Theory of human enterprise
Developing mathematical models of markets, innovation and organisations, so that we can predict them and enhance them through interventions.
Science has traditionally been concerned with the natural world. But as society gets more interconnected and organisations get bigger, the man-made world needs a science of its own.
The sentiment of borrowers and lenders in a financial network is what drives markets to success, but also to ruin. We develop mathematical methods to predict how distress spreads, and determine strategies to limit system-wide catastrophic failure. We determine the latent potential in countries and firms by applying spectral-like theories to their networks of products and capabilities.
Despite advances in our understanding of evolution, what drives innovation remains elusive. Technological innovation operates in an expanding space of building blocks, in which combinations of technologies become new technologies. We characterise innovation in a mathematical way, extracting concepts and conservation laws, so that we can predict and influence it.
Organisations have emergent properties and capabilities that we are just coming to terms with. The success of some wikis suggests that many non-interacting agents can produce creative works superior to what any one person could do alone. What is the mathematical basis for collective creativity, and what sectors can we apply it to? Can it be used to speed up discovery in physics and mathematics?
A complexity-science approach to digital twins of cities views them as self-organising phenomena, instead of machines or logistic systems.
Statistical physics contributes to new models and metrics for the study of financial network structure, dynamics, stability and instability.
Networks where risky banks are mostly exposed to other risky banks have higher levels of systemic risk than those with stable bank interactions.
Consistent valuation of interbank claims within an interconnected financial system can be found with a recursive update of banks' equities.
Insights from biology, physics and business shed light on the nature and costs of complexity and how to manage it in business organizations.
A theoretical model of recursive innovation suggests that new technologies are recursively built up from new combinations of existing ones.
Modern portfolio theory inspires a strategy for allocating renewable energy sources which minimises the impact of production fluctuations.
The distribution of product complexity helps explain why some technology sectors tend to exhibit faster innovation rates than other sectors.
Network users who have access to the network’s most informative node, as quantified by a novel index, the InfoRank, have a competitive edge.
Bipartite networks model the structures of ecological and economic real-world systems, enabling hypothesis testing and crisis forecasting.
Forecast errors for simple experience curve models facilitate more reliable estimates for the costs of technology deployment.
The large-scale structure of the interbank network changes drastically in times of crisis due to the effect of measures from central banks.
The usefulness of components and the complexity of products inform the best strategy for innovation at different stages of the process.
Complex networks model the links between financial institutions and how these channels can transition from diversifying to propagating risk.
Firms can harness the shifting importance of component building blocks to build better products and services and hence increase their chances of sustained success.
When people operate in echo chambers, they focus on information adhering to their system of beliefs. Debunking them is harder than it seems.
A new algorithm unveils complicated structures in the bipartite mapping between countries and products of the international trade network.
Processes believed to stabilize financial markets can drive them towards instability by creating cyclical structures that amplify distress.
In systems of innovation, the relative usefulness of different components changes as the number of components we possess increases.
Non-linear models of distress propagation in financial networks characterise key regimes where shocks are either amplified or suppressed.
Targeted immunisation policies limit distress propagation and prevent system-wide crises in financial networks according to sandpile models.
An extension of the Kelly criterion maximises the growth rate of multiplicative stochastic processes when limited resources are available.
Increasing the complexity of the network of contracts between financial institutions decreases the accuracy of estimating systemic risk.
Coupled distribution grids are more vulnerable to a cascading systemic failure but they have larger safe regions within their networks.
An adaptive network of oscillators in fragmented and incoherent states can re-organise itself into connected and synchronized states.
A formulation of Moore’s law estimates the probability that a given technology will outperform another at a certain point in the future.
News sentiment analysis and web browsing data are unilluminating alone, but inspected together, predict fluctuations in stock prices.
A new tool derived from information theory quantitatively identifies trees, hierarchies and community structures within complex networks.
When the number of tweets about an event peaks, the sentiment of those tweets correlates strongly with abnormal stock market returns.
Analysis of the hyperbolicity of real-world networks distinguishes between those which are aristocratic and those which are democratic.
Tweet volume is a good indicator of political parties' success in elections when considered over an optimal time window so as to minimise noise.
A dynamical microscopic theory of instability for financial networks reformulates the DebtRank algorithm in terms of basic accounting principles.
The speed of a financial crisis outbreak sets the maximum delay before intervention by central authorities is no longer effective.
Dynamical systems theory predicts the growth potential of countries with heterogeneous patterns of evolution where regression methods fail.
Less developed countries have to learn simple capabilities in order to start a stable industrialization and development process.
Time series data from networks of credit default swaps display no early warnings of financial crises without additional macroeconomic indicators.
When networks come under attack, a repairable architecture is superior to, and globally distinct from, an architecture that is robust.
The likelihood of stock prices bouncing on specific values increases due to memory effects in the time series data of the price dynamics.
The interplay between redundancies and smart reconfiguration protocols can improve the resilience of networked infrastructures to failures.
Fractal structures need very little mass to support a load; but for current designs, this makes them vulnerable to manufacturing errors.
The optimal architecture of a financial system is only dependent on its topology when the market is illiquid, and no topology is always superior.
A new non-monetary metric captures diversification, a dominant effect on the globalised market, and the effective complexity of products.
Coupled non-linear maps extract information about the competitiveness of countries to the complexity of their products from trade data.
A new concept, graph temperature, enables the prediction of distinct topological properties of real-world networks simultaneously.
The most efficient load-bearing fractals are designed as big structures under gentle loads, a common situation in aerospace applications.
Complex networks detect the driver institutions of an interbank market and ascertain that intervention policies should be time-scale dependent.
New mathematical tools can help infer financial networks from partial data to understand the propagation of distress through the network.
Network-based metrics to assess systemic risk and the importance of financial institutions can help tame the financial derivatives market.
The Yule-Simon distribution describes the diffusion of knowledge and ideas in a social network which in turn influences economic growth.
The transition from solid to hollow beams changes the scaling of stability versus loading analogously to increasing the hierarchical order by one.
Network theory finds unexpected interactions between the number of products a country produces and the number of countries producing each product.
A quantitative assessment of the non-monetary advantage of diversification represents a country’s hidden potential for development and growth.
Analysis of web search queries about a given stock, from the seemingly uncoordinated activity of many users, can anticipate the trading peak.
In single elimination competition the best indicator of success is a player's wealth: the accumulated wealth of all defeated players.