ArticlePublisher preview available

Choices in networks: a research framework

Authors:
To read the full-text of this research, you can request a copy directly from the authors.

Abstract and Figures

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.
This content is subject to copyright. Terms and conditions apply.
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 modelsthat
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 inter-
weave 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 alter-
natives. Networks are also endemic to contemporary business practice, where con-
sumers 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
https://doi.org/10.1007/s11002-020-09541-9
*Fred Feinberg
feinf@umich.edu
*Elizabeth Bruch
ebruch@umich.edu
Extended author information available on the last page of the article
Marketing Letters (2020) 31:349359
Published online: 2 October 2020
Content courtesy of Springer Nature, terms of use apply. Rights reserved.
... One of the crucial aspects of human decision making is that, as fundamentally social creatures, our preferences are strongly influenced by our social context. Viral trends, conformity, word-of-mouth, and signaling all play roles in behavior, including choices (Feinberg et al., 2020;Axsen & Kurani, 2012). Additionally, people with similar preferences, beliefs, and identities are more likely to be friends in the first place, a phenomenon known as homophily (McPherson et al., 2001). ...
... Together, these factors indicate that social network structure could be very informative in predicting choices. In economics and sociology, there has been growing interest in incorporating social factors into discrete choice models (McFadden, 2010;Maness et al., 2015;Feinberg et al., 2020). However, the methods used so far in these fields have largely been limited to simple feature-based summaries of social influence [e.g., what fraction of someone's friends have selected an item (Goetzke & Rave, 2011)]. ...
... More recently, there has been interest in the use of discrete choice in conjunction with networkbased analysis (Feinberg et al., 2020) enabled by rich data with both social and choice components . The traditional econometric approach to discrete choice modeling with social effects is to add terms to an individual's utility that depend on the actions or preferences of others (Brock & Durlauf, 2001;McFadden, 2010;Maness et al., 2015). ...
Article
Full-text available
Choices made by individuals have widespread impacts—for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase—moreover, data on these choices are increasingly abundant. Discrete choice models are a key tool for learning individual preferences from such data. Additionally, social factors like conformity and contagion influence individual choice. Traditional methods for incorporating these factors into choice models do not account for the entire social network and require hand-crafted features. To overcome these limitations, we use graph learning to study choice in networked contexts. We identify three ways in which graph learning techniques can be used for discrete choice: learning chooser representations, regularizing choice model parameters, and directly constructing predictions from a network. We design methods in each category and test them on real-world choice datasets, including county-level 2016 US election results and Android app installation and usage data. We show that incorporating social network structure can improve the predictions of the standard econometric choice model, the multinomial logit. We provide evidence that app installations are influenced by social context, but we find no such effect on app usage among the same participants, which instead is habit-driven. In the election data, we highlight the additional insights a discrete choice framework provides over classification or regression, the typical approaches. On synthetic data, we demonstrate the sample complexity benefit of using social information in choice models.
... One of the crucial aspects of human decision-making is that, as fundamentally social creatures, our preferences are strongly influenced by our social context. Viral trends, conformity, word-ofmouth, and signaling all play roles in behavior, including choices [4,17]. Additionally, people with similar preferences, beliefs, and identities are more likely to be friends in the first place, a phenomenon known as homophily [38]. ...
... Together, these factors mean that social network structure is often very informative in predicting choices. In economics and sociology, there has been growing interest in incorporating social factors into discrete choice models [17,35,36]. However, the methods used so far in these fields have largely been limited to simple feature-based summaries of social influence (e.g., what fraction of someone's friends have selected an item [20]). ...
... There is a long line of work in sociology and network science on understanding peoples' social behavior, including effects like contagion and herding [7,11,16]. More recently, there has been interest in the use of discrete choice in conjunction with network-based analysis [17] enabled by rich data with both social and choice components [1]. The traditional econometric approach to incorporating social interactions into discrete choice modeling is to add terms to an individual's utility that depend on the actions or preferences of others [10,35,36]. ...
Preprint
Choices made by individuals have widespread impacts--for instance, people choose between political candidates to vote for, between social media posts to share, and between brands to purchase--moreover, data on these choices are increasingly abundant. Discrete choice models are a key tool for learning individual preferences from such data. Additionally, social factors like conformity and contagion influence individual choice. Existing methods for incorporating these factors into choice models do not account for the entire social network and require hand-crafted features. To overcome these limitations, we use graph learning to study choice in networked contexts. We identify three ways in which graph learning techniques can be used for discrete choice: learning chooser representations, regularizing choice model parameters, and directly constructing predictions from a network. We design methods in each category and test them on real-world choice datasets, including county-level 2016 US election results and Android app installation and usage data. We show that incorporating social network structure can improve the predictions of the standard econometric choice model, the multinomial logit. We provide evidence that app installations are influenced by social context, but we find no such effect on app usage among the same participants, which instead is habit-driven. In the election data, we highlight the additional insights a discrete choice framework provides over classification or regression, the typical approaches. On synthetic data, we demonstrate the sample complexity benefit of using social information in choice models.
