The FHI-Amlin Research Collaboration on Systemic Risk of Modelling is pursuing better understanding and management of the systemic risks associated with modelling in the insurance industry through the strategic collaboration between the Future of Humanity Institute and Amlin. Systemic risks concern the unexpected collapse of an entire market, method of doing business or method of modelling, and are of great importance to managing risk on the large scale. The collaboration aims at enabling research into how systemic risks emerge from the usage of models and can be managed, disseminating this research, and educating towards a self-sustaining culture of accurate thinking about risk.
Decision-making and uncertainty
Risks that emerge from complex decision-making, including decision-making about risk itself, are the main focus of interest in this project. Distributed thinking in organisations, the increasing use of complex models, and a rapidly changing world pose many understudied challenges that FHI and Amlin are well placed to explore together in order to find better ways of managing large scale risks. This includes the problems of systemic risk in catastrophe modelling, how to maintain necessary institutional critical thinking despite biasing incentives, how to validate complex models, and how to handle changing uncertainty.
Systemic risks arise when connections within a system increase the chance of an individual failure becoming a system-wide failure. While financial risks of a systemic character have been very prominent recently, systemic risks exist in a range of domains: ecology, food security, technological infrastructure, and social systems. Systemic risks are of great importance because of their disruptiveness and because they pose challenges in detection, assignment of responsibility, and prevention. The normal problems of risk management are also further compounded by issues of complexity, uncertainty and ignorance: the systems and their dynamics are not fully known and may change over time (this point can be clearly illustrated by a comparison between current models and those used twenty years ago, even for relatively well-understood phenomena).
Modelling and systemic risks
One particular aspect that has been little studied is how modelling can contribute to the systemic risk. Many complex human systems attempt to adapt to conditions (reducing risk) by modelling the state of the world and future scenarios, yet these models can introduce further risks. For example, they might create interdependencies between parts of markets, promote biases such as institutional group-think, warp incentive structures, give a false sense of security, or reduce small and common errors while increasing the risk of large uncommon errors. This project aims to investigate systemic risks (in particular, of modelling of risk itself) and how they can be reduced.
The key objectives of this collaboration will be to
- describe and understand the factors of systemic risk in the domain of (risk) modelling,
- describe and understand methods of mitigating these risks, and
- propose practical applications in different domains (such as insurance) that follow from this.
These areas are under-researched at present, largely because of their interdisciplinary nature. A proper treatment requires considerations of statistical modelling, the theory and practice of risk management, handling high impact low-probability risks, distributed decision-making, cognitive bias, adaptive complex systems etc. The project would fill the knowledge gaps in this strategic topic, making use of the unique joint combination of strengths of Amlin and the FHI to tackle the big problems.
For a video summary of our latest conference, please see here.
The White Paper on Systemic Risk of Modelling in Insurance by the Systemic Risk of Modelling Working Party can be accessed here. The Paper aims to help the readers understand the systemic risk associated with Modelling practices within the insurance industry. This paper is specifically focuses on the practical understanding of Systemic Risk of Modelling and development of solutions for managing such risk within the insurance industry.