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1. Observational Learning with Fake Agents

   https://doi.org/10.1109/ISIT44484.2020.9174217  

   Poojary, Pawan; Berry, Randall (June 2020, 2020 IEEE International Symposium on Information Theory (ISIT))

   null (Ed.)

   It is common in online markets for agents to learn from other's actions. Such observational learning can lead to herding or information cascades in which agents eventually "follow the crowd". Models for such cascades have been well studied for Bayes-rational agents that choose pay-off optimal actions. In this paper, we additionally consider the presence of fake agents that seek to influence other agents into taking one particular action. To that end, these agents take a fixed action in order to influence the subsequent agents towards their preferred action. We characterize how the fraction of such fake agents impacts behavior of the remaining agents and show that in certain scenarios, an increase in the fraction of fake agents in fact reduces the chances of their preferred outcome. 

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2. First-Choice Maximality Meets Ex-ante and Ex-post Fairness

   https://doi.org/10.24963/ijcai.2023/303  

   Guo, Xiaoxi; Sikdar, Sujoy; Xia, Lirong; Cao, Yongzhi; Wang, Hanpin (August 2023, IJCAI)

   For the assignment problem where multiple indivis- ible items are allocated to a group of agents given their ordinal preferences, we design randomized mechanisms that satisfy first-choice maximality (FCM), i.e., maximizing the number of agents as- signed their first choices, together with Pareto- efficiency (PE). Our mechanisms also provide guarantees of ex-ante and ex-post fairness. The generalizedeager Boston mechanism is ex-ante envy-free, and ex-post envy-free up to one item (EF1). The generalized probabilistic Boston mech- anism is also ex-post EF1, and satisfies ex-ante ef- ficiency instead of fairness. We also show that no strategyproof mechanism satisfies ex-post PE, EF1, and FCM simultaneously. In doing so, we expand the frontiers of simultaneously providing efficiency and both ex-ante and ex-post fairness guarantees for the assignment problem. 

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3. Why ex post peer review encourages high-risk research while ex ante review discourages it

   https://doi.org/10.1073/pnas.2111615118  

   Gross, Kevin; Bergstrom, Carl T. (December 2021, Proceedings of the National Academy of Sciences)

   Peer review is an integral component of contemporary science. While peer review focuses attention on promising and interesting science, it also encourages scientists to pursue some questions at the expense of others. Here, we use ideas from forecasting assessment to examine how two modes of peer review—ex ante review of proposals for future work and ex post review of completed science—motivate scientists to favor some questions instead of others. Our main result is that ex ante and ex post peer review push investigators toward distinct sets of scientific questions. This tension arises because ex post review allows investigators to leverage their own scientific beliefs to generate results that others will find surprising, whereas ex ante review does not. Moreover, ex ante review will favor different research questions depending on whether reviewers rank proposals in anticipation of changes to their own personal beliefs or to the beliefs of their peers. The tension between ex ante and ex post review puts investigators in a bind because most researchers need to find projects that will survive both. By unpacking the tension between these two modes of review, we can understand how they shape the landscape of science and how changes to peer review might shift scientific activity in unforeseen directions. 

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4. Not Registered? Please Sign Up First: A Randomized Field Experiment on the Ex Ante Registration Request

   https://doi.org/10.1287/isre.2021.0999  

   Huang, Ni; Mojumder, Probal; Sun, Tianshu; Lv, Jinchi; Golden, Joseph M. (September 2021, Information Systems Research)

   Online commerce websites often request users to register in the online shopping process. Recognizing the challenges of user registration, many websites opt to delay their registration request until the end of the conversion funnel (i.e., ex post registration request). Our study explores an alternative approach by asking users to register with the website at the beginning of their shopping journey (i.e., ex ante registration request). Guided by a stylized analytical model, we conducted a large-scale randomized field experiment in partnership with an online retailer in the United States to examine how the ex ante request affects users’ registration decisions, short-term customer conversions, and long-term purchase behaviors. Specifically, we randomly assigned the new users in the website’s incoming traffic to one of two experimental groups: one with an ex ante registration request preceding the ex post request (treatment) and the other with only an ex post registration request (control). Our results show that the ex ante request leads to an increased probability of user registration; that is, the users in the treatment group, on average, are 58.08% relatively more likely to register with the website than those in the control group. Furthermore, the ex ante request leads to significant increases in customer purchases in the long run. Based on our estimation of the local average treatment effects, the ex ante registered users are 10.89% relatively more likely to make a purchase, place a 16.76% relatively greater number of orders, and generate 13.22% relatively higher total revenue for the firm in the long run. Finally, the ex ante request also does not impact customer conversion in the short-term. Further investigation into the long-term and short-term effects provides suggestive evidence on several potential mechanisms, such as firm-initiated interaction and screening of low-interest users. Our study provides managerial implications to the e-commerce websites on customer acquisition and contributes to the research on IT artifact design. 

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5. Learning From Strategic Agents: Accuracy, Improvement, and Causality, ICML

   Shavit, Yonadav; Edelman, Benjamin; Axelrod, Brian (July 2020, Proceedings of Machine Learning Research)

   In many predictive decision-making scenarios, such as credit scoring and academic testing, a decision-maker must construct a model that accounts for agents' incentives to ``game'' their features in order to receive better decisions. Whereas the strategic classification literature generally assumes that agents' outcomes are not causally dependent on their features (and thus strategic behavior is a form of lying), we join concurrent work in modeling agents' outcomes as a function of their changeable attributes. Our formulation is the first to incorporate a crucial phenomenon: when agents act to change observable features, they may as a side effect perturb unobserved features that causally affect their true outcomes. We consider three distinct desiderata for a decision-maker's model: accurately predicting agents' post-gaming outcomes (accuracy), incentivizing agents to improve these outcomes (improvement), and, in the linear setting, estimating the visible coefficients of the true causal model (causal precision). As our main contribution, we provide the first algorithms for learning accuracy-optimizing, improvement-optimizing, and causal-precision-optimizing linear regression models directly from data, without prior knowledge of agents' possible actions. These algorithms circumvent the hardness result of Miller et al. (2019) by allowing the decision maker to observe agents' responses to a sequence of decision rules, in effect inducing agents to perform causal interventions for free. 

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