activity
20172020
most citedRecognising and evaluating the effectiveness of extortion in the Iterated Prisoner's Dilemma

3 citations · 5 across the 4 of their papers we have counts for

collaborators

8 papers

stat.AP20201 cited

Segmentation analysis and the recovery of queuing parameters via the Wasserstein distance: a study of administrative data for patients with chronic obstructive pulmonary disease

Henry Wilde, Vincent Knight, Jonathan Gillard +1

This work uses a data-driven approach to analyse how the resource requirements of patients with chronic obstructive pulmonary disease (COPD) may change, quantifying how those chang…

cs.LG20201 cited

A novel initialisation based on hospital-resident assignment for the k-modes algorithm

Henry Wilde, Vincent Knight, Jonathan Gillard

This paper presents a new way of selecting an initial solution for the k-modes algorithm that allows for a notion of mathematical fairness and a leverage of the data that the commo…

cs.GT2019

Memory depth of finite state machine strategies for the iterated prisoner's dilemma

T. J. Gaffney, Marc Harper, Vincent A. Knight

We develop an efficient algorithm to determine the memory-depth of finite state machines and apply the algorithm to a collection of iterated prisoner's dilemma strategies. The calc…

cs.GT2019

Using a theory of mind to find best responses to memory-one strategies

Nikoleta E. Glynatsi, Vincent A. Knight

Memory-one strategies are a set of Iterated Prisoner's Dilemma strategies that have been praised for their mathematical tractability and performance against single opponents. This…

physics.soc-ph2019

A bibliometric study of research topics, collaboration and centrality in the Iterated Prisoner's Dilemma

Nikoleta E. Glynatsi, Vincent A. Knight

This manuscript explores the research topics and collaborative behaviour of authors in the field of the Prisoner's Dilemma using topic modeling and a graph theoretic analysis of th…

cs.DS2019

Evolutionary Dataset Optimisation: learning algorithm quality through evolution

Henry Wilde, Vincent Knight, Jonathan Gillard

In this paper we propose a novel method for learning how algorithms perform. Classically, algorithms are compared on a finite number of existing (or newly simulated) benchmark data…