SNF
SAMFUNNS-OG NAERINGSLIVSFORSKNING AS-er et selskap i NHH-miljøet med oppgave å initiere, organisere og utføre eksternfinansiert forskning. Norges Handelshøyskole og Stiftelsen SNF er aksjonaerer. Virksomheten drives med basis i egen stab og fagmiljøene ved NHH.SNF er ett av Norges ledende forsk ningsmiljø innen anvendt økonomisk-administrativ forskning, og har gode samarbeidsrelasjoner til andre forskningsmiljøer i Norge og utlandet. SNF utfører forskning og forsknings baserte utredninger for sentrale beslutningstakere i privat og offentlig sektor. Forskningen organiseres i programmer og prosjekter av langsiktig og mer kortsiktig karakter. Alle publikasjoner er offentlig tilgjengelig.
SNF
CENTRE FOR APPLIED RESEARCH AT NHH-is a company within the NHH group. Its objective is to initiate, organize and conduct externally financed research. The company shareholders are the Norwegian School of Economics (NHH) and the SNF Foundation. Research is carried out by SNF´s own staff as well as faculty members at NHH. SNF is one of Norway´s leading research environment within applied economic administrative research. It has excellent working relations with other research environments in Norway as well as abroad. SNF conducts research and prepares research-based reports for major decision-makers both in the private and the public sector. Research is organized in programmes and projects on a long-term as well as a short-term basis. All our publications are publicly available. While economists have discussed ecosystem-based management and similar concepts, little attention has been devoted to the art of modeling. Models of ecosystems or foodwebs that make economic analysis viable should capture as much as possible of system structure and dynamics while balancing biological and ecological detail against dimensionality and model complexity. Relevant models need a strong, empirical content, but data availability may inhibit modeling e orts. Models are bound to be nonlinear, and model and observational uncertainty should be observed. We suggest the data assimilation method ensemble Kalman ltering to improve modeling of ecosystems or foodwebs. To illustrate the method, we model the dynamics of the main, pelagic species in the Norwegian Sea. In order to reduce the parameter dimensionality, the species are modeled to rely on a common carrying capacity.We also take further methodological steps to deal with a still high number of parameters.Our best model captures much of the observed dynamics in the sh stocks, but the estimated model error is moderate.