... This article starts to fill this gap by studying how co-offending networks evolve over time. Specifically, it applies a recently developed approach in network science that considers the formation of social networks as the result of choices made by nodes (offenders, in our case) when joining a network (Opsahl and Hogan 2011;Overgoor et al. 2019;Feinberg et al. 2020). When a node joins a network-or, if it is already part of it, creates a new connection-it selects a 'target' from the pool of nodes that are already part of the network. ...
... ,Feinberg et al. (2020), andOvergoor et al. (2020). The model is an example of the more general discrete choice framework, which seeks to describe or predict the choices made by individuals from a discrete set of alternatives(McFadden 1981). ...
Article
Full-text available
This study aims to improve our understanding of criminal accomplice selection by studying the evolution of co-offending networks—i.e., networks that connect those who commit crimes together. To this end, we tested four growth mechanisms (popularity, reinforcement, reciprocity, and triadic closure) on three components observed in a network connecting criminal investigations (M=286 K) with adult offenders (N=274 K) in Bogotá (Colombia) between 2005 and 2018. The first component had 4286 offenders (component ‘A’), the second 227 (‘B’), and the third component 211 (‘C’). The evolution of these components was examined using temporal information in tandem with discrete choice models and simulations to understand the mechanisms that could explain how these components grew. The results show that they evolved differently during the period of interest. Popularity yielded negative statistically significant coefficients for ‘A’, suggesting that having more connections reduced the odds of connecting with incoming offenders in this network. Reciprocity and reinforcement yielded mixed results as we observed negative statistically significant coefficients in ‘C’ and positive statistically significant coefficients in ‘A’. Moreover, triadic closure produced positive, statistically significant coefficients in all the networks. The results suggest that a combination of growth mechanisms might explain how co-offending networks grow, highlighting the importance of considering offenders’ network-related characteristics when studying accomplice selection. Besides adding evidence about triadic closure as a universal property of social networks, this result indicates that further analyses are needed to understand better how accomplices shape criminal careers.
... Modeling the formation and growth of network is essential to explore the structure of networks [8,13,16]. Overgoor et al. [39] recently proposed a framework to model the growth of network as discrete choice of incoming and existing nodes. This framework is general enough to include many existing growth patterns, e.g. ...
Preprint
Multinomial Logit (MNL) is one of the most popular discrete choice models and has been widely used to model ranking data. However, there is a long-standing technical challenge of learning MNL from many real-world ranking data: exact calculation of the MNL likelihood of \emph{partial rankings} is generally intractable. In this work, we develop a scalable method for approximating the MNL likelihood of general partial rankings in polynomial time complexity. We also extend the proposed method to learn mixture of MNL. We demonstrate that the proposed methods are particularly helpful for applications to choice-based network formation modeling, where the formation of new edges in a network is viewed as individuals making choices of their friends over a candidate set. The problem of learning mixture of MNL models from partial rankings naturally arises in such applications. And the proposed methods can be used to learn MNL models from network data without the strong assumption that temporal orders of all the edge formation are available. We conduct experiments on both synthetic and real-world network data to demonstrate that the proposed methods achieve more accurate parameter estimation and better fitness of data compared to conventional methods.
... For instance, a posteriori analysis of hierarchies in high school friendship networks revealed that both unreciprocated and reciprocated relationships are critical to explaining observed network structure, yet they follow different attachment mechanisms [1]. Other models, focused on the dynamics of hierarchies over time, have incorporated nodes' preferences into the process by which new directed links are formed [17], bringing together network formation and discrete choice modeling [9,25]. Importantly, these statistically generative models allow for model comparison in explaining empirical data. ...
Article
Full-text available
Faculty hiring networks—who hires whose graduates as faculty—exhibit steep hierarchies, which can reinforce both social and epistemic inequalities in academia. Understanding the mechanisms driving these patterns would inform efforts to diversify the academy and shed new light on the role of hiring in shaping which scientific discoveries are made. Here, we investigate the degree to which structural mechanisms can explain hierarchy and other network characteristics observed in empirical faculty hiring networks. We study a family of adaptive rewiring network models, which reinforce institutional prestige within the hierarchy in five distinct ways. Each mechanism determines the probability that a new hire comes from a particular institution according to that institution’s prestige score, which is inferred from the hiring network’s existing structure. We find that structural inequalities and centrality patterns in real hiring networks are best reproduced by a mechanism of global placement power, in which a new hire is drawn from a particular institution in proportion to the number of previously drawn hires anywhere. On the other hand, network measures of biased visibility are better recapitulated by a mechanism of local placement power, in which a new hire is drawn from a particular institution in proportion to the number of its previous hires already present at the hiring institution. These contrasting results suggest that the underlying structural mechanism reinforcing hierarchies in faculty hiring networks is a mixture of global and local preference for institutional prestige. Under these dynamics, we show that each institution’s position in the hierarchy is remarkably stable, due to a dynamic competition that overwhelmingly favors more prestigious institutions. These results highlight the reinforcing effects of a prestige-based faculty hiring system, and the importance of understanding its ramifications on diversity and innovation in academia.
