Title: Analysis and interventions in large network games: graphon games and graphon contagion
Abstract: Many of today’s most promising technological systems involve very large numbers of autonomous agents that influence each other and make strategic decisions within a network structure. Examples include opinion dynamics, targeted marketing in social networks, economic exchange and international trade, product adoption and social contagion.
While traditional tools for the analysis of these systems assumed that a social planner has full knowledge of the network of interactions, when we turn to very large networks two issues emerge. First, collecting data about the exact network of interactions becomes very expensive or not at all possible because of privacy concerns. Second, methods for designing optimal interventions that rely on the exact network structure typically do not scale well with the population size.
To obviate these issues, in this talk I will present a framework in which the social planner designs interventions based on probabilistic instead of exact information about agent’s interactions. I will introduce the tool of “graphon games” as a way to formally describe strategic interactions in this setting and I will illustrate how this tool can be exploited to design interventions. I will cover two main applications: targeted budget allocation and optimal seeding in contagion processes. I will illustrate how the graphon approach leads to interventions that are asymptotically optimal in terms of the population size and can be computed without requiring exact network data.
Title: Closing the loop in Machine Learning: Learning to optimize with decision dependent data
Abstract: Learning algorithms are increasingly being deployed in a variety of real world systems with other autonomous decision processes and human decision-makers. Importantly, in many settings humans react to the decisions algorithms make. This calls into question the following classically held tenet in supervised machine learning: when it is arduous to model a phenomenon, observations thereof are representative samples from some static or otherwise independent distribution. Without taking such reactions into consideration at the time of design, machine learning algorithms are doomed to result in unintended consequences such as reinforcing institutional bias or incentivizing gaming or collusion. In this talk, we discuss several directions of research along which we have made progress towards closing the loop in ML including robustness to model misspecification in capturing strategic behavior, decision-dependent learning in the presence of competition ('multiplayer performative prediction'), and dynamic decision-dependent learning wherein the data distribution may drift in time. Open questions will be posed towards the end of the talk.
Title: Strategic Classification and the Quest for the Holy Grail
Abstract: Across a multitude of domains and applications, machine learning has become widespread as a tool for informing decisions about humans, and for humans. But most tools used in practice focus exclusively on mapping inputs to relevant outputs - and take no account of how humans respond to these outputs. This begs the question: how *should* we design learning systems when we know they will be used in social settings?
The goal of this talk is to initiate discussion regarding this question and the paths we can take towards possible answers. Building on strategic classification as an appropriate first step, I will describe some of our work, both recent and current, that aims to extend strategic classification towards more realistic strategic settings that include more elaborate forms of economic modeling. Finally, I will argue for a broader view of how we can approach learning problems that lie just outside the scope of classic supervised learning.
Title: Microfoundations of Algorithmic decisions
Abstract: When theorizing the causal effects that algorithmic decisions have on a population, an important modeling choice arises. We can model the change to a population in the aggregate, or we can model the response to a decision rule at the individual level. Standard economic microfoundations, for instance, ground the response in the utility-maximizing behavior of individuals.
Providing context from sociological and economic theory, I will argue why this methodological problem is of significant importance to machine learning. I will focus on the relationships and differences between two recent lines of work, called strategic classification and performative prediction. While performative prediction takes a macro-level perspective on distribution shifts induced by algorithmic predictions, strategic classification builds on standard economic microfoundations. Based on work with Meena Jagadeesan and Celestine Mendler-Dünner, I will discuss the serious shortcomings of standard microfoundations in the context of machine learning and speculate about the alternatives that we have.
Title: Improving Information from Manipulable Data
Abstract: Data-based decision-making must account for the manipulation of data by agents who are aware of how decisions are being made and want to affect their allocations. We study a framework in which, due to such manipulation, data becomes less informative when decisions depend more strongly on data. We formalize why and how a decisionmaker should commit to underutilizing data. Doing so attenuates information loss and thereby improves allocation accuracy.
Title: Revisiting Dynamics in Strategic ML
Abstract: Strategic classification concerns the problem of training a classifier that will ultimately observe data generated according to strategic agents’ responses. The commonly adopted setting is that the agents are fully rational and can best respond to a classifier, and the classifier is aiming to maximize its robustness to the strategic “manipulations”.
