# Behavioral Analytics for Myopic Agents

arXiv: https://arxiv.org/abs/1702.05496 · HTML: https://mlsanity.com/paper/1702.05496/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Feb 2017.

## Built on by (most cited first, 9 of 9)

- [Learning Optimal Prescriptive Trees from Observational Data](https://mlsanity.com/paper/2108.13628/) (2021)
- [Repeated Principal-Agent Games with Unobserved Agent Rewards and Perfect-Knowledge Agents](https://mlsanity.com/paper/2304.07407/) (2023)
- [Model Based Reinforcement Learning for Personalized Heparin Dosing](https://mlsanity.com/paper/2304.10000/) (2023)
- [Estimating and Incentivizing Imperfect-Knowledge Agents with Hidden Rewards](https://mlsanity.com/paper/2308.06717/) (2023)
- [Planning a Community Approach to Diabetes Care in Low- and Middle-Income Countries Using Optimization](https://mlsanity.com/paper/2305.06426/) (2023)
- [An Adaptive Optimization Approach to Personalized Financial Incentives in Mobile Behavioral Weight Loss Interventions](https://mlsanity.com/paper/2307.00444/) (2023)
- [Applications of 0-1 Neural Networks in Prescription and Prediction](https://mlsanity.com/paper/2402.18851/) (2024)
- [Finite-Time Guarantees for Multi-Agent Combinatorial Bandits with Nonstationary Rewards](https://mlsanity.com/paper/2508.20923/) (2025)
- [Integrating Explainable Machine Learning and Mixed-Integer Optimization for Personalized Sleep Quality Intervention](https://mlsanity.com/paper/2603.16937/) (2026)

## Abstract (arXiv)

Many multi-agent systems have the structure of a single coordinator providing behavioral or financial incentives to a large number of agents. Two challenges faced by the coordinator are a finite budget from which to allocate incentives, and an initial lack of knowledge about the utility function of the agents. Here, we present a behavioral analytics approach to solve the coordinator's problem when agents make decisions by maximizing utility functions that depend on prior system states, inputs, and other parameters that are initially unknown and subject to temporal dynamics. Our behavioral analytics framework involves three steps: first, we develop a behavioral model that describes the decision-making process of an agent; second, we use data to estimate behavioral model parameters for each agent and then use these estimates to predict future decisions of each agent; and third, we use the estimated behavioral model parameters to optimize a set of costly incentives to provide to each agent. In this paper, we describe a specific set of tools, models, and approaches that fit into this framework, and that adapt models and incentives as new information is collected by repeating the second and third steps of this framework. Furthermore, we prove that incentives computed by this adaptive approach are asymptotically optimal with respect to a given loss function that describes the coordinator's objective. We optimize incentives using a decomposition scheme, where each sub-problem solves the coordinator's problem for a single agent, and the master problem is a pure integer program. We conclude with a simulation study to evaluate the effectiveness of our behavioral analytics approach in designing personalized treatment plans for a weight loss program. The results show our approach maintains efficacy of the program while reducing costs by up to 60%, while adaptive heuristics provide less savings.