... Social norms and social capital shape micro-and macro-level outcomes in a society and hence social ties and networks play a crucial role in explaining economic, social and political outcomes (Bourdieu, 1986;Portes, 1998;Coleman, 1990;Putnam, 1993). Combining choice modelling with network analysis (Calastri et al., 2018;Feinberg et al., 2020;Neilson and Wichmann, 2014;Pink et al., 2020;Wichmann et al., 2016) is a powerful approach to investigate the choice processes involved in social tie formation and the causal determinants of partner choice as well as the effects of social networks on individuals' choice behaviour. Dynamic network analysesstudying social tie formation over timeallow for examining non-static choice behaviour. ...
Article
Full-text available
This paper argues that choice modelling is a gainful approach for all social sciences, while at the same time disciplines such as sociology and political science can contribute significantly to the future development of choice modelling. So far choice modelling has mainly been applied in disciplines that investigate types of consumption choices, be it marketing to investigate preferences for new products, transportation to analyse mode choices, or environmental economics to elicit preferences for public goods. However, using the information that can be gained from individual choices among mutually exclusive alternatives has gained increasing popularity in other disciplines as a powerful tool to test theoretical hypothesis and generate insights into individual behaviour. Examples are the acceptance of refugee shelters in peoples’ neighbourhood, the choice of where to commit a crime or the evolution of social networks. A good point of departure for an expansion of choice modelling within the social sciences is the common foundation that many disciplines share that are gathered under the umbrella of social sciences. Research traditions and theoretical models include rational choice concepts, and choice modelling can be linked to cross-cutting methods, including agent-based models, network analysis, and machine learning. At the same time, disciplines can complement each other in studying choice behaviour, as they can contribute concepts and tools less familiar to the other disciplines. Finally, all social science disciplines face challenges when it comes to issues such as causal analysis, heterogeneity in decision rules, joint decision making, or big data. Choice modelling and a cross-disciplinary dialogue can contribute to meeting these challenges.
Chapter
Data science in marketing has become critical in gaining sustained competitive advantage in a rapidly changing business environment. It involves using advanced analytics and scientific principles to extract valuable information from large volumes of data gathered from multiple sources, such as social media platforms. There are multiple benefits to using data science in marketing, including proper data-based planning, enhanced customization, enhanced forecasting through predictive analytics, effective ROI measuring, and improved pricing models. The research explains how companies can turn the potential and opportunities of these advanced analytics techniques into real company performance in a competitive marketing environment. This research aims to explore how firms can use marketing analytics and big data to improve capabilities and performance. Specifically, the study argues that big data and marketing analytics can be used to extract valuable and meaningful marketing information and insights that can be integrated to improve marketing effectiveness and performance.
Article
Full-text available
This research primarily aims at the development of new pathways to facilitate the resolving of the long debated issue of handling ties or the degree of indecisiveness precipitated in comparative information. The decision chaos is accommodated by the elegant application of the choice axiom ensuring intact utility when imperfect choices are observed. The objectives are facilitated by inducing an additional parameter in the probabilistic set up of Maxwell to retain the extent of indecisiveness prevalent in the choice data. The operational soundness of the proposed model is elucidated through the rigorous employment of Gibbs sampling—a popular approach of the Markov chain Monte Carlo methods. The outcomes of this research clearly substantiate the applicability of the proposed scheme in retaining the advantages of discrete comparative data when the freedom of no indecisiveness is permitted. The legitimacy of the devised mechanism is enumerated on multi-fronts such as the estimation of preference probabilities and assessment of worth parameters, and through the quantification of the significance of choice hierarchy. The outcomes of the research highlight the effects of sample size and the extent of indecisiveness exhibited in the choice data. The estimation efficiency is estimated to be improved with the increase in sample size. For the largest considered sample of size 100, we estimated an average confidence width of 0.0097, which is notably more compact than the contemporary samples of size 25 and 50.
