2025
In this short paper, we developed a stochastic closed-loop control model for oil production under demand uncertainty and market shocks. We derive the systems corresponding Hamilton-Jacobi-Bellman Partial Integro-Differential Equation (HJB-PIDE) and implement two different solvers.
2025
In this paper, we analyze multi-round scoring auctions where the auctioneers value function is unknown. We develop a greedy algorithm capable of multi-attribute value function estimation using information from only a few rounds of bidding. We apply our analysis to the case study of public works procurment.
2025
In this paper, we propose a novel quantum-statistical framework to model S. roselii’s behavioral responses to environmental stimuli. By leveraging quantum circuits with amplitude dampening and memory effects, we construct a quantum behavioral model that captures the probabilistic and hierarchical nature of S. roselii’s decision making.
2024
We introduce three axiomatic principles of energy-efficient decision making. We propose an optimal-control based model and Deep Q learning architecture that incorporates those principles to model animal wayfinding.
2024
We introduce the basics of quantum computing and simulation of quantum systems on classical computers. We then discuss noise in quantum systems and how instances of noise are classically modelled, along with the difficulties of simulating quantum noise on classical computers. We introduce an extension of the T-Gadget to classically simulate thousands of instances of dampening noise within reasonable memory constraints.
2024
In this paper, we explore the application of Deep Reinforcement Learning (DRL) to the domain of autonomous equity trading, with a particular focus on the use of Deep Q Networks (DQNs) coupled with risk-sensitive loss objectives, to develop trading agents capable of navigating complex financial market conditions.
2024
In this paper, we develop a conceptual, mathematical, and computational framework for modeling market exchange as a series of dynamically interacting cognitive processes. Specifically, we show how advertisers can build trust and gain confidence in their pricing power to the point that they erode trust and undermine the efficacy of their advertising.