The Incomplete‐information Split‐stream Fish War: Examining the Implications of Competing Risks

Abstract

ABSTRACT. . It is now widely recognized that climactic regime shifts, which aperiodically alter a harvested fish stock's biomass and spatial distribution, may lead to distorted fisheries management decisions which negatively impact the fishery, both biologically and economically. This is particularly true for trans‐boundary migratory stocks, where optimal management relies on coordination among independent nation‐states. Unanticipated changes in stock distribution and abundance can upset expectations of national authorities, leading them to sanction inappropriate harvesting levels by their separately managed fleets targeting the same breeding fish stock. Our theoretical studies are based on a spatially‐distributed stochastic model, which we have called the “split‐stream model,‘ where two separately managed fleets harvest simultaneously at two separate sites. Our key assumption is that competing fleet managers, when harvesting noncooperatively, hold incomplete and asymmetric private information of current stock recruitment and spatial distribution. When subsequently negotiating to coordinate their harvests, they agree that they will share their information and then bargain over partition of the gains from their cooperation. This bargaining process takes into account the fleet's relative competitive strengths, particularly due to private information asymmetries. In this present article we introduce a more complex information structure than had been assumed in our earlier work (McKelvey and Golubtsov [2002], McKelvey, Miller and Golubtsov [2003], Mckelvey et al. [2004]). Specifically, both stock‐growth and stock‐split parameters vary stochastically and asynchronously. Thus, when harvesting noncooperatively, each fleet may possess private knowledge which is unavailable to the other. We examine the interplay of the harvesting game's information structure with other fishery characteristics, such as the fleets' economics and operating characteristics and their attitudes toward risk, to determine the implications of such structure for the outcome of the harvesting game. All of these changes are made to capture new conceptual phenomena and expand the range of applicability of the model.

Panic-Based Overfishing in Transboundary Fisheries

“…Furthermore, instead of deriving the set of sustainable harvests in repeated-game and imperfect-monitoring settings, here a coordination game is studied where the existence of private information may contribute to equilibrium refinement. In addition, McKelvey et al (2003) and Golubtsov and McKelvey (2007) study fishery games where the fishing fleets observe private noisy signals about the stock growth and stock-split parameters. Using simulations and assigning numeric precisions to the signals, they compare the outcome of cooperative and non-cooperative harvest strategies.…”

Comparing Proactive and Reactive Management: Managing a Transboundary Fish Stock Under Changing Environment

“…We used dynamic programming (DP) to find the optimal policies at each time step in a noncooperative game. Proactive harvest decision making in absence of a trend in the stock distribution was studied by Golubtsov and McKelvey [] using a DP algorithm. In contrast to their work, our model includes a rising temperature trend over time.…”