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Maths in the Social Sciences Research Group

Maths in the Social Sciences Research Group (MISSRG) is for anyone doing research or interested in the broad area of social science and its mathematical applications.

Currently meeting for a group project Fridays at 3pm on MS Teams!

Please email S.Forbes.1@warwick.ac.uk for any questions and to be added to the mailing list and Teams group. Keep an eye on this page for future updates!

Group positions:

President: Sam Forbes

Joint vice-president: Charlie Pilgrim

Joint vice-president: Kendal Foster

Plan:

Weekly talks (15-30mins) by members of the group or outside speakers followed by questions and discussion.

Collaboration on a group social science project.

Future Talks

Past Talks

19.3.21 - Anja Janischewski (PhD student at Chemnitz University of Technology, Germany)

Title: Price bubble definitions and strict local martingales
Abstract: Financial market bubbles and crashes are often present in public press. Commonly, bubble prices are defined as deviations from fundamental values. However, definitions of bubbles and of fundamentals vary accross the literature. In this talk, I give a brief overview of the different branches of literature and the definitions used. In the second part of the talk, I discuss the meaning of using strict local martingales as a model for price bubbles in mathematical finance.

12.3.21 - Xueying Zhao

Title: (De)marketing to Manage Consumer Quality Inferences

Abstract: Savvy consumers attribute a product’s market performance to its intrinsic quality as well as the seller’s marketing push. The authors study how sellers should optimize their marketing decisions in response. They find that a seller can benefit from “demarketing” its product, meaning visibly toning down its marketing efforts. Demarketing lowers expected sales ex ante but improves product quality image ex post, as consumers attribute good sales to superior quality and lackluster sales to insufficient marketing. The authors derive conditions under which demarketing can be a recommendable business strategy. A series of experiments confirm these predictions.

(Miklos-Thal and Zhang, 2013)

5.3.21 - Charlie Pilgrim

Title: Entropy Rising In The Attention Economy
Abstract: In the modern interconnected world there are ever increasing demands on our limited attention in what has been called "the attention economy". Our behaviour of choosing which media to consume is very similar to how animals forage for food, and optimal foraging models from ecology can be applied to our information gathering behaviour. This behaviour doesn't happen in isolation - media producers are adapting to generate media that we find more attractive and so gets more attention. I will describe a model for this system and present supporting empirical evidence of changes in the entropy of language (a proxy for information density) across time and between media categories.
26.2.21 - Yijie Zhou
Title: What leads to instability in financial networks?
Abstract: In ecosystems, the trade-off between network complexity and instability has been well studied. However, the natural complexity of the financial institutions and the various types of contracts between them makes it difficult to study the financial networks. What's the meaning of a financial network being unstable? What are the pathways to instability? In this talk, I will review the paper by Bardoscia (2017) which provides possible answers to these questions.
Bardoscia, M., Battiston, S., Caccioli, F., & Caldarelli, G. (2017). Pathways towards instability in financial networks. Nature Communications, 8(1), 1-7.

19.2.21 - Jack Bara

Title: Cooperation on Dynamic Networks
Abstract: To tackle large (global) issues such as climate change requires cooperation at multiple scales, from individuals choosing to recycle to international trade. When agents act negatively, they may be punished by mutual defection or by unilaterally burning bridges. Such coevolutionary processes have been gaining attention in recent years and as such I wish to give some preliminary insight and results on cooperative games on dynamic networks.

5.2.21- Ed Hill

Title: Vaccination and Non-Pharmaceutical Interventions: When can the UK relax about COVID-19?
Abstract: The dynamics of vaccination against SARS-CoV-2 are made complex by age-dependent factors, changing levels of infection and the potential relaxation of non-pharmaceutical interventions (NPIs) as the perceived risk declines

We consider the interaction between the UK vaccination programme and future relaxation (or removal) of NPIs using an age-structured mathematical model. The model is matched to a range of epidemiological data in the UK and includes the roll-out of a two-dose vaccination programme targeted at specific age groups

While the novel vaccines against SARS-CoV-2 offer a potential exit strategy for this outbreak, this is highly contingent on the infection-blocking (or transmission-blocking) action of the vaccine and the population uptake, both of which need to be carefully monitored as vaccine programmes are rolled out in the UK and other countries.

