Recent Projects

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Learning quadratic games on networks

Individuals, or organizations, cooperate with or compete against one another in a wide range of practical situations. The understanding of such strategic interactions plays a crucial role in modeling the collective decision-making process. Strategic interactions are commonly modeled and analyzed as games played on networks, which are assumed known a priori. We tackle the inverse problem and propose a novel framework for learning quadratic network games, in particular, the structure of the network, based on the Nash equilibrium of such games, and test the framework on real-world examples showing that it outperforms existing methods by accounting for strategic dependencies of actions. The proposed framework has both theoretical and practical implications for understanding strategic interactions in a network environment. Preprint.
Here is a 5-min pitch in the Imagination In Action event.


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Contextual centrality: going beyond network structure

Centrality is a fundamental network property which ranks nodes by their structural importance. However, structural importance may not suffice to predict successful diffusions in a wide range of applications, such as word-of-mouth marketing and political campaigns. In particular, nodes with high structural importance may contribute negatively to the objective of the diffusion. To address this problem, we propose contextual centrality, which integrates structural positions, the diffusion process, and, most importantly, nodal contributions to the objective of the diffusion. We perform an empirical analysis of the adoption of microfinance in Indian villages and weather insurance in Chinese villages. Results show that contextual centrality of the first-informed individuals has higher predictive power towards the eventual adoption outcomes than other standard centrality measures. Interestingly, when the product of diffusion rate p and the largest eigenvalue λ is larger than one and diffusion period is long, contextual centrality linearly scales with eigenvector centrality. This approximation reveals that contextual centrality identifies scenarios where a higher diffusion rate of individuals may negatively influence the cascade payoff. Further simulations on the synthetic and real-world networks show that contextual centrality has the advantage of selecting an individual whose local neighborhood generates a high cascade payoff when pλ<1. Under this condition, stronger homophily leads to higher cascade payoff. Our results suggest that contextual centrality captures more complicated dynamics on networks and has significant implications for applications, such as information diffusion, viral marketing, and political campaigns. Preprint.


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Measuring social influence within and across multi-dimensional homophilous communities

Behavioral similarity on networks is driven by both homophily and social influence. While homophily drives the formation of communities with similar behaviors and characteristics, social influence may be positive or negative and occurs both within and between communities. In this paper, we propose a generative model and a joint analysis of both the network formation process and the diffusion of behavioral influence across different empirically-identified communities. We analyze adoption decisions for microfinance and weather insurance in Indian and Chinese villages and show that although people tend to form links within communities, there are also strong positive and negative social influences between communities. Our framework facilitates the quantification of influences underlying decision cliques and is thus a useful tool for driving information diffusion, viral marketing, and technology adoptions.


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Inferring app preference via geometric information fusion

Many real-world behavioral data cover various facets of human behaviors, creating opportunities for marketing companies to understand demand and preferences of customers with complementary information. However, to effectively combine data with multi-modal nature and complex structures is challenging. In this study, we focus on the inference of individual mobile App preferences complemented by information about user mobility. Specifically, we generate a diverse set of geographic proximity networks, which capture both the similarity in location preferences and the hidden social influence between individuals. We then propose a novel matrix completion framework to infer unobserved App adoption by integrating two types of information, namely, the observed App preference and the multi-view proximity networks, in a recent geometric deep learning model. We compare our methods with several baseline models in an inference task using real-world App adoption data, where the improved performances highlight the advantage of incorporating multi-view and geometric information in a principled manner. Besides, our results suggest that geographical proximity in residential areas and parks are particularly useful for predicting App adoption, which sheds light on using new types of information for App recommendation. The framework proposed in our study can be further generalized to a variety of application settings where the fusion of behavior decisions and mobility information is beneficial.