Article
Full-text available
Networks have become increasingly important to model complex systems comprised of interacting elements. Network data mining has a large number of applications in many disciplines including protein-protein interaction networks, social networks, transportation networks, and telecommunication networks. Different empirical studies have shown that it is possible to predict new relationships between elements attending to the topology of the network and the properties of its elements. The problem of predicting new relationships in networks is called link prediction. Link prediction aims to infer the behavior of the network link formation process by predicting missed or future relationships based on currently observed connections. It has become an attractive area of study since it allows us to predict how networks will evolve. In this survey we will review the general-purpose techniques at the heart of the link prediction problem, which can be complemented by domain-specific heuristic methods in practice.
Conference Paper
We provide a framework for modeling social network formation through conditional multinomial logit models from discrete choice and random utility theory, in which each new edge is viewed as a “choice” made by a node to connect to another node, based on (generic) features of the other nodes available to make a connection. This perspective on network formation unifies existing models such as preferential attachment, triadic closure, and node fitness, which are all special cases, and thereby provides a flexible means for conceptualizing, estimating, and comparing models. The lens of discrete choice theory also provides several new tools for analyzing social network formation; for example, the significance of node features can be evaluated in a statistically rigorous manner, and mixtures of existing models can be estimated by adapting known expectation-maximization algorithms. We demonstrate the flexibility of our framework through examples that analyze a number of synthetic and real-world datasets. For example, we provide rigorous methods for estimating preferential attachment models and show how to separate the effects of preferential attachment and triadic closure. Non-parametric estimates of the importance of degree show a highly linear trend, and we expose the importance of looking carefully at nodes with degree zero. Examining the formation of a large citation graph, we find evidence for an increased role of degree when accounting for age.
Article
Goals are constructs that direct choice behavior by guiding a decision maker towards desirable (or away from undesirable) end-states. Oftentimes, consumers are motivated to satisfy multiple goals within a single choice. While recognizing this possibility, the literature has not directly formulated models of choice as a multi-goal problem. We develop such a model, referred to as the Multiple-Goal-Based-Choice-Model, that incorporates 1) simultaneous multiple goal pursuit and 2) context-driven goal adaptation, but 3) does not require a priori identification of the number or nature of the goals. Goal adaptation within a single choice instance, allied to repeated choices, is the key to empirical identification of multiple latent goals. The proposed model is tested and supported using discrete choice experimental data on digital cameras via multiple validation exercises. The model can lead to significantly different policy implications with regards to consumers’ valuation for new product designs, compared to extant utility-based choice models.
Article
Seeded marketing campaigns (SMCs) involve firms sending products to selected customers and encouraging them to spread word of mouth (WOM). Prior research has examined certain aspects of this increasingly popular form of marketing communication, such as seeding strategies and their efficacy. Building on prior research, this study investigates the effects of SMCs that extend beyond the generation of WOM for a campaign’s focal product by considering how seeding can affect WOM spillover effects at the brand and category levels. The authors introduce a framework of SMC-related spillover effects, and empirically estimate these with a unique data set covering 390 SMCs for products from 192 different cosmetics brands. Multiple spillover effects are found, suggesting that while SMCs can be used primarily to stimulate WOM for a focal product, marketers must also account for brand- and category-level WOM spillover effects. Specifically, seeding increases conversations about that product among nonseed consumers, and, interestingly, decreases WOM about other products from the same brand and about competitors’ products in the same category as the focal product. These findings indicate that marketers can use SMCs to focus online WOM on a particular product by drawing consumers away from talking about other related, but off-topic, products. Data, as supplemental material, are available at http://dx.doi.org/10.1287/mksc.2016.1001 .
Article
Customer relationship management (CRM) campaigns have traditionally focused on maximizing the profitability of the targeted customers. The authors demonstrate that in business settings characterized by network externalities, a CRM campaign that is aimed at changing the behavior of specific customers propagates through the social network, thereby also affecting the behavior of nontargeted customers. Using a randomized field experiment involving nearly 6,000 customers of a mobile telecommunication provider, they find that the social connections of targeted customers increase their consumption and become less likely to churn, due to a campaign that was neither targeted at them nor offered them any direct incentives. The authors estimate a social multiplier of 1.28. That is, the effect of the campaign on first-degree connections of targeted customers is 28% of the effect of the campaign on the targeted customers. By further leveraging the randomized experimental design, the authors show that, consiste...
Article
Systems as diverse as genetic networks or the World Wide Web are best described as networks with complex topology. A common property of many large networks is that the vertex connectivities follow a scale-free power-law distribution. This feature was found to be a consequence of two generic mech-anisms: (i) networks expand continuously by the addition of new vertices, and (ii) new vertices attach preferentially to sites that are already well connected. A model based on these two ingredients reproduces the observed stationary scale-free distributions, which indicates that the development of large networks is governed by robust self-organizing phenomena that go beyond the particulars of the individual systems.