{
  "id": "1406.7639",
  "version": "v2",
  "published": "2014-06-30T09:15:59.000Z",
  "updated": "2015-06-18T15:15:13.000Z",
  "title": "Signalling and obfuscation for congestion control",
  "authors": [
    "Jakub Marecek",
    "Robert Shorten",
    "Jia Yuan Yu"
  ],
  "comment": "in International Journal of Control, 2015",
  "doi": "10.1080/00207179.2015.1033758",
  "categories": [
    "math.OC",
    "cs.MA",
    "math.DS"
  ],
  "abstract": "We aim to reduce the social cost of congestion in many smart city applications. In our model of congestion, agents interact over limited resources after receiving signals from a central agent that observes the state of congestion in real time. Under natural models of agent populations, we develop new signalling schemes and show that by introducing a non-trivial amount of uncertainty in the signals, we reduce the social cost of congestion, i.e., improve social welfare. The signalling schemes are efficient in terms of both communication and computation, and are consistent with past observations of the congestion. Moreover, the resulting population dynamics converge under reasonable assumptions.",
  "revisions": [
    {
      "version": "v1",
      "updated": "2014-06-30T09:15:59.000Z",
      "title": "Congestion Management by Obfuscation",
      "abstract": "We aim to reduce congestion through better signaling. That is, we propose signals that a central agent with real-time data on the congestion across a number of resources should send to the users, who compete for the limited resources, in order to improve social welfare. We model a heterogeneous population of users and the interactions of this population with the central agent over time. Even in our intentionally simple model, our findings are counter-intuitive: communicating too much information about the state of congestion can be detrimental to future congestion. Moreover, the population dynamics resulting from repeated interactions, where some information is withheld, while ensuring that the signals remain consistent with the past observations, converge in distribution.",
      "comment": null,
      "journal": null,
      "doi": null
    },
    {
      "version": "v2",
      "updated": "2015-06-18T15:15:13.000Z"
    }
  ],
  "analyses": {
    "keywords": [
      "congestion control",
      "social cost",
      "obfuscation",
      "resulting population dynamics converge",
      "signalling schemes"
    ],
    "tags": [
      "journal article"
    ],
    "publication": {
      "publisher": "Taylor-Francis"
    },
    "note": {
      "typesetting": "TeX",
      "pages": 0,
      "language": "en",
      "license": "arXiv",
      "status": "editable",
      "adsabs": "2014arXiv1406.7639M"
    }
  }
}