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Maximizing Utility or Avoiding Losses? Uncovering Decision Rule-Heterogeneity in Sociological Research with an Application to Neighbourhood Choice

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  • Ulf Liebe
  • Sander van Cranenburgh
  • Caspar Chorus

Abstract

Empirical studies on individual behaviour often, implicitly or explicitly, assume a single type of decision rule. Other studies do not specify behavioural assumptions at all. We advance sociological research by introducing (random) regret minimization, which is related to loss aversion, into the sociological literature and by testing it against (random) utility maximization, which is the most prominent decision rule in sociological research on individual behaviour. With an application to neighbourhood choice, in a sample of four European cities, we combine stated choice experiment data and discrete choice modelling techniques and find a considerable degree of decision rule-heterogeneity, with a strong prevalence of regret minimization and hence loss aversion. We also provide indicative evidence that decision rules can affect expected neighbourhood demand at the macro level. Our approach allows identifying heterogeneity in decision rules, that is, the degree of regret/loss aversion, at the level of choice attributes such as the share of foreigners when comparing neighbourhoods, and can improve sociological practice related to linking theories and social research on decision-making.

Suggested Citation

  • Ulf Liebe & Sander van Cranenburgh & Caspar Chorus, 2025. "Maximizing Utility or Avoiding Losses? Uncovering Decision Rule-Heterogeneity in Sociological Research with an Application to Neighbourhood Choice," Sociological Methods & Research, , vol. 54(1), pages 275-314, February.
  • Handle: RePEc:sae:somere:v:54:y:2025:i:1:p:275-314
    DOI: 10.1177/00491241231186657
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    References listed on IDEAS

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    1. Train,Kenneth E., 2009. "Discrete Choice Methods with Simulation," Cambridge Books, Cambridge University Press, number 9780521766555, August.
    2. Caspar G. Chorus, 2012. "A Random Regret Minimization-based Discrete Choice Model," SpringerBriefs in Business, in: Random Regret-based Discrete Choice Modeling, edition 127, chapter 0, pages 5-15, Springer.
    3. Caspar G. Chorus, 2012. "Random Regret-based Discrete Choice Modeling," SpringerBriefs in Business, Springer, edition 127, number 978-3-642-29151-7, January.
    4. Stephane Hess & Amanda Stathopoulos & Andrew Daly, 2012. "Allowing for heterogeneous decision rules in discrete choice models: an approach and four case studies," Transportation, Springer, vol. 39(3), pages 565-591, May.
    5. Hess, Stephane & Palma, David, 2019. "Apollo: A flexible, powerful and customisable freeware package for choice model estimation and application," Journal of choice modelling, Elsevier, vol. 32(C), pages 1-1.
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