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1. Incentive Effects Capture Variability in Task-General Control Allocation.

   Cheng, Z; Leng, X; Shenhav, A (June 2025, Proceedings of the 47th Annual Meeting of the Cognitive Science Society.)

   To understand how people vary in their cognitive control engagement, researchers use different laboratory tasks and compare performance on trials that are more versus less control-demanding (e.g., congruency effects). However, previous research has struggled to uncover consistent patterns of correlation across cognitive control tasks, leading to questions about the utility of these tasks and the existence of task-general control. The current study sought to test whether these validity concerns may center on the stimulus-driven nature of congruency effects, rather than the tasks themselves. To overcome this obstacle, we varied task incentives while holding stimulus features constant. We show both theoretically and empirically that the effects of incentives on control allocation correlate across tasks. Together, findings support task-general control processes that operate across different contexts. 

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2. Promoting Multidimensional Equity through Collaborative Routing using Incentive Mechanisms

   Wang, C; Peeta, S (January 2024, TRB)

   Penalty-based strategies, such as congestion pricing, have been employed to improve traffic network efficiency, but they face criticism for their negative impact on users and equity concerns. Collaborative routing, which allows users to negotiate route choices, offers a solution that considers individual heterogeneity. Personalized incentives can encourage such collaboration and are more politically acceptable than penalties. This study proposes a collaborative routing strategy that uses personalized incentives to guide users towards desired traffic states while promoting multidimensional equity. Three equity dimensions are considered: accessibility equity (equal access to jobs, services, and education), inclusion equity (route suggestions and incentives that do not favor specific users), and utility equity (envy-free solutions where no user feels others have more valuable incentives). The strategy prioritizes equitable access to societal services and activities, ensuring accessibility equity in routing solutions. Inclusion equity is maintained through non-negative incentives that consider user heterogeneity without excluding anyone. An envy-free compensation mechanism achieves utility equity by eliminating envy over incentive-route bundles. A constrained traffic assignment (CTA) formulation and consensus optimization variant are then devised to break down the centralized problem into smaller, manageable parts and a decentralized algorithm is developed for scalability in large transportation networks and user populations. Numerical studies investigate the model's enhancement of equity dimensions and the impact of hyperparameters on system objective tradeoffs and demonstrate the algorithm convergence. 

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3. Topic-aware Incentive Mechanism for Task Diffusion in Mobile Crowdsourcing through Social Network

   https://doi.org/10.1145/3487580  

   Xu, Jia; Zhou, Yuanhang; Chen, Gongyu; Ding, Yuqing; Yang, Dejun; Liu, Linfeng (February 2022, ACM Transactions on Internet Technology)

   Crowdsourcing has become an efficient paradigm to utilize human intelligence to perform tasks that are challenging for machines. Many incentive mechanisms for crowdsourcing systems have been proposed. However, most of existing incentive mechanisms assume that there are sufficient participants to perform crowdsourcing tasks. In large-scale crowdsourcing scenarios, this assumption may be not applicable. To address this issue, we diffuse the crowdsourcing tasks in social network to increase the number of participants. To make the task diffusion more applicable to crowdsourcing system, we enhance the classic Independent Cascade model so the influence is strongly connected with both the types and topics of tasks. Based on the tailored task diffusion model, we formulate the Budget Feasible Task Diffusion ( BFTD ) problem for maximizing the value function of platform with constrained budget. We design a parameter estimation algorithm based on Expectation Maximization algorithm to estimate the parameters in proposed task diffusion model. Benefitting from the submodular property of the objective function, we apply the budget-feasible incentive mechanism, which satisfies desirable properties of computational efficiency, individual rationality, budget-feasible, truthfulness, and guaranteed approximation, to stimulate the task diffusers. The simulation results based on two real-world datasets show that our incentive mechanism can improve the number of active users and the task completion rate by 9.8% and 11%, on average. 

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4. Personalized Freight Route Recommendations with System Optimality Considerations: A Utility Learning Approach

   Aristotelis-Angelos Papadopoulos; Ioannis Kordonis; Maged Dessouky; Petros Ioannou (January 2023, IEEE transactions on intelligent transportation systems)

   Traffic congestion has a negative economic and environmental impact. Traffic conditions become even worse in areas with high volume of trucks. In this paper, we propose a coordinated pricing-and-routing scheme for truck drivers to efficiently route trucks into the network and improve the overall traffic conditions. A basic characteristic of our approach is the fact that we provide personalized routing instructions based on drivers’ individual routing preferences. In contrast with previous works that provide personalized routing suggestions, our approach optimizes over a total system-wide cost through a combined pricing-and-routing scheme that satisfies the budget balance on average property and ensures that every truck driver has an incentive to participate in the proposed mechanism by guaranteeing that the expected total utility of a truck driver (including payments) in case he/she decides to participate in the mechanism, is greater than or equal to his/her expected utility in case he/she does not participate. Since estimating a utility function for each individual truck driver is computationally intensive, we first divide the truck drivers into disjoint clusters based on their responses to a small number of binary route choice questions and we subsequently propose to use a learning scheme based on the Maximum Likelihood Estimation (MLE) principle that allows us to learn the parameters of the utility function that describes each cluster. The estimated utilities are then used to calculate a pricing-and-routing scheme with the aforementioned characteristics. Simulation results in the Sioux Falls network demonstrate the efficiency of the proposed pricing-and-routing scheme. 

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5. The Fair Value of Data Under Heterogeneous Privacy Constraints in Federated Learning

   Justin Kang, Ramtin Pedarsani (February 2024, Transactions on machine learning research)

   Modern data aggregation often involves a platform collecting data from a network of users with various privacy options. Platforms must solve the problem of how to allocate incentives to users to convince them to share their data. This paper puts forth an idea for a \textit{fair} amount to compensate users for their data at a given privacy level based on an axiomatic definition of fairness, along the lines of the celebrated Shapley value. To the best of our knowledge, these are the first fairness concepts for data that explicitly consider privacy constraints. We also formulate a heterogeneous federated learning problem for the platform with privacy level options for users. By studying this problem, we investigate the amount of compensation users receive under fair allocations with different privacy levels, amounts of data, and degrees of heterogeneity. We also discuss what happens when the platform is forced to design fair incentives. Under certain conditions we find that when privacy sensitivity is low, the platform will set incentives to ensure that it collects all the data with the lowest privacy options. When the privacy sensitivity is above a given threshold, the platform will provide no incentives to users. Between these two extremes, the platform will set the incentives so some fraction of the users chooses the higher privacy option and the others chooses the lower privacy option. 

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