# Algorithmic Approaches to Sequential Decision-Making and Social Epistemology

arXiv: https://arxiv.org/abs/2607.20636 · HTML: https://mlsanity.com/paper/2607.20636/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 107 references that are in the map.
- First posted on arXiv 22 Jul 2026.

## Builds on (most cited first, 8 of 8)

- [Introduction to Online Convex Optimization](https://mlsanity.com/paper/1909.05207/) (2019)
- [Introduction to Multi-Armed Bandits](https://mlsanity.com/paper/1904.07272/) (2019)
- [Efficient Automatic CASH via Rising Bandits](https://mlsanity.com/paper/2012.04371/) (2020)
- [Offline-to-online hyperparameter transfer for stochastic bandits](https://mlsanity.com/paper/2501.02926/) (2025)
- [The Pandora's Box Problem with Sequential Inspections](https://mlsanity.com/paper/2507.07508/) (2025)
- [Algorithm Design and Stronger Guarantees for the Improving Multi-Armed Bandits Problem](https://mlsanity.com/paper/2511.10619/) (2025)
- [Pessimism Traps and Algorithmic Interventions](https://mlsanity.com/paper/2406.04462/) (2024)
- [A Theoretical Model for Grit in Pursuing Ambitious Ends](https://mlsanity.com/paper/2503.02952/) (2025)

## Abstract (arXiv)

As humans, we face many decisions that require us to choose between sticking to something and giving up. This thesis uses algorithmic tools to derive insights about such decision-making problems in theoretical models, studying both near-optimal methods and outcomes of social and behavioral influences. Along the way, this thesis sheds light on what we gain and what we lose as we move from a messy and complex real world setting to a very general abstract model by studying various points along this spectrum. In Part I, we study algorithms for sequential decision-making in the improving multi-armed bandits problem. We provide nearly matching upper and lower bounds in the general case. Then, we then ask what is possible if we have access to similar instances to the one we wish to deploy our algorithm on. To that end, we provide guarantees in the data-driven algorithm design framework, showing that a polynomial number of samples is sufficient for learning good algorithms from a class of algorithms. In Part II, we study algorithmic approaches for problems in social epistemology. We start by analyzing what role theoretical models can play in the study of social problems. We then study social and behavioral influences in decision-making requiring investment. First, we provide mathematical formalism in which to study the formation of pessimism traps, a phenomenon identified by philosophers in which agents are influenced by their predecessors to engage in less-ambitious goals. We develop financial interventions to sustainably shift communities out of these traps. The second problem we study is the influence of grit as a behavioral trait in ambitious decision-making. Overall, these works seek to theoretically model phenomena in social epistemology and provide a framework for intervening algorithmically.