Choices in networks: a research framework

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Choices in networks: a research framework Fred Feinberg 1 & Elizabeth Bruch 2 & Michael Braun 3 & Brett Hemenway Falk 4 & Nina Fefferman 5 & Elea McDonnell Feit 6 & John Helveston 7 & Daniel Larremore 8 & Blakeley B. McShane 9 & Alice Patania 10 & Mario L. Small 11 # Springer Science+Business Media, LLC, part of Springer Nature 2020

Abstract Networks are ubiquitous in life, structuring options available for choice and influencing their relative attractiveness. In this article, we propose an integration of network science and choice theory beyond merely incorporating metrics from one area into models of the other. We posit a typology and framework for “network-choice models” that highlight the distinct ways choices occur in and influence networked environments, as well as two specific feedback processes that guide their mutual interaction, emergent valuation and contingent options. In so doing, we discuss examples, data sources, methodological challenges, anticipated benefits, and research pathways to fully interweave network and choice models. Keywords Choice models . Networks . Decision theory . Computational social science .

Marketing . Data science

1 Introduction Many critical life decisions are intrinsically situated in networks: forming a social circle, evaluating housing options, and seeking a romantic partner all transpire in networked environments with interdependencies among decision-makers and/or alternatives. Networks are also endemic to contemporary business practice, where consumers mutually interact through firm platforms: collaboration tools (e.g., Dropbox, Google Drive), communications (WhatsApp, Skype), transport (Uber, Lyft), lodging (HomeAway, Flipkey), retailing (Amazon, Alibaba), and payment (PayPal, Venmo), among others. Consumer networks enable firms to leverage “social multipliers”—for

* Fred Feinberg [email protected] * Elizabeth Bruch [email protected] Extended author information available on the last page of the article

Marketing Letters

example, running effective referral campaigns—and growing the network increases member value for interactive services like dating sites and multiplayer games. Owing to recent data and methodological advances, choice modeling has begun to accommodate the impact, structure, and constraints of networks. Analogously, while network models accommodate “generative” accounts of node connection (e.g., preferential attachment, triadic closure), these await fuller integration into utility-driven “decision processes” common in choice modeling. We propose a framework for combining choice theory with network modeling, specifically in problem domains that, like marketing and sociology, naturally encompass them both. Such a synthesis is enabled by suitably granular information—often disintermediated, large-scale online activity data—and computational power that allow researchers to capture interrelations between choices and networks, especially their mutual feedback and co-evolution. We argue that models representing complex and/or goal-directed decision processes ca

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