Toward a leading indicator of catastrophic shifts in complex systems: Assessing changing conditions in nation states

Abstract

The 20th century was characterized by substantial change on a global scale. There were multiple wars and unrest, social and political transitions, technological innovation and widespread development that impacted every corner of the earth. In order to assess the sustainability implications of these changes, we conducted a study of three advanced nations particularly affected during this time: France, Germany and the United States (USA). All three nation states withstood these changes and yet continued to thrive, which speaks to their resilience. However, we were interested in determining whether any of these countries underwent a regime shift during this period and if they did, whether there was advanced warning that transition was imminent. This study seeks to evaluate systemic trends in each country by exploring key variables that describe its condition over time. We use Fisher Information to assess changing conditions in the nation states based on trends in social, economic and environmental variables and employ Bayes Theorem as an objective means of determining whether declines in Fisher information provide early warning signals of critical transitions. Results indicate that while the United States was relatively stable and France experienced a great deal of change during this period, only Germany appeared to undergo a regime shift. Further, each country exhibited decreasing Fisher information when approaching significant events (e.g., World Wars, Great Depression), and reflected unique mechanisms linked to dynamic changes in each nation state. This study highlights the potential of using trends in Fisher information as a sentinel for evaluating dynamic change and assessing resilience in coupled human and natural systems.

Enhancing quantitative approaches for assessing community resilience

“…Despite the challenges presented by insufficient data or the desire for long term temporal studies (Hicks et al, 2016;Nystr€ om and Folke, 2001), some EWSs have been applied to assess aspects of resilience including regime shifts in coupled human and natural systems (e.g. Eason and Cabezas, 2012;Gonzalez-Mejia et al, 2014;Karunanithi et al, 2011;Spielmann et al, 2016;Vance et al, 2017). For example, Spielmann et al (2016) used EWSs for research on the change of human settlement size and social institutions under the impacts of climate change and population relocation in the prehistoric U.S. Southwest from 1050 AD to 1375 AD.…”

Detecting dynamic system regime boundaries with Fisher information: the case of ecosystems

“…Regime shift occurs when one or more thresholds have been reached (e.g., a catastrophic disturbance, or the loss of too many species). In previous research, Fisher information has been used retroactively, to identify regime thresholds after regime shifts have occurred (Mayer et al 2006;Sundstrom et al 2017;Vance et al 2017). Identifying regime thresholds without first observing a regime shift is a different problem.…”

“…Information theory has been applied to assess the sustainability of dynamic systems (Eason and Cabezas 2012), mainly to detect transitions from one dynamic regime to another (Mayer et al 2006;Karunanithi et al 2008;Spanbauer et al 2014;Eason et al 2016;Sundstrom et al 2017;Vance et al 2017). The "ball and cup" mental model has been central to this work (Gunderson 2000).…”

An Information Theory-Based Approach to Assessing Spatial Patterns in Complex Systems

“…It has been adapted into an index that reflects the dynamic order within a system by collapsing patterns in the underlying system variables into a measure that can be tracked to assess systemic change [58]. This form of Fisher information has been used to assess sustainability, political instability and resilience, and it has been proposed as an EWI in a variety of human and natural systems at multiple spatial scales (e.g., [17,57,59,[62][63][64][65][66]). However, it has primarily been employed to evaluate temporal dynamics with time as a natural ordering parameter.…”