There has been much talk about using social distancing as a response to the Coronavirus outbreak. Here’s a video of how social distancing can be used:
Close Contact, where the population is infected very rapidly. This is not A Good Thing – the national health service is overwhelmed, and the death rate goes up as a result
Extreme Distancing, where the viral spread takes too long. This is not A Good Thing – the social and economic costs are huge
Optimal Distancing, where the spread is controlled and the human, social, and economic costs are optimized. This is A Good Thing. It is also very tricky to get right.
This network map shows each MP that voted for each of 5 propositions: Parliamentary Sovereignty, Confirmatory Public Vote, Customs Union, Common Market 2.0, or the Withdrawal Agreement. The large dots show the number of MPs that voted for each proposition. It shows that Parliamentary Sovereignty and the Confirmatory Public Vote are unlikely to be in any consensus (unless with Common Market 2.0 &/or Customs Union), whereas a consensus between the Withdrawal Agreement and either Common Market 2.0 &/or Customs Union may be a possible way to form a Parliamentary majority.
Note the colours are indicative only, and that these votes were whipped by either the Labour Party or the Conservative Party (for instance, Cabinet ministers were instructed to vote only for the Withdrawal Agreement, so the blue dots to the right of the Withdrawal Agreement dot are likely to include the Cabinet).
Wednesday’s indicative votes in the House of Commons produced no definitive answer of the way forward. By using social network analysis showing the size of each voting bloc and ‘Hamming distances’ (ironically usually used for error correction), we can map how close MPs are to each other, giving an indication of how a coalition could be made if each block of MPs flipped their vote in order to form a Parliamentary consensus.
Brexit is currently turning out to a failed experiment in direct democracy, something I pointed out nearly three years ago.
However, with the House of Commons opening up data, it does allow us a rare insight into the goings on of the population and the MPs that are our representatives.
One interesting data source is released by the UK Parliament showing the voting record of every MP for every ‘division’ (vote). One particularly interesting vote was that done on Wednesday 27 March, where MPs were able to cast their votes for 8 motions:
By making a so-called bipartite network, we can map individual MPs to the votes for which they voted yes. This results in a map of MPs shown below.
(c) Dr Duncan Robertson duncanrobertson.com
While this is interesting, it doesn’t really show the distance between MPs’ voting intentions.
We can redraw the map by using the distance between MPs according to the votes they cast. We can do this by constructing a binary string of their votes. For simplicity, we count only the ‘aye’ or yes votes, and ignore abstentions and nos.
For instance, if an MP voted yes, no, no, yes, no, yes, yes, no, they would be given a string of 10010110, whereas if another MP voted no, no, yes, no, yes, yes, no, yes, they would be given a string of 00101101. So, what is the ‘distance’ between 10010110 and 00101101? For this, we use the Hamming distance – count the number of locations where there is a difference. In this case, the Hamming distance between the MPs is 6.
By constructing a graph of Hamming distances of 1, we can construct neighbours of individual groups of MPs. This is shown in the graph below.
I have listed the votes in the following order:
Common Market 2.0 Confirmatory Public Vote Contingent Preferential Arrangement Customs union EFTA and EEA Labour’s Alternative Plan No Deal Revocation to Avoid No Deal
(c) Dr Duncan Robertson duncanrobertson.com
However, this isn’t very useful, as it doesn’t show the type of MP that voted for each of these. So we can relabel the nodes with a representative MP from that bloc.
From this, you can work out the number of intermediate MPs to get to any other MPs. What is quite interesting is that every MP was just one vote away from another – no-one is isolated. Which, in some little way, gives us hope.
(c) Duncan Robertson duncanrobertson.com
We can then weight the edges to show the possible coalition that could be made if these blocs were to join. And here it is:
The size of each circle represents the number of MPs that voted the same way as the representative MP named on the circle, and the thickness of the links shows how many MPs would join together if one vote were flipped.
If the linked blocs join up, you can see how there could be a path to a Parliamentary majority – for the blocs to join, it would mean switching one vote from ‘aye’ to ‘no’ or vice versa.
For completeness, the list of MPs and their associated binary string is linked here. You can find the MPs that are part of each bloc by searching for the MP name in the label on the network graph. The Hamming distance between each and every MP is available on request. I leave it to the reader to construct an affinity matrix – or what I would call currently describe as a ‘Matrix of Hate’ for each MP pair.
This paper , published in the journal Risk Analysis, sets out a review of the different methods used for modelling the spread of an idea, disease, etc. over space.
ABSTRACT
Within risk analysis and more broadly, the decision behind the choice of which modelling technique to use to study the spread of disease, epidemics, fires, technology, rumors, or more generally spatial dynamics, is not well documented.
While individual models are well defined and the modeling techniques are well understood by practitioners, there is little deliberate choice made as to the type of model to be used, with modelers using techniques that are well accepted in the field, sometimes with little thought as to whether alternative modelling techniques could or should be used.