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Beyond exposure: the paradoxical nature of social influence

Social influence manifests and drives the adoption decisions in a wide range of behaviors. The cascade model and complex contagion have been widely adopted to explain the diffusion of social influence. We argue that social influence goes beyond merely exposure. Preference elicitation, another form of social influence, potentially acts as an opposing effect to and jointly influences decision-making with exposure. However, unraveling the two effects is an empirically challenging task since they happen simultaneously during a conversation and the content of the conversation is hardly observed. We utilize future adoption decisions as the behavioral signal predicting the elicited preference. After controlling for observed covariates to isolate social influence with matched sampling, we observe a drastic difference in the adoption outcomes with ego network consists of different behavioral signals in three offline settings - visiting a newly-opened grocery store, adopting the micro-finance, and enrolling in weather insurance. To further disentangle the impacts of exposure and preference elicitation, we leverage the fixed effect of exposure to the existence of the product and the variation in ego network structures. With such a framework, we observe the paradoxical nature of social influence with the potential opposing effect of exposure and preference elicitation. Our study provides empirical evidence to enrich influence models and informs more effective interventions.

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The Ripple Effect of Social Influence in Phone Networks

Several empirical works have shown that social influence propagates beyond direct neighbors in relatively costless online decision-making settings mainly due to the exposure effect explained by simple or complex contagion. Yet precisely how influence plays a role in costly offline behaviors and propagates through a social network and especially what is the underlying mechanisms that drive such propagation remain unclear. In this study, we leverage a high-resolution mobile phone data and a new behavioral matching method, based on revealed preference theory, to study how social influence propagates and affects individual off-line behavior. Our results show that propagation within the network persists in shaping individual decision-making through up to more than three degrees of separation regarding attending an international cultural performance in Andorra degrees of separation and regarding visiting a newly opened retail store in Mérida, Mexico. We find that exposure to behavior does not suffice to explain this social influence’s ripple effect, but it can be rather explained by a compound mechanism based on local communication and information aggregation. Based on those ideas we propose a Bayesian network-based learning model that can better predict individual adoption behavior than exposure-based models. This means that social learning is a paramount ingredient to understand the diffusion of influence in social networks, and it might have far-reaching implications in such domains as viral marketing, public health management, political campaign, and social mobilization, where it is often desirable to trigger a costly behavioral change in large-scale populations. Paper.

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Synergistic data-driven travel demand management based on phone records

Traffic congestion has increasingly threatened urban development economically, societally, and environmentally. Leisure trips contribute to 79% of the total travel demand. However, leisure trips suffer from the ‘richer-get-richer’ effect, leading to congestion exacerbation. We address this issue with a novel data-driven travel demand management framework by recommending locations based on phone records. In particular, we infer unobserved location preferences using Matrix Factorization from longitudinal mobility histories. We then formulate a constrained optimization problem to maximize preferences regarding recommended locations while accounting for constraints imposed by road capacity. Our case study shows that under full compliance rate, congestion falls by 52% at the cost of 31% less location satisfaction. Under 60% compliance rate, 41% travel delay is saved with a 17% reduction in satisfaction. This study highlights the effectiveness of the synergy among collective behaviors in improving system efficiency. Paper.




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Bayesian Models of Cognition in the Wisdom of the Crowd

There is disagreement in the literature as to the role of social learning and influence on group accuracy. We believe that a missing piece of this line of research is to model of how humans update their belief distributions. Using a novel dataset (of 17K predictions from 2K people) that was collected in a series of live online experimental studies, we use models the literature of cognitive science to investigate how individuals learn from each other, and how they build belief distributions of the future prices of real assets (such as the S&P 500 and WTI Oil prices). Finally, we create a metric that estimates how much each individual prefers to use peer belief distributions instead of the past price distribution to update their belief, and we use this metric to filter for more ‘social-learning’ individuals in the group to see if their aggregate estimate is more accurate than that of the whole ‘crowd’. We observe that filtering using our novel metric outperforms other previous works’ metric of resistance to social influence (that we also reproduce), indicating that the ability to process social information is beneficial to group accuracy. We extend our finding to a very different domain and dataset where there is no explicit social information exposure by first estimating social-learning of individuals (using a hidden Markov model) and using it again to improve group accuracy, showing that finding individuals who are better at social learning can improve group accuracy in a very different domain. Preprint.