1. Develop new-generation models of financial networks incorporating complex financial instruments that are prone to generate unforeseen systemic effect
2. Provide new tools to quantify gains in the accuracy of risk estimation from future regulations and information disclosures, in particular with respect to non-conventional banking
3. Identify key actors and instruments to contribute to the design of a more sustainable climate-financial system.
4. Develop an ICT knowledge sharing platform to enable the crowd-sourcing of data collection and data analysis regarding policy-relevant socio-economic networks.
5. Develop an ICT infrastructure to gather semantic web data for policy relevant socio-economic networks.
6. Demonstrate the possibility to empower citizens by enabling real-time collective mapping of networks of influence in policy making processes.
The overall work plan of the SIMPOL Project consists of three main work packages (WP) whose activity covers the goal of our project.
WP1 – Financial Networks for policy modelling. In the first instance we believe it is necessary to develop financial network models that incorporate complex instruments in order to provide insights to regulators regarding contracts data disclosure and capital requirements. More specifically we want to develop network models of shadow banking by incorporating also network dependencies related to climate finance and those obtained via crowdsourcing. At this point we have the mathematical instruments to measure and investigate the robustness of different financial system architecture in order to contribute to the public policy debate.
WP2 – Modelling Climate Finance Policies. At the same time, we want to analyse the interactions among the various actors and instruments involved in climate finance. Since climate change is a paradigmatic case of global issue with planetary scale and a long-term temporal horizon, the nexus Finance-Climate is a paramount challenge in policy modelling. Moreover, the complexity of financial innovations often hampers the transparency that is needed in climate related agreements. In this respect, we aim to develop network models of climate finance that incorporate financial instruments not taken into account to far. We also develop macro models of climate finance and models of networks of influence revolving around climate-finance policies.
WP3 – Collective Mapping of Influence Networks. Finally, we build empirical socio-economic networks from a large variety of resources available. Because the sustainability of the financial system is a societal issue, the input of civic society cannot be neglected. The aim of this WP is to demonstrate the feasibility empowering citizens and increase decision-making transparency around financial regulations. It is not necessary to engage civic society in the discussion of the details of policies and their effects. Instead, it is possible and socially desirable to involve civic society in uncovering the network economic interests revolving around policies. To avoid cold-start problems, we follow a mixed strategy whereby we both engage users (with the incentive that they can in exchange gain privileged access to the platform) and we mine existing open datasets. To this end, we will first develop a simple infrastructure, based on the semantic web, to construct, visualize, explore and extend the networks. The networks will be initially constructed by crawling public, open data on the web. Next, the interested public will be able to explore and contribute partial data. This will be used to construct new economic networks and to enrich those that already exist in the database. In this WP, we will also monitor streams of big data ranging from news to financial blogs and social media (such as Twitter) to extract and aggregate the public pulse about socio-economic issues, thus providing additional links and properties to the networks.
WP4 Management. We define the instruments to set up collaboration with nodes and to deliver the objectives of our project.
WP5 Dissemination. We make a plan on the dissemination instruments we shall use in the course of the activity, including scientific papers, papers targeted at the broader public, conferences and schools.
The following table lists the partner institutions to the SIMPOL Project.
|University of Zurich||Switzerland|
|IMT Alti Studi Lucca||Italy|
|Global Climate Forum||Germany|
|Université Paris I Panthéon-Sorbonne||France|
|Institute Jozef Stefan, Ljubljana||Slovenija|
|London Institute for Mathematical Sciences||UK|
Research Output for the First Year
Work performed and main results in the period M1-M11
The main results of the works of the SIMPOL project address some fundamental research questions in three inter-related areas.
Financial Networks Models
The questions here concern (1) how to incorporate complex financial instruments in the state-of-art of contagion models and estimate systemic risk; and (2) how to estimate the accuracy on the measures of systemic impact, in order to determine the minimal necessary level of financial data disclosure. In this respect,
1. We have developed a model to compute the default probability in a network of derivative contracts and our results indicate that little errors on the evaluations of the contracts and their structure can lead to large errors on the estimation of default probability. We are also working on models of contagion with rehypotecation.
2. We have continued with our collaboration with the Bank of England (BoE), the BundesBank and the European Central Bank (ECB) by providing code and developing algorithms for systemic risk. During the first year of SIMPOL, our DebtRank methodology has been included at the ECB among the tools to monitor the EU payment system TARGET2.
The questions here concern (1) who are the main actors in climate finance and how they interact; (2) What are the macro-economic impacts of climate finance; and (3) what is the structure of networks of influence in climate policy. In this respect,
1. We have carried out a network analysis of the European Carbon Market (EU-ETS) looking at the relationships between market inefficiencies and network topology.
2. We have surveyed existing climate finance instruments as well as macro-economic models for climate policy. We developed an initial agent-based model with real-financial linkages in order to investigate the impacts of climate finance on macro-economic dynamics.
3. We developed initial maps of networks of influence in several climate/finance contexts:
a. roll-call votes in the European Parliament; the EU Transparency Register of lobbies
b. ownership and board interlocks data among companies involved in finance and climate
c. Twitter network and sentiment analysis around climate issues. Twitter activity around policy issues such as the 2014 ECB Asset Quality Review.
Collective Mapping of Influence Networks in Policy Making
The questions we address here are (1) which knowledge representation formalism is appropriate to represent and efficiently analyze (large) socio-economic networks from heterogeneous sources; (2) which technologies should be selected to implement the infrastructure (e.g. back-end database, web-based graphical user interface, front-end visualizations); (3) how to crowd-source the task of mapping the network of influence into a collective task by means of semantic web and big data mining technologies; (4) how to (semi)automatically transform various instances of open data into the selected, graph-based knowledge representation formalism? In this respect,
1. We have implemented a web portal http://simpol.ijs.si/ which uses Neo4j as back-end database and follows the Semantic Web and Linked Open Data (LOD) principles, as recommended by the World Wide Web consortium (W3C). The portal supports all the planned SIMPOL activities to build socio-economic networks: selection and inspection of existing networks in the database, extraction of new networks from open data, import/export interface to crowdsourcing; monitoring of news and blogs and social media. The SIMPOL knowledge-sharing platform, with all its components, is available for downloading and installing.
2. We have designed a general-purpose workflow, which takes any instance of Linked Open Data (LOD) in the standard RDF formalism, and imports it into the graph-based Neo4j database. The workflow and its implementation are open-source, freely available on GitHub (the main open source software repository). As initial test, we have (semi) automatically extracted and inserted them into the Neo4j database: DBpedia (structured subset of Wikipedia), reegle (dataset of renewable energy resources), European Parliament (dataset of members and political groups), Climate change sceptics (dataset of U.S. climate change counter-movement organizations).
3. We started a preliminary analysis of social media data focusing on queries log data and data of interaction of users on Facebook. We looked at the interplay between true and false news on Facebook, with specific interest on scientific news and conspiracy ideas related to climate change.
Diagrams illustrating and promoting the work of the project
(Left). Ownership network among top firms in finance and energy sectors (Right). Board interlock network among corporations in the EU Transparency register
(Left). Twitter communities and sentiment around climate issues. (Right). Twitter communities in the Asset Quality Review dataset (October 2014).
Screenshot from the SIMPOL knowledge base platform. A full-text search engine has been implemented. In the example searching for a given organization yields the results in one or more subgraphs in the Neo4j database.
The relations are combined across subgraphs and the resulting graph can be exported.
Original publication date: 1 April 2015.