
propose a Nash bargaining-based joint head selection and airtime allocation
scheme for data dissemination within the group. Specifically, the bargaining
game of joint head selection and airtime allocation is first formulated. Then,
Nash bargaining solution (NBS) based optimization problems are proposed for a
homogeneous case and a more general heterogeneous case. For both cases, the
existence of solution to the optimization problem is proved, which guarantees
Pareto optimality and proportional fairness. Next, an algorithm, allowing
distributed implementation, for join head selection and airtime allocation is
introduced. Finally, numerical results are presented to evaluate the
performance, validate intuitions and derive insights of the proposed scheme
The smart home in the mind and in the practice of digital natives. The case of “Sapienza” University
Smart home e giovani: quale la percezione? La presente indagine pilota, effettuata da un gruppo di studiosi dell’Università Sapienza di Roma mira ad analizzarne i risultati, rappresentando una ricognizione essenziale di quello che è l’universo dei giovani in relazione al mondo smart e alla domotica. L’Ateneo Sapienza sposa appieno la sfida lanciata da Horizon 2020 con il progetto ReStart4Smart, un laboratorio pratico in cui poter conoscere e sperimentare, fare ricerca e innovare, condividere e divulgare, tanto problemi quanto, e più possibile, soluzioni ambientali ed abitative. Chi sono realmente i nativi digitali? E qual è il loro livello di conoscenza della smart home? Quali i valori e quali i comportamenti concreti in relazione all’utilizzo intelligente delle nuove tecnologie
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