Collective phenomena in community ecology

Traditional theoretical ecology describes low-dimensional patterns (few species, few traits...) used as archetypes for intuition. Network models combine many such patterns into more complex systems, but this comes with hard empirical challenges: predictions are sensitive to the whole structure of species interactions, and this structure is difficult to measure precisely and exhaustively.

New approaches, inspired by statistical mechanics, try to make more general predictions on these high-dimensional systems. The underlying assumption is that large systems that have emerged through many concurrent processes, rather than being precisely designed (or co-evolved), tend to exhibit only properties that are most robust to changes in structure. Such robust properties can easily be captured in null models such as random interactions, stochastic dynamics, etc. Hence a different, collective kind of simplicity appears at the other end of complexity.

Our questions are:

  • Under which conditions is this sort of emergent simplicity found in theoretical and empirical ecosystems?
  • Can we probe such high-dimensional properties, measure them, see them reflected in ecosystem stability?
  • In which ways must we deviate from this simple limit to better understand real systems? How can we combine low-dimensional structures and high-dimensional robustness?

Modeling and measuring subjective values

How to connect values in decision theory, instruments in psychometrics, and norms in classical sociology and anthropology?

Many difficulties have emerged from decades of striving to model human behavior and experience on the basis of utility (e.g. wellbeing, preferences, and other notions from behavioral economics and decision theory). That framework assumes that goals and values are a stable or slowly-changing property of individuals, and that the object of theory is to explain shorter time scales: how humans will (or should) therefore use resources and strategies to achieve these goals.

When dissecting that framework, we should avoid naive objections: humans do occasionally behave in such a way, and it may have been fair to hope that these occasional behaviors would help investigate the complexity of human values. Much like a physical instrument may convert temperature or forces into a more easily measurable electric current, revealed preferences were hoped to convert subjective properties into objectively-measurable actions in a market setting. Yet it seems to us that the physical instrument succeeds because all forms of physical energy and work are really interchangeable, whereas in the second case, what is measured is very specific to the market setting - and so we end up making a theory of the instrument (the electric current) rather than of the target (temperature or forces).

Rather than modeling humans as optimizing (or even satisficing) agents, and slowly complicating their objective function to explain anomalous behaviors (along the lines of prospect theory), we believe that a more elegant formulation can be constructed by drawing inspiration from:

- Generative and multiagent models of the mind: the free energy principle, predictive processing, etc.
- Psychological results on conformity, influence and learning
- Social norms and cultural anthropology

Together, these sources draw a picture of humans as being in a constant process of discovering and reevaluating their own objectives. Classical decision theory is not well-equipped to deal with situations where objectives change at least as much as actions -- for instance, where an individual can deduce their own objective from what they are currently doing (resulting in commitment and consistency "biases").

Spatial structure and regimes in ecological dynamics

Numerous studies in ecology have considered the effects of spatial structure on various properties of populations, communities and ecosystems. Very few have asked how the interplay between local and spatial processes can lead to well defined regimes with distinct dynamics, or how changing the spatial structure (as might occur due to landscape fragmentation) can switch the system between these regimes.

As it turns out, by focusing on the role of dimensional properties in these systems (e.g. system size or rate of biomass growth), we can make predictions about specific systems from general considerations, and we can better compare distinct systems, that might otherwise seem completely unrelated.

The aims of this project are:
- Find the relevant dynamical regimes for different premises about ecosystem properties (e.g. strong spatial heterogeneity), and derive predictions for transitions between them.
- Validate such theoretical results with data from observations and experiments, based on past studies.
- Explore how and when these dynamical regimes can qualitatively change due to alterations of the landscape.