This talk revisits a couple of dynamics concepts in the above formulation. The first question we try to revisit is: are all changes considered undesirable? We observe that in many application settings, changes in agents’ profile X can lead to true improvement in their target variable Y [1,2]. This observation requires us to revisit the objective function of the learner, and study the possibility of inducing an improved population from the agents. The second question we revisit is: do agents respond rationally? Inspired by evolutionary game theory, we introduce a dynamical agent response model using replicator dynamics to model agents’ potentially non-fully rational responses to a sequence of classifiers . We characterize the dynamics of this model and offer observations of its fairness implication in such a long-term dynamical environment.
 Linear Classifiers that Encourage Constructive Adaptation, Yatong Chen, Jialu Wang and Yang Liu, 2021.
 Induced Domain Adaptation, Yang Liu, Yatong Chen, Jiaheng Wei, 2021.
 Unintended Selection: Persistent Qualification Rate Disparities and Interventions, Reilly Raab and Yang Liu, Neural Information Processing Systems (NeurIPS), 2021
Title: Online intermediation in legacy industries: The effect of reservation platforms on restaurants’ prices and survival
Abstract: Across multiple industries, new online platforms are interjecting themselves as digital intermediaries in previously direct business-to-consumer transactions. A reasonable concern is that these platforms, once they become dominant, can leverage their unique position to extract surplus from both sides participating in the transaction and lead to different welfare outcomes than platform participants expected (and experienced) when they first joined the platform. We study the effects that OpenTable (an online restaurant reservation platform) had on restaurants’ prices and their likelihood of survival in NYC, during a period the platform expanded to cover most restaurants in the city. We develop an analytical model to understand restaurants’ adoption decision, and the effect of adoption on prices and consumer surplus. The model shows how the platform can induce a prisoner’s dilemma where restaurants have incentives to join the platform to poach customers from competitors or to protect its clientele from competitors. However, once all restaurants join, none of them will attract additional customers, and the costs of the platform will be passed down to diners through price increases. As the popularity of the platform grows, the platform can charge a higher fee to restaurants until extracting all the benefits it creates. To test the predictions of the model, we create a dataset containing prices, survival, and OpenTable participation for over 5,000 restaurants in NYC between 2005 and 2016. Our analysis suggests that as the platform became prevalent, the costs of the platform were passed down to consumers through price and restaurants saw no benefits in terms of survival.
Title: Algorithmic Monoculture and Social Welfare
Abstract: As algorithms are increasingly applied to screen applicants for high-stakes decisions in employment, education, lending, and other domains, concerns have been raised about the effects of "algorithmic monoculture", in which many decision-makers all rely on the same algorithm. This concern invokes analogies to agriculture, where a monocultural system runs the risk of severe harm from unexpected shocks. We present a set of basic models characterizing the potential risks from algorithmic monoculture, showing that monocultural convergence on a single algorithm by a group of decision-making agents, even when the algorithm is more accurate for any one agent in isolation, can reduce the overall quality of the decisions being made by the full collection of agents. Our results rely on minimal assumptions, and involve a combination of game-theoretic arguments about competing decision-makers with the development of a probabilistic framework for analyzing systems that use multiple noisy estimates of a set of alternatives. The talk is based on joint work with Manish Raghavan.
Title: Leveraging strategic interactions for causal discovery
Abstract: Machine Learning algorithms often prompt individuals to strategically modify their observable attributes to receive more favorable predictions. As a result, the distribution the predictive model is trained on may differ from the one it operates on in deployment. While such distribution shifts, in general, hinder accurate predictions, our work identifies a unique opportunity associated with shifts due to strategic responses. We show that we can use strategic responses effectively to recover causal relationships between the observable features and outcomes we wish to predict. More specifically, we study a game-theoretic model in which a principal deploys a sequence of models to predict an outcome of interest (e.g., college GPA) for a sequence of strategic agents (e.g., college applicants). In response, strategic agents invest efforts and modify their features for better predictions. In such settings, unobserved confounding variables can influence both an agent's observable features (e.g., high school records) and outcomes. Therefore, standard regression methods generally produce biased estimators. To address this issue, our work establishes a novel connection between strategic responses to machine learning models and instrumental variable (IV) regression, by observing that the sequence of deployed models can be viewed as an instrument that affects agents' observable features but does not directly influence their outcomes. Therefore, two-stage least squares (2SLS) regression can recover the causal relationships between observable features and outcomes. Beyond causal recovery, we can build on our 2SLS method to address two additional relevant optimization objectives: agent outcome maximization and predictive risk minimization.
This work is joint with Keegan Harris, Daniel Ngo, Logan Stapleton, and Hoda Heidari.