Learning Fair And Effective Points-Based Rewards Programs

• URL: http://arxiv.org/abs/2506.03911v1
• Date: Wed, 04 Jun 2025 13:05:16 GMT
• Title: Learning Fair And Effective Points-Based Rewards Programs
• Authors: Chamsi Hssaine, Yichun Hu, Ciara Pike-Burke,
• Abstract summary: Points-based rewards programs have come under scrutiny due to accusations of unfair practices in their implementation.<n>We study the problem of fairly designing points-based rewards programs, with a focus on two obstacles that put fairness at odds with their effectiveness.<n>We show that an individually fair rewards program that uses the same redemption threshold for all customers suffers a loss in revenue of at most a factor of $1+ln 2$.<n>We propose a learning algorithm that limits the risk of point devaluation due to experimentation by only changing the redemption threshold $O(log T)$ times.
• Score: 4.465134753953128
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Points-based rewards programs are a prevalent way to incentivize customer loyalty; in these programs, customers who make repeated purchases from a seller accumulate points, working toward eventual redemption of a free reward. These programs have recently come under scrutiny due to accusations of unfair practices in their implementation. Motivated by these concerns, we study the problem of fairly designing points-based rewards programs, with a focus on two obstacles that put fairness at odds with their effectiveness. First, due to customer heterogeneity, the seller should set different redemption thresholds for different customers to generate high revenue. Second, the relationship between customer behavior and the number of accumulated points is typically unknown; this requires experimentation which may unfairly devalue customers' previously earned points. We first show that an individually fair rewards program that uses the same redemption threshold for all customers suffers a loss in revenue of at most a factor of $1+\ln 2$, compared to the optimal personalized strategy that differentiates between customers. We then tackle the problem of designing temporally fair learning algorithms in the presence of demand uncertainty. Toward this goal, we design a learning algorithm that limits the risk of point devaluation due to experimentation by only changing the redemption threshold $O(\log T)$ times, over a horizon of length $T$. This algorithm achieves the optimal (up to polylogarithmic factors) $\widetilde{O}(\sqrt{T})$ regret in expectation. We then modify this algorithm to only ever decrease redemption thresholds, leading to improved fairness at a cost of only a constant factor in regret. Extensive numerical experiments show the limited value of personalization in average-case settings, in addition to demonstrating the strong practical performance of our proposed learning algorithms.

Related papers

• Learning Recommender Systems with Soft Target: A Decoupled Perspective [49.83787742587449]
  We propose a novel decoupled soft label optimization framework to consider the objectives as two aspects by leveraging soft labels. We present a sensible soft-label generation algorithm that models a label propagation algorithm to explore users' latent interests in unobserved feedback via neighbors.
  arXiv  Detail & Related papers  (2024-10-09T04:20:15Z)
• Walking the Values in Bayesian Inverse Reinforcement Learning [66.68997022043075]
  Key challenge in Bayesian IRL is bridging the computational gap between the hypothesis space of possible rewards and the likelihood. We propose ValueWalk - a new Markov chain Monte Carlo method based on this insight.
  arXiv  Detail & Related papers  (2024-07-15T17:59:52Z)
• Provably Efficient Interactive-Grounded Learning with Personalized Reward [44.64476717773815]
  Interactive-Grounded Learning (IGL) is a powerful framework in which a learner aims at maximizing unobservable rewards. We provide the first provably efficient algorithms with sublinear regret under realizability. We propose two algorithms, one based on explore-then-exploit and the other based on inverse-gap weighting.
  arXiv  Detail & Related papers  (2024-05-31T08:21:09Z)
• Behavior Alignment via Reward Function Optimization [23.92721220310242]
  We introduce a new framework that integrates auxiliary rewards reflecting a designer's domain knowledge with the environment's primary rewards. We evaluate our method's efficacy on a diverse set of tasks, from small-scale experiments to high-dimensional control challenges.
  arXiv  Detail & Related papers  (2023-10-29T13:45:07Z)
• STARC: A General Framework For Quantifying Differences Between Reward Functions [52.69620361363209]
  We provide a class of pseudometrics on the space of all reward functions that we call STARC metrics.<n>We show that STARC metrics induce both an upper and a lower bound on worst-case regret.<n>We also identify a number of issues with reward metrics proposed by earlier works.
  arXiv  Detail & Related papers  (2023-09-26T20:31:19Z)
• Reward Teaching for Federated Multi-armed Bandits [18.341280891539746]
  This work focuses on clients who always maximize their individual cumulative rewards, and introduces a novel idea of reward teaching'' A phased approach, called Teaching-After-Learning (TAL), is first designed to encourage and discourage clients' explorations separately. Rigorous analyses demonstrate that when facing clients with UCB1, TWL outperforms TAL in terms of the dependencies on sub-optimality gaps.
  arXiv  Detail & Related papers  (2023-05-03T22:01:10Z)
• Network Revenue Management with Demand Learning and Fair Resource-Consumption Balancing [16.37657820732206]
  We study the price-based network revenue management (NRM) problem with both demand learning and fair resource-consumption balancing. We propose a primal-dual-type online policy with the Upper-Confidence-Bound (UCB) demand learning method to maximize the regularized revenue. Our algorithm achieves a worst-case regret of $widetilde O(N5/2sqrtT)$, where $N$ denotes the number of products and $T$ denotes the number of time periods.
  arXiv  Detail & Related papers  (2022-07-22T15:55:49Z)
• Designing Rewards for Fast Learning [18.032654606016447]
  We look at how reward-design choices impact learning speed and seek to identify principles of good reward design that quickly induce target behavior. We propose a linear-programming based algorithm that efficiently finds a reward function that maximizes action gap and minimizes subjective discount.
  arXiv  Detail & Related papers  (2022-05-30T19:48:52Z)
• Simultaneous Double Q-learning with Conservative Advantage Learning for Actor-Critic Methods [133.85604983925282]
  We propose Simultaneous Double Q-learning with Conservative Advantage Learning (SDQ-CAL) Our algorithm realizes less biased value estimation and achieves state-of-the-art performance in a range of continuous control benchmark tasks.
  arXiv  Detail & Related papers  (2022-05-08T09:17:16Z)
• Achieving Counterfactual Fairness for Causal Bandit [18.077963117600785]
  We study how to recommend an item at each step to maximize the expected reward. We then propose the fair causal bandit (F-UCB) for achieving the counterfactual individual fairness.
  arXiv  Detail & Related papers  (2021-09-21T23:44:48Z)
• Policy Gradient Bayesian Robust Optimization for Imitation Learning [49.881386773269746]
  We derive a novel policy gradient-style robust optimization approach, PG-BROIL, to balance expected performance and risk. Results suggest PG-BROIL can produce a family of behaviors ranging from risk-neutral to risk-averse.
  arXiv  Detail & Related papers  (2021-06-11T16:49:15Z)
• Confidence-Budget Matching for Sequential Budgeted Learning [69.77435313099366]
  We formalize decision-making problems with querying budget. We consider multi-armed bandits, linear bandits, and reinforcement learning problems. We show that CBM based algorithms perform well in the presence of adversity.
  arXiv  Detail & Related papers  (2021-02-05T19:56:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.