Complexity Science and Evolution Unit (Ulf Dieckmann)

How the unit combines complexity science, socio-economics, game theory, physics, mathematics, computer science, ecology, evolution, nonlinear feedbacks, nonequilibrium dynamics, multiple scales, emergent phenomena, tipping points, systemic risks

Systems governing human wellbeing and ecosystem services are complex, involving nonlinear feedbacks, nonequilibrium dynamics, multiple scales, emergent phenomena, tipping points, and systemic risks. The structure and function of such complex systems are driven by self-organization and adaptation, with complexity science describing how self-organization shapes the interaction structure among system agents and evolutionary theory describing how adaptation shapes the adaptable characteristics of system agents. While social and biological forms of adaptation operate through fundamentally different mechanisms – social learning of memes and biological inheritance of genes –, their mathematical descriptions have in common essential features rooted in replicator dynamics. Taken together, complexity science and evolution provide the most powerful toolbox we have available for understanding and managing the challenges posed by complexity in social and biological systems.

The Complexity Science and Evolution Unit analyzes the dynamics of complex adaptive systems. Since this necessitates a pluralistic spectrum of approaches, methods are selected, combined, and developed as problems require, drawing on complexity science, evolution, socio-economics, ecology, game theory, theoretical physics, applied mathematics, and computer science. Key questions addressed include how to promote prosocial behavior, how to understand and manage biodiversity formation and loss, and how to utilize living resources sustainably. The unit’s specific research areas are outlined below.

Social dilemmas and governance of common goods. Social dilemmas are posing pervasive challenges to the functioning of societies, occurring whenever goods important for collective wellbeing are under the threat of selfish actors. Overcoming such dilemmas requires promoting cooperative action through governance solutions based on diverse mechanisms such as positive and negative incentives, appropriate rules and regulations, conditional cooperation and participation, as well as competition and movement among social groups. Building on and combining these mechanisms, we explore how to design governance solutions that are effective and efficient.

Biodiversity dynamics and speciation. Even though biodiversity has become recognized as a key determinant of ecosystem services, biodiversity dynamics are only partially understood. In the ongoing quest to comprehend how species form, mounting attention is being devoted to parapatric speciation (advancing despite incipient species not being geographically isolated), ecological speciation (driven by selection pressures originating from biotic interactions), and adaptive speciation (occurring when evolving populations escape from being trapped at a fitness minimum). We investigate how ecological and evolutionary forces drive biodiversity formation and loss.

Sustainable fisheries management and fishing-induced evolution. Once believed to be virtually inexhaustible, living aquatic resources have become overexploited. Promoting sustainable fisheries from an ecological perspective requires understanding the complex adaptive systems involving biotic resources and their environments, ecosystem services, management interventions and their political determinants, and the socio-economic interplay of fishers, consumers, and market forces. Promoting sustainable fisheries from an evolutionary perspective requires recognizing how fishing imposes changes not only on the numbers of fish but also on their functional traits. We analyze fishing from both angles.

Systemic risk and network dynamics. Systemic risk describes the likelihood of cascading failures in networks and occurs in a wide range of domains including disease dynamics, ecosystems, financial networks, supply chains, power grids, and transportation networks. A typical example is the spread of contagious diseases percolating across social contact networks: even when health systems are well geared to treating individual infections, they can be overwhelmed by infection cascades spiraling out of control. Across domains, we study how to assess, model, predict, and mitigate such dynamics.

Evolutionary community ecology and eco-evolutionary vegetation dynamics. The structure and function of all ecosystems have been shaped by evolution. Evolutionary community models based on functional traits explain how ecological settings determine, in turn, biotic environments, selection pressures, coevolutionary dynamics, and thus, changes to the ecological settings. We apply this approach to diverse ecosystems including food webs and to vegetation dynamics in particular, where it enables us to predict from first principles how the compositions of plant biomes around the globe follow from regional environmental conditions.

Adaptive dynamics theory and models. Adaptive dynamics theory describes the evolutionary and coevolutionary dynamics of phenotypic traits driven by natural selection in realistic social and ecological settings. Moving beyond classical evolutionary frameworks postulating fitness functions, adaptive dynamics theory stands out by deriving them from the underlying population dynamics. Having contributed to the foundations and applications of adaptive dynamics theory since its inception, we develop innovative theory and models on topics including species packing, function-valued evolution, evolutionary bifurcations, environmental-feedback dimension, and evolution of pattern formation.

Simplifying spatial complexity. Spatial structures are ubiquitous in nature, and neither ecological nor evolutionary dynamics can be accurately understood without accounting for them. Nowadays, this is often accomplished by brute-force numerical simulations, while corresponding analytical methods have fallen behind needs. New avenues for overcoming this shortcoming and simplifying spatial complexity are opened up by realizing that classical models of well-mixed populations are special cases in a broader theoretical framework that uses as state variables the spatial densities of singlets, pairs, triplets, etc. of individuals. We research how truncating such moment hierarchies at the triplet level yields powerful approximations.

Disease ecology and evolution. Humans, animals, and plants live under the constant threat of contracting contagious diseases. Pathogens frequently jump between species and incessantly adapt their functional traits, facilitating the transmission of variant pathogens. This is creating moving targets for individual and collective efforts at disease protection, as evidenced by the ongoing Covid-19 pandemic. The success of public health interventions thus crucially depends on accurately forecasting not only pathogen spread but also pathogen evolution. Strengthening the young field of evolutionary epidemiology, we devise new methods for predicting changes in the virulence of pathogens and the resistance of their hosts.