Invited Talks

Francesca Parise
(Cornell University )

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.

Lillian Ratliff
(U. of Washington )

Title: Closing the loop in Machine Learning: Learning to optimize with decision dependent data

Nir Rosenfeld
(Technion)

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.

Moritz Hardt
(UC Berkeley)

Title: Microfoundations of Algorithmic decisions

Yang Liu
(UC Santa Cruz)

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 [3]. We characterize the dynamics of this model and offer observations of its fairness implication in such a long-term dynamical environment.

References:

[1] Linear Classifiers that Encourage Constructive Adaptation, Yatong Chen, Jialu Wang and Yang Liu, 2021.

[2] Induced Domain Adaptation, Yang Liu, Yatong Chen, Jiaheng Wei, 2021.

[3] Unintended Selection: Persistent Qualification Rate Disparities and Interventions, Reilly Raab and Yang Liu, Neural Information Processing Systems (NeurIPS), 2021

Navin Kartik
(Columbia University)

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.

Cristobal Cheyre
(Cornell University)

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.

Jon Kleinberg
(Cornell University)

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.

Steven Wu
(CMU)

Title: Leveraging strategic interactions for causal discovery