Carlo Jaeger

Complexity theory and financial regulation

Economic policy needs interdisciplinary network analysis and behavioral modeling Traditional economic theory could not explain, much less predict, the near collapse of the financial system and its long-lasting effects on the global economy. Since the 2008 crisis, there has been increasing interest in using ideas from complexity theory to make sense of economic and financial markets. Concepts, such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon, but actual use of complexity models and results remains at an early stage. Recent insights and techniques offer potential for better monitoring and management of highly interconnected economic and financial systems and, thus, may help anticipate and manage future crises.

THREE VIEWS OF TWO DEGREES

Limiting global warming to 2°Celsius above pre-industrial global mean temperature has become a widely endorsed goal for climate policy. It has also been severely criticized. We show how the limit emerged out of a marginal remark in an early paper about climate policy and distinguish three possible views of it. The catastrophe view sees it as the threshold separating a domain of safety from a domain of catastrophe. The cost-benefit view sees it as a strategy to optimize the relation between the costs and benefits of climate policy. The focal point view sees it as a solution to a complex coordination problem. We argue that the focal point view is the most appropriate. It leads to an emphasis on implementing effective steps towards a near-zero emissions economy, without panicking in the face of a possible temporary overshooting. After several decades of practical experiences, the focal point may or may not be redefined on the basis of knowledge gathered thanks to these experiences.

COMPLEX SYSTEMS Complexity theory and financial regulation

Economic policy needs interdisciplinary network analysis and behavioral modeling Traditional economic theory could not explain, much less predict, the near collapse of the financial system and its long-lasting effects on the global economy. Since the 2008 crisis, there has been increasing interest in using ideas from complexity theory to make sense of economic and financial markets. Concepts, such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon, but actual use of complexity models and results remains at an early stage. Recent insights and techniques offer potential for better monitoring and management of highly interconnected economic and financial systems and, thus, may help anticipate and manage future crises.

Impact Factors and Risk Analysis of Tropical Cyclones on a Highway Network

Coastal areas typically have high social and economic development and are likely to suffer huge losses due to tropical cyclones. These cyclones have a great impact on the transportation network, but there have been a limited number of studies about tropical-cyclone-induced transportation network functional damages, especially in Asia. This study develops an innovative measurement and analytical tool for highway network functional damage and risk in the context of a tropical cyclone, with which we explored the critical spatial characteristics of tropical cyclones with regard to functional damage to a highway network by developing linear regression models to quantify their relationship. Furthermore, we assessed the networks functional risk and calculated the return periods under different damage levels. In our analyses, we consider the real-world highway network of Hainan province, China. Our results illustrate that the most important spatial characteristics were location (in particular, the midlands), travel distance, landfalling status, and origin coordinates. However, the trajectory direction did not obviously affect the results. Our analyses indicate that the highway network of Hainan province may suffer from a 90% functional damage scenario every 4.28 years. These results have critical policy implications for the transport sector in reference to emergency planning and disaster reduction.

Choice for China： What Role for Vocational Education in Green Growth？

Green growth cannot succeed without significant changes in the education system and the closely related social division of labor. This paper combines historical evidence and a game-theoretic analysis to study the relation between vocational education and green growth. It is found that a low-vocation and a high-vocation equilibrium can be distinguished in the interplay between education and labor markets, and that a high-vocation equilibrium is better suited for green growth. At the present stage of development, there are tendencies in both directions in China. Therefore, China has the possibility to successfully implement a green growth strategy by developing a strong vocational education with Chinese characteristics.