29.1.21 - Paul Wilson

Title: Use of 3D Printing in the Exhibition Hall: Museum Visitor Preferences

Abstract: Social science research methods are powerful tools for exploring a whole host of different research fields, with wide-ranging applications. Here, Paul will talk about one particular application of such methods in a rather unusual circumstance, user experience in museums.
Museums are in a strange place, caught between decades of unchanged practice since the early 20th century, drastically dwindling budgets and a technological landscape rapidly opening up within an affordable price range. 3D prints are one such development, which promises to help museums forever change the manner in which they present content to audiences, both with the exhibition gallery and overseas.
Here Paul will discuss a research approach to better understand the visitor perspective of 3D printed replicas for use within the gallery and to help determine the best practices going forward in creating good replicas for audiences of all ages using a robust suite of methods perfect for evaluating user experience.

22.1.21 - Bhavan Chahal

Title: Using deep learning to infer house prices from online images
Abstract: House prices are of interest at both a micro- and macroeconomic level due to issues such as housing inequality. The variation in amenities, air pollution, and other important factors in a particular location can determine where individuals choose to buy a property. By simply walking through a neighbourhood, we can understand a large amount of information about its socio-economic status and likely house prices. In recent times, vast quantities of online images have become available from sources such as Geograph and Zoopla. Simultaneously, huge leaps in what can automatically be inferred from an image have occurred, drawing on recent advances in deep learning. Can we use deep learning and this vast corpus of images to automatically infer house prices of neighbourhoods across London?

15.1.21 Annika Stechemesser

Title: Using Twitter to understand societal impacts of climate change

Abstract: Climate change already has profound impacts on ecosystems around the world. In the last 100 years, the global average temperature has increased by more than one degree Celsius. Understanding influences of climate change on human behaviour is essential for developing effective mitigation and adaption strategies. In this talk we will illustrate how data from the social media platform Twitter can be used to investigate relationships between heat stress and negative sentiment and psychological well-being using examples from the literature. Furthermore, we will discuss an excerpt of current research where natural language processing techniques are applied to Twitter data in order to investigate the temperature dependence of hate speech online in Europe and the United States.

4.12.20 Shuaib Choudhry

Title: Generalised Trophic Analysis applied to Urban and Social systems

Abstract: Many complex systems can be represented by graphs, e.g. food webs, social networks, financial networks and many more. Trophic coherence, a measure of a network’s organisation, has been shown to be linked to a network’s structural and dynamical aspects such as cyclicity, stability and normality; trophic levels of nodes can reveal their functional properties and rank the nodes accordingly. But trophic levels and hence trophic coherence can only be defined on networks with well defined sources, known as basal nodes. Thus trophic analysis of networks had been restricted until now. In this presentation I introduce a generalisation of trophic analysis, that can be defined on any simple graph. I will then show interpretations of novel network metrics in this framework applied to two contexts: Urban Networks and Information Transmission. This will illustrate new insights gained on the topological and dynamical aspects of networks within these respective domains, related to core-periphery, network imputation and influence of nodes on the flow dynamics.

27.11.20 Elena Kochkina

Title: “Natural Language Processing for Rumour Verification in Social Media Conversations"

Abstract: Online misinformation had a dramatic impact on our society in recent years, affecting people in many sectors, from finance, health to politics. The importance of combating the spread of fake news has been acknowledged by the public, companies and governments. The problem is being addressed through policies, journalistic work, media literacy of the population, social media platforms response and research. Our research aims to tackle this problem using Machine Learning and Natural Language Processing methods to propose systems for automated rumour verification that would assist journalists in their work. In this talk Elena will discuss various types of existing approaches to automated rumour verification, overview recent advances and outline open challenges that rumour verification models are facing, and share her view on how to tackle them.

