Score Design for Multi-Criteria Incentivization
We present a framework for designing scores to summarize performance metrics. Our design has two multi-criteria objectives: (1) improving on scores should improve all performance metrics, and (2) achieving pareto-optimal scores should achieve pareto-optimal metrics. We formulate our design to minimize the dimensionality of scores while satisfying the objectives. We give algorithms to design scores, which are provably minimal under mild assumptions on the structure of performance metrics. This framework draws motivation from real-world practices in hospital rating systems, where misaligned scores and performance metrics lead to unintended consequences.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Strengthening Subcommunities: Towards Sustainable Growth in AI Research
AI's rapid growth has been felt acutely by scholarly venues, leading to growing pains within the peer review process. These challenges largely center on the inability of specific subareas to identify and evaluate work th…
Decentralized autonomous organization and blockchain-based incentivization framework for community-based facilities management
Traditional facility management often relies on centralized decision-making structures that limit stakeholder participation, leading to misalignment with occupant needs and reduced satisfaction. This paper proposes a nov…
Perturbation CheckLists for Evaluating NLG Evaluation Metrics
Natural Language Generation (NLG) evaluation is a multifaceted task requiring assessment of multiple desirable criteria, e.g., fluency, coherency, coverage, relevance, adequacy, overall quality, etc. Across existing data…
Data-to-Text Generationnlg evaluationText GenerationPredicting ratings in multi-criteria recommender systems via a collective factor model
In a multi-criteria recommender system, users are allowed to give an overall rating to an item and provide a score on each of its attribute. Finding an effective method to exploit a user s multi-criteria ratings to predi…
AttributeRecommendation SystemsDynamic multi-agent deep reinforcement learning-based pricing and incentivization approach in multimodal transportation networks
In multimodal transportation systems, shared mobility services (SMSs) are promoted for their potential to enhance flexibility and reduce congestion. However, SMS demand is often concentrated in high-density areas, which …
Reinforcement Learning