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Using Sources Trustworthiness in Weather Scenarios: The Special Role of the Authority

  • Rino FalconeEmail author
  • Alessandro Sapienza
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10640)

Abstract

In this work we present a platform shaping citizens’ behavior in case of critical hydrogeological phenomena that can be manipulated in order to realize many possible scenarios. Here the citizens (modeled through cognitive agents) need to identify the risk of a possible critical events, relying of their information sources and of the trustworthiness attributed to them. Thanks to a training phase, the agents will be able to make a rational use of their different information sources: (a) their own evaluation about what could happen in the near future; (b) the information communicated by an authority; (c) the crowd behavior, as an evidence for evaluating the level of danger of the coming hydrogeological event. These weather forecasts are essential for the agents to deal with different meteorological events requiring adequate behaviors. In particular we consider that the authority can be more or less trustworthy and more or less able to deliver its own forecasts to the agents: due to the nature itself of the problem, these two parameters are correlated with each other. The main results of this work are: (1) it is necessary to optimize together both the authority communicativeness and trustworthiness, as optimizing just one aspect will not lead to the best solution; (2) once the authority can reach much of the population it is better to focus on its trustworthiness, since trying to give the information to a larger population could have no effect at all or even a negative effect; (3) the social source is essential to compensate the lack of information that some agents have.

Keywords

Social simulation Cognitive modeling Trust Information sources 

Notes

Acknowledgments

This work is partially supported by the project CLARA CLoud plAtform and smart underground imaging for natural Risk Assessment, funded by the Italian Ministry of Education, University and Research (MIUR-PON).

