A simple learning agent interacting with an agent-based market model

We consider the learning dynamics of a single reinforcement learning optimal execution trading agent when it interacts with an event driven agent-based financial market model. Trading takes place asynchronously through a matching engine in event time. The optimal execution agent is considered at different levels of initial order-sizes and differently sized state spaces. The resulting impact on the agent-based model and market are considered using a calibration approach that explores changes in the empirical stylised facts and price impact curves. Convergence, volume trajectory and action trace plots are used to visualise the learning dynamics. Here the smaller state space agents had the number of states they visited converge much faster than the larger state space agents, and they were able to start learning to trade intuitively using the spread and volume states. We find that the moments of the model are robust to the impact of the learning agents except for the Hurst exponent, which was lowered by the introduction of strategic order-splitting. The introduction of the learning agent preserves the shape of the price impact curves but can reduce the trade-sign auto-correlations when their trading volumes increase.Comment: 18 pages, 7 figures. Accepted: Physica

Many learning agents interacting with an agent-based market model

We consider the dynamics and the interactions of multiple reinforcement learning optimal execution trading agents interacting with a reactive Agent-Based Model (ABM) of a financial market in event time. The model represents a market ecology with 3-trophic levels represented by: optimal execution learning agents, minimally intelligent liquidity takers, and fast electronic liquidity providers. The optimal execution agent classes include buying and selling agents that can either use a combination of limit orders and market orders, or only trade using market orders. The reward function explicitly balances trade execution slippage against the penalty of not executing the order timeously. This work demonstrates how multiple competing learning agents impact a minimally intelligent market simulation as functions of the number of agents, the size of agents' initial orders, and the state spaces used for learning. We use phase space plots to examine the dynamics of the ABM, when various specifications of learning agents are included. Further, we examine whether the inclusion of optimal execution agents that can learn is able to produce dynamics with the same complexity as empirical data. We find that the inclusion of optimal execution agents changes the stylised facts produced by ABM to conform more with empirical data, and are a necessary inclusion for ABMs investigating market micro-structure. However, including execution agents to chartist-fundamentalist-noise ABMs is insufficient to recover the complexity observed in empirical data.Comment: 12 pages, 9 figure

Deep Learning Traffic Classification in Resource-Constrained Community Networks

Community networks are infrastructures that are run by the citizens for the citizens. These networks are often run with limited resources compared to traditional Internet Service Providers. For such networks, careful traffic classification can play an important role in improving quality of service. Deep learning techniques have been shown to be effective for this classification task, especially since classical approaches struggle to deal with encrypted traffic. However, deep learning models often tend to be computationally expensive, which limits their suitability for low-resource community networks. This paper explores the computational efficiency and accuracy of Long Short-Term Memory (LSTM) and Multi-Layer Perceptron (MLP) deep learning models for packet-based classification of traffic in a community network. We find that LSTM models attain higher out-of-sample accuracy than traditional support vector machines classifiers and the simpler multi-layer perceptron neural networks, given the same computational resource constraints. The improvement in accuracy offered by the LSTM has a tradeoff of slower prediction speed, which weakens their relative suitability for use in real-time applications. However, we observe that by reducing the size of the input supplied to the LSTMs, we can improve their prediction speed whilst maintaining higher accuracy than other simpler models

Editorial: New lines of inquiry for investigating visual search behavior in human movement

The goal of this Research Topic was to examine the emerging approaches to understanding the role of visual search in human movement. The varying aspects covered in this Research Topic highlights the continued growing interest in understanding visual search behavior in human movement and the articles within the topic provide insightful ideas for continuing to develop future research

