Algorithmic Approaches to Sequential Decision-Making and Social Epistemology

2026-07-22

AI Summary

This study addresses sequential decision-making in humans concerning persistence versus abandonment, and investigates phenomena such as pessimism traps and insufficient ambition arising from behavioral biases and social influence in social cognition. By constructing an enhanced multi-armed bandit model, the work introduces data-driven algorithmic design into sequential decision-making for the first time and provides a formal characterization of pessimism traps. Theoretical analysis yields nearly tight upper and lower bounds on sample complexity under general conditions, demonstrating that polynomially many samples suffice to learn near-optimal policies. Furthermore, the paper proposes a sustainable community intervention mechanism that effectively disrupts pessimism traps, thereby bridging abstract theories in social epistemology with the complexities of real-world decision-making.

Abstract

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.

Problem

• sequential decision-making
• social epistemology
• pessimism traps
• behavioral influences
• multi-armed bandits

Innovation

• improving multi-armed bandits
• data-driven algorithm design
• social epistemology
• pessimism traps
• behavioral interventions