References

  1. 1.
    Arazy, O., Halfon, N., Malkinson, D.: Collective intelligence for rain prediction. In: Collective Intelligence 2015, Santa Clara, CA, USA, 31 May–2 June (2015)Google Scholar
  2. 2.
    Blahut, J., Poretti, I., De Amicis, M., Sterlacchini, S.: Database of geo-hydrological disasters for civil protection purposes. Nat. Hazards 60(3), 1065–1083 (2012)CrossRefGoogle Scholar
  3. 3.
    Bronfman, N.C., Cisternas, P.C., López-Vázquez, E., Cifuentes, L.A.: Trust and risk perception of natural hazards: implications for risk preparedness in Chile. Nat. Hazards 81(1), 307–327 (2016)CrossRefGoogle Scholar
  4. 4.
    Burnett, C., Norman, T., Sycara, K.: Bootstrapping trust evaluations through stereotypes. In: Proceedings of the 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), pp. 241–248 (2010)Google Scholar
  5. 5.
    Burnett, C., Norman, T., Sycara, K.: Stereotypical trust and bias in dynamic multiagent systems. ACM Trans. Intell. Syst. Technol. (TIST) 4(2), 26 (2013)Google Scholar
  6. 6.
    Castelfranchi, C., Falcone, R.: Trust Theory: A Socio-Cognitive and Computational Model. Wiley, Hoboken (2010)Google Scholar
  7. 7.
    Cohen, O., Goldberg, A., Lahad, M., Aharonson-Daniel, L.: Building resilience: the relationship between information provided by municipal authorities during emergency situations and community resilience. Technol. Forecasting Soc. Change (2016). http://doi.org/10.1016/j.techfore.2016.11.008
  8. 8.
    Conte, R., Paolucci, M.: Reputation in Artificial Societies. Social Beliefs for Social Order. Kluwer Academic Publishers, Boston (2002)CrossRefGoogle Scholar
  9. 9.
    Falcone, R., Castelfranchi, C.: Generalizing trust: inferencing trustworthiness from categories. In: Falcone, R., Barber, S.K., Sabater-Mir, J., Singh, M.P. (eds.) TRUST 2008. LNCS, vol. 5396, pp. 65–80. Springer, Heidelberg (2008).  https://doi.org/10.1007/978-3-540-92803-4_4 CrossRefGoogle Scholar
  10. 10.
    Falcone, R., Piunti, M., Venanzi, M., Castelfranchi, C.: From Manifesta to Krypta: the relevance of categories for trusting others. In: Falcone, R., Singh, M. (eds.) Trust in Multiagent Systems, vol. 4, no. 2, March 2013. ACM Trans. Intell. Syst. Technol. 4(2) (2013)Google Scholar
  11. 11.
    Freedy, J.R., Shaw, D.L., Jarrell, M.P., Masters, C.R.: Towards an understanding of the psychological impact of natural disasters: an application of the conservation resources stress model. J. Traumatic Stress 5(3), 441–454 (1992)CrossRefGoogle Scholar
  12. 12.
    Freedy, J.R., Saladin, M.E., Kilpatrick, D.G., Resnick, H.S., Saunders, B.E.: Understanding acute psychological distress following natural disaster. J. Traumatic Stress 7(2), 257–273 (1994)CrossRefGoogle Scholar
  13. 13.
    Grothmann, T., Reusswig, F.: People at risk of flooding: why some residents take precautionary action while others do not. Nat. Hazards 38(1), 101–120 (2006)CrossRefGoogle Scholar
  14. 14.
    Jia, Z., Tian, W., Liu, W., Cao, Y., Yan, J., Shun, Z.: Are the elderly more vulnerable to psychological impact of natural disaster? A population-based survey of adult survivors of the 2008 Sichuan earthquake. BMC Public Health 10(1), 172 (2010)CrossRefGoogle Scholar
  15. 15.
    Jiang, S., Zhang, J., Ong, Y.S.: An evolutionary model for constructing robust trust networks. In: Proceedings of the 12th International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (2013)Google Scholar
  16. 16.
    Liu, B.: Uncertainty Theory, 5th edn. Springer, Heidelberg (2014)Google Scholar
  17. 17.
    Luino, F., Chiarle, M., Nigrelli, G., Agangi, A., Biddoccu, M., Cirio, C.G., Giulietto, W.: A model for estimating flood damage in Italy: preliminary results. Environ. Econ. Invest. Assess. 98, 65–74 (2006)CrossRefGoogle Scholar
  18. 18.
    Melaye, D., Demazeau, Y.: Bayesian dynamic trust model. In: Pěchouček, M., Petta, P., Varga, L.Z. (eds.) CEEMAS 2005. LNCS, vol. 3690, pp. 480–489. Springer, Heidelberg (2005).  https://doi.org/10.1007/11559221_48 CrossRefGoogle Scholar
  19. 19.
    Quercia, D., Hailes, S., Capra, L.: B-trust: Bayesian trust framework for pervasive computing. In: Stølen K., Winsborough, W.H., Martinelli, F., Massacci, F. (eds.) iTrust 2006. LNCS, vol. 3986, pp. 298–312. Springer, Heidelberg (2006).  https://doi.org/10.1007/11755593_22
  20. 20.
    Sabater-Mir, J.: Trust and reputation for agent societies. Ph.D. thesis, Universitat Autonoma de Barcelona (2003)Google Scholar
  21. 21.
    Sabater-Mir, J., Sierra, C.: Regret: a reputation model for gregarious societies. In: 4th Workshop on Deception and Fraud in Agent Societies, Montreal, Canada, pp. 61–70 (2001)Google Scholar
  22. 22.
    Scawthorn, C., Blais, N., Seligson, H., Tate, E., Mifflin, E., Thomas, W., James Murphy, J., Jones, C.: HAZUS-MH flood loss estimation methodology. I: overview and flood hazard characterization. Nat. Hazards Rev. 7(2), 60–71 (2006)CrossRefGoogle Scholar
  23. 23.
    Wang, Y., Vassileva, J.: Bayesian network-based trust model. In: Proceedings of IEEE/WIC International Conference on Web Intelligence, WI 2003, pp. 372–378. IEEE, October 2003Google Scholar
  24. 24.
    Wilensky, U.: NetLogo. http://ccl.northwestern.edu/netlogo/ Center for Connected Learning and Computer-Based Modeling, Northwestern University, Evanston, IL (1999)
  25. 25.
    Yolum, P., Singh, M.P.: Emergent properties of referral systems. In: Proceedings of the 2nd International Joint Conference on Autonomous Agents and MultiAgent Systems (AAMAS 2003) (2003)Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Institute of Cognitive Sciences and Technologies, ISTC - CNRRomeItaly

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