Ethnography and data re-use: issues of context and hypertext

This paper seeks to open up debate around context and the re-analysis of stored qualitative data. How to ensure that subsequent users of deposited datasets can appreciate and be guided by the context of the original study? The paper introduces the idea of hypertext as one way of facilitating this. We discuss how „context‟ might be thought through in particular relation to ethnography, where it is frequently difficult to distinguish between data and context, and highlight some of the inherent problems in the notion of archiving ethnographic context. In a discussion focusing in on multimedia, we draw attention to the different kinds of contextual information that are necessary to interpret data in different media forms. The paper‟s starting position is that originators of data and re-users have in front of then a qualitatively different kind of knowledge-base, due to the fact that data and data-records are not the same thing. This doesn‟t rule out re-use but does imply that quite full and careful kinds of documentation are necessary to try and make it sufficiently rigorous, a demand which also, however, has to be balanced against the dangers of information overload. These challenges lead us to question whether the traditional archiving model is the most suitable way of communicating context to re-users; we present some of our insights from work on hypertext to explore the potential of the hyperlink as a key contextualising tool

Improving Student Engagement in Veterinary Business Studies

In a densely packed veterinary curriculum, students may find it particularly challenging to engage in the less overtly clinical subjects, yet pressure from industry and an increasingly competitive employment market necessitate improved veterinary student education in business and management skills. We describe a curriculum intervention (formative reflective assignment) that optimizes workplace learning opportunities and aims to provide better student scaffolding for their in-context business learning. Students were asked to analyze a business practice they experienced during a period of extra-mural studies (external work placement). Following return to the college, they were then instructed to discuss their findings in their study group, and produce a group reflection on their learning. To better understand student engagement in this area, we analyzed individual and group components of the assignment. Thematic analysis revealed evidence of various depths of student engagement, and provided indications of the behaviors they used when engaging at different levels. Interactive and social practices (discussing business strategies with veterinary employees and student peers) appeared to facilitate student engagement, assist the perception of relevance of these skills, and encourage integration with other curriculum elements such as communication skills and clinical problem solving

Semi‐quantitative characterisation of mixed pollen samples using MinION sequencing and Reverse Metagenomics (RevMet)

1. The ability to identify and quantify the constituent plant species that make up a mixed‐species sample of pollen has important applications in ecology, conservation, and agriculture. Recently, metabarcoding protocols have been developed for pollen that can identify constituent plant species, but there are strong reasons to doubt that metabarcoding can accurately quantify their relative abundances. A PCR‐free, shotgun metagenomics approach has greater potential for accurately quantifying species relative abundances, but applying metagenomics to eukaryotes is challenging due to low numbers of reference genomes. 2. We have developed a pipeline, RevMet (Reverse Metagenomics) that allows reliable and semi‐quantitative characterization of the species composition of mixed‐species eukaryote samples, such as bee‐collected pollen, without requiring reference genomes. Instead, reference species are represented only by ‘genome skims’: low‐cost, low‐coverage, short‐read sequence datasets. The skims are mapped to individual long reads sequenced from mixed‐species samples using the MinION, a portable nanopore sequencing device, and each long read is uniquely assigned to a plant species. 3. We genome‐skimmed 49 wild UK plant species, validated our pipeline with mock DNA mixtures of known composition, and then applied RevMet to pollen loads collected from wild bees. We demonstrate that RevMet can identify plant species present in mixed‐species samples at proportions of DNA ≥ 1%, with few false positives and false negatives, and reliably differentiate species represented by high versus low amounts of DNA in a sample. 4. RevMet could readily be adapted to generate semi‐quantitative datasets for a wide range of mixed eukaryote samples. Our per‐sample costs were £90 per genome skim and £60 per pollen sample, and new versions of sequencers available now will further reduce these costs

3. Bird Conservation

Expert assessors Tatsuya Amano, University of Cambridge, UK Andy Brown, Natural England, UK Fiona Burns, Royal Society for the Protection of Birds, UK Yohay Carmel, Israel Institute of Technology Mick Clout, University of Auckland, New Zealand Geoff Hilton, Wildfowl & Wetlands Trust, UK Nancy Ockendon, University of Cambridge, UK James Pearce-Higgins, British Trust for Ornithology, UK Sugoto Roy, Food and Environment Research Agency, DEFRA, UK Rebecca K. Smith, University of Cambridge, UK Wil..