20.11.20 Alex Holmes and Emma Southall

Title: Emma and Alex make some plots that spread much further through government than they expected

Abstract: Remark: this is ongoing work with confidential data, as such this talk should not be recorded, screenshotted or shared with anyone outside of the mathsys department.
From the start of June, we have been contributing towards the Schools team in Warwick’s Infectious Disease Research for the COVID-19 modelling response. We reported on the partial reopening of schools at the start of June, analysing the spatial and school variability in attendance and correlations with prosperity and geographical prevalence within England. We are continuing this data analysis for all schools from September. The current work includes spatial analysis of correlations between covid cases (students and teachers) within schools and community transmission (pillar 2 data) at the LTLA level; and the geographical variation of testing behaviour associated with the reopening of schools.

13.11.20 James Price

Title: The Dynamics of Wealth Inequality

Abstract: In random processes with multiplicative dynamics the average value is not guaranteed to match the typical value. For example, the average wage in a population could rise but most agents could see a decrease in their wages. This talk will explore the properties of a model based on Geometric Brownian Motion with an added mechanism that transfers wealth between agents. Particularly we will focus on how the rate of reallocation affects the difference between the average and the typical.

6.11.20 Sam Forbes

Title: Fitting the wealth distribution

Abstract: Wealth is a quantity that can be positive and negative both literally and metaphorically. I will present a piecewise distribution that fits to both positive and negative wealth. It has been known for many years that both a lognormal and power law distribution fit to different parts of positive wealth. What distributions fits to all of positive wealth? Come to the talk to find out!

30.10.20 Kendal Foster

Title: Model Selection: A Halloween Special

Abstract: Many of us use mathematical models as a tool to help explain simplified versions of real-world phenomena. As newer models look to usurp established ones, how exactly do we determine which model is indeed better? This notion of a "better" model can take many different forms, and we will discuss several tools that can directly compare models. However, each of these tools also has its own biases, so we must be aware of how we evaluate and select models.

23.10.20 Charlie Pilgrim

Title: Historical Trends in Linguistic Complexity

Abstract: It has been suggested that as social systems grow in size, languages will become less complex.
We will discuss this idea with a quick overview of the literature, and then present some results regarding the changes in Zipf's law over the last 200 years.

13.3.20 - Connah Johnson

Title: Abstraction of bio-orientated software tools to social science problems.

Abstract: Many biological systems are spatially organized. The development of computational tools for modeling spatially organized biological systems has to date focused on either so-called agent-based computational models or on physico-chemical models based on partial differential equations [1,2]. Here, we aim to combine the benefits of agent-based models with PDE-based ones, by introducing structures representing cells and their attributes into the PDE-based modeling platforms called CHASTE [3]. While this software has been developed for the study of biological systems the analogies between microbial communities and social communities are plentiful. In this talk I would like to introduce an abstraction of this bio-orientated tool and discuss possible social applications.

[1] An, G. et al. (2017), Otimization and Control of Agent-Based Models in Biology: A Perspective, Bull Math Biol, https://doi.org/10.1007/s11538-016-0225-6
[2] Kondo, S. et al. (2010), Reaction-Diffusion Model as a Framework for Understanding Biological Pattern Formation, Science, https://doi.org/10.1126/science.1179047
[3] Mirams, G et al. (2013), Chaste: an open source C++ library for computational physiology and biology, PloS Comput. Biol. https://doi.org/10.1371/journal.pcbi.1002970

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6.3.20 - Mohammad Noorbakhsh

