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Posts by Collection
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publications
A Model for Trust Driven Advertising
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.
Autonomous Trading Using Deep Q Learning
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.
Simulating Quantum Circuits with Non-Clifford Noise
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.
A Model Predictive Control and Deep Q Learning Approach to Wayfinding
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.
A QuantumStatistical Model of Decision Making in a Single-Cell Eukaryote
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.
Blind Multi-Stage Scoring Auctions with Two-Sided Uncertainty
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.
Closed Loop Control with Jump Processes: OPEC Oil Production Cases Study
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.
talks
Modeling Trust Based Markets
Published:
Cognitive processes underlie economic relations. In this talk, we discuss 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. Customers conversely orient towards advertisers seeking information or turn away from them as unreliable communicators. These behaviors and the patterns they generate occur inside a state space of unallocated perceived value. They constitute a small subset of the full range of possible strategic...
Classical Simulation of Non-Clifford Noise Channels
Published:
We introduce the basics of quantum computing and simulation of quantum systems on classical computers. We then discuss noise in quantum systems and how it is classically modelled, along with the difficulties of simulating quantum noise on classical computers. Our primary question is how to efficiently simulate quantum noise by leveraging existing techniques based upon the Gottesman-Knill theorem, which provides efficient simulation of circuits containing only Clifford gates. To this end we develop theory and practical implementations of the K-Gadget, a method to compress and simualte thousands of instances of dampening noise within reasonable memory constraints.
The Ontology and Architecture of Intelligence
Published:
We suggest the problem of intelligence is to locate viable solutions in infinite search spaces using finite resources. Here we present our work on conceptualizing and formalizing some of the features of intelligence in dynamic multi-level control systems. We begin by presenting a new ontology needed for understanding intelligence, illustrated by the controversy around beables in quantum theory. We then present a model of situationally appropriate information-processing in a single-celled organism, using quantum computing formalisms. We show how multi-level control systems can emerge from information-processing constraints within a system of multi-dimensional optimization and explore the information flow between levels within...
A Quantum Statistical Model of Decision Making in a Single-Cell Eukaryote
Published:
The single-celled protist Stentor roselii has long been observed to exhibit complex decision-making behaviors, yet existing machine learning and classical computational models have struggled to replicate its actions. 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. Our results suggest that quantum statistical theory provides a promising tool for representing and simulating biological decision processes.
A Pruned Shortest Path Algorithm for L_0 Optimization Problems with Rockafellian Perturbations
Published:
In this presentation we propose an optimization-based approach to timeseries breakpoint detection and attribution, where the target time-series is a function of an arbitrary number of “feature” timeseries. We tackle this problem by first introducing a general loss function with $L_0$ regularization along with a dual formulation. We then reformulate the dual into a shortest path problem which we solve by developing several specialized lazy path finding algorithms. We conclude with some theoretical analysis of the efficiency of our proposed algorithms along with a use case in marketing strategy
Blind Multi-Stage Scoring Auctions with Value Function Estimation
Published:
Government procurement often suffers from subjectivity because reviewers rely on variable and partially unknown value functions to score multi-attribute bids. We address this challenge with a few-shot value function estimation framework and propose a greedy online heuristic solver that incrementally approximates both the structure and weights of the underlying value function. We apply the algorithm to a public works case study, where our results show that a 7-attribute value function can be accurately recovered in under 20 iterations, enabling bidders to adapt quickly and effectively in multi-stage scoring auctions.
teaching
Teaching experience 1
Undergraduate course, University 1, Department, 2014
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Teaching experience 2
Workshop, University 1, Department, 2015
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