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A Discrete-Choice Approach to Modeling Social Influence on Individual Decision Making

Author

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  • Antonio Páez
  • Darren M Scott
  • Erik Volz

    (Department of Integrative Biology, University of Texas at Austin, 1 University Station C0930 Austin, TX 78712, USA)

Abstract

Individual decision making is commonly studied using discrete choice models. Models of this type are applied extensively to the study of travel behavior, residential location, and employment decisions, among other topics of interest. A notable characteristic of the underlying economic theory is the assumption that individuals seek to maximize utility on the basis of their personal attributes and the attributes of the alternatives available to them. This approach ignores the interrelated nature of decision making in social situations—in other words, the role that social structures play in shaping behavior. In this paper we describe a multinomial discrete choice approach to analyzing individual behavior in social situations where position in a social network may encourage or discourage different courses of action. By means of a simulation example, we explore some properties of the model, in particular the effect of network topology.

Suggested Citation

  • Antonio Páez & Darren M Scott & Erik Volz, 2008. "A Discrete-Choice Approach to Modeling Social Influence on Individual Decision Making," Environment and Planning B, , vol. 35(6), pages 1055-1069, December.
    • Handle: RePEc:sae:envirb:v:35:y:2008:i:6:p:1055-1069
    DOI: 10.1068/b3320t
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    References listed on IDEAS

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    Cited by:

    1. Smirnov, Oleg A. & Egan, Kevin J., 2012. "Spatial random utility model with an application to recreation demand," Economic Modelling, Elsevier, vol. 29(1), pages 72-78.
    2. López, Fernando A. & Páez, Antonio & Carrasco, Juan A. & Ruminot, Natalia A., 2017. "Vulnerability of nodes under controlled network topology and flow autocorrelation conditions," Journal of Transport Geography, Elsevier, vol. 59(C), pages 77-87.
    3. Wang, Chih-Hao & Akar, Gulsah & Guldmann, Jean-Michel, 2015. "Do your neighbors affect your bicycling choice? A spatial probit model for bicycling to The Ohio State University," Journal of Transport Geography, Elsevier, vol. 42(C), pages 122-130.
    4. Pike, Susan & Lubell, Mark, 2018. "The conditional effects of social influence in transportation mode choice," Research in Transportation Economics, Elsevier, vol. 68(C), pages 2-10.
    5. Chih-Hao Wang & Hongwei Dong, 2017. "Responding to the Drought: A Spatial Statistical Approach to Investigating Residential Water Consumption in Fresno, California," Sustainability, MDPI, vol. 9(2), pages 1-15, February.
    6. Lin, Tao & Wang, Donggen & Zhou, Meng, 2018. "Residential relocation and changes in travel behavior: what is the role of social context change?," Transportation Research Part A: Policy and Practice, Elsevier, vol. 111(C), pages 360-374.
    7. Wei, Fangfang & Jia, Ning & Ma, Shoufeng, 2016. "Day-to-day traffic dynamics considering social interaction: From individual route choice behavior to a network flow model," Transportation Research Part B: Methodological, Elsevier, vol. 94(C), pages 335-354.
    8. Di Porto Edoardo & Revelli Federico, 2009. "Central Command, Local Hazard and the Race to the Top," Department of Economics and Statistics Cognetti de Martiis. Working Papers 200909, University of Turin.
    9. Maness, Michael & Cirillo, Cinzia & Dugundji, Elenna R., 2015. "Generalized behavioral framework for choice models of social influence: Behavioral and data concerns in travel behavior," Journal of Transport Geography, Elsevier, vol. 46(C), pages 137-150.
    10. Smirnov, Oleg A., 2010. "Modeling spatial discrete choice," Regional Science and Urban Economics, Elsevier, vol. 40(5), pages 292-298, September.
    11. Kowald, Matthias & van den Berg, Pauline & Frei, Andreas & Carrasco, Juan-Antonio & Arentze, Theo & Axhausen, Kay & Mok, Diana & Timmermans, Harry & Wellman, Barry, 2013. "Distance patterns of personal networks in four countries: a comparative study," Journal of Transport Geography, Elsevier, vol. 31(C), pages 236-248.
    12. Shen, Yu & de Abreu e Silva, João & Martínez, Luis Miguel, 2014. "Assessing High-Speed Rail’s impacts on land cover change in large urban areas based on spatial mixed logit methods: a case study of Madrid Atocha railway station from 1990 to 2006," Journal of Transport Geography, Elsevier, vol. 41(C), pages 184-196.
    13. Pan, Xiaofeng & Rasouli, Soora & Timmermans, Harry, 2019. "Modeling social influence using sequential stated adaptation experiments: A study of city trip itinerary choice," Transportation Research Part A: Policy and Practice, Elsevier, vol. 130(C), pages 652-672.
    14. Wang, Xiaokun (Cara) & Kockelman, Kara M. & Lemp, Jason D., 2012. "The dynamic spatial multinomial probit model: analysis of land use change using parcel-level data," Journal of Transport Geography, Elsevier, vol. 24(C), pages 77-88.

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