Things are different today: the challenge of global systemic risks

Abstract While most OECD countries have been rather successful in reducing risks to human lives, health, and the quality of the environment, the record for new global risks such as climate change, pandemics, financial breakdowns, and social inequality is much less convincing. This is the challenge of systemic risks. Since the global financial crisis, it has received rapidly growing attention. However, considerable conceptual confusion mars research on and practical responses to this challenge. We undertake an effort of conceptual clarification, starting with the paradigmatic example of the financial crisis. This leads to a view of global systems as involving an interplay between micro- and macrodynamics internal to the system, with the system simultaneously interacting with its environment. Such dynamics typically show periods of stability, punctuated by situations opening up several possible futures. Alternative global futures, like other prospects, constitute risks for an agent if she considers some of these futures as less desirable than others. Agents may have lexicographic preferences over futures they would like to avoid, so as to consider some futures as just undesirable, but others as catastrophic. If an agent expects some of the relevant futures at a bifurcation point of a global system to be catastrophic in this sense, they are faced with a systemic risk.

Complexity theory and financial regulation Economic policy needs interdisciplinary network analysis and behavioral modeling

Economic policy needs interdisciplinary network analysis and behavioral modeling Traditional economic theory could not explain, much less predict, the near collapse of the financial system and its long-lasting effects on the global economy. Since the 2008 crisis, there has been increasing interest in using ideas from complexity theory to make sense of economic and financial markets. Concepts, such as tipping points, networks, contagion, feedback, and resilience have entered the financial and regulatory lexicon, but actual use of complexity models and results remains at an early stage. Recent insights and techniques offer potential for better monitoring and management of highly interconnected economic and financial systems and, thus, may help anticipate and manage future crises.

Systemic Risks: A Homomorphic Approach on the Basis of Complexity Science

Although the notion of systemic risk gained prominence with respect to financial systems, it is a generic term that refers to risks of increasing importance in many domains—risks that cannot be tackled by conventional techniques of risk management and governance. We build on a domain-overarching definition of systemic risks by highlighting crucial properties that distinguish them from conventional risks and plain disasters. References to typical examples from various domains are included. Common features of systemic risks in different domains—such as the role of agents and emergence phenomena, tipping and cascading, parameters indicating instability, and historicity—turn out to be more than noncommittal empirical observations. Rather these features can be related to fundamental theory for relatively simple and well-understood systems in physics and chemistry. A crucial mechanism is the breakdown of macroscopic patterns of whole systems due to feedback reinforcing actions of agents on the microlevel, where the reinforcement is triggered by boundary conditions moving beyond critical tipping points. Throughout the whole article, emphasis is placed on the role of complexity science as a basis for unifying the phenomena of systemic risks in widely different domains.

Correction to: Towards Quantitatively Understanding the Complexity of Social-Ecological Systems—From Connection to Consilience

The original publication was published without Acknowledgments section. The missing section is printed here.

Towards Quantitatively Understanding the Complexity of Social-Ecological Systems—From Connection to Consilience

The complexity of social-ecological systems (SES) is rooted in the outcomes of node activities connected by network topology. Thus far, in network dynamics research, the connectivity degree (CND), indicating how many nodes are connected to a given node, has been the dominant concept. However, connectivity focuses only on network topology, neglecting the crucial relation to node activities, and thereby leaving system outcomes largely unexplained. Inspired by the phenomenon of “consensus of wills and coordination of activities” often observed in disaster risk management, we propose a new concept of network characteristic, the consilience degree (CSD), aiming to measure the way in which network topology and node activities together contribute to system outcomes. The CSD captures the fact that nodes may assume different states that make their activities more or less compatible. Connecting two nodes with in/compatible states will lead to outcomes that are un/desirable from the perspective of the SES in question. We mathematically prove that the CSD is a generalized CND, and the CND is a special case of CSD. As a general, fundamental concept, the CSD can facilitate the development of a new framework of network properties, models, and theories that allows us to understand patterns of network behavior that cannot be explained in terms of connectivity alone. We further demonstrate that a co-evolutionary mechanism can naturally improve the CSD. Given the generality of co-evolution in SES, we argue that the CSD is an inherent attribute rather than an artificial concept, which underpins the fundamental importance of the CSD to the study of SES.