In this paper, we divide modelling techniques for spatial transmission into four main categories: population-level models, where a macro-level estimate of the infected population is required; cellular models, where the transmission takes place between connected domains, but is restricted to a fixed topology of neighboring cells; network models, where host-to-host transmission routes are modelled, either as planar spatial graphs or where short cuts can take place as in social networks; and finally agent-based models which model the local transmission between agents, either as host-to-host geographical contacts, or by modelling the movement of the disease vector, with dynamic movement of hosts and vectors possible, on a Euclidian space or a more complex space deformed by the existence of information about the topology of the landscape using GIS techniques. We summarize these techniques by introducing a taxonomy classifying these modeling approaches.
Finally, we present a framework for choosing the most appropriate spatial modelling method, highlighting the links between seemingly disparate methodologies, bearing in mind that the choice of technique rests with the subject expert.
One of the most challenging issues for policy makers dealing with bio-security threats is their dynamic nature: diseases may spread quickly and deadly among vulnerable populations and pandemics may cause many casualties.
Finding the appropriate response to threats is a major challenge. Whilst models exist for understanding of the dynamics of the threats themselves, responses can be largely ad-hoc or ‘firefighting’. The aim of this research is to produce robust responses for dynamic threats.
The research will build up as follows, from low to high complexity: static responses to static threats; static responses to dynamic threats; dynamic responses to static threats; and dynamic responses to dynamic threats.
We will use a variety of methods to define the best response: cellular automata, network analysis, spatial modelling, agent-based modelling, and the generation of dynamic fitness landscapes.
This PhD studentship is most suitable for candidates with a background in a quantitative discipline such as management science, operations research, engineering, physics and other natural sciences.
I am using airline data to construct a network of competition in the airline industry. As part of this, I am listing the routes that are the most competitive – not necessarily the ones that have the most flights, but the ones that have the most competitors.
And here they are
Map generated from GCMap.com
HKG-ICN Hong Kong – Incheon, Seoul (South Korea)
Not shown: EastarJet
TPE-NRT Taipei (Taiwan) – Narita, Tokyo (Japan)
Not shown: Vanilla Air, Tiger Air, Scoot, Transasia
SIN-CGK Singapore – Jakarta (Indonesia)
Not shown: Indonesian Air Asia, Scoot, JetStar Asia
SIN-DPS Singapore – Denpasar, Bali (Indonesia)
Not shown: Qantas, Qatar, Indonesia Air Asia, Scoot (nb JetStar and JetStar Asia are different airlines)
Please note that these data are a few years old, are preliminary and not completely accurate, and airlines come and go on such competitive routes.
I am looking for high quality, numerate, candidates to fill these exciting PhD studentships with me as a (co-) supervisor at Loughborough’s School of Business and Economics. Please note that this post has been updated with new links (in blue, below).
The first is modelling dynamic responses to dynamic threats; the second is using analytics in traditional industries. Please see the links below for further details and how to apply. Note that for further information, you will need to click on the blue links below.
One of the most challenging issues for policy makers dealing with bio-security threats is their dynamic nature: diseases may spread quickly and deadly among vulnerable populations and pandemics may cause many casualties.
Finding the appropriate response to threats is a major challenge. Whilst models exist for understanding of the dynamics of the threats themselves, responses can be largely ad-hoc or ‘firefighting’. The aim of this research is to produce robust responses for dynamic threats.
The research will build up as follows, from low to high complexity: static responses to static threats; static responses to dynamic threats; dynamic responses to static threats; and dynamic responses to dynamic threats.
We will use a variety of methods to define the best response: cellular automata, network analysis, spatial modelling, agent-based modelling, and the generation of dynamic fitness landscapes.
This PhD studentship is most suitable for candidates with a background in a quantitative discipline such as management science, operations research, engineering, physics and other natural sciences.
The rise of business analytics has given rise to enormous opportunities within the private sector, but these benefits have yet to be fully realized in public services and regulated industries such as energy, water, and transportation networks. Conversely, governments are mandating collection of data by installing smart metering devices. This gives rise to the need for innovative ways of thinking in industries that are still largely based on traditional economic thinking involving conventional assumptions on optimization and behaviour.
As an example, the energy sector is characterised by strongly defined market structures with incumbents and an ultimate need for energy network security, which not only prevents the quick adoption of technical changes but also translates into regulatory outcomes, such as price caps.
This exciting PhD opportunity will integrate theoretical and empirical approaches and spans two strengths of Loughborough’s School of Business and Economics: microeconomics and particularly rigorous analysis of the determinants of productivity and performance (including cost modelling) and management science (including simulation and network analysis).
We are therefore seeking a student with a quantitative background (whether in economics, management science, engineering, physics or other natural sciences). A willingness to learn new techniques such as, cost modelling, performance measurement, agent-based modelling and network analysis is desired.
This presentation from the EURO 2016 conference in Poznan, Poland, and from the GDN conference in Bellingham, WA, USA, joint work with Leroy White of Warwick Business School, shows how combining formal and informal organizational networks enables decisions to flow more freely around organizations, but at a cost, leading to an optimal size of informal organizational networks. If organizations can control these, this leads to implications for optimal information flows in companies.
