Fieldmost

Fieldmost is a theoretical framework used to model spatially distributed systems by identifying, at each location, the most influential field component among a set of candidate fields and using that component to drive local dynamics. The approach formalizes a local selection rule in which the field with the greatest perceived influence determines update rules and interactions with neighboring sites.

Origin and etymology: The term fieldmost was introduced in a 2019 preprint by researchers at the Institute for Complex Systems. It combines field-theoretic ideas with a maximization principle, drawing on terminology that echoes selecting the most significant local field.

Theoretical formulation: At each site i, several field channels f1(i), f2(i), ..., fm(i) are available. The fieldmost operator selects fi = argmaxk |fk(i)| and the local state is updated according to a function F that depends on fi and, if present, neighboring states. The framework can be implemented within stochastic or deterministic update schemes and can be integrated with existing field theories or lattice models.

Applications: In materials science, fieldmost has been used to model domain growth in magnetic materials where the dominant local magnetization field governs domain wall motion. In geophysics, it has been applied to seismic field mapping under multi-source models. In computational neuroscience, variants of fieldmost appear in neural field simulations where the strongest input field at a node shapes activity. In network simulations, the approach can simplify multi-field interactions by reducing the effective input to a single channel per node.

Reception and status: Fieldmost is the subject of ongoing research. Proponents argue that the local selection principle captures essential nonlinearity with reduced complexity, while critics note potential neglect of synergistic field interactions and sensitivity to how candidate fields are defined. Further work aims to establish formal properties and benchmarks for its use. See also field theory, spatial statistics, Ising model.