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1. Quickest Change Detection using Time Inconsistent Anticipatory and Quantum Decision Modeling

   https://doi.org/10.1109/Allerton49937.2022.9929427  

   Krishnamurthy, Vikram; Snow, Luke (September 2022, 2022 58th Annual Allerton Conference on Communication, Control, and Computing)

   This paper considers quickest detection scheme where the change in an underlying parameter influencing human decisions is to be detected by only observing the human decisions. Stemming from behavioral economics and mathematical psychology, we propose two generative models for the human decision maker. Namely, we consider an anticipatory decision making model and a quantum decision model. From a decision theoretic point of view, anticipatory models are time inconsistent, meaning that Bellman's principle of optimality does not hold. The appropriate formalism is thus the subgame Nash equilibrium. We show that the interaction between anticipatory agents and sequential quickest detection results in unusual (nonconvex) structure of the quickest change detection policy. In contrast the quantum decision model, despite its mathematical complexity, results in the typical convex quickest detection policy. The optimal quickest detection policy is shown to perform strictly worse than classical quickest detection for both models, via a Blackwell dominance argument. The model and structural results provided contribute to an understanding of the dynamics of human-sensor interfacing. 

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2. Mix and Match: Characterizing Heterogeneous Human Behavior in AI-assisted Decision Making

   https://doi.org/10.1609/hcomp.v12i1.31604  

   Lu, Zhuoran; Amin_Mahmoo, Syed Hasan; Li, Zhuoyan; Yin, Ming (October 2024, Proceedings of the AAAI Conference on Human Computation and Crowdsourcing)

   AI-assisted decision-making systems hold immense potential to enhance human judgment, but their effectiveness is often hindered by a lack of understanding of the diverse ways in which humans take AI recommendations. Current research frequently relies on simplified, ``one-size-fits-all'' models to characterize an average human decision-maker, thus failing to capture the heterogeneity of people's decision-making behavior when incorporating AI assistance. To address this, we propose Mix and Match (M&M), a novel computational framework that explicitly models the diversity of human decision-makers and their unique patterns of relying on AI assistance. M&M represents the population of decision-makers as a mixture of distinct decision-making processes, with each process corresponding to a specific type of decision-maker. This approach enables us to infer latent behavioral patterns from limited data of human decisions under AI assistance, offering valuable insights into the cognitive processes underlying human-AI collaboration. Using real-world behavioral data, our empirical evaluation demonstrates that M&M consistently outperforms baseline methods in predicting human decision behavior. Furthermore, through a detailed analysis of the decision-maker types identified in our framework, we provide quantitative insights into nuanced patterns of how different individuals adopt AI recommendations. These findings offer implications for designing personalized and effective AI systems based on the diverse landscape of human behavior patterns in AI-assisted decision-making across various domains. 

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3. Algorithmic Decision-Making under Agents with Persistent Improvement

   Xie, Tian; Tan, Xuwei; Zhang, Xueru (August 2024, 7th AAAI Conference on AI, Ethics, and Society)

   This paper studies algorithmic decision-making under human's strategic behavior, where a decision maker uses an algorithm to make decisions about human agents, and the latter with information about the algorithm may exert effort strategically and improve to receive favorable decisions. Unlike prior works that assume agents benefit from their efforts immediately, we consider realistic scenarios where the impacts of these efforts are persistent and agents benefit from efforts by making improvements gradually. We first develop a dynamic model to characterize persistent improvements and based on this construct a Stackelberg game to model the interplay between agents and the decision-maker. We analytically characterize the equilibrium strategies and identify conditions under which agents have incentives to improve. With the dynamics, we then study how the decision-maker can design an optimal policy to incentivize the largest improvements inside the agent population. We also extend the model to settings where 1) agents may be dishonest and game the algorithm into making favorable but erroneous decisions; 2) honest efforts are forgettable and not sufficient to guarantee persistent improvements. With the extended models, we further examine conditions under which agents prefer honest efforts over dishonest behavior and the impacts of forgettable efforts. 

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4. Fairness Interventions as (Dis)Incentives for Strategic Manipulation

   X. Zhang, M. Khalili (July 2022, International Conference on Machine Learning (ICML))

   Although machine learning (ML) algorithms are widely used to make decisions about individuals in various domains, concerns have arisen that (1) these algorithms are vulnerable to strategic manipulation and "gaming the algorithm"; and (2) ML decisions may exhibit bias against certain social groups. Existing works have largely examined these as two separate issues, e.g., by focusing on building ML algorithms robust to strategic manipulation, or on training a fair ML algorithm. In this study, we set out to understand the impact they each have on the other, and examine how to characterize fair policies in the presence of strategic behavior. The strategic interaction between a decision maker and individuals (as decision takers) is modeled as a two-stage (Stackelberg) game; when designing an algorithm, the former anticipates the latter may manipulate their features in order to receive more favorable decisions. We analytically characterize the equilibrium strategies of both, and examine how the algorithms and their resulting fairness properties are affected when the decision maker is strategic (anticipates manipulation), as well as the impact of fairness interventions on equilibrium strategies. In particular, we identify conditions under which anticipation of strategic behavior may mitigate/exacerbate unfairness, and conditions under which fairness interventions can serve as (dis)incentives for strategic manipulation. 

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5. Who Leads and Who Follows in Strategic Classification?

   Zrnic, Tijana; Mazumdar, Eric; Sastry, Shankar; Jordan, Michael I (July 2021, ArXivorg)

   null (Ed.)

   As predictive models are deployed into the real world, they must increasingly contend with strategic behavior. A growing body of work on strategic classification treats this problem as a Stackelberg game: the decision-maker "leads" in the game by deploying a model, and the strategic agents "follow" by playing their best response to the deployed model. Importantly, in this framing, the burden of learning is placed solely on the decision-maker, while the agents' best responses are implicitly treated as instantaneous. In this work, we argue that the order of play in strategic classification is fundamentally determined by the relative frequencies at which the decision-maker and the agents adapt to each other's actions. In particular, by generalizing the standard model to allow both players to learn over time, we show that a decision-maker that makes updates faster than the agents can reverse the order of play, meaning that the agents lead and the decision-maker follows. We observe in standard learning settings that such a role reversal can be desirable for both the decision-maker and the strategic agents. Finally, we show that a decision-maker with the freedom to choose their update frequency can induce learning dynamics that converge to Stackelberg equilibria with either order of play. 

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