Title: "Reinforcement Learning for FX trading ((Currency trading)"
Abstract: "Reinforcement learning (RL) is a branch of machine learning in which an agent learns to act within a certain environment in order to maximize its total reward, which is defined in relationship to the actions it takes. Traditionally, reinforcement learning has been applied to the playing of several Atari games, but more recently,more applications of reinforcement learning have come up. Particularly, in finance, several trading challenges can be formulated as a game in which an agent can be designed to maximize a reward. Reinforcement learning presents a unique opportunity to model the complexities of trading in which traditional supervised learning models may not be able to explore. FX trading is one such financial problem."
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27.2.20 - Trystan Leng

Title: Network structure and the spread of sexually transmitted infections
Abstract: In this talk I will discuss the importance of modelling network structure explicitly when modelling the spread of sexually transmitted infections, introduce approximate network models (e.g. pairwise approximations) and some difficulties in their application to diseases where recovery from infection does not confer immunity, then go on to discuss some of the difficulties posed by the behavioural data available for sexual networks.
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14.2.20 - Kutlwano Bashe

Title: Inferring Value through Behavioural Asset Pricing Models

Abstract: Stock prices are used as a measure of value for assets in financial markets. However, they tend to be affected by factors having little to do with the fundamentals of the asset. These factors tend to distort market prices of assets leading to inefficient capital allocation. In conjunction with behavioural asset pricing models, we use Bayesian filtering techniques on stock price time series data to infer the fundamental value. Can we use the inferred values to detect bubbles in financial markets and predict the risk of a financial crisis?

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7.2.20 - Bhavan Chahal

Title: Using deep learning to infer house prices from online images
Abstract: House prices are of interest at both a micro- and macroeconomic level due to issues such as housing inequality. The variation in amenities, air pollution, and other important factors in a particular location can determine where individuals choose to buy a property. By simply walking through a neighbourhood, we can understand a large amount of information about its socio-economic status and likely house prices. In recent times, vast quantities of online images have become available from sources such as Google Street View and Zoopla. Simultaneously, huge leaps in what can automatically be inferred from an image have occurred, drawing on recent advances in deep learning. Can we use deep learning and this vast corpus of images to automatically infer house prices of neighbourhoods across London?
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31.1.20 - Charlotte Roman

Title: Route choice in intelligent traffic systems
Abstract: I will talk about how to use congestion games to answer some of the following questions. When does the distribution of information about route choices negatively effect expected journey times? How do biased traffic lights affect route choice? Can biased traffic lights improve network inefficiencies? How will drivers respond to intelligent traffic lights? Are intelligent traffic lights fair?
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6.12.19- Jack Bara

Title: Information Gerrymandering and Undemocratic Decisions
Abstract: With a general election just under a week away, it is more important than ever to critically analyse polls and their effects on vote-shares. In particular through information gerrymandering, "the structure of [an] influence network can sway the vote outcome towards one party, even when both parties have equal sizes and each player has the same influence. A small number of zealots, when strategically placed on the influence network, can also induce information gerrymandering and thereby bias vote outcomes."
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29.11.19 - Charlie Pilgrim

Title: Evolution and communication

Abstract: We'll start with a crash course on evolutionary algorithms, with interesting examples. Then we will see that in action with an overview of Martin Nowak's fantastic paper "The evolution of language". Finally I will talk a bit about my own research model, which I want everyone to brutally criticise.

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22.11.19- Kendal Foster

Title: Using Response Times to Model Learning

Abstract: I will discuss the model that I developed this past summer; it uses response time data from a cognitive science experiment to determine whether or not individual participants learned a pattern. This model was designed to explain the learning phenomenon in this particular experiment, but it can also be generalized to other situations involving simple learning tasks.

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15.11.19 - Sam Forbes

Title: Applications of a monopoly model

Abstract: I have been using an agent based model as an application to the wealth distribution which under certain conditions leads to a heavy tailed distribution and monopoly. The characteristics of the model may be applicable to other areas. In this talk I will give a brief overview of the model and discuss it's potential application to city populations.