Matching and Sorting in Online Dating

By Günter J. Hitsch, Ali Hortaçsu, Dan Ariely. 2010

696 citations recorded by the source corpus.

Subfields: Experimental and Cognitive Psychology, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/e761b4df4f9d07de7e14795a47033c29ea310aeb

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In Experimental and Cognitive Psychology

In Sociology and Political Science

Citations in the corpus

Broadband Internet: An Information Superhighway to Sex Crime? (2013, background): “Hitsch et al. (2010), for example, find that search frictions”
Demographic research with non-representative internet data (2015, background): “There is a fairly large literature: a few examples include works on matching algorithms (Hitsch et al., 2010), the demography of online dating users (Valkenburg and Peter, 2007) and the social demography and economics of online dating (Sautter et al.”
The Dating Preferences of Liberals and Conservatives (2013, background): “…process of assortation, and potentially political polarization as well, as individuals can easily select potential mates that are similar to themselves by searching through detailed profiles before investing the time and energy involved with courtship (Feliciano et al. 2009; Hitsch et al. 2010).”
Beauty is the Promise of Happiness? (2011, positive): “At the same time a smaller, but also burgeoning literature on the effects of beauty on various outcomes has been created (e.g., Hamermesh and Biddle, 1994; Möbius and Rosenblat, 2006; Mocan and Tekin, 2010; Hitsch et al, 2010).”
Field Experiments on Discrimination (2016, background): “In a meta-analysis across 48 comparisons of direct report and list randomization, Holbrook and Krosnick (2010) found that 63 percent of the estimates for socially undesirable behaviors were significantly larger when elicited through list randomization.”
Fatter Attraction: Anthropometric and Socioeconomic Matching on the Marriage Market (2012, background): “Finally, Hitsch et al. (2010), working on online dating, introduce several dimensions by modelling individual utility as a linear valuation of the mates attributes within a Gale-Shapley framework (in which transfers between mates are ruled out).”
Contagion of pro- and anti-social behavior among peers and the role of social proximity (2019, background): “Common preferences or interests are often the first step in creating a common identity and successful matching (Hitsch et al., 2010).”
Algorithmics of Matching Under Preferences (2013, background): “• assigning children to daycare places in Denmark [375]; • school placement in Boston [1, 4], Hungary [77, 90], New York [2] and Singapore [567](11); • higher education admission in China [593,594], Germany [111], Hungary [77, 90], Spain [491] and Turkey [64, 60, 61]); • awarding tuition exchange sc”
Machine behaviour (2019, background): “Algorithms shape romantic matches via online dating 35,36 .”
A Supply and Demand Framework for Two-Sided Matching Markets (2016, neutral): “With respect to schools, the literature has emphasized the importance of competition and choice to private efficiency of allocations, and spillover effects (see Hoxby, 2000 and references therein). More closely related to Hatfield et al. (2011a) are papers that consider whether competition gives sch”
Educational Assortative Mating and Household Income Inequality (2019, background): “Hitsch et al. (2010) begin by estimating the extent to which the actual matches achieved at an online dating site can be explained by preferences alone.”