The Ensemble Kalman Filter for Multidimensional Bioeconomic Models

Abstract

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 CENTER 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. AbstractTo serve the needs for integrating economic considerations into management decisions in ecosystem frameworks, we need to build models that capture observed system dynamics and incorporate existing knowledge of ecosystems while at the same time serve the needs of economics analysis. The main constraint for models to serve in economic analysis is dimensionality. In addition, models should be stable in order to apply in long-term management analysis. We use the ensemble Kalman filter to fit relatively simple models to ecosystem or foodweb data and estimate parameters that are stable over the observed variability in the data. The filter also provides a lower bound on the noise terms that a stochastic analysis require. In the present article, we apply the filter to model the main interactions in the Barents Sea ecosystem.

A Bridge between Continuous and Discrete-Time Bioeconomic Models: Seasonality in Fisheries

“…where growth is measured in 1,000 tons, =0.00045371 C r is the intrinsic growth rate, and 3,703 C K  (1,000 tons) is the carrying capacity of the environment. The estimated parameter values are based on the same data set that was used in Kvamsdal and Sandal (2015), who provide a detailed description of technical and implementation details.…”

Greed is good: Heuristic adaptations for resilience in renewable resource management

“…It is also assumed that there is no correlation between the stochastic terms. This is a simplifying assumption that is computationally convenient, but the cod (a demersal fish) and the capelin (pelagic) have rather different natures and the assumption is not completely unreasonable (Kvamsdal & Sandal, 2015). The diffusion matrix is thus specified as: $${\sigma }_{11}=0.2$$; $${\sigma }_{12}=0$$; $${\sigma }_{21}=0$$ and $${\sigma }_{22}=0.2$$.…”

Using the best of two worlds: A bio‐economic stock assessment (BESA) method using catch and price data

“…Anderson et al, 2017; Branch et al, 2011; Costello et al, 2012; Froese et al, 2017; Martell & Froese, 2013; Thorson et al, 2013), selectivity (Winker et al, 2020) or standardized catch per unit effort (CPUE) (Pedersen & Berg, 2017; Winker et al, 2018). Standardized stock indices, often based on commercial CPUE, also complements catch data in bio‐economic state space models (Aeberhard et al, 2018; Auger‐Méthé et al, 2021; Ekerhovd & Kvamsdal, 2017; Froese et al, 2017; ICES, 2020; Kvamsdal & Sandal, 2015; Meyer & Millar, 1999; Millar & Meyer, 2000; Winker et al, 2018 provide overviews of these methods). These data‐limited assessment methods are valuable avenues to uncover latent information about stock size and parameters, although their performance varies and may depend on the specific stock (Bouch et al, 2020; Pons et al, 2020).…”