Complexity Science and Evolution Unit

The system governing human well-being and ecosystem services is complex, involving nonlinear feedback, non-equilibrium dynamics, multiple scales, emergent phenomena, tipping points, and system risks. The structure and function of such complex systems are driven by self-organization and adaptation. Complexity science explains how self-organization shapes the interaction structure among system agents, while evolution theory describes how adaptation forms the adaptable characteristics of system agents. Social and biological adaptations function through fundamentally different mechanisms—social learning of memes and biological inheritance of genes—yet share essential features rooted in replicator dynamics. Overall, complexity science and evolution provide a powerful toolbox for understanding and managing the challenges posed by complexity in social and biological systems.

• Complex Systems

Complexity science and evolution units analyze the dynamics of complex adaptive systems. This requires a spectrum of diverse approaches, with methods selected, combined, and developed according to the problem at hand. This involves leveraging complexity science, evolution, social economics, ecology, game theory, theoretical physics, applied mathematics, and computer science. Key questions addressed include how to promote pro-social behavior, understand and manage the formation and loss of biodiversity, and sustainably utilize living resources.

• Super-Interdisciplinary Sensibility (blu3mo)

The specific research areas of the unit are as follows:

• Governance of social dilemmas and common goods. Social dilemmas present broad challenges to the functioning of society, arising when goods crucial for the welfare of a group are threatened by self-interested actors. Overcoming such dilemmas requires promoting cooperative behavior through governance solutions based on positive and negative incentives, appropriate rules and regulations, conditional cooperation and participation, as well as various mechanisms such as competition and mobility between social groups. By combining these mechanisms, the unit explores ways to design effective and efficient governance solutions.

  • From economics and game theory

• Dynamics of biodiversity and speciation. Despite biodiversity being recognized as a critical determinant of ecosystem services, the dynamics of biodiversity are only partially understood. Ongoing exploration to understand how species are formed includes increasing focus on parapatric speciation that progresses despite not being geographically isolated, ecologically driven speciation resulting from biotic interactions, and adaptive speciation that occurs when evolving populations escape the minimum fitness value. The unit investigates how ecological and evolutionary forces drive the formation and loss of biodiversity.

  • Evolutionary?- Sustainable fisheries management and the evolution of fisheries. Aquatic resources, once considered almost inexhaustible, are now being overexploited. To promote sustainable fisheries from an ecological perspective, it is necessary to understand complex adaptive systems that include biological resources and their environment, ecosystem services, management interventions and their political decision-making factors, as well as the socio-economic interactions of fishermen, consumers, and market forces. From an evolutionary perspective, it is important to recognize that fisheries not only affect the abundance of fish but also their functional characteristics. We will analyze fisheries from both perspectives.
  • It seems like a discussion combining natural ecosystems and economics, right?

• Systemic risk and network dynamics. Systemic risk explains the potential for cascading failures within a network, occurring in various fields such as disease dynamics, ecosystems, financial networks, supply chains, power grids, and transportation networks. A typical example is the spread of infectious diseases through social contact networks. Even if the healthcare system is suitable for treating individual infections, it can be overwhelmed by uncontrollable chains of infections. In various fields, we study methods to evaluate, model, predict, and mitigate such dynamics.

  • Sounds like Network Science.

• Evolutionary community ecology and ecological-evolutionary vegetation dynamics. The structure and function of all ecosystems have been shaped by evolution. Evolutionary community models based on functional characteristics explain how ecological environments determine biological environments, selection pressures, and coevolutionary dynamics, thus leading to changes in ecological environments. By applying this approach to diverse ecosystems including food webs and particularly vegetation dynamics, it becomes possible to predict how the composition of plant biomes worldwide is derived from first principles based on regional environmental conditions.

  • First principles? Sounds intriguing.

• Adaptive dynamics theory and models. Adaptive dynamics theory explains the evolutionary and coevolutionary dynamics of phenotypic traits driven by natural selection in realistic social and ecological environments. Going beyond the classical evolutionary framework assuming fitness functions, adaptive dynamics theory stands out by deriving them from underlying population dynamics. Since the inception of adaptive dynamics theory, we have developed innovative theories and models on topics such as species packing, functional value evolution, evolutionary branching, environmental feedback dimensions, and evolutionary pattern formation.

  • They have their own theory, it seems.

• Simplifying spatial complexity. Spatial structures are ubiquitous in nature, and ecological and evolutionary dynamics cannot be accurately understood without considering them. Currently, this is often achieved through powerful numerical simulations, but corresponding analytical methods are lagging behind. Overcoming this shortcoming and simplifying spatial complexity opens up a new path by recognizing that well-mixed population classical models are a special case of a broader theoretical framework using spatial densities of individuals such as singletons, pairs, triplets, as state variables. We are researching methods that cut off such moment hierarchies at the triplet level to bring about powerful approximations.

  • Still not quite clear.- The ecology and evolution of diseases. Humans, animals, and plants are constantly threatened by infectious diseases. Pathogens frequently cross species boundaries, continuously adapting their functional characteristics and promoting the spread of mutated pathogens. This creates moving targets for individual and population disease protection efforts, as evidenced by the ongoing Covid-19 pandemic. Therefore, the success of public health interventions heavily relies not only on controlling the spread of pathogens but also accurately predicting their evolution. Strengthening the young field of evolutionary epidemiology, new methods are being devised to predict changes in pathogen toxicity and host resistance.

  • [Public Health] seems like something [mu373] would be doing.

• Each of these topics seems very interesting, but the themes are too broad, making it seem like “Is this all one lab!?” (blu3mo)